CN112883847A - Intelligent supervision method for municipal road engineering construction project based on big data and image analysis technology - Google Patents
Intelligent supervision method for municipal road engineering construction project based on big data and image analysis technology Download PDFInfo
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Abstract
The invention discloses a municipal road engineering construction project intelligent supervision method based on big data and image analysis technology, which is characterized in that by obtaining the side surface image of a cement concrete pavement core sample of each detection subarea, carrying out image preprocessing operation, simultaneously obtaining the area proportion of each cement concrete material type in the side surface image of the cement concrete pavement core sample of each detection subarea, comparing the area proportion with standard data, further obtaining the limit load of the cement concrete pavement core sample of each detection subarea, determining the corresponding compression strength grade and the compression strength influence coefficient, counting the comprehensive quality influence coefficient of the cement concrete pavement and the corresponding quality grade of the cement concrete pavement, displaying the comprehensive quality influence coefficient and the corresponding quality grade of the cement concrete pavement, having the characteristics of high reality and high reliability, being beneficial to reducing the probability of sudden damage of concrete and prolonging the service life of the concrete pavement, provides powerful technical support for the safety application of municipal roads.
Description
Technical Field
The invention belongs to the technical field of municipal road monitoring, and particularly relates to a municipal road engineering construction project intelligent supervision method based on big data and an image analysis technology.
Background
With the rapid development of social economy, the construction of municipal roads rises rapidly, and cement concrete is widely applied to the field of municipal roads due to the remarkable advantages of high compressive strength, easily available raw materials, low cost and the like. However, concrete is easily damaged in the long-term service process, the damage degree is accumulated continuously along with time, and the concrete is easily released and erupted intensively under the action of sudden natural disasters or external loads, so that immeasurable economic loss and extremely bad social influence are caused. Therefore, real-time monitoring and evaluation of concrete, particularly of important structures, is essential.
The existing municipal road detection method mainly adopts external detection technologies including deflection, acoustic emission, ground detection radar, ultrasound, infrared thermal image and image technologies, but the external detection technology lacks spontaneity and has hysteresis, requires large manpower and equipment resources, is mostly positioned on the surface at detection positions, and is difficult to detect hidden cracks from bottom to top.
Disclosure of Invention
Aiming at the problems, the invention provides an intelligent supervision method for municipal road engineering construction projects based on big data and image analysis technology, which comprises the steps of obtaining a side image of a core sample of a cement concrete pavement of each detection subarea, carrying out image preprocessing operation, obtaining the area proportion of each cement concrete material type in the side image of the core sample of the cement concrete pavement of each detection subarea, comparing the area proportion with standard data, further obtaining the limit load of the core sample of the cement concrete pavement of each detection subarea, determining the corresponding compressive strength grade and the compressive strength influence coefficient, counting the comprehensive quality influence coefficient of the cement concrete pavement and the corresponding quality grade of the cement concrete pavement, displaying the comprehensive quality influence coefficient and the corresponding quality grade of the cement concrete pavement, and solving the problems in the background technology.
The purpose of the invention can be realized by the following technical scheme:
the intelligent supervision method for the municipal road engineering construction project based on the big data and image analysis technology comprises the following steps:
s1: acquiring a side image of the core sample of the cement concrete pavement of each detection subarea, and performing image preprocessing operation;
s2: acquiring the area proportion of each cement concrete material type in the core sample side image of the cement concrete pavement of each detection subarea, and comparing the area proportion with standard data;
s3: acquiring the limit load of the core sample of the cement concrete pavement of each detection subarea, and determining the corresponding compressive strength grade and the compressive strength influence coefficient;
s4: according to the data obtained in S2 and S3, the comprehensive quality influence coefficient of the cement-concrete pavement and the corresponding quality grade of the cement-concrete pavement are counted and displayed;
the intelligent municipal road engineering construction project supervision method based on the big data and image analysis technology uses a municipal road engineering construction project intelligent supervision system based on the big data and image analysis technology, and comprises a region division module, a core sample acquisition module, an image preprocessing module, a feature extraction module, a compressive strength detection module, a database, a modeling analysis server, a management server and a display terminal;
the core sample acquisition module is respectively connected with the region division module, the image acquisition module and the compressive strength detection module, the image preprocessing module is respectively connected with the image acquisition module and the characteristic extraction module, the modeling analysis server is respectively connected with the compressive strength detection module, the characteristic extraction module, the database and the management server, the database is respectively connected with the characteristic extraction module and the management server, and the display terminal is connected with the management server;
the regional division module is used for carrying out regional division on the detected municipal road, dividing the detected whole municipal road into a plurality of detection subregions which have the same area and are mutually connected, numbering the divided detection subregions according to a preset sequence, and sequentially marking the detection subregions as 1,2, a.
