CN112883085B - Bridge dynamic load safety online real-time monitoring, analyzing, early warning and management platform based on big data and cloud computing - Google Patents

Abstract

The invention discloses a bridge dynamic load safety online real-time monitoring, analyzing, early warning and managing platform based on big data and cloud computing, which comprises a bridge deck area dividing module, an area load-bearing load detecting module, a database, an area load-bearing dynamic danger coefficient counting module, an area total load-bearing load counting module, an area wind power parameter detecting module, an area wind power load counting module, a management cloud platform, an early warning module and a display terminal, wherein the bridge is subjected to load-bearing load and wind power load detection at set time points, and then the dynamic danger coefficient of the whole comprehensive load of the bridge is counted according to the detection result, the load detection index is perfected, the comprehensive and comprehensive safety detection of the dynamic load of the bridge is realized, the defects of the traditional dynamic load detection of the bridge are overcome, the reliability of the detection result is improved, and the safety problem of the bridge structure caused by the fact that the wind load detection of the bridge is not carried out is greatly reduced.

Description

Bridge dynamic load safety online real-time monitoring, analyzing and early warning management platform based on big data and cloud computing

Technical Field

The invention belongs to the technical field of bridge dynamic load monitoring, and particularly relates to a bridge dynamic load safety online real-time monitoring, analyzing and early warning management platform based on big data and cloud computing.

Background

Bridge engineering is a basic component of a highway traffic network in China and plays an important role in a highway traffic system in China, so that the safety of the bridge engineering needs to be monitored to ensure the quality of the highway bridge engineering. The load test is an important bridge detection method, has great practicability for maintenance work of the bridge, and can scientifically realize design of bridge bearing capacity; on the other hand, the firmness degree of the bridge can be mastered so as to make regular maintenance work. Therefore, the load test is particularly important for the inspection of the bridge, which directly influences the service life and the safety of the bridge.

The current bridge load detection mode comprises static load detection and dynamic load detection, wherein the dynamic load detection can reflect the structural stability of the bridge in the operation process and is more suitable for the load detection of the bridge. However, most of the traditional bridge dynamic load detection modes only aim at the bearing load of the bridge, influence of the wind load in the environment on the stability of the bridge structure is ignored, the load detection index is single, the detection result reliability is low, and comprehensive safety detection of the bridge dynamic load cannot be realized. In view of the above, the invention designs a bridge dynamic load safety online real-time monitoring, analyzing and early warning management platform based on big data and cloud computing.

Disclosure of Invention

The invention aims to provide a bridge dynamic load safety online real-time monitoring, analyzing and early warning management platform based on big data and cloud computing, which can be used for detecting the bearing load and the wind load of a bridge at each set time point, perfecting the load detection index, realizing comprehensive and comprehensive safety detection of the bridge dynamic load and solving the problems in the background art.

The purpose of the invention can be realized by the following technical scheme:

a bridge dynamic load safety online real-time monitoring, analyzing and early warning management platform based on big data and cloud computing comprises a bridge deck area dividing module, an area load-bearing load detection module, a database, an area load-bearing dynamic risk coefficient statistical module, an area total load-bearing load statistical module, an area wind power parameter detection module, an area wind power load statistical module, a management cloud platform, an early warning module and a display terminal;

the bridge deck area division module is respectively connected with the area bearing load detection module and the area wind power parameter detection module, the area bearing load detection module is respectively connected with the area bearing load dynamic danger coefficient statistical module and the area total bearing load statistical module, the area wind power parameter detection module is connected with the area wind power load statistical module, the area bearing load dynamic danger coefficient statistical module, the area total bearing load statistical module and the area wind power load statistical module are all connected with the management cloud platform, and the management cloud platform is respectively connected with the early warning module and the display terminal;

the bridge deck area dividing module is used for dividing a bridge deck area into a plurality of local areas according to the length of a bridge and a set local area dividing method, numbering the divided local areas in a sequence from near to far away from a bridgehead, and sequentially marking the divided local areas as 1,2.. i.. n, and meanwhile, dividing the divided local areas according to the width of the bridge and dividing the divided local areas into sub-areas according to the set sub-area dividing method, so that the sub-areas divided by the local areas are numbered and respectively marked as 1,2.. j.. m;

