CN112949977A - Intelligent supervision management system for periodic acceptance check of engineering project quality based on big data and artificial intelligence - Google Patents
Intelligent supervision management system for periodic acceptance check of engineering project quality based on big data and artificial intelligence Download PDFInfo
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Abstract
The invention discloses an intelligent supervision management system for stepwise acceptance check of engineering project quality based on big data and artificial intelligence, which divides a tunnel internal top layer area to be accepted into sub-areas, simultaneously lays monitoring points for the sub-areas, detects the distance between each monitoring point position in each sub-area and the circle center of a corresponding central arc line, analyzes the comparison difference of the distance between each monitoring point position in each sub-area and the circle center of the corresponding central arc line, calculates the standard difference of the distance difference between the central arc line of each sub-area and the circle center, collects gray level images at each monitoring point position in each sub-area, acquires the thickness of each layer of each gray level enhanced image in each sub-area, calculates the volume of each layer in the tunnel internal top layer area to be accepted, simultaneously calculates the comprehensive quality influence coefficient of the tunnel internal top layer to be accepted, displays the comprehensive quality influence coefficient, and carries out comparison treatment measures by related personnel, therefore, the engineering quality of the top layer in the tunnel is improved, and the safety of people is guaranteed.
Description
Technical Field
The invention relates to the technical field of engineering project quality management, in particular to an intelligent supervision management system for periodic acceptance inspection of engineering project quality based on big data and artificial intelligence.
Background
Ensuring the quality of the engineering project is the key of the supervision work of the tunnel engineering. The quality of the engineering project is related to the life and property safety of tunnel workers and passengers, so that the quality inspection and acceptance supervision of the reinforced tunnel engineering project is the most important part of supervision work.
At present, the existing quality inspection, acceptance and supervision method for tunnel engineering projects mainly adopts personnel supervision, the personnel supervision level is low, a scientific system method is lacked, the arc radius of the top layer in the tunnel cannot be comprehensively and accurately known, the problem of uneven stress of the top layer area in the tunnel exists, the engineering quality of the top layer in the tunnel is reduced, thereby increasing the potential safety hazard of people going out, bringing huge physical and mental damage to people, simultaneously carrying out destructive sampling on the layer surface of the top layer in the tunnel by a supervisor, analyzing the volume of each layer surface in the sample to judge the quality of the tunnel engineering project, thus not only reducing the accuracy and reliability of the supervision data, but also reducing the aesthetic effect of the top layer in the tunnel, and then make tunnel engineering project quality's acceptance efficiency receive the influence, in order to solve above problem, design the engineering project quality stage nature acceptance intelligence supervision management system based on big data and artificial intelligence now.
Disclosure of Invention
The invention aims to provide an intelligent supervision and management system for stepwise acceptance inspection of engineering project quality based on big data and artificial intelligence, which divides a top layer area in a tunnel to be accepted into sub-areas, simultaneously lays monitoring points for the sub-areas, detects the distance between the center of each monitoring point in each sub-area and a corresponding central arc line, analyzes the comparison difference between the distance between the center of each monitoring point in each sub-area and the corresponding central arc line, calculates the standard difference between the distance between the central arc line of each sub-area and the center of the circle, collects gray level images at the positions of the monitoring points in each sub-area, acquires the thickness of each level enhancement image in each sub-area, calculates the volume of each level in the top layer area in the tunnel to be accepted, calculates the comprehensive quality influence coefficient of the top layer in the tunnel to be accepted, and displays the comprehensive quality influence coefficient, the problems existing in the background technology are solved.
The purpose of the invention can be realized by the following technical scheme:
the intelligent supervision management system for the periodic acceptance check of the engineering project quality based on big data and artificial intelligence comprises a region division module, a monitoring point arrangement module, a tunnel radius detection module, a tunnel radius analysis module, a gray level image acquisition module, a gray level image processing module, a layer thickness acquisition module, a layer thickness analysis module, an analysis server, a cloud management center, a display terminal and a storage database;
the analysis server is respectively connected with the tunnel radius analysis module, the layer thickness analysis module, the cloud management center and the storage database, the monitoring point arrangement module is respectively connected with the region division module, the tunnel radius detection module and the gray level image acquisition module, the tunnel radius analysis module is respectively connected with the tunnel radius detection module and the storage database, the gray level image processing module is respectively connected with the gray level image acquisition module and the layer thickness acquisition module, the layer thickness acquisition module is connected with the layer thickness analysis module, and the cloud management center is respectively connected with the storage database and the display terminal;
the area dividing module is used for dividing a top layer area in the tunnel to be checked and accepted, dividing the top layer area into a plurality of sub-areas with the same volume according to a tunnel length equal division mode, sequentially numbering the sub-areas according to a set sequence, wherein the number of the sub-areas is 1,2, 1.