The core sample acquisition module comprises a core drilling machine and is used for coring the cement concrete pavement of each detection subarea and respectively sending the core samples of the cement concrete pavement acquired by each detection subarea to the image acquisition module and the compressive strength detection module;
the image acquisition module comprises a high-definition camera and is used for acquiring side images of the core sample of the cement concrete pavement of each detection sub-region and sending the acquired side images of the core sample of the cement concrete pavement of each detection sub-region to the image preprocessing module;
the image preprocessing module receives the core sample side images of the cement concrete pavement of each detection subarea sent by the image acquisition module, performs image segmentation on the received core sample side images of the cement concrete pavement of each detection subarea, splices core sample characteristic areas obtained by image segmentation, removes background images outside the core sample characteristic areas of the cement concrete pavement, changes the reserved core sample characteristic areas of the cement concrete pavement into images with consistent size and without deflection angles of core samples through geometric normalization processing, performs gray change processing and image enhancement processing simultaneously to obtain the processed core sample side images of the cement concrete pavement of each detection subarea, and sends the processed core sample side images of the cement concrete pavement of each detection subarea to the characteristic extraction module;
the characteristic extraction module receives the processed cement concrete pavement core sample side images of all the detection sub-regions sent by the image preprocessing module, amplifies the received cement concrete pavement core sample side images of all the detection sub-regions, compares the characteristics of all the cement concrete materials in the cement concrete pavement core sample side images of all the detection sub-regions with the characteristics corresponding to different cement concrete material types stored in the database to obtain the characteristics of all the cement concrete material types in the cement concrete pavement core sample side images of all the detection sub-regions, further obtains the characteristics of all the cement concrete material types in the cement concrete pavement core sample side images of all the detection sub-regions, and sequentially marks the characteristics of all the cement concrete material types as 1,2, a.a.j, a.j.a.j.a., k, forming a cement concrete material category characteristic set Ai(ai1,ai2,...,aij,...,aik)，aij represents the jth cement concrete material type characteristic in the cement concrete pavement core sample side image of the ith detection subarea, and the characteristic extraction module respectively sends the cement concrete material type characteristic set and the cement concrete pavement core sample side images of the detection subareas to the modeling analysis server;
the compressive strength detection module comprises a compression testing machine for receiving the core samples of the cement concrete pavement, which are obtained by each detection subarea and sent by the core sample obtaining module, gradually applying pressure to the core samples of the cement concrete pavement obtained by each detection subarea, stopping applying pressure when the core sample of the cement concrete pavement in the detection subarea is close to damage and begins to deform until the core sample of the cement concrete pavement in the detection subarea is damaged, recording the limit load of the core sample of the cement concrete pavement obtained by the detection subarea, further obtaining the ultimate load of the core sample of the cement concrete pavement obtained by each detection subarea to form an ultimate load set B (B1, B2, B.., bi, B.., bg), wherein bi represents the ultimate load of the core sample of the cement concrete pavement of the ith detection subarea, and the compressive strength detection module sends the ultimate load set to the modeling analysis server;
the database is used for storing the characteristics corresponding to different cement concrete material types, storing the image area of the side surface of the core sample of the cement concrete pavement, storing the standard proportion of the area occupied by each cement concrete material type, storing the limit load range corresponding to each compression strength grade, storing the compression strength influence coefficient corresponding to each compression strength grade, and storing the comprehensive quality evaluation coefficient range corresponding to each cement concrete pavement quality grade;
the modeling analysis server receives the characteristic set of the types of the cement concrete materials sent by the characteristic extraction module and the side images of the core samples of the cement concrete pavements of all the detection sub-regions, and obtains the occupied area of the types of the cement concrete materials for the received side images of the core samples of the cement concrete pavements of all the detection sub-regions to form a set C of the area of the cement concrete materialsi(ci1,ci2,...,cij,...,cik)，cij is the area occupied by the jth cement concrete material type in the core sample side image of the cement concrete pavement of the ith detection subarea, the modeling analysis server extracts the area of the side image of the core sample of the cement concrete pavement stored in the database, and the area proportion occupied by each cement concrete material type in the core sample side image of the cement concrete pavement of each detection subarea is counted according to the set of the area of the cement concrete material to form the area occupied by the cement concrete materialRatio set Di(di1,di2,...,dij,...,dik)，dij is the area proportion occupied by the jth cement concrete material type in the cement concrete pavement core sample side image of the ith detection subarea, the modeling analysis server compares the area proportion occupied by each cement concrete material type in the cement concrete pavement core sample side image of each detection subarea with the standard area proportion occupied by each cement concrete material type stored in the database to form a cement concrete material area proportion comparison set Di′(di′1,di′2,...,di′j,...,di′k)，di' j is the difference value between the area proportion occupied by the jth cement concrete material type in the cement concrete pavement core sample side image of the ith detection subarea and the area standard proportion occupied by the jth cement concrete material type;
