CN117541234B - Engineering maintenance diagnosis system and method based on big data - Google Patents
Engineering maintenance diagnosis system and method based on big data Download PDFInfo
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Abstract
The invention discloses a large data-based engineering maintenance diagnosis system and a large data-based engineering maintenance diagnosis method, which relate to the field of data processing systems for management, wherein bridge pier data and bridge deck data are respectively stored in a data storage after being separated, the bridge pier data are extracted and imported into a bridge pier threat calculation strategy to calculate threat coefficients of a bridge pier, the bridge deck data are extracted and imported into the bridge deck threat calculation strategy to calculate threat coefficients of the bridge deck, the threat coefficients of the bridge pier and the threat coefficients of the bridge deck obtained through calculation are substituted into a bridge threat value calculation formula to calculate a bridge threat value, whether the bridge threat value is larger than or equal to a set threat threshold value is judged, if yes, the subarea is set as an abnormal bridge body, and maintenance personnel are reminded of maintenance; if not, the subarea is set as a safety bridge body, reminding is not needed, the judging speed of the viaduct damage is effectively improved, and the accuracy of the viaduct threat maintenance diagnosis is improved.
Description
Technical Field
The invention relates to the field of data processing systems for management, in particular to an engineering maintenance diagnosis system and method based on big data.
Background
At present, maintenance of large projects such as viaducts is performed in a mode of manual inspection and diagnosis through timing maintenance, so that real-time maintenance and diagnosis of the projects cannot be performed, in addition, a few large-span viaducts are provided with a viaduct health monitoring system for monitoring the operation state in real time, however, on one hand, the bridge health monitoring system is provided with a few sensors only on key parts or key components of a main body structure, and therefore, effective real-time monitoring of all parts or all diseases of a bridge is impossible; on the other hand, the mass bridge health monitoring data is difficult to directly guide the maintenance of the bridge, the association degree of various monitoring data is low, the management and maintenance of the bridge can not be effectively serviced, and the problems exist in the prior art;
For example, in the patent with publication number CN106442720a, an acoustic vibration type track bridge health monitoring device, system and method are disclosed, the monitoring device includes a sound collecting device and a communication processing device, wherein the sound collecting device is used for collecting a sound spectrum generated when a wheel excitation source vibrates to impact the track bridge, and the communication processing device is used for transmitting the sound spectrum; the bridge health monitoring system comprises a monitoring device and a cloud computing server, wherein the sound collecting equipment is used for collecting a sound frequency spectrum generated when a wheel excitation source vibrates and impacts a track bridge, and sending signals to the cloud computing server in real time. The cloud computing server is used for storing and analyzing the sound spectrum; the bridge health monitoring method is based on a sound monitoring fault diagnosis method, and utilizes the frequency spectrum characteristics of sound transmitted in the bridge structure to statistically analyze the deformation of the bridge structure and judge the bridge health condition. The invention simplifies bridge monitoring field facilities, saves engineering cost, and reduces maintenance workload of an online monitoring system through intensive management;
Meanwhile, for example, in chinese patent with publication number CN107609304B, a PHM-based fault diagnosis prediction system for a long-span railroad bridge is disclosed, which includes: the invention further provides a PHM-based fault diagnosis and prediction method for the large-span railway bridge. The beneficial effects of the invention are as follows: the 3S network architecture, BIM and GIS technology association and archiving design, construction, operation and maintenance information are adopted, and the diagnosis and prediction of bridge diseases and the evaluation of bridge health conditions are realized through comprehensive monitoring of train, track, bridge and bridge environments and big data processing of manual inspection information, so that a decision basis is provided for bridge maintenance.
However, the above patents all have the problems proposed in the background art: the maintenance of large projects such as viaducts is carried out by means of manual inspection and diagnosis through timing maintenance, so that real-time maintenance and diagnosis of the projects cannot be carried out, and in addition, a few large-span viaducts are provided with a viaduct health monitoring system for monitoring the operation state in real time, however, on one hand, the bridge health monitoring system is provided with a small number of sensors only on key parts or key components of a main body structure, so that the bridge health monitoring system cannot effectively monitor all parts or all diseases of a bridge in real time; on the other hand, the mass bridge health monitoring data is difficult to directly guide the maintenance of the bridge, the association degree of various monitoring data is low, and the bridge maintenance cannot be effectively served.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an engineering maintenance diagnosis system and method based on big data, the invention divides areas of a viaduct to be maintained, separates bridge pier and bridge deck data, stores the separated bridge pier and bridge deck data in a data storage, extracts bridge pier data to be imported into a bridge pier threat calculation strategy to calculate threat coefficients of the bridge pier, extracts bridge deck data to be imported into the bridge deck threat calculation strategy to calculate threat coefficients of the bridge deck, substitutes the threat coefficients of the bridge pier and the threat coefficients of the bridge deck into a bridge threat value calculation formula to calculate a bridge threat value, judges whether the threat value of the bridge body is greater than or equal to a set threat threshold, if so, sets the subarea as an abnormal bridge body, and reminds maintenance personnel of maintenance; if not, the subarea is set as a safety bridge body, reminding is not needed, the judging speed of the viaduct damage is effectively improved, and the accuracy of the viaduct threat maintenance diagnosis is improved.