the area bearing load detection module comprises a plurality of pressure sensors which are respectively arranged at the bottom of the bridge floor of each sub-area corresponding to each local area of the bridge floor and used for detecting the bearing load of each sub-area corresponding to each local area of the bridge floor at each set time point, and the obtained bearing load of each sub-area corresponding to each local area of the bridge floor at each time point forms an area time point bearing load set F_{Press and press} ^ij(f_{Pressing and pressing} ^ij1,f_{Press and press} ^ij2...,f_{Press and press} ^ijt,...,f_{Press and press} ^ijz)，f_{Press and press} ^ijthe bearing load of the jth sub-area of the ith local area at the tth time point is represented by t, the t is represented by the time point, and the t is 1,2, z;

the database is used for storing safe bearing loads capable of being borne by a bridge deck, storing dynamic danger coefficients of the safe bearing loads corresponding to local areas of the bridge, storing comparison difference values of safe comprehensive loads of adjacent local areas of the bridge, and storing weight coefficients of the bridge load corresponding to total bearing loads and effective wind loads;

the dynamic risk coefficient statistic module of the regional bearing load receives the regional time point bearing load set sent by the regional bearing load detection module, sequentially extracts the bearing load of each sub-region corresponding to each local region at each time point from the received regional time point bearing load set according to the number sequence of the local region and the number sequence of each sub-region corresponding to each local region, and thus the bearing load of the same sub-region of the same local region at each time point is subjected to the bearing load subtraction operation of two adjacent time points, so that the bearing load difference value of the two adjacent time points of the same sub-region of the same local region is obtained, and the bearing load difference set delta F of the two adjacent time points is formed_{Pressing and pressing} ^ij[Δf_{Press and press} ^ij1,Δf_{Press and press} ^ij2...,Δf_{Press and press} ^ijt,...,Δf_{Press and press} ^ij(z-1)]，Δf_{Press and press} ^ijt is represented as a bearing load difference value between the bearing load of the jth sub-area of the ith local area at the tth time point and the bearing load of the t +1 th time point, at the moment, the safe bearing load capable of being borne by the bridge deck is extracted from the database, and therefore the bearing load dynamic risk coefficients of each sub-area corresponding to each local area of the bridge deck are counted by combining the bearing load difference set of the adjacent time points and the safe bearing load capable of being borne by the bridge deck and are sent to the management cloud platform;

the area total bearing load counting module receives the area time point bearing load set sent by the area bearing load detection module, counts the total bearing load of each local area of the bridge deck at each time point according to the received area time point bearing load set, and sends the total bearing load to the management cloud platform;

the regional wind parameter detection module comprises a plurality of wind anemometers which are respectively arranged at the side positions of each local region and used for detecting the wind speed and the wind direction angle of each local region at each set time point, and the obtained wind speed and wind direction angle of each local region at each time point form a regional wind parameter set Q_w ⁱ(q_w ⁱ1,q_w ⁱ2...,q_w ⁱt,...,q_w ⁱz)，q_w ⁱt is a numerical value corresponding to the wind parameter of the ith local area at the t-th time point, w is a wind parameter, w is p1 and p2 which are respectively expressed as wind speed and wind direction angle, and the regional wind parameter detection module sends the regional wind parameter combination to the regional wind load statistics module;

the regional wind load counting module receives the regional wind parameter set sent by the regional wind parameter detection module and acquires the bridge side area of each local region, so that the effective wind load of each local region at each time point is counted according to the regional wind parameter set and the bridge side area of each local region and then sent to the management cloud platform;

the management cloud platform receives the dynamic bearing load risk coefficients of each sub-area corresponding to each local area of the bridge deck sent by the dynamic bearing load risk coefficient statistical module of the areas, sorts the dynamic bearing load risk coefficients of each sub-area corresponding to each local area of the bridge deck according to the descending order, further screens out the maximum value of the dynamic bearing load risk coefficient corresponding to each local area, compares the maximum value with the dynamic bearing load risk coefficient corresponding to the local area of the bridge set in the database, if the maximum value of the dynamic bearing load risk coefficient corresponding to a certain local area is larger than the dynamic bearing load risk coefficient corresponding to the local area of the bridge, indicates that the local area is dangerous, the local area is marked as a dangerous local area, counts the serial number of the dangerous local area at the moment, and sends the local area to the display terminal, meanwhile, sending an early warning instruction to an early warning module;

the management cloud platform respectively receives the total load bearing of each local area at each time point sent by the area total load bearing statistic module and the effective wind load of each local area at each time point sent by the area wind load statistic module, and calculates the comprehensive load of each local area at each time point according to the received total load bearing of each local area at each time point and the effective wind load of each local area at each time point, and forms a time point local area comprehensive load set D^t(D^t1,D^t2...,D^ti,...,D^tn)，D^ti is expressed as the comprehensive load of the ith local area at the tth time point, so that the comprehensive loads of the adjacent local areas at the same time point are subtracted according to the sequence of the time points to obtain the comprehensive load difference value of the adjacent local areas at the same time, and a comprehensive load comparison set of the adjacent local areas at the time point is formed