The monitoring point laying module is used for receiving the serial numbers of all sub-areas in the tunnel internal top layer area to be checked and accepted, sent by the area dividing module, laying the monitoring points of all sub-areas in the tunnel internal top layer area to be checked and accepted, sequentially numbering the positions of all the monitoring points in all the sub-areas according to the laying sequence, counting the position serial numbers of all the monitoring points in all the sub-areas in the tunnel internal top layer area to be checked and accepted, and forming a position number set A of all the monitoring points in all the sub-areas in the tunnel internal top layer area to be checked and acceptedi m(ai 1,ai 2,...,ai j,...,ai m)，ai jExpressed as the jth monitoring point in the ith sub-area in the top layer area inside the tunnel to be checkedPosition numbering, namely respectively sending a position number set of each monitoring point in each sub-area in the tunnel inner top layer area to be checked and accepted to a tunnel radius detection module and a gray level image acquisition module;
the tunnel radius detection module comprises a plurality of ultrasonic ranging sensors, wherein the plurality of ultrasonic ranging sensors are respectively installed at the circle centers of central circular arc lines in all sub-areas, the plurality of ultrasonic ranging sensors are in one-to-one correspondence with the circle centers of the central circular arc lines in all sub-areas and are used for receiving position number sets of monitoring points in all sub-areas in a tunnel internal top layer area to be checked and received, the position number sets are sent by a monitoring point arrangement module, the distance from each monitoring point in each sub-area in the tunnel internal top layer area to be checked and received to the circle center of the corresponding central circular arc line is respectively detected by the plurality of ultrasonic ranging sensors, the distance from each monitoring point in each sub-area to the circle center of the corresponding central circular arc line is counted, and a circle center distance set R from eachiA(ria1,ria2,...,riaj,...,riam)，riajThe distance between the jth monitoring point in the ith sub-area and the circle center of the corresponding central arc line is represented, and the distance set of the circle center between each monitoring point in each sub-area and the corresponding central arc line is sent to the tunnel radius analysis module;
the tunnel radius analysis module is used for receiving the circle center distance set of the monitoring point positions in each subarea from the corresponding central circular arc line, which is sent by the tunnel radius detection module, extracting the standard radius of the central circular arc line of the top layer in the tunnel, which is stored in the storage database, from the circle center, and comparing the received circle center distance of each monitoring point position in each subarea from the corresponding central circular arc line with the standard radius to obtain the circle center distance contrast difference value set delta R of each monitoring point position in each subarea in the top layer area in the tunnel to be checked and accepted from the corresponding central circular arc lineiA(Δria1,Δria2,...,Δriaj,...,Δriam)，ΔriajWithin the top zone of the interior of the tunnel denoted as acceptanceComparing the circle center distance between the jth monitoring point position in the ith sub-area and the corresponding central arc line with the standard radius, and sending the comparison difference value set of the circle center distance between each monitoring point position in each sub-area in the tunnel inner top layer area to be checked and accepted and the corresponding central arc line to the analysis server;
the analysis server is used for receiving a central distance comparison difference value set, sent by the tunnel radius analysis module, of each monitoring point in each subregion in the tunnel internal top layer region to be checked and accepted from the corresponding central arc line, calculating a standard deviation of a distance difference value of the central arc line of each subregion in the tunnel internal top layer region to be checked and accepted from the center of a circle, counting the standard deviation of the distance difference value of the central arc line of each subregion in the tunnel internal top layer region to be checked and accepted from the center of a circle, and sending the standard deviation of the distance difference value of the central arc line of each subregion in the tunnel internal top layer region to be checked and accepted from the center of a circle to the cloud management center;
the grayscale image acquisition module comprises an x-ray detector and a grayscale image processing module, wherein the x-ray detector is used for receiving a position number set of each monitoring point in each sub-region in the tunnel internal top layer region to be checked and accepted, which is sent by the monitoring point arrangement module, scanning each monitoring point position in each sub-region in the tunnel internal top layer region to be checked and accepted through the x-ray detector, acquiring grayscale images at each monitoring point position in each sub-region in the tunnel internal top layer region to be checked and accepted, counting grayscale images at each monitoring point position in each sub-region in the tunnel internal top layer region to be checked and accepted, and sending the grayscale images at each monitoring point position in each sub-region in the tunnel internal top layer region to be checked and accepted to the grayscale image processing module;