In order to achieve the above purpose, the present invention provides the following technical solutions:
The engineering maintenance diagnosis method based on big data comprises the following specific steps:
S1, dividing a viaduct to be maintained into n subareas, and extracting pier and bridge deck data of the subareas, wherein the pier data comprise pier surface concave area and depth data, pier surface crack length, width data and quantity data, the bridge deck data comprise bridge deck crack length and width data, bridge deck bulge, concave depth and area data and dislocation amplitude data of bridge deck joints, and the dislocation amplitude data of the bridge deck joints are longitudinal displacement of the joints of two bridge deck templates;
S2, respectively storing bridge pier data and bridge deck data in a data storage after separating the bridge pier data and the bridge deck data;
S3, extracting pier data, and leading the pier data into a pier threat calculation strategy to calculate threat coefficients of the piers;
S4, extracting bridge deck data, and leading the bridge deck data into a bridge deck threat calculation strategy to calculate the threat coefficient of the bridge deck;
s5, substituting the threat coefficient of the bridge pier and the threat coefficient of the bridge deck obtained by calculation into a bridge threat value calculation formula to calculate a bridge threat value;
s6, judging whether the threat value of the bridge body is larger than or equal to a set threat threshold value, if so, setting the subarea as an abnormal bridge body, and reminding maintenance personnel of maintenance; if not, the subarea is set as a safety bridge body, and reminding is not needed.
Specifically, the specific content of S1 includes the following steps:
S11, taking an overhead bridge to be maintained, acquiring the bridge pier positions of the overhead bridge, dividing the overhead bridge into areas according to the number of bridge piers, dividing the areas into subareas of the number of bridge piers, wherein each subarea comprises at least one bridge pier and half of bridge decks on two sides of the bridge pier;
S12, acquiring bridge deck and pier pictures of each sub-area by using an unmanned aerial vehicle, processing the photographed images of the unmanned aerial vehicle, acquiring crack length, width and number data of the bridge deck and the piers, and simultaneously acquiring concave area and depth data of the surfaces of the bridge deck and the piers and bridge deck junction bump height data.
Specifically, the specific steps of S3 are as follows:
S31, extracting pier data of each sub-area, wherein the pier surface depression area and depth data, the pier surface crack length, the maximum width data and the number data are included, the number of pier surface depressions is set to be m 1, and the number of pier surface cracks is set to be m 2;
s32, substituting the extracted pier surface depression area, depth data and pier surface depression quantity into a pier depression threat coefficient calculation formula to calculate pier depression threat coefficients, wherein the pier depression threat coefficient calculation formula is as follows: Wherein/> Is the area of the ith concave on the bridge pier,/>Is the depth of the ith concave on the pier,/>Is a safety standard value of the set concave area,/>The depth safety standard value of the set concave is set;
S33, substituting the extracted length, maximum width and number of the surface cracks of the bridge pier into a bridge pier crack coefficient calculation formula to calculate a bridge pier crack coefficient, wherein the bridge pier crack coefficient calculation formula is as follows: wherein/> Is the i-th crack length data on the bridge pier,/>Is the width data of the ith crack on the pier,/>Is a safe standard value of the crack length on the bridge pier, i.e./>The safety standard value of the width of the crack on the bridge pier is set;
S34, substituting the extracted and calculated pier sinking threat coefficients and pier crack coefficients into a pier threat calculation formula to calculate threat coefficients of the pier, wherein the pier threat coefficient calculation formula is as follows: s d＝s1+s2.
Specifically, the specific steps of S4 include the following:
S41, extracting bridge deck data of each sub-area comprises acquiring crack length, width and number data r 1 of the bridge deck, pit and bulge area, depth data and number data r 2 of the bridge deck and bulge height data of bridge deck joints;
s42, substituting the length of the extracted bridge deck surface cracks, the maximum width data and the number of the bridge pier surface cracks into a bridge deck crack threat coefficient calculation formula to calculate a bridge deck crack threat coefficient, wherein the bridge deck crack threat coefficient calculation formula is as follows: Wherein/> For the i-th crack length data on the bridge deck,/>For the data of the width of the ith crack on the bridge deck,Is a set safety standard value of the crack length on the bridge deck,/>The safety standard value of the width of the crack on the bridge deck is set;
S43, substituting the extracted bridge deck junction uplift height data, the bridge deck debouching and uplift area, the depth data and the quantity data into a bridge deck flattening threat coefficient calculation formula to calculate a bridge deck flattening threat coefficient, wherein the debouching quantity is r 3, the uplift quantity is r 4, and the bridge deck flattening threat coefficient calculation formula is Wherein/>Depth of i-th recess on deck,/>Is the area of the ith recess on the bridge deck,/>Is a depth safety standard value of the dent on the bridge deck,Is the standard value of the area safety of the dent on the bridge deck,/>For the height of the ith bump on the deck,/>Is the area of the ith bump on the bridge deck,/>Is the safety standard value of the height of the bump on the bridge deck,/>Is the standard value of the area of the ith bump on the bridge deck, and l m is the bump height at the bridge deck joint,/>A 1 is a bridge floor depression duty ratio coefficient, a 2 is a bridge floor elevation duty ratio coefficient, and a 1+a2 =1;
S44, substituting the calculated threat coefficients of the bridge deck cracks and the bridge deck leveling threat coefficients into a bridge deck threat calculation formula to calculate the threat coefficients of the bridge deck, wherein the bridge deck threat coefficient calculation formula is as follows: s m＝s3+s4.
Specifically, the step S5 includes the following specific steps:
s51, extracting pier threat coefficients S d and bridge deck threat coefficients S m of the calculated subareas;
S52, substituting the obtained bridge pier threat coefficient S d and the bridge deck threat coefficient S m into a bridge threat value calculation formula to calculate a bridge threat value, wherein the calculation formula of the bridge threat value is as follows: Where e is the base of the exponential function.