ΔD^ti is expressed as a comparison difference value between the comprehensive load of the ith local area at the tth time point and the comprehensive load of the (i + 1) th local area, so that a safety comprehensive load comparison difference value of the adjacent local area of the bridge is extracted from the database, and the dynamic risk coefficient of the whole comprehensive load of the bridge is counted according to the time point adjacent local area comprehensive load comparison set and the safety comprehensive load comparison difference value of the adjacent local area and is sent to the display terminal;

the early warning module receives a local early warning instruction sent by the management cloud platform and carries out local area early warning;

and the display terminal receives and displays the dangerous local area number and the whole comprehensive load dynamic danger coefficient of the bridge sent by the management cloud platform.

In one embodiment, the set local region dividing method includes the steps of:

s1, counting the length of the bridge from the bridge head to the bridge tail, and recording the counted length as the length of the bridge;

and S2, evenly dividing the length of the bridge into equal parts, wherein the areas where the equal divided lengths are located are local areas.

In one embodiment, the method comprises: the set subregion dividing method comprises the following steps:

h1, counting the width of the bridge;

h2, evenly dividing the width of the bridge of each local area into equal parts, and dividing each local area into sub-areas by the equal width dividing points.

In one embodiment, the local regions correspond to the dynamic risk system of the partial regions for the load-bearing loadThe calculation formula of the number is

η_ijExpressed as the dynamic risk coefficient of the bearing load of the ith local area corresponding to the jth sub-area, f_{Pressure 0}Expressed as the safe load bearing load that the bridge deck can bear.

In one embodiment, the calculation formula of the effective wind load of each local area at each time point is

f_{Wind power} ^itThe effective wind load of the ith local area at the t time point is expressed, c is expressed by an air resistance coefficient, rho is expressed by air density, and the standard case can be 1.293g/l, s_iBridge side area, q, expressed as ith local area_p1 ⁱt、q_p2 ⁱAnd t is respectively expressed as the wind speed and the wind direction angle of the ith local area at the tth time point, wherein the wind direction angle refers to the included angle between the wind direction and the horizontal plane of the bridge deck.

In one embodiment, the calculation formula of the total load bearing capacity of each local area of the bridge deck at each time point is

F_{Total pressure} ^itExpressed as the total load bearing of the ith local area of the bridge at the t-th time point.

In one embodiment, the calculation formula of the comprehensive load of each local area at each time point is as follows

a. b represents a bridge load weight coefficient corresponding to the total load bearing load and the effective wind load, and a + b is 1.

In one embodiment, the calculation formula of the overall comprehensive load dynamic risk coefficient of the bridge is

Expressed as the overall integrated load dynamic risk factor, Δ D, of the bridge₀Expressed as the safe combined load contrast difference of the adjacent local areas of the bridge.

The invention has the following beneficial effects:

(1) the invention carries out local area division on the bridge deck area, carries out sub-area division on each divided local area, and simultaneously carries out bearing load detection on each sub-area of each divided local area at each set time point, thereby counting the total bearing load of each local area of the bridge deck at each time point, simultaneously carries out wind load detection on each local area at each set time point, thereby counting the comprehensive load of each local area at each time point, and carries out comparison of the comprehensive loads of adjacent local areas at each time point according to the sequence of the time points, thereby counting the whole comprehensive load dynamic danger coefficient of the bridge according to the comparison result, realizing comprehensive safety detection on the dynamic load of the bridge, making up the defect of single load detection index existing in the traditional bridge dynamic load detection, and improving the detection index of the dynamic load of the bridge, the reliability of the detection result is improved, the problem of bridge structure safety caused by the fact that wind load detection of the bridge is not carried out is greatly solved, and the dynamic load detection level of the bridge is improved.