the grayscale image processing module is used for receiving grayscale images at the positions of monitoring points in each sub-region in the tunnel internal top layer region to be checked and accepted, which are sent by the grayscale image acquisition module, normalizing the grayscale images at the positions of the monitoring points in each sub-region in the tunnel internal top layer region to be checked and accepted, converting the normalized grayscale images into grayscale images in a fixed standard form, filtering and denoising the converted grayscale images and enhancing the images, counting the grayscale enhanced images at the positions of the monitoring points in each sub-region in the tunnel internal top layer region to be checked and accepted, and sending the grayscale enhanced images at the positions of the monitoring points in each sub-region in the tunnel internal top layer region to be checked and accepted to the layer thickness acquisition module;
the layer thickness acquisition module is used for receiving the gray level enhanced images at the positions of the monitoring points in the sub-regions of the tunnel internal top layer region to be checked and received, which are sent by the gray level image processing module, acquiring the thickness of each layer of the gray level enhanced images in the sub-regions of the tunnel internal top layer region to be checked and received, counting the thickness of each layer of the gray level enhanced images in the sub-regions of the tunnel internal top layer region to be checked and received, and forming a layer thickness set D of the gray level enhanced images in the sub-regions of the tunnel internal top layer region to be checked and receivediX(d1 ix,d2 ix,...,dj ix,...,dm ix)，dj ix is expressed as the thickness of the x-th layer of the j-th gray level enhanced image in the ith sub-region in the tunnel inner top layer region to be checked and accepted, and x is x1,x2,x3,x4,x5，x1,x2,x3,x4,x5Respectively representing a reinforced concrete base layer surface, a leveling layer surface, a waterproof protection layer surface and a facing layer surface in the top layer inside the tunnel, and sending the thickness set of each layer surface of each gray level enhancement image in each sub-area in the top layer area inside the tunnel to be checked to a layer surface thickness analysis module;
the layer thickness analysis module is used for receiving the layer thickness sets of the gray level enhanced images in all the sub-areas in the tunnel internal top layer area to be checked and accepted, sent by the layer thickness acquisition module, calculating the average thickness of all the layers in all the sub-areas in the tunnel internal top layer area to be checked and accepted, counting the average thickness of all the layers in all the sub-areas in the tunnel internal top layer area to be checked and accepted, and forming the average thickness set of all the layers in all the sub-areas in the tunnel internal top layer area to be checked and accepted
The average thickness of the xth layer surface in the ith sub-area in the tunnel internal top layer area to be checked and accepted is represented, and the average thickness set of each layer surface in each sub-area in the tunnel internal top layer area to be checked and accepted is sent to the analysis server;
the analysis server is used for receiving an average thickness set of each layer in each sub-area in the tunnel internal top layer area to be checked and accepted, which is sent by the layer thickness analysis module, extracting a proportional coefficient of preset gray image data and actual data stored in a storage database, a standard arc length of the tunnel internal top layer and a standard tunnel length, calculating the volume of each layer in the tunnel internal top layer area to be checked and accepted, counting the volume of each layer in the tunnel internal top layer area to be checked and accepted, and sending the volume of each layer in the tunnel internal top layer area to be checked and accepted to the cloud management center;
the cloud management center is used for receiving standard deviation of distance difference values between central arc lines of all sub-areas in the tunnel internal top layer area to be checked and accepted and all layer volumes in the tunnel internal top layer area to be checked and accepted, which are sent by the analysis server, extracting plan standard volumes of all layers in the tunnel internal top layer area and quality influence proportional coefficients of all layers in the tunnel internal top layer area, which are stored in the storage database, calculating comprehensive quality influence coefficients of the tunnel internal top layer to be checked and accepted, and sending the comprehensive quality influence coefficients of the tunnel internal top layer to be checked and accepted to the display terminal;
the display terminal is used for receiving the comprehensive quality influence coefficient of the top layer in the tunnel to be checked and received, which is sent by the cloud management center, and displaying the comprehensive quality influence coefficient;
the storage database is used for storing the standard radius of the central arc line of the top layer in the tunnel from the center of the circle, and simultaneously storing the proportional coefficient mu of the preset gray image data and the actual data and the standard arc length L of the top layer in the tunnelSign boardAnd standard tunnel length kSign boardAnd storing the planned standard volume of each layer in the top layer area inside the tunnel and the quality influence proportionality coefficient of each layer in the top layer area inside the tunnel.
Furthermore, the monitoring point arrangement module arranges a plurality of monitoring points on the central arc line of each sub-area in an evenly distributed manner, wherein the arc length distances between the monitoring point positions in each sub-area are equal, and the number of the monitoring points arranged on the central arc line of each sub-area is the same.