the modeling analysis server receives the limit load set sent by the compressive strength detection module, extracts the limit load of the core sample of the cement concrete pavement obtained by each detection subarea in the limit load set, compares the limit load of the core sample of the cement concrete pavement of each detection subarea with the limit load range corresponding to each compressive strength grade stored in the database, obtains the compressive strength grade corresponding to the limit load of the core sample of the cement concrete pavement of the detection subarea, extracts the compressive strength influence coefficient corresponding to each compressive strength grade stored in the database, and forms a compressive strength influence coefficient set
The coefficient of influence of the compressive strength of the core sample of the cement-mud soil pavement is expressed as i detection subareas;
the modeling analysis server calculates the comprehensive quality evaluation coefficient of the cement concrete pavement according to the compression strength influence coefficient set and the cement concrete material area ratio comparison set, and sends the calculated comprehensive quality evaluation coefficient of the cement concrete pavement to the management server;
the management server receives the comprehensive quality evaluation coefficient of the cement concrete pavement sent by the modeling analysis server, compares the received comprehensive quality evaluation coefficient of the cement concrete pavement with the comprehensive quality evaluation coefficient range of the cement concrete pavement corresponding to each cement concrete pavement quality grade stored in the database, if the comprehensive quality evaluation coefficient of the cement concrete pavement is within the comprehensive quality evaluation coefficient range of the cement concrete pavement corresponding to the first-level cement concrete pavement quality grade, the quality grade of the cement concrete pavement is first level, if the comprehensive quality evaluation coefficient of the cement concrete pavement is within the comprehensive quality evaluation coefficient range of the cement concrete pavement corresponding to the second-level cement concrete pavement quality grade, the quality grade of the cement concrete pavement is second level, and if the comprehensive quality evaluation coefficient of the cement concrete pavement is within the comprehensive quality evaluation coefficient range of the cement concrete pavement corresponding to the third-level cement concrete pavement quality grade, the quality evaluation coefficient of the cement concrete pavement is second-level Within the coefficient range, the quality grade of the cement concrete pavement is three-grade, and the management server respectively sends the comprehensive quality evaluation coefficient of the cement concrete pavement and the corresponding quality grade of the cement concrete pavement to a display terminal;
and the display terminal receives and displays the comprehensive quality evaluation coefficient of the cement concrete pavement and the corresponding quality grade of the cement concrete pavement, which are sent by the management server.
Further, the cement concrete material types comprise granulated blast furnace slag, steel slag, fly ash and limestone.
Further, the calculation formula of the area proportion occupied by each cement concrete material type in the cement concrete pavement core sample side surface image of each detection subarea is
dij represents the area proportion occupied by the jth cement concrete material type in the cement concrete pavement core sample side image of the ith detection subarea, and S represents the area of the cement concrete pavement core sample side image.
Furthermore, the upper limit value of the comprehensive quality evaluation coefficient range of the cement concrete pavement corresponding to the quality grade of the primary cement concrete pavement is smaller than the lower limit value of the comprehensive quality evaluation coefficient range of the cement concrete pavement corresponding to the quality grade of the secondary cement concrete pavement, and the upper limit value of the comprehensive quality evaluation coefficient range of the cement concrete pavement corresponding to the quality grade of the secondary cement concrete pavement is smaller than the lower limit value of the comprehensive quality evaluation coefficient range of the cement concrete pavement corresponding to the quality grade of the tertiary cement concrete pavement.
Further, the upper limit value of the limit load range corresponding to the first-level compressive strength grade is smaller than the lower limit value of the limit load range corresponding to the second-level compressive strength grade, and the upper limit value of the limit load range corresponding to the second-level compressive strength grade is smaller than the lower limit value of the limit load range corresponding to the third-level compressive strength grade.
Furthermore, the magnitude sequence of the compression strength influence coefficients corresponding to the compression strength grades is omega 1 < omega 2 < omega 3.
Further, the calculation formula of the comprehensive quality evaluation coefficient of the cement concrete pavement is
The coefficient of influence of the compressive strength of the core sample of the cement-concrete soil pavement expressed as i detection subareas, di' j is the difference between the area proportion of the jth cement concrete material type in the core sample side image of the cement concrete pavement of the ith detection subarea and the area standard proportion of the jth cement concrete material type, and dij represents the area proportion occupied by the jth cement concrete material type in the cement concrete pavement core sample side image of the ith detection subarea.
Has the advantages that:
(1) the invention obtains the side images of the core sample of the cement concrete pavement of each detection subarea, and carries out image preprocessing operation, meanwhile, the area proportion of each cement concrete material type in the core sample side image of the cement concrete pavement of each detection subarea is obtained and compared with standard data, further acquiring the limit load of the core sample of the cement concrete pavement of each detection subarea, determining the corresponding compressive strength grade and the compressive strength influence coefficient, to count the comprehensive quality influence coefficient of the cement concrete pavement and the corresponding quality grade of the cement concrete pavement, and displays the concrete, has the characteristics of high authenticity and high reliability, is beneficial to reducing the probability of sudden damage of the concrete, the service life of the concrete pavement is prolonged, and powerful technical support is provided for the safe application of the municipal road.
(2) The invention provides reliable early-stage data preparation and reference basis for later statistics of the comprehensive quality evaluation coefficient of the cement concrete pavement by acquiring the side images and the ultimate load of the core sample of the cement concrete pavement in each detection sub-region, and has the characteristics of high authenticity and high data accuracy and precision.