(2) According to the invention, the load-bearing load detection is carried out on each sub-area of each divided local area at each set time point, and the load-bearing load of each sub-area of each detected local area is compared with the load-bearing load of each adjacent two time points, so that the dynamic risk coefficient of the load-bearing load of each sub-area corresponding to each local area of the bridge floor is counted according to the comparison value, the dynamic risk condition of the load-bearing load of the bridge area is intuitively reflected by the dynamic risk coefficient of the load-bearing load counted, the defect that the traditional bridge dynamic load-bearing load safety detection only carries out the whole load-bearing load safety detection is overcome, and the range of the bridge dynamic load-bearing safety detection is expanded.

(3) According to the method, the maximum value of the dynamic risk coefficient of the bearing load corresponding to each local area is screened out from the dynamic risk coefficients of the bearing load corresponding to each sub-area of the bridge deck, and is compared with the dynamic risk coefficient of the safe bearing load corresponding to each local area of the bridge, so that the dangerous local area is screened out, early warning and display are carried out, bridge supervisors can know the position of the dangerous local area in time, targeted local maintenance of the bridge is carried out, and damage to the stability of the whole structure of the bridge caused by the fact that the dangerous local area is not maintained in time is avoided.

Drawings

The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.

Fig. 1 is a schematic diagram of the module connection of the present invention.

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.

Referring to fig. 1, the online real-time monitoring, analyzing, early warning and managing platform for bridge dynamic load safety based on big data and cloud computing comprises a bridge deck area dividing module, an area load bearing load detection module, a database, an area load bearing dynamic risk coefficient statistical module, an area total load bearing statistical module, an area wind power parameter detection module, an area wind power load statistical module, a management cloud platform, an early warning module and a display terminal, wherein the bridge deck area dividing module is respectively connected with the area load bearing load detection module and the area wind power parameter detection module, the area load bearing load detection module is respectively connected with the area load bearing dynamic risk coefficient statistical module and the area total load bearing statistical module, the area wind power parameter detection module is connected with the area wind power load statistical module, the area load bearing dynamic risk coefficient statistical module, the early warning module and the display terminal, The regional total bearing load counting module and the regional wind load counting module are both connected with the management cloud platform, and the management cloud platform is respectively connected with the early warning module and the display terminal.

The bridge deck area dividing module is used for dividing a bridge deck area into a plurality of local areas according to a set local area dividing method by taking the length of a bridge as a dividing basis, wherein the set local area dividing method comprises the following steps:

s1, counting the length of the bridge from the bridge head to the bridge tail, and recording the counted length as the length of the bridge;

s2, evenly dividing the length of the bridge into equal parts, wherein the areas where the equal division lengths are located are local areas;

and numbering the divided local areas according to a distance sequence from the bridge head to the bridge head, wherein the sequence is marked as 1,2.

H1, counting the width of the bridge;

h2, uniformly dividing the width of each local area bridge into equal parts, and dividing each local area into sub-areas by each equal width dividing point, thereby numbering the sub-areas divided by each local area, wherein the sub-areas are respectively marked as 1,2.

The embodiment lays a foundation for carrying out bearing load detection and wind load detection on the rear surface by dividing the bridge deck area into local areas and sub-areas.

The area bearing load detection module comprises a plurality of pressure sensors which are respectively arranged at the bottom of the bridge floor of each sub-area corresponding to each local area of the bridge floor and are used for detecting the bearing load of each sub-area corresponding to each local area of the bridge floor at each set time point to obtain the bearing load of each sub-area corresponding to each local area of the bridge floor at each time pointLoad forming area time point bearing load set F_{Press and press} ^ij(f_{Press and press} ^ij1,f_{Press and press} ^ij2...,f_{Press and press} ^ijt,...,f_{Pressing and pressing} ^ijz)，f_{Press and press} ^ijAnd t is the bearing load of the jth sub-area of the ith local area at the tth time point, t is the time point, t is 1,2, z, and the area bearing load detection module respectively sends the area time point bearing load set to the area bearing load dynamic risk coefficient statistical module and the area total bearing load statistical analysis module.

The database is used for storing safe bearing loads which can be borne by the bridge deck, storing dynamic danger coefficients of the safe bearing loads corresponding to local areas of the bridge, storing comparison difference values of safe comprehensive loads of adjacent local areas of the bridge, and storing weight coefficients of the bridge load corresponding to the total bearing load and the effective wind load.