The engineering maintenance diagnosis system based on big data is realized based on the engineering maintenance diagnosis method based on big data, and specifically comprises the following steps: the bridge threat system comprises an area dividing module, a data storage module, a bridge pier threat coefficient calculating module, a bridge threat coefficient calculating module, a control module, a threat value comparing module and an abnormal bridge prompting module, wherein the area dividing module is used for dividing areas of a viaduct to be maintained, the data storage module is used for respectively storing bridge piers and bridge deck data in a data storage after separating the bridge piers and the bridge deck data, the bridge pier threat coefficient calculating module is used for extracting threat coefficients of bridge piers, which are calculated in a bridge pier threat calculating strategy, and the control module is used for controlling the operation of the area dividing module, the data storage module, the bridge pier threat coefficient calculating module, the bridge threat coefficient calculating module, the threat value comparing module and the abnormal bridge prompting module.
Specifically, the bridge deck threat coefficient calculation module is used for extracting threat coefficients of a bridge deck in a bridge deck threat calculation strategy of bridge deck data import, the bridge body threat value calculation module is used for substituting the threat coefficients of the bridge pier obtained through calculation and the threat coefficients of the bridge deck into a bridge body threat value calculation formula to calculate a bridge body threat value, the threat value comparison module is used for judging whether the bridge body threat value is greater than or equal to a set threat threshold value, and the abnormal bridge body prompting module is used for prompting maintenance personnel to maintain an abnormal bridge body.
Specifically, the pier threat coefficient calculation module comprises a subarea pier data extraction unit, a pier depression threat coefficient calculation unit, a pier crack coefficient calculation unit and a pier threat coefficient calculation unit, wherein the subarea pier data extraction unit is used for extracting pier data of each subarea, the pier data comprises pier surface depression area and depth data, pier surface crack length, maximum data and quantity data, the pier depression threat coefficient calculation unit is used for substituting the extracted pier surface depression area, depth data and pier surface depression quantity into a pier depression threat coefficient calculation formula to calculate pier depression threat coefficients, and the pier crack coefficient calculation unit is used for extracting the calculated pier depression threat coefficients and pier crack coefficients to be substituted into the pier threat calculation formula to calculate the threat coefficients of the piers.
Specifically, the bridge deck threat coefficient calculation module comprises a sub-area bridge deck data extraction unit, a bridge deck crack threat coefficient calculation unit, a bridge deck leveling threat coefficient calculation unit and a bridge deck threat coefficient calculation unit, wherein the sub-area bridge deck data extraction unit is used for extracting bridge deck data of each sub-area, including crack length, width and number data of a bridge deck, pit and bulge area of the bridge deck, depth data and number data, the bridge deck crack threat coefficient calculation unit is used for substituting the extracted bridge deck surface crack length, maximum width data and bridge deck surface crack number into a bridge deck crack threat coefficient calculation formula to calculate a bridge deck crack threat coefficient, the bridge deck leveling threat coefficient calculation unit is used for substituting the extracted bridge deck junction bulge height data and the pit and bulge area of the bridge deck, depth data and number data into a bridge deck leveling threat coefficient calculation formula to calculate a bridge deck leveling threat coefficient, and the bridge deck leveling threat coefficient calculation unit is used for substituting the calculated bridge deck crack threat coefficient and the bridge deck leveling threat coefficient into the bridge deck leveling threat coefficient calculation formula to calculate the threat coefficient of the bridge deck.
Specifically, an electronic device includes: a processor and a memory, wherein the memory stores a computer program for the processor to call;
the processor executes the engineering maintenance diagnosis method based on big data by calling the computer program stored in the memory.
Specifically, a computer readable storage medium stores instructions that, when executed on a computer, cause the computer to perform an engineering repair diagnosis method based on big data as described above.
Compared with the prior art, the invention has the beneficial effects that:
Dividing areas of the viaduct to be maintained, respectively storing bridge pier and bridge deck data in a data storage after separating, extracting the threat coefficients of the bridge pier calculated by the bridge pier data in a bridge deck threat calculation strategy, extracting the threat coefficients of the bridge deck calculated by the bridge deck data in the bridge deck threat calculation strategy, substituting the threat coefficients of the bridge pier and the threat coefficients of the bridge deck obtained by calculation into a bridge threat value calculation formula to calculate a bridge threat value, judging whether the bridge threat value is greater than or equal to a set threat threshold, if so, setting the subarea as an abnormal bridge, and reminding maintenance personnel of maintenance; if not, the subarea is set as a safety bridge body, reminding is not needed, the judging speed of the viaduct damage is effectively improved, and the accuracy of the viaduct threat maintenance diagnosis is improved.
Drawings
FIG. 1 is a schematic flow chart of an engineering maintenance diagnosis method based on big data;
FIG. 2 is a schematic diagram of a specific flow of step S3 of the engineering maintenance diagnosis method based on big data;
FIG. 3 is a schematic diagram of the overall architecture of the engineering maintenance diagnostic system based on big data of the present invention;
FIG. 4 is a schematic diagram of a bridge pier threat coefficient calculation module of the engineering maintenance diagnosis system based on big data;
FIG. 5 is a schematic diagram of a bridge deck threat coefficient calculation module of the engineering maintenance diagnosis system based on big data.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments.