(3) According to the invention, at the display terminal, the comprehensive quality evaluation coefficient and the corresponding quality grade of the cement concrete pavement are displayed, so that the detection data of the municipal road are provided for relevant departments, and timely personnel can conveniently take different measures to safely maintain the municipal road according to the detection data of the municipal road, thereby greatly improving the use safety of the municipal road.
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 diagram of the steps of the method of the present invention.
FIG. 2 is a flow chart of the system 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 intelligent supervision method for the municipal road engineering construction project based on the big data and image analysis technology comprises the following steps:
s1: acquiring a side image of the core sample of the cement concrete pavement of each detection subarea, and performing image preprocessing operation;
s2: acquiring the area proportion of each cement concrete material type in the core sample side image of the cement concrete pavement of each detection subarea, and comparing the area proportion with standard data;
s3: acquiring the limit load of the core sample of the cement concrete pavement of each detection subarea, and determining the corresponding compressive strength grade and the compressive strength influence coefficient;
s4: according to the data obtained in S2 and S3, the comprehensive quality influence coefficient of the cement-concrete pavement and the corresponding quality grade of the cement-concrete pavement are counted and displayed;
referring to fig. two, the intelligent supervision method for the municipal road engineering construction project based on the big data and image analysis technology uses an intelligent supervision system for the municipal road engineering construction project based on the big data and image analysis technology, and comprises an area division module, a core sample acquisition module, an image preprocessing module, a feature extraction module, a compressive strength detection module, a database, a modeling analysis server, a management server and a display terminal;
the core sample acquisition module is respectively connected with the region division module, the image acquisition module and the compressive strength detection module, the image preprocessing module is respectively connected with the image acquisition module and the characteristic extraction module, the modeling analysis server is respectively connected with the compressive strength detection module, the characteristic extraction module, the database and the management server, the database is respectively connected with the characteristic extraction module and the management server, and the display terminal is connected with the management server;
the regional division module is used for carrying out regional division on the detected municipal road, dividing the detected whole municipal road into a plurality of detection subregions which have the same area and are mutually connected, numbering the divided detection subregions according to a preset sequence, and sequentially marking the detection subregions as 1,2, a.
The core sample acquisition module comprises a core drilling machine and is used for coring the cement concrete pavement of each detection subarea and respectively sending the core samples of the cement concrete pavement acquired by each detection subarea to the image acquisition module and the compressive strength detection module;
the image acquisition module comprises a high-definition camera and is used for acquiring side images of the core sample of the cement concrete pavement of each detection sub-region and sending the acquired side images of the core sample of the cement concrete pavement of each detection sub-region to the image preprocessing module;
the image preprocessing module receives the core sample side images of the cement concrete pavement of each detection subarea sent by the image acquisition module, performs image segmentation on the received core sample side images of the cement concrete pavement of each detection subarea, splices core sample characteristic areas obtained by image segmentation, removes background images outside the core sample characteristic areas of the cement concrete pavement, changes the reserved core sample characteristic areas of the cement concrete pavement into images with consistent size and without deflection angles of core samples through geometric normalization processing, performs gray change processing and image enhancement processing simultaneously to obtain the processed core sample side images of the cement concrete pavement of each detection subarea, and sends the processed core sample side images of the cement concrete pavement of each detection subarea to the characteristic extraction module;
the characteristic extraction module receives the processed core sample side images of the cement concrete pavement of each detection subarea sent by the image preprocessing module, amplifies the received core sample side images of the cement concrete pavement of each detection subarea, and amplifies the characteristics of each cement concrete material in the core sample side images of the cement concrete pavement of each detection subarea and the characteristics corresponding to different types of cement concrete materials stored in the databaseComparing to obtain the characteristics of each cement concrete material type in the cement concrete pavement core sample side image of each detection subarea, further obtaining the characteristics of each cement concrete material type in the cement concrete pavement core sample side image of each detection subarea, and sequentially marking the characteristics of each cement concrete material type as 1,2, ai(ai1,ai2,...,aij,...,aik)，aij represents the jth cement concrete material type characteristic in the cement concrete pavement core sample side image of the ith detection subarea, and the characteristic extraction module respectively sends the cement concrete material type characteristic set and the cement concrete pavement core sample side images of the detection subareas to the modeling analysis server;
the compressive strength detection module comprises a compression testing machine for receiving the core samples of the cement concrete pavement, which are obtained by each detection subarea and sent by the core sample obtaining module, gradually applying pressure to the core samples of the cement concrete pavement obtained by each detection subarea, stopping applying pressure when the core sample of the cement concrete pavement in the detection subarea is close to damage and begins to deform until the core sample of the cement concrete pavement in the detection subarea is damaged, recording the limit load of the core sample of the cement concrete pavement obtained by the detection subarea, further obtaining the ultimate load of the core sample of the cement concrete pavement obtained by each detection subarea to form an ultimate load set B (B1, B2, B.., bi, B.., bg), wherein bi represents the ultimate load of the core sample of the cement concrete pavement of the ith detection subarea, and the compressive strength detection module sends the ultimate load set to the modeling analysis server;
in the embodiment, the reliable early-stage data preparation and reference basis is provided for later-stage statistics of the comprehensive quality evaluation coefficient of the cement concrete pavement by acquiring the side images and the limit load of the core sample of the cement concrete pavement of each detection sub-region, and the method has the characteristics of high authenticity and high data accuracy and precision.