The dynamic danger coefficient statistic module of the regional bearing load receives a regional time point bearing load set sent by the regional bearing load detection module, sequentially extracts the bearing load of each sub-region corresponding to each local region at each time point from the received regional time point bearing load set according to the number sequence of the local region and the number sequence of each sub-region corresponding to each local region, and thus the bearing load of the same sub-region of the same local region at each time point is subjected to the bearing load subtraction operation of two adjacent time points, so that the bearing load difference value of two adjacent time points of the same sub-region of the same local region is obtained, and a bearing load difference set delta F at the adjacent time points is formed_{Press and press} ^ij[Δf_{Press and press} ^ij1,Δf_{Press and press} ^ij2...,Δf_{Press and press} ^ijt,...,Δf_{Press and press} ^ij(z-1)]，Δf_{Press and press} ^ijt is the bearing load difference value between the bearing load of the jth sub-area of the ith local area at the tth time point and the bearing load of the (t + 1) th time point, at the moment, the safe bearing load capable of being born by the bridge deck is extracted from the database, and therefore each local area of the bridge deck is counted by combining the bearing load difference set of the adjacent time points and the safe bearing load capable of being born by the bridge deckDynamic risk coefficient of load bearing of each sub-area corresponding to domain

η_ijExpressed as the dynamic risk factor of the load-bearing load of the ith partial area corresponding to the jth sub-area, f_{Pressure 0}The safe bearing load that represents bridge deck can bear to send to the management cloud platform.

In the embodiment, the load-bearing load detection is carried out on each sub-area of each divided local area at each set time point, and the load-bearing load of each sub-area of each detected local area is compared with the load-bearing load of each adjacent two time points, so that the dynamic risk coefficient of the load-bearing load of each sub-area corresponding to each local area of the bridge floor is counted according to the comparison value, the dynamic risk condition of the load-bearing load of each bridge area is directly reflected by the dynamic risk coefficient of the load-bearing load counted, the defect that the traditional bridge dynamic load-bearing load safety detection only carries out the whole load-bearing load safety detection is overcome, and the range of the bridge dynamic load-bearing safety detection is expanded.

The area total bearing load counting module receives the area time point bearing load set sent by the area bearing load detection module, and then counts the total bearing load set of each local area of the bridge deck at each time point according to the received area time point bearing load set

F_{Total pressure} ^itAnd the total load bearing capacity of the ith local area of the bridge at the tth time point is represented and is sent to the management cloud platform.

The regional wind parameter detection module comprises a plurality of wind anemometers which are respectively arranged at the side positions of each local region and used for detecting the wind speed and the wind direction angle of each local region at each set time point, and the obtained wind speed and wind direction angle of each local region at each time point form a regional wind parameter set Q_w ⁱ(q_w ⁱ1,q_w ⁱ2...,q_w ⁱt,...,q_w ⁱz)，q_w ⁱt is a numerical value corresponding to the wind parameter of the ith local area at the tth time point, w is a wind parameter, w is p1 and p2 which are respectively expressed as wind speed and wind direction angle, and the regional wind parameter detection module sends the regional wind parameter combination to the regional wind load statistics module.

The regional wind load statistic module receives the regional wind parameter set sent by the regional wind parameter detection module and acquires the bridge side area of each local region, so that the effective wind load of each local region at each time point is counted according to the regional wind parameter set and the bridge side area of each local region

f_{Wind power} ^itThe effective wind load of the ith local area at the t time point is expressed, c is expressed by an air resistance coefficient, rho is expressed by air density, and the standard case can be 1.293g/l, s_iBridge side area, q, expressed as ith local area_p1 ⁱt、q_p2 ⁱAnd t is respectively expressed as the wind speed and the wind direction angle of the ith local area at the tth time point, wherein the wind direction angle is the included angle between the wind direction and the horizontal plane of the bridge floor, and then the wind direction angle is sent to the management cloud platform.

The management cloud platform receives the dynamic risk coefficients of the bearing load of each sub-area corresponding to each local area of the bridge floor sent by the dynamic risk coefficient statistical module of the bearing load of the area, sorts the dynamic risk coefficients of the bearing load of each sub-area corresponding to each local area of the bridge floor according to the descending order, and further screens out the maximum value of the dynamic risk coefficient of the bearing load corresponding to each local area, so as to compare the maximum value of the dynamic risk coefficient of the bearing load corresponding to each local area of the bridge with the dynamic risk coefficient of the safe bearing load corresponding to the local area of the bridge set in the database, if the maximum value of the dynamic risk coefficient of the bearing load corresponding to a certain local area is larger than the dynamic risk coefficient of the safe bearing load corresponding to the local area of the bridge, the local area is marked as a dangerous local area, at this time, the serial number of the dangerous local area is counted, and the local area is sent to the display terminal, and simultaneously, sending an early warning instruction to an early warning module.