Furthermore, the ultrasonic ranging sensor is used for transmitting a beam of ultrasonic pulse to the position of the monitoring point, receiving the echo reflected by the position of the monitoring point by the electronic element, converting the echo into an electric signal, counting the time from the transmission to the reception of the ultrasonic wave, and analyzing the distance between the position of each monitoring point and the center of the corresponding central arc line according to the known sound velocity.
Further, the standard deviation calculation formula of the distance difference value between the central arc line of each sub-area in the tunnel inner top layer area to be checked and accepted and the circle center is
σiExpressed as the standard deviation of the distance difference between the central arc line of the ith sub-area in the top layer area in the tunnel to be checked and accepted and the circle center, delta riajAnd the comparison difference value between the circle center distance between the jth monitoring point position in the ith sub-area in the tunnel inner top layer area to be checked and accepted and the corresponding central circular arc line and the standard radius is expressed, and m is the number of the monitoring points distributed on the central circular arc line of each sub-area.
Further, the calculation formula of the average thickness of each layer in each sub-area of the top layer area in the tunnel to be checked and accepted is as
Expressed as the average thickness of the x-th layer in the ith sub-area in the tunnel inner top layer area to be checked, x is x1,x2,x3,x4,x5，dj ix is expressed as the x-th layer thickness of the j-th gray level enhanced image in the ith sub-region in the tunnel inner top layer region to be checked and accepted, and m is expressed in each sub-regionThe number of monitoring points distributed on the heart arc line.
Further, the volume calculation formula of each layer in the top layer area in the tunnel to be checked and accepted is
VxExpressed as the x-th floor volume in the inner top floor area of the tunnel to be checked, x ═ x1,x2,x3,x4,x5，LSign boardExpressed as the standard arc length of the top layer in the tunnel, mu expressed as the proportionality coefficient of the preset gray image data and the actual data,
expressed as the average thickness of the x-th layer surface in the ith sub-area in the tunnel inner top layer area to be checked, n is expressed as the number of sub-areas divided by the tunnel inner top layer area to be checked, kSign boardExpressed as the standard tunnel length of the top layer inside the tunnel.
Further, the calculation formula of the comprehensive quality influence coefficient of the top layer in the tunnel to be checked is
Xi is expressed as the comprehensive quality influence coefficient of the top layer in the tunnel to be checked and received, lambdaxExpressed as the mass influence proportionality coefficient of the x-th layer in the tunnel inner top layer area, wherein x is x1,x2,x3,x4,x5，VxExpressed as the x-th layer volume, V ', in the inner top layer area of the tunnel to be inspected'xExpressed as the planned standard volume of the x-th level in the top layer area inside the tunnel, e is expressed as a natural number and is equal to 2.718, n is expressed as the number of sub-areas divided by the top layer area inside the tunnel to be checked and accepted, and sigma is expressed as the number of sub-areas divided by the top layer area inside the tunneliAnd expressing the standard deviation of the distance difference between the central arc line of the ith sub-area in the tunnel inner top layer area to be checked and accepted and the circle center.
Has the advantages that:
(1) the intelligent supervision management system for the stepwise acceptance of the engineering project quality based on big data and artificial intelligence provided by the invention divides the top layer area in the tunnel to be accepted into each subarea, simultaneously lays the monitoring points of each subarea, detects the distance between the position of each monitoring point in each subarea and the circle center of the corresponding central arc line, adopts a scientific system method to avoid the problem of low artificial supervision level, thereby comprehensively and accurately knowing the arc radius of the top layer in the tunnel, analyzes the comparison difference value of the distance between each monitoring point in each subarea and the circle center of the corresponding central arc line, calculates the standard difference value of the distance difference value between the central arc line of each subarea and the circle center, avoids the problem of uneven stress of the top layer area in the tunnel, and provides reliable reference data for later-stage calculation of the comprehensive quality influence coefficient of the top layer in the tunnel to be accepted, simultaneously, the gray level images of the positions of the monitoring points in each sub-area are collected, the thickness of each layer of the gray level enhancement images in each sub-area is obtained, the occurrence of an event that supervision personnel destructively sample the layer of the top layer inside the tunnel is avoided, the attractive effect of the top layer inside the tunnel is guaranteed, the volume of each layer in the top layer area inside the tunnel to be checked and accepted is calculated, and therefore the accuracy and the reliability of supervision data are improved.
(2) According to the invention, the comprehensive quality influence coefficient of the top layer inside the tunnel to be checked is calculated through the cloud management center, so that the checking efficiency of the tunnel engineering project quality is increased, the checking efficiency is displayed, the comprehensive quality influence condition of the top layer inside the tunnel is visually displayed, and relevant personnel perform corresponding treatment measures, so that the engineering quality of the top layer inside the tunnel is improved, the safety hazard of people in trip is reduced, and the physical and mental health of people is ensured.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram 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 and management system for checking and accepting engineering project quality in stages based on big data and artificial intelligence comprises a region division module, a monitoring point arrangement module, a tunnel radius detection module, a tunnel radius analysis module, a gray image acquisition module, a gray image processing module, an aspect thickness acquisition module, an aspect thickness analysis module, an analysis server, a cloud management center, a display terminal and a storage database.