The database is used for storing the characteristics corresponding to different types of cement concrete materials, the types of the cement concrete materials comprise granulated blast furnace slag, steel slag, fly ash and limestone, the image area of the side surface of a core sample of the cement concrete pavement is stored, the standard proportion of the area occupied by each type of the cement concrete materials is stored, the limit load range corresponding to each compression strength grade is stored, the upper limit numerical value of the limit load range corresponding to the first-level compression strength grade is smaller than the lower limit numerical value of the limit load range corresponding to the second-level compression strength grade, the upper limit numerical value of the limit load range corresponding to the second-level compression strength grade is smaller than the lower limit numerical value of the limit load range corresponding to the third-level compression strength grade, the compression strength influence coefficients corresponding to each compression strength grade are stored, the magnitude sequence corresponding to the compression strength influence coefficients corresponding to each compression strength grade is omega 1 < omega 2, storing the comprehensive quality evaluation coefficient range of the cement concrete pavement corresponding to each quality grade of the cement concrete pavement, wherein the upper limit value of the comprehensive quality evaluation coefficient range of the cement concrete pavement corresponding to the quality grade of the primary cement concrete pavement is smaller than the lower limit value of the comprehensive quality evaluation coefficient range of the cement concrete pavement corresponding to the quality grade of the secondary cement concrete pavement, and the upper limit value of the comprehensive quality evaluation coefficient range of the cement concrete pavement corresponding to the quality grade of the secondary cement concrete pavement is smaller than the lower limit value of the comprehensive quality evaluation coefficient range of the cement concrete pavement corresponding to the quality grade of the tertiary cement concrete pavement;
the modeling analysis server receives the characteristic set of the types of the cement concrete materials sent by the characteristic extraction module and the side images of the core samples of the cement concrete pavements of all the detection sub-regions, and obtains the occupied area of the types of the cement concrete materials for the received side images of the core samples of the cement concrete pavements of all the detection sub-regions to form a set C of the area of the cement concrete materialsi(ci1,ci2,...,cij,...,cik)，cij is the area occupied by the jth cement concrete material type in the core sample side image of the cement concrete pavement of the ith detection subarea, the modeling analysis server extracts the side image area of the core sample of the cement concrete pavement stored in the database, and counts the side image of the core sample of the cement concrete pavement of each detection subarea according to the area set of the cement concrete materialThe calculation formula of the area proportion of each cement concrete material type in the core sample side image of the cement concrete pavement of each detection subarea is
dij represents the area proportion occupied by the jth cement concrete material type in the cement concrete pavement core sample side image of the ith detection subarea, S represents the area of the cement concrete pavement core sample side image, and a cement concrete material area proportion set D is formedi(di1,di2,...,dij,...,dik)，dij is the area proportion occupied by the jth cement concrete material type in the cement concrete pavement core sample side image of the ith detection subarea, the modeling analysis server compares the area proportion occupied by each cement concrete material type in the cement concrete pavement core sample side image of each detection subarea with the standard area proportion occupied by each cement concrete material type stored in the database to form a cement concrete material area proportion comparison set D'i(d′i1,d′i2,...,d′i j,...,d′ik)，d′ij is the difference value between the area proportion occupied by the jth cement concrete material type in the cement concrete pavement core sample side image of the ith detection subarea and the area standard proportion occupied by the jth cement concrete material type;
the modeling analysis server receives the limit load set sent by the compressive strength detection module, extracts the limit load of the core sample of the cement concrete pavement obtained by each detection subarea in the limit load set, compares the limit load of the core sample of the cement concrete pavement of each detection subarea with the limit load range corresponding to each compressive strength grade stored in the database, obtains the compressive strength grade corresponding to the limit load of the core sample of the cement concrete pavement of the detection subarea, extracts the compressive strength influence coefficient corresponding to each compressive strength grade stored in the database, and forms a compressive strength influence coefficient set
The coefficient of influence of the compressive strength of the core sample of the cement-mud soil pavement is expressed as i detection subareas;
the modeling analysis server calculates the comprehensive quality evaluation coefficient of the cement concrete pavement according to the compression strength influence coefficient set and the cement concrete material area ratio comparison set, and the following steps are as follows: the calculation formula of the comprehensive quality evaluation coefficient of the cement concrete pavement is
The coefficient of influence of the compressive strength of the core sample of the cement-concrete soil pavement expressed as i detection subareas, di' j is the difference between the area proportion of the jth cement concrete material type in the core sample side image of the cement concrete pavement of the ith detection subarea and the area standard proportion of the jth cement concrete material type, and dij represents the area proportion occupied by the jth cement concrete material type in the cement concrete pavement core sample side image of the ith detection subarea, and sends the statistical comprehensive quality evaluation coefficient of the cement concrete pavement to the management server;