This embodiment is through screening out the dynamic danger coefficient maximum value of bearing load that each local area corresponds among the dynamic danger coefficient of bearing load of each subregion from each local area of bridge floor corresponds, and compare its safe bearing load dynamic danger coefficient that corresponds with the bridge local area, and then screen out dangerous local area, and early warning and demonstration, be convenient for the bridge supervisory personnel in time know dangerous local area position, and then carry out the local maintenance of pertinence bridge, avoid leading to causing harm to bridge overall structure's stability because of not in time carrying out dangerous local area's maintenance.

The management cloud platform respectively receives the total load bearing of each local area at each time point sent by the area total load bearing statistic module and the effective wind load of each local area at each time point sent by the area wind load statistic module, and calculates the comprehensive load of each local area at each time point according to the received total load bearing of each local area at each time point and the effective wind load of each local area at each time point

a. b is respectively expressed as the weight coefficient of the bridge load corresponding to the total load bearing load and the effective wind load, a + b is 1, and a time point local area comprehensive load set D is formed^t(D^t1,D^t2...,D^ti,...,D^tn)，D^ti is expressed as the comprehensive load of the ith local area at the tth time point, and therefore, according to the sequence of each time point, the comprehensive loads of the adjacent local areas at the same time point are subjected to subtraction operation to obtain the comprehensive load difference value of the adjacent local areas at the same time point, and a comprehensive load comparison set of the adjacent local areas at the time point is formed

ΔD^ti is expressed as a comparison difference between the comprehensive load of the ith local area and the comprehensive load of the (i + 1) th local area at the tth time point, thereby extracting the safety comprehensive load comparison difference of the adjacent local areas of the bridge from the database so as to be based onCounting the dynamic danger coefficient of the integral comprehensive load of the bridge by the comprehensive load comparison set of the adjacent local areas and the safety comprehensive load comparison difference value of the adjacent local areas at the time point

Expressed as the overall integrated load dynamic risk factor, Δ D, of the bridge₀And the comparison difference is expressed as the safe comprehensive load comparison difference of the adjacent local areas of the bridge and is sent to the display terminal.

The dynamic risk coefficient of the whole comprehensive load of the bridge counted by the embodiment synthesizes the bearing load and the wind load of the bridge, thereby realizing the quantitative display of the dynamic risk condition of the comprehensive load of the bridge to the bridge structure, making up the defect of single load detection index in the traditional dynamic load detection of the bridge, perfecting the detection index of the dynamic load of the bridge, improving the reliability of the detection result, greatly reducing the safety problem of the bridge structure caused by the fact that the wind load detection of the bridge is not carried out, improving the dynamic load detection level of the bridge, and simultaneously providing reliable reference basis for the whole maintenance of the bridge structure for bridge supervisors by the dynamic risk coefficient of the whole comprehensive load of the bridge counted by the dynamic risk coefficient of the whole comprehensive load of the bridge.

The early warning module receives a local early warning instruction sent by the management cloud platform, and carries out local area early warning to remind bridge supervisors to pay attention.

And the display terminal receives and displays the dangerous local area number and the whole comprehensive load dynamic danger coefficient of the bridge sent by the management cloud platform.

The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.

Claims (8)

1. The utility model provides a bridge dynamic load safety on-line real-time supervision analysis early warning management platform based on big data and cloud calculate which characterized in that: the system comprises a bridge deck area division module, an area bearing load detection module, a database, an area bearing load dynamic danger coefficient statistical module, an area total bearing load statistical module, an area wind power parameter detection module, an area wind power load statistical module, a management cloud platform, an early warning module and a display terminal;

the bridge deck area division module is respectively connected with the area bearing load detection module and the area wind power parameter detection module, the area bearing load detection module is respectively connected with the area bearing load dynamic danger coefficient statistical module and the area total bearing load statistical module, the area wind power parameter detection module is connected with the area wind power load statistical module, the area bearing load dynamic danger coefficient statistical module, the area total bearing load statistical module and the area wind power load statistical module are all connected with the management cloud platform, and the management cloud platform is respectively connected with the early warning module and the display terminal;

the bridge deck area dividing module is used for dividing a bridge deck area into a plurality of local areas according to the length of a bridge and a set local area dividing method, numbering the divided local areas in a sequence from near to far away from a bridgehead, and sequentially marking the divided local areas as 1,2.. i.. n, and meanwhile, dividing the divided local areas according to the width of the bridge and dividing the divided local areas into sub-areas according to the set sub-area dividing method, so that the sub-areas divided by the local areas are numbered and respectively marked as 1,2.. j.. m;