The analysis server is respectively connected with the tunnel radius analysis module, the layer thickness analysis module, the cloud management center and the storage database, the monitoring point arrangement module is respectively connected with the region division module, the tunnel radius detection module and the gray level image acquisition module, the tunnel radius analysis module is respectively connected with the tunnel radius detection module and the storage database, the gray level image processing module is respectively connected with the gray level image acquisition module and the layer thickness acquisition module, the layer thickness acquisition module is connected with the layer thickness analysis module, and the cloud management center is respectively connected with the storage database and the display terminal.
The area dividing module is used for dividing a top layer area in the tunnel to be checked and accepted, dividing the top layer area into a plurality of sub-areas with the same volume according to a tunnel length equal division mode, numbering the sub-areas in sequence according to a set sequence, wherein the number of the sub-areas is 1,2,.
The monitoring point laying module is used for receiving the serial numbers of all sub-areas in the top layer area inside the tunnel to be checked and accepted, which are sent by the area dividing module, and entering the sub-areas in the top layer area inside the tunnel to be checked and acceptedArranging a plurality of monitoring points on a central arc line of each subregion in an evenly distributed mode, wherein the arc length distance between the monitoring point positions in each subregion is equal, the number of the monitoring points arranged on the central arc line of each subregion is the same, sequentially numbering the monitoring points in each subregion according to the arrangement sequence, counting the position numbers of the monitoring points in each subregion in the top layer region in the tunnel to be checked, and forming a position number set A of the monitoring points in each subregion in the top layer region in the tunnel to be checkedi m(ai 1,ai 2,...,ai j,...,ai m)，ai jAnd the position number of the jth monitoring point in the ith sub-area in the tunnel internal top layer area to be checked is expressed, and the position number sets of the monitoring points in all the sub-areas in the tunnel internal top layer area to be checked are respectively sent to the tunnel radius detection module and the gray level image acquisition module.
The tunnel radius detection module comprises a plurality of ultrasonic ranging sensors, wherein the plurality of ultrasonic ranging sensors are respectively arranged at the circle centers of central circular arc lines in each subregion, the plurality of ultrasonic ranging sensors correspond to the circle centers of the central circular arc lines in each subregion one by one and are used for receiving a position number set of each monitoring point in each subregion in the tunnel inner top layer region to be checked and received, which is sent by the monitoring point arrangement module, transmitting a beam of ultrasonic pulse to the monitoring point position through the ultrasonic ranging sensors, receiving echo reflected by the monitoring point position by an electronic element and converting the echo into an electric signal, counting the time from transmitting to receiving of ultrasonic waves, analyzing the distance between each monitoring point position and the circle center of the corresponding central circular arc line according to the known sound velocity, adopting a scientific system method, avoiding the problem of low manual supervision level, and comprehensively and accurately knowing the circular arc radius of the tunnel inner top layer, and counting the distance between each monitoring point in each sub-area and the center of the corresponding central arc line to form a set R of the distance between each monitoring point in each sub-area and the center of the corresponding central arc lineiA(ria1,ria2,...,riaj,...,riam)，riajAnd the distance between the jth monitoring point in the ith sub-area and the circle center of the corresponding central arc line is represented, and the distance set of the circle center between each monitoring point in each sub-area and the corresponding central arc line is sent to the tunnel radius analysis module.
The tunnel radius analysis module is used for receiving the circle center distance set of the monitoring point positions in each subarea from the corresponding central circular arc line, which is sent by the tunnel radius detection module, extracting the standard radius of the central circular arc line of the top layer in the tunnel, which is stored in the storage database, from the circle center, and comparing the received circle center distance of each monitoring point position in each subarea from the corresponding central circular arc line with the standard radius to obtain the circle center distance contrast difference value set delta R of each monitoring point position in each subarea in the top layer area in the tunnel to be checked and accepted from the corresponding central circular arc lineiA(Δria1,Δria2,...,Δriaj,...,Δriam)，ΔriajAnd the comparison difference value is expressed as the comparison difference value between the circle center distance between the jth monitoring point position in the ith sub-area in the tunnel internal top layer area to be checked and the corresponding central arc line and the standard radius, and the comparison difference value set of the circle center distance between each monitoring point position in each sub-area in the tunnel internal top layer area to be checked and accepted and the corresponding central arc line is sent to the analysis server.