Example 1
Referring to fig. 1-2, an embodiment of the present invention is provided: the engineering maintenance diagnosis method based on big data comprises the following specific steps:
S1, dividing a viaduct to be maintained into n subareas, and extracting pier and bridge deck data of the subareas, wherein the pier data comprise pier surface concave area and depth data, pier surface crack length, width data and quantity data, the bridge deck data comprise bridge deck crack length and width data, bridge deck bulge, concave depth and area data and dislocation amplitude data of bridge deck joints, and the dislocation amplitude data of the bridge deck joints are longitudinal displacement of the joints of two bridge deck templates;
what should be stated here is: the specific content of S1 comprises the following steps:
S11, taking an overhead bridge to be maintained, acquiring the bridge pier positions of the overhead bridge, dividing the overhead bridge into areas according to the number of bridge piers, dividing the areas into subareas of the number of bridge piers, wherein each subarea comprises at least one bridge pier and half of bridge decks on two sides of the bridge pier;
S12, acquiring bridge deck and pier pictures of each sub-area by using an unmanned aerial vehicle, processing the images shot by the unmanned aerial vehicle, acquiring crack length, width and number data of the bridge deck and the piers, and simultaneously acquiring concave area and depth data of the surfaces of the bridge deck and the piers and ridge height data of the bridge deck joint;
It should be noted that, the unmanned aerial vehicle is used to acquire the bridge deck and pier images of each sub-area, the unmanned aerial vehicle acquires the bridge deck and pier images along the overpass, and the data of the crack length, width and number of the bridge deck and the pier can be obtained through simple image processing and manual measurement, and meanwhile, the data of the concave area and depth of the bridge deck and the pier surface and the data of the bump height at the bridge deck connection position are acquired, which is the prior art;
It should be noted that the following is a brief frame of Python code for implementing flying the unmanned aerial vehicle along the overpass and collecting the image, and note that this is just a starting point, and further development and testing are required according to specific hardware and requirements.
```python
import time
import dronekit
import cv2
# Is connected to unmanned plane
vehicle＝dronekit.connect('/dev/ttyUSB0',wait_ready＝True)
# Initialization camera
cap＝cv2.VideoCapture(0)
# Set image storage path
image_save_path＝'/path/to/image/folder/'
# Flight to the start of overpass
Target_ latitude =40.0# is replaced with the actual longitude and latitude
Target_ longitude = -75.0# is replaced by actual longitude and latitude
Target_all=50.0# is replaced with target height (in meters)
# Definition flight control function
def fly_to_bridge():
# Send instruction to fly unmanned aerial vehicle to target site
vehicle.simple_goto(dronekit.LocationGlobal(target_latitude,target_longitude,target_altitude))
Sleep (30) # waits for enough time to fly
# Start flying
fly_to_bridge()
# Start image acquisition
while True:
ret,frame＝cap.read()
if ret:
Where # an image processing code (e.g., save image, detect object, etc.) may be added
timestamp＝time.time()
image_filename＝f'{timestamp}.jpg'
cv2.imwrite(image_save_path+image_filename,frame)
# Close camera and disconnect with unmanned aerial vehicle's connection
cap.release()
vehicle.close()``
S2, respectively storing bridge pier data and bridge deck data in a data storage after separating the bridge pier data and the bridge deck data;
S3, extracting pier data, and leading the pier data into a pier threat calculation strategy to calculate threat coefficients of the piers;
what should be stated here is: the specific steps of S3 are as follows:
S31, extracting pier data of each sub-area, wherein the pier surface depression area and depth data, the pier surface crack length, the maximum data and the number data are included, the number of pier surface depressions is set to be m1, and the number of pier surface cracks is set to be m2;
s32, substituting the extracted pier surface depression area, depth data and pier surface depression quantity into a pier depression threat coefficient calculation formula to calculate pier depression threat coefficients, wherein the pier depression threat coefficient calculation formula is as follows: Wherein/> Is the area of the ith concave on the bridge pier,/>Is the depth of the ith concave on the pier,/>Is a safety standard value of the set concave area,/>The depth safety standard value of the set concave is set; the safety standard values for determining the bridge pier cracks, the pit areas and the pit depths need to consider a plurality of factors including the types, materials, the positions and the sizes of the cracks and the local climate conditions, and in general, the parameters need to be evaluated and determined by structural engineers and bridge specialists, 50 bridge specialists can be invited to evaluate the safety standard values, and then the safety standard values for the bridge pier cracks, the pit areas and the pit depths are obtained by an averaging mode;
S33, substituting the extracted length, maximum width and number of the surface cracks of the bridge pier into a bridge pier crack coefficient calculation formula to calculate a bridge pier crack coefficient, wherein the bridge pier crack coefficient calculation formula is as follows: wherein/> Is the i-th crack length data on the bridge pier,/>Is the width data of the ith crack on the pier,/>Is a safe standard value of the crack length on the bridge pier, i.e./>The safety standard value of the width of the crack on the bridge pier is set;
S34, substituting the extracted and calculated pier sinking threat coefficients and pier crack coefficients into a pier threat calculation formula to calculate threat coefficients of the pier, wherein the pier threat coefficient calculation formula is as follows: s d＝s1+s2;
S4, extracting bridge deck data, and leading the bridge deck data into a bridge deck threat calculation strategy to calculate the threat coefficient of the bridge deck;
it should be noted that the specific steps of S4 include the following:
S41, extracting bridge deck data of each sub-area comprises acquiring crack length, width and number data r 1 of the bridge deck, pit and bulge area, depth data and number data r 2 of the bridge deck and bulge height data of bridge deck joints;
s42, substituting the length of the extracted bridge deck surface cracks, the maximum width data and the number of the bridge pier surface cracks into a bridge deck crack threat coefficient calculation formula to calculate a bridge deck crack threat coefficient, wherein the bridge deck crack threat coefficient calculation formula is as follows: Wherein/> For the i-th crack length data on the bridge deck,/>For the data of the width of the ith crack on the bridge deck,/>Is a set safety standard value of the crack length on the bridge deck,/>The safety standard value of the width of the crack on the bridge deck is set;
S43, substituting the extracted bridge deck junction uplift height data, the bridge deck debouching and uplift area, the depth data and the quantity data into a bridge deck flattening threat coefficient calculation formula to calculate a bridge deck flattening threat coefficient, wherein the debouching quantity is r 3, the uplift quantity is r 4, and the bridge deck flattening threat coefficient calculation formula is Wherein/>Depth of i-th recess on deck,/>Is the area of the ith recess on the bridge deck,/>Is a depth safety standard value of the dent on the bridge deck,Is the standard value of the area safety of the dent on the bridge deck,/>For the height of the ith bump on the deck,/>Is the area of the ith bump on the bridge deck,/>Is the safety standard value of the height of the bump on the bridge deck,/>Is the standard value of the area of the ith bump on the bridge deck, and l m is the bump height at the bridge deck joint,/>A 1 is a bridge floor depression duty ratio coefficient, a 2 is a bridge floor elevation duty ratio coefficient, and a 1+a2 =1;