the area bearing load detection module comprises a plurality of pressure sensors which are respectively arranged at the bottom of the bridge floor of each sub-area corresponding to each local area of the bridge floor and used for detecting the bearing load of each sub-area corresponding to each local area of the bridge floor at each set time point, and the obtained bearing load of each sub-area corresponding to each local area of the bridge floor at each time point forms an area time point bearing load set F_{Press and press} ^ij(f_{Press and press} ^ij1,f_{Press and press} ^ij2...,f_{Press and press} ^ijt,...,f_{Press and press} ^ijz)，f_{Press and press} ^ijthe bearing load of the jth sub-area of the ith local area at the tth time point is represented by t, the t is represented by the time point, and the t is 1,2, z;

the database is used for storing safe bearing loads capable of being borne by a bridge deck, storing dynamic danger coefficients of the safe bearing loads corresponding to local areas of the bridge, storing comparison difference values of safe comprehensive loads of adjacent local areas of the bridge, and storing weight coefficients of the bridge load corresponding to total bearing loads and effective wind loads;

the dynamic risk coefficient statistic module of the regional bearing load receives the regional time point bearing load set sent by the regional bearing load detection module, sequentially extracts the bearing load of each sub-region corresponding to each local region at each time point from the received regional time point bearing load set according to the number sequence of the local region and the number sequence of each sub-region corresponding to each local region, and thus the bearing load of the same sub-region of the same local region at each time point is subjected to the bearing load subtraction operation of two adjacent time points, so that the bearing load difference value of the two adjacent time points of the same sub-region of the same local region is obtained, and the bearing load difference set delta F of the two adjacent time points is formed_{Press and press} ^ij[Δf_{Press and press} ^ij1,Δf_{Press and press} ^ij2...,Δf_{Press and press} ^ijt,...,Δf_{Press and press} ^ij(z-1)]，Δf_{Press and press} ^ijt is represented as a bearing load difference value between the bearing load of the jth sub-area of the ith local area at the tth time point and the bearing load of the t +1 th time point, at the moment, the safe bearing load capable of being borne by the bridge deck is extracted from the database, and therefore the bearing load dynamic risk coefficients of each sub-area corresponding to each local area of the bridge deck are counted by combining the bearing load difference set of the adjacent time points and the safe bearing load capable of being borne by the bridge deck and are sent to the management cloud platform;

the area total bearing load counting module receives the area time point bearing load set sent by the area bearing load detection module, counts the total bearing load of each local area of the bridge deck at each time point according to the received area time point bearing load set, and sends the total bearing load to the management cloud platform;

the regional wind parameter detection module comprises a plurality of wind anemometers which are respectively arranged at the side positions of each local region and used for detecting the wind speed and the wind direction angle of each local region at each set time point, and the obtained wind speed and wind direction angle of each local region at each time point form a regional wind parameter set Q_w ⁱ(q_w ⁱ1,q_w ⁱ2...,q_w ⁱt,...,q_w ⁱz)，q_w ⁱt is a numerical value corresponding to the wind parameter of the ith local area at the t-th time point, w is a wind parameter, w is p1 and p2 which are respectively expressed as wind speed and wind direction angle, and the regional wind parameter detection module sends the regional wind parameter combination to the regional wind load statistics module;

the regional wind load counting module receives the regional wind parameter set sent by the regional wind parameter detection module and acquires the bridge side area of each local region, so that the effective wind load of each local region at each time point is counted according to the regional wind parameter set and the bridge side area of each local region and then sent to the management cloud platform;

the management cloud platform receives the dynamic risk coefficients of the bearing load of each sub-area corresponding to each local area of the bridge floor sent by the dynamic risk coefficient statistical module of the bearing load of the area, sorts the dynamic risk coefficients of the bearing load of each sub-area corresponding to each local area of the bridge floor according to the descending order, and further screens out the maximum value of the dynamic risk coefficient of the bearing load corresponding to each local area, so as to compare the maximum value of the dynamic risk coefficient of the bearing load corresponding to each local area of the bridge with the dynamic risk coefficient of the safe bearing load corresponding to the local area of the bridge set in the database, if the maximum value of the dynamic risk coefficient of the bearing load corresponding to a certain local area is larger than the dynamic risk coefficient of the safe bearing load corresponding to the local area of the bridge, the local area is marked as a dangerous local area, at the moment, the serial number of the dangerous local area is counted and sent to the display terminal, meanwhile, sending an early warning instruction to an early warning module;