The analysis server is used for receiving a central distance comparison difference value set of each monitoring point position in each subarea in the tunnel inner top layer area to be checked and accepted, which is sent by the tunnel radius analysis module, and calculating a standard deviation of a central arc line-to-centre distance difference value of each subarea in the tunnel inner top layer area to be checked and accepted
σiExpressed as the standard deviation of the distance difference between the central arc line of the ith sub-area in the top layer area in the tunnel to be checked and accepted and the circle center, delta riajThe distance is expressed as the position distance pair of the jth monitoring point in the ith sub-area in the tunnel inner top layer area to be checked and acceptedAnd counting the standard difference of the distance difference between the central arc line of each subarea in the tunnel internal top layer area to be checked and accepted, sending the standard difference of the distance difference between the central arc line of each subarea in the tunnel internal top layer area to be checked and accepted and the center of a circle to a cloud management center, avoiding the problem of uneven stress of the tunnel internal top layer area, and providing reliable reference data for calculating the comprehensive quality influence coefficient of the tunnel internal top layer to be checked and accepted in the later period.
The grayscale image acquisition module comprises an x-ray detector and is used for receiving a position number set of each monitoring point in each sub-region in the tunnel internal top layer region to be checked and accepted, which is sent by the monitoring point arrangement module, scanning each monitoring point position in each sub-region in the tunnel internal top layer region to be checked and accepted through the x-ray detector, acquiring grayscale images of each monitoring point position in each sub-region in the tunnel internal top layer region to be checked and accepted, counting grayscale images of each monitoring point position in each sub-region in the tunnel internal top layer region to be checked and accepted, and sending the grayscale images of each monitoring point position in each sub-region in the tunnel internal top layer region to be checked and accepted to the grayscale image processing module.
The grayscale image processing module is used for receiving grayscale images at the positions of monitoring points in all sub-areas in the tunnel internal top layer area to be checked and accepted, which are sent by the grayscale image acquisition module, normalizing the grayscale images at the positions of the monitoring points in all sub-areas in the tunnel internal top layer area to be checked and accepted, converting the normalized grayscale images into grayscale images in a fixed standard form, filtering and denoising the converted grayscale images and enhancing the images, counting the grayscale enhanced images at the positions of the monitoring points in all sub-areas in the tunnel internal top layer area to be checked and accepted, and sending the grayscale enhanced images at the positions of the monitoring points in all sub-areas in the tunnel internal top layer area to be checked and accepted to the layer thickness acquisition module.
The layer thickness acquisition module is used for receiving all sub-regions in the tunnel inner top layer region to be checked and received, which are sent by the gray level image processing moduleThe gray level enhancement images at the positions of the monitoring points in the region are obtained, the thickness of each layer of the gray level enhancement images in each sub-region in the tunnel internal top layer region to be checked and accepted is obtained, the occurrence of an event that supervision personnel destructively sample the layer of the tunnel internal top layer is avoided, the attractive effect of the tunnel internal top layer is ensured, the thickness of each layer of each gray level enhancement image in each sub-region in the tunnel internal top layer region to be checked and accepted is counted, and a thickness set D of each layer of each gray level enhancement image in each sub-region in the tunnel internal top layer region to be checked and accepted is formediX(d1 ix,d2 ix,...,dj ix,...,dm ix)，dj ix is expressed as the thickness of the x-th layer of the j-th gray level enhanced image in the ith sub-region in the tunnel inner top layer region to be checked and accepted, and x is x1,x2,x3,x4,x5，x1,x2,x3,x4,x5The method comprises the steps of respectively representing a reinforced concrete foundation layer surface, a leveling layer surface, a waterproof protection layer surface and a facing layer surface in a top layer inside the tunnel, and sending the thickness set of each layer surface of each gray level enhancement image in each sub-area in the top layer area inside the tunnel to be checked and accepted to a layer surface thickness analysis module.
The layer thickness analysis module is used for receiving the layer thickness sets of the gray level enhanced images in the sub-regions of the tunnel internal top layer region to be checked and accepted sent by the layer thickness acquisition module and calculating the average thickness of the layers in the sub-regions of the tunnel internal top layer region to be checked and accepted
Expressed as the average thickness of the x-th layer in the ith sub-area in the tunnel inner top layer area to be checked, x is x1,x2,x3,x4,x5，dj ix is expressed as the th of the jth gray scale enhancement image in the ith sub-area in the top layer area inside the tunnel to be checked and acceptedx layer thicknesses, m represents the number of monitoring points distributed on the central circular arc line of each subregion, the average thickness of each layer in each subregion in the tunnel internal top layer region to be checked and accepted is counted, and an average thickness set of each layer in each subregion in the tunnel internal top layer region to be checked and accepted is formed
And sending the average thickness set of each layer in each sub-area of the tunnel internal top layer area to be checked and accepted to an analysis server.