S44, substituting the calculated threat coefficients of the bridge deck cracks and the bridge deck leveling threat coefficients into a bridge deck threat calculation formula to calculate the threat coefficients of the bridge deck, wherein the bridge deck threat coefficient calculation formula is as follows: s m＝s3+s4;
s5, substituting the threat coefficient of the bridge pier and the threat coefficient of the bridge deck obtained by calculation into a bridge threat value calculation formula to calculate a bridge threat value;
it should be noted that, S5 includes the following specific steps:
s51, extracting pier threat coefficients S d and bridge deck threat coefficients S m of the calculated subareas;
S52, substituting the obtained bridge pier threat coefficient S d and the bridge deck threat coefficient S m into a bridge threat value calculation formula to calculate a bridge threat value, wherein the calculation formula of the bridge threat value is as follows: Wherein e is the base of the exponential function;
S6, judging whether the threat value of the bridge body is larger than or equal to a set threat threshold value, if so, setting the subarea as an abnormal bridge body, and reminding maintenance personnel of maintenance; if not, the subarea is set as a safety bridge body, prompting is not needed, the threat threshold value is used for substituting the verification data of 500 groups of bridges into the calculated threat value of the bridge body, then 500 experts in the field are used for finding out abnormal bridge bodies, and the abnormal bridge bodies are substituted into data fitting software for fitting, so that the threat threshold value, the bridge deck depression duty ratio coefficient and the bridge deck rising duty ratio coefficient are obtained.
The bridge threat calculation method includes dividing areas of the viaduct to be maintained, separating bridge pier and bridge deck data, storing the bridge pier and bridge deck data in a data storage, extracting bridge pier data, importing the bridge pier data into a bridge pier threat calculation strategy to calculate threat coefficients of the bridge pier, extracting bridge deck data, importing the bridge deck data into the bridge deck threat calculation strategy to calculate threat coefficients of the bridge deck, substituting the threat coefficients of the bridge pier and the threat coefficients of the bridge deck obtained through calculation into a bridge threat value calculation formula to calculate a bridge threat value, judging whether the bridge threat value is greater than or equal to a set threat threshold, if so, setting the subareas as abnormal bridges, and reminding maintenance personnel of maintenance; if not, the subarea is set as a safety bridge body, reminding is not needed, the judging speed of the viaduct damage is effectively improved, and the accuracy of the viaduct threat maintenance diagnosis is improved.
Example 2
As shown in fig. 3, the engineering maintenance diagnosis system based on big data is implemented based on the engineering maintenance diagnosis method based on big data, and specifically includes: the bridge threat system comprises a region division module, a data storage module, a bridge pier threat coefficient calculation module, a bridge threat value calculation module, a control module, a threat value comparison module and an abnormal bridge prompt module, wherein the region division module is used for dividing regions of a viaduct to be maintained, the data storage module is used for respectively storing bridge piers and bridge deck data in a data storage after separating the bridge piers and the bridge deck data, the bridge pier threat coefficient calculation module is used for extracting bridge pier data to be imported into a bridge pier threat calculation strategy to calculate threat coefficients of the bridge piers, and the control module is used for controlling the operation of the region division module, the data storage module, the bridge pier threat coefficient calculation module, the bridge threat value calculation module, the threat value comparison module and the abnormal bridge prompt module; the bridge threat coefficient calculation module is used for extracting the threat coefficient of the bridge deck in the bridge threat calculation strategy of leading the bridge deck data into the bridge threat calculation strategy, the bridge threat value calculation module is used for substituting the threat coefficient of the bridge pier and the threat coefficient of the bridge deck obtained through calculation into the bridge threat value calculation formula to calculate the bridge threat value, the threat value comparison module is used for judging whether the bridge threat value is greater than or equal to a set threat threshold value, and the abnormal bridge prompting module is used for prompting maintenance personnel to maintain an abnormal bridge;
as shown in fig. 4, in this embodiment, the pier threat coefficient calculation module includes a sub-region pier data extraction unit, a pier recess threat coefficient calculation unit, a pier crack coefficient calculation unit, and a pier threat coefficient calculation unit, where the sub-region pier data extraction unit is configured to extract pier data of each sub-region including pier surface recess area and depth data, pier surface crack length, maximum data, and number data, the pier recess threat coefficient calculation unit is configured to substitute the extracted pier surface recess area, depth data, and pier surface recess number into a pier recess threat coefficient calculation formula to calculate a pier recess threat coefficient, and the pier crack coefficient calculation unit is configured to extract the calculated pier recess threat coefficient and pier crack coefficient into the pier threat coefficient calculation formula to calculate a threat coefficient of the pier;
As shown in fig. 5, in this embodiment, the bridge deck threat coefficient calculation module includes a sub-area bridge deck data extraction unit, a bridge deck crack threat coefficient calculation unit, a bridge deck leveling threat coefficient calculation unit and a bridge deck threat coefficient calculation unit, the sub-area bridge deck data extraction unit is used for extracting bridge deck data of each sub-area including obtaining crack length, width and number data of the bridge deck, pit and bump area, depth data and number data of the bridge deck, the bridge deck crack threat coefficient calculation unit is used for substituting the extracted bridge deck surface crack length, maximum data and number of bridge deck surface cracks into the bridge deck crack threat coefficient calculation formula to calculate a bridge deck crack threat coefficient, the bridge deck leveling threat coefficient calculation unit is used for substituting the extracted bridge deck junction bump height data and pit and bump area, depth data and number data of the bridge deck into the bridge deck leveling threat coefficient calculation formula to calculate a bridge deck leveling threat coefficient, and the bridge deck threat coefficient calculation unit is used for substituting the calculated crack threat coefficient and bridge deck leveling threat coefficient into the bridge deck threat coefficient calculation formula.