the management cloud platform respectively receives the total load bearing of each local area at each time point sent by the area total load bearing statistic module and the effective wind load of each local area at each time point sent by the area wind load statistic module, and calculates the comprehensive load of each local area at each time point according to the received total load bearing of each local area at each time point and the effective wind load of each local area at each time point, and forms a time point local area comprehensive load set D^t(D^t1,D^t2...,D^ti,...,D^tn)，D^ti is expressed as the comprehensive load of the ith local area at the tth time point, so that the comprehensive loads of the adjacent local areas at the same time point are subtracted according to the sequence of the time points to obtain the comprehensive load difference value of the adjacent local areas at the same time, and a comprehensive load comparison set of the adjacent local areas at each time point is formed

ΔD^ti is expressed as a comparison difference value between the comprehensive load of the ith local area at the tth time point and the comprehensive load of the (i + 1) th local area, so that a safety comprehensive load comparison difference value of the adjacent local area of the bridge is extracted from the database, and the dynamic risk coefficient of the whole comprehensive load of the bridge is counted according to the comprehensive load comparison set of the adjacent local area at each time point and the safety comprehensive load comparison difference value of the adjacent local area and is sent to the display terminal;

the early warning module receives a local early warning instruction sent by the management cloud platform and carries out local area early warning;

and the display terminal receives and displays the dangerous local area number and the whole comprehensive load dynamic danger coefficient of the bridge sent by the management cloud platform.

2. The bridge dynamic load safety online real-time monitoring, analyzing and early warning management platform based on big data and cloud computing as claimed in claim 1, wherein: the set local area dividing method comprises the following steps:

s1, counting the length of the bridge from the bridge head to the bridge tail, and recording the counted length as the length of the bridge;

and S2, evenly dividing the length of the bridge into equal parts, wherein the areas where the equal length is located are local areas.

3. The bridge dynamic load safety online real-time monitoring, analyzing and early-warning management platform based on big data and cloud computing as claimed in claim 1, characterized in that: the set subregion dividing method comprises the following steps:

h1, counting the width of the bridge;

h2, evenly dividing the width of the bridge of each local area into equal parts, and dividing each local area into sub-areas by the equal width dividing points.

4. The bridge dynamic load safety online real-time monitoring, analyzing and early warning management platform based on big data and cloud computing as claimed in claim 1, wherein: the calculation formula of the dynamic risk coefficient of the bearing load of each sub-area corresponding to each local area is

η_ijExpressed as the dynamic risk coefficient of the bearing load of the ith local area corresponding to the jth sub-area, f_{Pressure 0}Expressed as the safe load bearing load that the bridge deck can bear.

5. The bridge dynamic load safety online real-time monitoring, analyzing and early warning management platform based on big data and cloud computing as claimed in claim 1, wherein: the calculation formula of the effective wind load of each local area at each time point is

f_{Wind power} ^itIs expressed as the effective wind load of the ith local area at the t-th time point, c is expressed as the air resistance coefficient, rho is expressed as the air density, and s is expressed as_iBridge side area, q, expressed as ith local area_p1 ⁱt、q_p2 ⁱAnd t is respectively expressed as the wind speed and the wind direction angle of the ith local area at the tth time point, wherein the wind direction angle refers to the included angle between the wind direction and the horizontal plane of the bridge deck.

6. The bridge dynamic load safety online real-time monitoring, analyzing and early warning management platform based on big data and cloud computing as claimed in claim 1, wherein: the calculation formula of the total bearing load of each local area of the bridge deck at each time point is

F_{Total pressure} ^itExpressed as the total load bearing of the ith local area of the bridge at the t-th time point.

7. The bridge dynamic load safety online real-time monitoring, analyzing and early warning management platform based on big data and cloud computing as claimed in claim 1, wherein: the calculation formula of the comprehensive load of each local area at each time point is

a. b represents a bridge load weight coefficient corresponding to the total load bearing load and the effective wind load, and a + b is 1.

8. The bridge dynamic load safety online real-time monitoring, analyzing and early warning management platform based on big data and cloud computing as claimed in claim 1, wherein: the calculation formula of the dynamic risk coefficient of the integral comprehensive load of the bridge is

Expressed as the overall integrated load dynamic risk factor, Δ D, of the bridge₀Expressed as the safe combined load contrast difference of the adjacent local areas of the bridge.

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