the management server receives the comprehensive quality evaluation coefficient of the cement concrete pavement sent by the modeling analysis server, compares the received comprehensive quality evaluation coefficient of the cement concrete pavement with the comprehensive quality evaluation coefficient range of the cement concrete pavement corresponding to each cement concrete pavement quality grade stored in the database, if the comprehensive quality evaluation coefficient of the cement concrete pavement is within the comprehensive quality evaluation coefficient range of the cement concrete pavement corresponding to the first-level cement concrete pavement quality grade, the quality grade of the cement concrete pavement is first level, if the comprehensive quality evaluation coefficient of the cement concrete pavement is within the comprehensive quality evaluation coefficient range of the cement concrete pavement corresponding to the second-level cement concrete pavement quality grade, the quality grade of the cement concrete pavement is second level, and if the comprehensive quality evaluation coefficient of the cement concrete pavement is within the comprehensive quality evaluation coefficient range of the cement concrete pavement corresponding to the third-level cement concrete pavement quality grade, the quality evaluation coefficient of the cement concrete pavement is second-level Within the coefficient range, the quality grade of the cement concrete pavement is three-grade, and the management server respectively sends the comprehensive quality evaluation coefficient of the cement concrete pavement and the corresponding quality grade of the cement concrete pavement to a display terminal;
the display terminal receives the comprehensive quality evaluation coefficient of the cement concrete pavement and the corresponding quality grade of the cement concrete pavement sent by the management server, displays the comprehensive quality evaluation coefficient of the cement concrete pavement and the corresponding quality grade, provides detection data of the municipal road for relevant departments, facilitates timely personnel to take different measures according to the detection data of the municipal road to perform safety maintenance on the municipal road, and greatly improves the use safety of the municipal road.
According to the method, the side images of the core sample of the cement concrete pavement of each detection subarea are obtained, the image preprocessing operation is carried out, the area proportion occupied by each cement concrete material type in the side images of the core sample of the cement concrete pavement of each detection subarea is obtained at the same time, the comparison is carried out with standard data, the limit load of the core sample of the cement concrete pavement of each detection subarea is further obtained, the corresponding compression strength grade and the compression strength influence coefficient are determined, the comprehensive quality influence coefficient of the cement concrete pavement and the corresponding quality grade of the cement concrete pavement are counted and displayed, and the method has the characteristics of high authenticity and high reliability, is favorable for reducing the probability of sudden damage of concrete, prolongs the service life of the concrete pavement, and provides powerful technical support for the safety application of municipal roads.
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 (7)
1. Municipal road engineering construction project intelligent supervision method based on big data and image analysis technology is characterized in that: the method comprises the following steps:
s1: acquiring a side image of the core sample of the cement concrete pavement of each detection subarea, and performing image preprocessing operation;
s2: acquiring the area proportion of each cement concrete material type in the core sample side image of the cement concrete pavement of each detection subarea, and comparing the area proportion with standard data;
s3: acquiring the limit load of the core sample of the cement concrete pavement of each detection subarea, and determining the corresponding compressive strength grade and the compressive strength influence coefficient;
s4: according to the data obtained in S2 and S3, the comprehensive quality influence coefficient of the cement-concrete pavement and the corresponding quality grade of the cement-concrete pavement are counted and displayed;
the intelligent municipal road engineering construction project supervision method based on the big data and image analysis technology uses a municipal road engineering construction project intelligent supervision system based on the big data and image analysis technology, and comprises a region division module, a core sample acquisition module, an image preprocessing module, a feature extraction module, a compressive strength detection module, a database, a modeling analysis server, a management server and a display terminal;
the core sample acquisition module is respectively connected with the region division module, the image acquisition module and the compressive strength detection module, the image preprocessing module is respectively connected with the image acquisition module and the characteristic extraction module, the modeling analysis server is respectively connected with the compressive strength detection module, the characteristic extraction module, the database and the management server, the database is respectively connected with the characteristic extraction module and the management server, and the display terminal is connected with the management server;
the regional division module is used for carrying out regional division on the detected municipal road, dividing the detected whole municipal road into a plurality of detection subregions which have the same area and are mutually connected, numbering the divided detection subregions according to a preset sequence, and sequentially marking the detection subregions as 1,2, a.