Example 3
The present embodiment provides an electronic device including: a processor and a memory, wherein the memory stores a computer program for the processor to call;
The processor executes an engineering repair diagnosis method based on big data as described above by calling a computer program stored in the memory.
The electronic device may vary greatly in configuration or performance, and can include one or more processors (Central Processing Units, CPU) and one or more memories, where the memories store at least one computer program that is loaded and executed by the processors to implement an engineering repair diagnostic method based on big data provided by the above-described method embodiments. The electronic device can also include other components for implementing the functions of the device, for example, the electronic device can also have wired or wireless network interfaces, input-output interfaces, and the like, for inputting and outputting data. The present embodiment is not described herein.
Example 4
The present embodiment proposes a computer-readable storage medium having stored thereon an erasable computer program;
the computer program, when run on a computer device, causes the computer device to perform one of the above-described big data based engineering repair diagnostic methods.
For example, the computer readable storage medium can be Read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), compact disk Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM), magnetic tape, floppy disk, optical data storage device, and the like.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
It should be understood that determining B from a does not mean determining B from a alone, but can also determine B from a and/or other information.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions in accordance with embodiments of the present invention are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by way of wired or/and wireless networks from one website site, computer, server, or data center to another. Computer readable storage media can be any available media that can be accessed by a computer or data storage devices, such as servers, data centers, etc. that contain one or more collections of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely one, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (6)
1. The engineering maintenance diagnosis method based on big data is characterized by comprising the following specific steps:
s1, dividing regions of a viaduct to be maintained into n subareas, and extracting pier and bridge deck data of the subareas;
S2, respectively storing bridge pier data and bridge deck data in a data storage after separating the bridge pier data and the bridge deck data;
S3, extracting pier data, and leading the pier data into a pier threat calculation strategy to calculate threat coefficients of the piers;
S4, extracting bridge deck data, and leading the bridge deck data into a bridge deck threat calculation strategy to calculate the threat coefficient of the bridge deck;
s5, substituting the threat coefficient of the bridge pier and the threat coefficient of the bridge deck obtained by calculation into a bridge threat value calculation formula to calculate a bridge threat value;
S6, judging whether the threat value of the bridge body is larger than or equal to a set threat threshold value, if so, setting the subarea as an abnormal bridge body, and reminding maintenance personnel of maintenance; if not, the subarea is set as a safety bridge body, and reminding is not needed; the specific steps of the S3 are as follows:
S31, extracting pier data of each sub-area, wherein the pier surface depression area and depth data, the pier surface crack length, the maximum width data and the number data are included, the number of pier surface depressions is set to be m 1, and the number of pier surface cracks is set to be m 2;
S32, substituting the extracted pier surface depression area and depth data and pier surface depression quantity into a pier depression threat coefficient calculation formula to calculate a pier depression threat coefficient S 1, wherein the pier depression threat coefficient calculation formula is as follows: Wherein/> Is the area of the ith concave on the bridge pier,/>Is the depth of the ith concave on the pier,/>Is a safety standard value of the set concave area,/>The depth safety standard value of the set concave is set; the specific step of S3 further includes the following:
s33, substituting the extracted length, maximum width and number of the surface cracks of the bridge pier into a bridge pier crack coefficient calculation formula to calculate a bridge pier crack coefficient S 2, wherein the bridge pier crack coefficient calculation formula is as follows: wherein/> Is the i-th crack length data on the bridge pier,/>Is the width data of the ith crack on the pier,/>Is a safe standard value of the crack length on the bridge pier, i.e./>The safety standard value of the width of the crack on the bridge pier is set;
S34, substituting the extracted and calculated pier sinking threat coefficients and pier crack coefficients into a pier threat calculation formula to calculate threat coefficients of the pier, wherein the pier threat coefficient calculation formula is as follows: s d＝s1+s2; the specific steps of S4 include the following:
S41, extracting bridge deck data of each sub-area comprises acquiring crack length, width and number data r 1 of the bridge deck, pit and bulge area, depth data and number data r 2 of the bridge deck and bulge height data of bridge deck joints;
s42, substituting the length of the extracted bridge deck surface cracks, the maximum width data and the number of the bridge pier surface cracks into a bridge deck crack threat coefficient calculation formula to calculate a bridge deck crack threat coefficient, wherein the bridge deck crack threat coefficient calculation formula is as follows: Wherein/> For the i-th crack length data on the bridge deck,/>For the data of the width of the ith crack on the bridge deck,/>Is a set safety standard value of the crack length on the bridge deck,/>The safety standard value of the width of the crack on the bridge deck is set;
S43, substituting the extracted bridge deck junction uplift height data, the bridge deck debouching and uplift area, the depth data and the quantity data into a bridge deck flattening threat coefficient calculation formula to calculate a bridge deck flattening threat coefficient, wherein the debouching quantity is r 3, the uplift quantity is r 4, and the bridge deck flattening threat coefficient calculation formula is Wherein/>Depth of i-th recess on deck,/>Is the area of the ith recess on the bridge deck,/>Is a depth safety standard value of the pit on the bridge deck,/>Is the standard value of the area safety of the dent on the bridge deck,/>For the height of the ith bump on the deck,/>Is the area of the ith bump on the bridge deck,/>Is the safety standard value of the height of the bump on the bridge deck,/>Is the standard value of the area of the ith bump on the bridge deck, and l m is the bump height at the bridge deck joint,/>A 1 is a bridge floor depression duty ratio coefficient, a 2 is a bridge floor elevation duty ratio coefficient, and a 1+a2 =1;
S44, substituting the calculated threat coefficients of the bridge deck cracks and the bridge deck leveling threat coefficients into a bridge deck threat calculation formula to calculate the threat coefficients of the bridge deck, wherein the bridge deck threat coefficient calculation formula is as follows: s m＝s3+s4; the step S5 comprises the following specific steps:
s51, extracting pier threat coefficients S d and bridge deck threat coefficients S m of the calculated subareas;
S52, substituting the obtained bridge pier threat coefficient S d and the bridge deck threat coefficient S m into a bridge threat value calculation formula to calculate a bridge threat value, wherein the calculation formula of the bridge threat value is as follows: Where e is the base of the exponential function.