The analysis server is used for receiving the average thickness set of each layer in each sub-area in the tunnel internal top layer area to be checked and accepted, sent by the layer thickness analysis module, extracting the proportional coefficient of the preset gray image data and the actual data stored in the storage database, the standard arc length of the tunnel internal top layer and the standard tunnel length, and calculating the volume of each layer in the tunnel internal top layer area to be checked and accepted
VxExpressed as the x-th floor volume in the inner top floor area of the tunnel to be checked, x ═ x1,x2,x3,x4,x5，LSign boardExpressed as the standard arc length of the top layer in the tunnel, mu expressed as the proportionality coefficient of the preset gray image data and the actual data,
expressed as the average thickness of the x-th layer surface in the ith sub-area in the tunnel inner top layer area to be checked, n is expressed as the number of sub-areas divided by the tunnel inner top layer area to be checked, kSign boardThe standard tunnel length of the top layer inside the tunnel is represented, the sizes of all layers in the top layer area inside the tunnel to be checked and accepted are counted, and the sizes of all layers in the top layer area inside the tunnel to be checked and accepted are sent to the cloud management center, so that the accuracy and the reliability of the supervision data are improved.
The cloud management center is used for receiving centers of all sub-areas in the tunnel inner top layer area to be checked and accepted and sent by the analysis serverStandard deviation of distance difference between the arc line and the circle center and the volume of each layer in the top layer area in the tunnel to be checked and accepted, extracting the planned standard volume of each layer in the top layer area in the tunnel and the mass influence proportional coefficient of each layer in the top layer area in the tunnel stored in the storage database, and calculating the comprehensive mass influence coefficient of the top layer in the tunnel to be checked and accepted
Xi is expressed as the comprehensive quality influence coefficient of the top layer in the tunnel to be checked and received, lambdaxExpressed as the mass influence proportionality coefficient of the x-th layer in the tunnel inner top layer area, wherein x is x1,x2,x3,x4,x5，VxExpressed as the x-th layer volume, V ', in the inner top layer area of the tunnel to be inspected'xExpressed as the planned standard volume of the x-th level in the top layer area inside the tunnel, e is expressed as a natural number and is equal to 2.718, n is expressed as the number of sub-areas divided by the top layer area inside the tunnel to be checked and accepted, and sigma is expressed as the number of sub-areas divided by the top layer area inside the tunneliThe standard deviation is expressed as the distance difference between the central arc line of the ith sub-area in the tunnel inner top layer area to be checked and accepted and the circle center, so that the checking and accepting efficiency of the tunnel engineering project quality is improved, and the comprehensive quality influence coefficient of the tunnel inner top layer to be checked and accepted is sent to the display terminal.
The display terminal is used for receiving the comprehensive quality influence coefficient of the top layer inside the tunnel to be checked and received, which is sent by the cloud management center, displaying the comprehensive quality influence coefficient, visually displaying the comprehensive quality influence condition of the top layer inside the tunnel, and performing corresponding processing measures by related personnel, so that the engineering quality of the top layer inside the tunnel is improved, the potential safety hazard of people in trip is reduced, and the physical and mental health of people is guaranteed.
The storage database is used for storing the standard radius of the central arc line of the top layer in the tunnel from the center of the circle, and simultaneously storing the proportional coefficient mu of the preset gray image data and the actual data and the standard arc length L of the top layer in the tunnelSign boardAnd standard tunnel length kSign boardAnd storing the planned standard volume of each layer in the top layer area inside the tunnel and the quality influence ratio of each layer in the top layer area inside the tunnelThe coefficients are illustrated.
The foregoing is merely exemplary and illustrative of the principles of the present invention and various modifications, additions and substitutions of the specific embodiments described herein may be made by those skilled in the art without departing from the principles of the present invention or exceeding the scope of the claims set forth herein.
Claims (7)
1. Engineering project quality stage acceptance intelligent supervision management system based on big data and artificial intelligence, its characterized in that: the system comprises a region division module, a monitoring point arrangement module, a tunnel radius detection module, a tunnel radius analysis module, a gray level image acquisition module, a gray level image processing module, a layer thickness acquisition module, a layer thickness analysis module, an analysis server, a cloud management center, a display terminal and a storage database;
the analysis server is respectively connected with the tunnel radius analysis module, the layer thickness analysis module, the cloud management center and the storage database, the monitoring point arrangement module is respectively connected with the region division module, the tunnel radius detection module and the gray level image acquisition module, the tunnel radius analysis module is respectively connected with the tunnel radius detection module and the storage database, the gray level image processing module is respectively connected with the gray level image acquisition module and the layer thickness acquisition module, the layer thickness acquisition module is connected with the layer thickness analysis module, and the cloud management center is respectively connected with the storage database and the display terminal;
the area dividing module is used for dividing a top layer area in the tunnel to be checked and accepted, dividing the top layer area into a plurality of sub-areas with the same volume according to a tunnel length equal division mode, sequentially numbering the sub-areas according to a set sequence, wherein the number of the sub-areas is 1,2, 1.