The core sample acquisition module comprises a core drilling machine and is used for coring the cement concrete pavement of each detection subarea and respectively sending the core samples of the cement concrete pavement acquired by each detection subarea to the image acquisition module and the compressive strength detection module;
the image acquisition module comprises a high-definition camera and is used for acquiring side images of the core sample of the cement concrete pavement of each detection sub-region and sending the acquired side images of the core sample of the cement concrete pavement of each detection sub-region to the image preprocessing module;
the image preprocessing module receives the core sample side images of the cement concrete pavement of each detection subarea sent by the image acquisition module, performs image segmentation on the received core sample side images of the cement concrete pavement of each detection subarea, splices core sample characteristic areas obtained by image segmentation, removes background images outside the core sample characteristic areas of the cement concrete pavement, changes the reserved core sample characteristic areas of the cement concrete pavement into images with consistent size and without deflection angles of core samples through geometric normalization processing, performs gray change processing and image enhancement processing simultaneously to obtain the processed core sample side images of the cement concrete pavement of each detection subarea, and sends the processed core sample side images of the cement concrete pavement of each detection subarea to the characteristic extraction module;
the characteristic extraction module receives the processed core sample side images of the cement concrete pavement of each detection subarea sent by the image preprocessing module, amplifies the received core sample side images of the cement concrete pavement of each detection subarea, compares the characteristics of each cement concrete material in the core sample side images of the cement concrete pavement of each detection subarea with the characteristics corresponding to different types of cement concrete materials stored in the database to obtain the characteristics of each type of cement concrete material in the core sample side images of the cement concrete pavement of the detection subarea, and further obtains the characteristics of each type of cement concrete material in the core sample side images of the cement concrete pavement of the detection subareaThe characteristics of each cement concrete material type in the cement concrete pavement core sample side image of each detection subarea are sequentially marked as 1,2, ai(ai1,ai2,...,aij,...,aik)，aij represents the jth cement concrete material type characteristic in the cement concrete pavement core sample side image of the ith detection subarea, and the characteristic extraction module respectively sends the cement concrete material type characteristic set and the cement concrete pavement core sample side images of the detection subareas to the modeling analysis server;
the compressive strength detection module comprises a compression testing machine for receiving the core samples of the cement concrete pavement, which are obtained by each detection subarea and sent by the core sample obtaining module, gradually applying pressure to the core samples of the cement concrete pavement obtained by each detection subarea, stopping applying pressure when the core sample of the cement concrete pavement in the detection subarea is close to damage and begins to deform until the core sample of the cement concrete pavement in the detection subarea is damaged, recording the limit load of the core sample of the cement concrete pavement obtained by the detection subarea, further obtaining the ultimate load of the core sample of the cement concrete pavement obtained by each detection subarea to form an ultimate load set B (B1, B2, B.., bi, B.., bg), wherein bi represents the ultimate load of the core sample of the cement concrete pavement of the ith detection subarea, and the compressive strength detection module sends the ultimate load set to the modeling analysis server;
the database is used for storing the characteristics corresponding to different cement concrete material types, storing the image area of the side surface of the core sample of the cement concrete pavement, storing the standard proportion of the area occupied by each cement concrete material type, storing the limit load range corresponding to each compression strength grade, storing the compression strength influence coefficient corresponding to each compression strength grade, and storing the comprehensive quality evaluation coefficient range corresponding to each cement concrete pavement quality grade;
the modeling analysis server receives the cement concrete material category characteristic set sent by the characteristic extraction module and the water of each detection subareaObtaining the area occupied by each cement concrete material type of the received cement concrete pavement core sample side image of each detection subarea to form a cement concrete material area set Ci(ci1,ci2,...,cij,...,cik)，cij is the area occupied by the jth cement concrete material type in the core sample side image of the cement concrete pavement of the ith detection subarea, the modeling analysis server extracts the area of the core sample side image of the cement concrete pavement stored in the database, and the area proportion occupied by each cement concrete material type in the core sample side image of the cement concrete pavement of each detection subarea is counted according to the set of the areas of the cement concrete materials to form a set D of the area occupied by the cement concrete materialsi(di1,di2,...,dij,...,dik)，dij is the area proportion occupied by the jth cement concrete material type in the cement concrete pavement core sample side image of the ith detection subarea, the modeling analysis server compares the area proportion occupied by each cement concrete material type in the cement concrete pavement core sample side image of each detection subarea with the standard area proportion occupied by each cement concrete material type stored in the database to form a cement concrete material area proportion comparison set D'i(d′i1,d′i2,...,d′ij,...,d′ik)，d′ij is the difference value between the area proportion occupied by the jth cement concrete material type in the cement concrete pavement core sample side image of the ith detection subarea and the area standard proportion occupied by the jth cement concrete material type;
the modeling analysis server receives the limit load set sent by the compressive strength detection module, extracts the limit load of the core sample of the cement concrete pavement obtained by each detection subarea in the limit load set, compares the limit load of the core sample of the cement concrete pavement of each detection subarea with the limit load range corresponding to each compressive strength grade stored in the database, and obtains the compressive strength grade corresponding to the limit load of the core sample of the cement concrete pavement of the detection subareaExtracting the compression strength influence coefficients corresponding to the compression strength grades stored in the database to form a compression strength influence coefficient set
The coefficient of influence of the compressive strength of the core sample of the cement-mud soil pavement is expressed as i detection subareas;
the modeling analysis server calculates the comprehensive quality evaluation coefficient of the cement concrete pavement according to the compression strength influence coefficient set and the cement concrete material area ratio comparison set, and sends the calculated comprehensive quality evaluation coefficient of the cement concrete pavement to the management server;
the management server receives the comprehensive quality evaluation coefficient of the cement concrete pavement sent by the modeling analysis server, compares the received comprehensive quality evaluation coefficient of the cement concrete pavement with the comprehensive quality evaluation coefficient range of the cement concrete pavement corresponding to each cement concrete pavement quality grade stored in the database, if the comprehensive quality evaluation coefficient of the cement concrete pavement is within the comprehensive quality evaluation coefficient range of the cement concrete pavement corresponding to the first-level cement concrete pavement quality grade, the quality grade of the cement concrete pavement is first level, if the comprehensive quality evaluation coefficient of the cement concrete pavement is within the comprehensive quality evaluation coefficient range of the cement concrete pavement corresponding to the second-level cement concrete pavement quality grade, the quality grade of the cement concrete pavement is second level, and if the comprehensive quality evaluation coefficient of the cement concrete pavement is within the comprehensive quality evaluation coefficient range of the cement concrete pavement corresponding to the third-level cement concrete pavement quality grade, the quality evaluation coefficient of the cement concrete pavement is second-level Within the coefficient range, the quality grade of the cement concrete pavement is three-grade, and the management server respectively sends the comprehensive quality evaluation coefficient of the cement concrete pavement and the corresponding quality grade of the cement concrete pavement to a display terminal;
and the display terminal receives and displays the comprehensive quality evaluation coefficient of the cement concrete pavement and the corresponding quality grade of the cement concrete pavement, which are sent by the management server.