2. The engineering repair diagnosis method based on big data according to claim 1, wherein the specific content of S1 comprises the following steps:
S11, taking an overhead bridge to be maintained, acquiring the bridge pier positions of the overhead bridge, dividing the overhead bridge into areas according to the number of bridge piers, dividing the areas into subareas of the number of bridge piers, wherein each subarea comprises at least one bridge pier and half of bridge decks on two sides of the bridge pier;
S12, acquiring bridge deck and pier pictures of each sub-area by using an unmanned aerial vehicle, processing the photographed images of the unmanned aerial vehicle, acquiring crack length, width and number data of the bridge deck and the piers, and simultaneously acquiring concave area and depth data of the surfaces of the bridge deck and the piers and bridge deck junction uplift height data.
3. A big data based engineering maintenance diagnostic system, which is implemented based on the big data based engineering maintenance diagnostic method according to any one of claims 1-2, characterized in that it specifically comprises: the bridge threat system comprises an area dividing module, a data storage module, a bridge pier threat coefficient calculating module, a bridge threat coefficient calculating module, a control module, a threat value comparing module and an abnormal bridge prompting module, wherein the area dividing module is used for dividing areas of a viaduct to be maintained, the data storage module is used for respectively storing bridge piers and bridge deck data in a data storage after separating the bridge piers and the bridge deck data, the bridge pier threat coefficient calculating module is used for extracting threat coefficients of bridge piers, which are calculated in a bridge pier threat calculating strategy, and the control module is used for controlling the operation of the area dividing module, the data storage module, the bridge pier threat coefficient calculating module, the bridge threat coefficient calculating module, the threat value comparing module and the abnormal bridge prompting module.
4. The engineering maintenance diagnosis system based on big data as set forth in claim 3, wherein the bridge threat coefficient calculation module is configured to extract bridge deck data, import the bridge deck data into a bridge deck threat calculation strategy, calculate a bridge threat value by substituting the calculated threat coefficient of the bridge pier and the calculated threat coefficient of the bridge deck into a bridge threat value calculation formula, and determine whether the bridge threat value is greater than or equal to a set threat threshold, and the abnormal bridge prompting module is configured to prompt maintenance personnel to maintain an abnormal bridge.
5. The engineering maintenance diagnosis system based on big data according to claim 4, wherein the pier threat coefficient calculation module comprises a sub-region pier data extraction unit, a pier depression threat coefficient calculation unit, a pier crack coefficient calculation unit and a pier threat coefficient calculation unit, the sub-region pier data extraction unit is used for extracting pier data of each sub-region, the pier surface depression area and depth data, the pier surface crack length, the maximum data and the number data, the pier depression threat coefficient calculation unit is used for substituting the extracted pier surface depression area, depth data and pier surface depression number into a pier depression threat coefficient calculation formula to calculate pier depression threat coefficients, and the pier crack coefficient calculation unit is used for extracting the calculated pier depression threat coefficients and the pier crack coefficients into the threat coefficient calculation formula to calculate pier threat coefficients.
6. The large data-based engineering maintenance diagnostic system according to claim 5, wherein the bridge deck threat coefficient calculation module comprises a sub-area bridge deck data extraction unit, a bridge deck crack threat coefficient calculation unit, a bridge deck leveling threat coefficient calculation unit and a bridge deck threat coefficient calculation unit, the sub-area bridge deck data extraction unit is used for extracting bridge deck data of each sub-area, including obtaining crack length, width and number data of a bridge deck, pit and ridge area of the bridge deck, depth data and number data, the bridge deck crack threat coefficient calculation unit is used for substituting the extracted bridge deck surface crack length, maximum width data and bridge deck surface crack number into a bridge deck crack threat coefficient calculation formula to calculate a bridge deck crack threat coefficient, the bridge deck leveling threat coefficient calculation unit is used for substituting the extracted bridge deck junction ridge height data and pit and ridge area of the bridge deck, depth data and number data into a leveling threat coefficient calculation formula, and the bridge deck threat coefficient calculation unit is used for substituting the calculated bridge deck crack threat coefficient and the bridge deck leveling threat coefficient into the calculated bridge deck leveling threat coefficient.
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Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AU2003236468A1 (en) * | 2002-08-30 | 2004-03-18 | Slider Guide Pty Ltd | An Anchor Assembly for a Road Post |
| CN106971630A (en) * | 2017-03-27 | 2017-07-21 | 中公智联（北京）科技有限公司 | Navigation bridge pier anticollision monitoring early-warning system |
| KR20180102339A (en) * | 2017-03-07 | 2018-09-17 | 한국건설기술연구원 | A system and method for evaluating ground subsidence risk of sewer pipe surrounding ground, and a recording medium having computer readable program for executing the method |
| CN111486902A (en) * | 2020-06-04 | 2020-08-04 | 许辉 | Large-span bridge safety performance real-time monitoring system based on big data |
| CN111505010A (en) * | 2020-04-28 | 2020-08-07 | 张地林 | Bridge safety detection system based on cloud platform |
| CN111709664A (en) * | 2020-06-28 | 2020-09-25 | 张玉红 | Bridge structure safety monitoring management system based on big data |
| CN112950543A (en) * | 2021-02-02 | 2021-06-11 | 岑杰 | Bridge maintenance method and system, storage medium and intelligent terminal |
| CN114626680A (en) * | 2022-02-15 | 2022-06-14 | 中电建路桥集团有限公司 | Safety assessment method and device for reinforced concrete bridge |
| CN114757373A (en) * | 2022-04-07 | 2022-07-15 | 保银信科信息技术(湖北)有限公司 | Bridge monitoring analysis method and device based on digital model analysis and computer storage medium |
| CN217579818U (en) * | 2022-06-07 | 2022-10-14 | 中铁大桥科学研究院有限公司 | Pier reinforcing apparatus |
| CN115186361A (en) * | 2022-07-29 | 2022-10-14 | 四川鼎诺泰成交通设备有限公司 | Urban road model establishing and analyzing method, system and storage medium |
| CN116202432A (en) * | 2023-04-14 | 2023-06-02 | 西北大学 | Intelligent regional landslide monitoring and management method |
| CN116543114A (en) * | 2023-07-05 | 2023-08-04 | 江苏狄诺尼信息技术有限责任公司 | Lifting three-dimensional visual underground engineering sand table demonstration system |
| CN116910423A (en) * | 2023-09-12 | 2023-10-20 | 中国土木工程集团有限公司 | Bridge deformation monitoring method and monitoring system |
| CN116980958A (en) * | 2023-09-22 | 2023-10-31 | 上海优立检测技术股份有限公司 | Radio equipment electric fault monitoring method and system based on data identification |
| CN117093842A (en) * | 2023-08-31 | 2023-11-21 | 四川省公路规划勘察设计研究院有限公司 | Geological deformation upper risk bridge monitoring demand assessment method and system |
-
2023
- 2023-12-04 CN CN202311641831.2A patent/CN117541234B/en active Active
Patent Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AU2003236468A1 (en) * | 2002-08-30 | 2004-03-18 | Slider Guide Pty Ltd | An Anchor Assembly for a Road Post |
| KR20180102339A (en) * | 2017-03-07 | 2018-09-17 | 한국건설기술연구원 | A system and method for evaluating ground subsidence risk of sewer pipe surrounding ground, and a recording medium having computer readable program for executing the method |
| CN106971630A (en) * | 2017-03-27 | 2017-07-21 | 中公智联（北京）科技有限公司 | Navigation bridge pier anticollision monitoring early-warning system |
| CN111505010A (en) * | 2020-04-28 | 2020-08-07 | 张地林 | Bridge safety detection system based on cloud platform |
| CN111486902A (en) * | 2020-06-04 | 2020-08-04 | 许辉 | Large-span bridge safety performance real-time monitoring system based on big data |
| CN111709664A (en) * | 2020-06-28 | 2020-09-25 | 张玉红 | Bridge structure safety monitoring management system based on big data |
| CN112950543A (en) * | 2021-02-02 | 2021-06-11 | 岑杰 | Bridge maintenance method and system, storage medium and intelligent terminal |
| CN114626680A (en) * | 2022-02-15 | 2022-06-14 | 中电建路桥集团有限公司 | Safety assessment method and device for reinforced concrete bridge |
| CN114757373A (en) * | 2022-04-07 | 2022-07-15 | 保银信科信息技术(湖北)有限公司 | Bridge monitoring analysis method and device based on digital model analysis and computer storage medium |
| CN217579818U (en) * | 2022-06-07 | 2022-10-14 | 中铁大桥科学研究院有限公司 | Pier reinforcing apparatus |
| CN115186361A (en) * | 2022-07-29 | 2022-10-14 | 四川鼎诺泰成交通设备有限公司 | Urban road model establishing and analyzing method, system and storage medium |
| CN116202432A (en) * | 2023-04-14 | 2023-06-02 | 西北大学 | Intelligent regional landslide monitoring and management method |
| CN116543114A (en) * | 2023-07-05 | 2023-08-04 | 江苏狄诺尼信息技术有限责任公司 | Lifting three-dimensional visual underground engineering sand table demonstration system |
| CN117093842A (en) * | 2023-08-31 | 2023-11-21 | 四川省公路规划勘察设计研究院有限公司 | Geological deformation upper risk bridge monitoring demand assessment method and system |
| CN116910423A (en) * | 2023-09-12 | 2023-10-20 | 中国土木工程集团有限公司 | Bridge deformation monitoring method and monitoring system |
| CN116980958A (en) * | 2023-09-22 | 2023-10-31 | 上海优立检测技术股份有限公司 | Radio equipment electric fault monitoring method and system based on data identification |
Non-Patent Citations (3)
| Title |
|---|
| 城市桥梁运营期风险评估与对策研究;宋建;《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》;20120715(第07期);第C034-147页 * |
| 深部厚煤层沿底巷道变形破坏机理及帮顶协同支护研究;彭杨皓;《中国博士学位论文全文数据库工程科技Ⅰ辑》;20230215(第02期);第B021-39页 * |
| 近断层地震动脉冲识别及考虑波动效应的桥梁地震破坏分析;夏春旭;《中国博士学位论文全文数据库 工程科技Ⅱ辑》;20210115(第01期);第C034-38页 * |
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