2. The intelligent supervision method for the municipal road engineering construction project based on big data and image analysis technology according to claim 1, characterized in that: the cement concrete material types comprise granulated blast furnace slag, steel slag, fly ash and limestone.
3. The intelligent supervision method for the municipal road engineering construction project based on big data and image analysis technology according to claim 1, characterized in that: the calculation formula of the area proportion occupied by each cement concrete material type in the core sample side surface image of the cement concrete pavement of each detection subarea is
dij represents the area proportion occupied by the jth cement concrete material type in the cement concrete pavement core sample side image of the ith detection subarea, and S represents the area of the cement concrete pavement core sample side image.
4. The intelligent supervision method for the municipal road engineering construction project based on big data and image analysis technology according to claim 1, characterized in that: the upper limit value of the comprehensive quality evaluation coefficient range of the cement concrete pavement corresponding to the quality grade of the first-level cement concrete pavement is smaller than the lower limit value of the comprehensive quality evaluation coefficient range of the cement concrete pavement corresponding to the quality grade of the second-level cement concrete pavement, and the upper limit value of the comprehensive quality evaluation coefficient range of the cement concrete pavement corresponding to the quality grade of the second-level cement concrete pavement is smaller than the lower limit value of the comprehensive quality evaluation coefficient range of the cement concrete pavement corresponding to the quality grade of the third-level cement concrete pavement.
5. The intelligent supervision method for the municipal road engineering construction project based on big data and image analysis technology according to claim 1, characterized in that: the upper limit value of the limit load range corresponding to the first-level compressive strength grade is smaller than the lower limit value of the limit load range corresponding to the second-level compressive strength grade, and the upper limit value of the limit load range corresponding to the second-level compressive strength grade is smaller than the lower limit value of the limit load range corresponding to the third-level compressive strength grade.
6. The intelligent supervision method for the municipal road engineering construction project based on big data and image analysis technology according to claim 1, characterized in that: the magnitude sequence corresponding to the compression strength influence coefficients corresponding to the compression strength grades is omega 1 < omega 2 < omega 3.
7. The intelligent supervision method for the municipal road engineering construction project based on big data and image analysis technology according to claim 1, characterized in that: the calculation formula of the comprehensive quality evaluation coefficient of the cement concrete pavement is
Coefficient of influence of compressive strength of core sample of cement-concrete pavement expressed as i detection subareas, d'ij is the difference between the area proportion of the jth cement concrete material type in the core sample side image of the cement concrete pavement of the ith detection subarea and the standard area proportion of the jth cement concrete material type, dij represents the area proportion occupied by the jth cement concrete material type in the cement concrete pavement core sample side image of the ith detection subarea.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113781425A (en) * | 2021-09-07 | 2021-12-10 | 中国三峡建工(集团)有限公司 | Concrete leveling analysis method and device |
| CN115130179A (en) * | 2022-06-23 | 2022-09-30 | 中冶检测认证有限公司 | Method for determining safety of concrete structure containing steel slag aggregate |
| CN116894610A (en) * | 2023-09-11 | 2023-10-17 | 山东万世机械科技有限公司 | Pavement leveling quality analysis management system based on concrete laser leveling machine |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113781425A (en) * | 2021-09-07 | 2021-12-10 | 中国三峡建工(集团)有限公司 | Concrete leveling analysis method and device |
| CN115130179A (en) * | 2022-06-23 | 2022-09-30 | 中冶检测认证有限公司 | Method for determining safety of concrete structure containing steel slag aggregate |
| CN116894610A (en) * | 2023-09-11 | 2023-10-17 | 山东万世机械科技有限公司 | Pavement leveling quality analysis management system based on concrete laser leveling machine |
| CN116894610B (en) * | 2023-09-11 | 2023-12-01 | 山东万世机械科技有限公司 | Pavement leveling quality analysis management system based on concrete laser leveling machine |
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