The monitoring point laying module is used for receiving the serial numbers of all sub-areas in the tunnel internal top layer area to be checked and accepted, which are sent by the area dividing module, and for receiving all sub-areas in the tunnel internal top layer area to be checked and acceptedLaying monitoring points, sequentially numbering the positions of the monitoring points in each subarea according to the laying sequence, counting the position numbers of the monitoring points in each subarea in the tunnel internal top layer area to be checked and accepted, and forming a position number set A of the monitoring points in each subarea in the tunnel internal top layer area to be checked and acceptedi m(ai 1,ai 2,...,ai j,...,ai m)，ai jThe method comprises the steps of expressing the position number of the jth monitoring point in the ith sub-area in the tunnel internal top layer area to be checked and accepted, and respectively sending the position number sets of the monitoring points in all sub-areas in the tunnel internal top layer area to be checked and accepted to a tunnel radius detection module and a gray level image acquisition module;
the tunnel radius detection module comprises a plurality of ultrasonic ranging sensors, wherein the plurality of ultrasonic ranging sensors are respectively installed at the circle centers of central circular arc lines in all sub-areas, the plurality of ultrasonic ranging sensors are in one-to-one correspondence with the circle centers of the central circular arc lines in all sub-areas and are used for receiving position number sets of monitoring points in all sub-areas in a tunnel internal top layer area to be checked and received, the position number sets are sent by a monitoring point arrangement module, the distance from each monitoring point in each sub-area in the tunnel internal top layer area to be checked and received to the circle center of the corresponding central circular arc line is respectively detected by the plurality of ultrasonic ranging sensors, the distance from each monitoring point in each sub-area to the circle center of the corresponding central circular arc line is counted, and a circle center distance set R from eachiA(ria1,ria2,...,riaj,...,riam)，riajThe distance between the jth monitoring point in the ith sub-area and the circle center of the corresponding central arc line is represented, and the distance set of the circle center between each monitoring point in each sub-area and the corresponding central arc line is sent to the tunnel radius analysis module;
the tunnel radius analysis module is used for receiving the circle center distance set of each monitoring point position in each sub-area from the corresponding central circular arc line sent by the tunnel radius detection module, extracting and storing the data baseComparing the received circle center distance between each monitoring point in each subarea and the corresponding central arc line with the standard radius to obtain a comparison difference value set delta R of the circle center distance between each monitoring point in each subarea in the tunnel inner top layer to be checked and accepted and the corresponding central arc lineiA(Δria1,Δria2,...,Δriaj,...,Δriam)，ΔriajExpressed as the comparison difference value between the circle center distance between the jth monitoring point position in the ith sub-area in the tunnel internal top layer area to be checked and the corresponding central arc line and the standard radius, and sending the comparison difference value set of the circle center distance between each monitoring point position in each sub-area in the tunnel internal top layer area to be checked and the corresponding central arc line to the analysis server;
the analysis server is used for receiving a central distance comparison difference value set, sent by the tunnel radius analysis module, of each monitoring point in each subregion in the tunnel internal top layer region to be checked and accepted from the corresponding central arc line, calculating a standard deviation of a distance difference value of the central arc line of each subregion in the tunnel internal top layer region to be checked and accepted from the center of a circle, counting the standard deviation of the distance difference value of the central arc line of each subregion in the tunnel internal top layer region to be checked and accepted from the center of a circle, and sending the standard deviation of the distance difference value of the central arc line of each subregion in the tunnel internal top layer region to be checked and accepted from the center of a circle to the cloud management center;
the grayscale image acquisition module comprises an x-ray detector and a grayscale image processing module, wherein the x-ray detector is used for receiving a position number set of each monitoring point in each sub-region in the tunnel internal top layer region to be checked and accepted, which is sent by the monitoring point arrangement module, scanning each monitoring point position in each sub-region in the tunnel internal top layer region to be checked and accepted through the x-ray detector, acquiring grayscale images at each monitoring point position in each sub-region in the tunnel internal top layer region to be checked and accepted, counting grayscale images at each monitoring point position in each sub-region in the tunnel internal top layer region to be checked and accepted, and sending the grayscale images at each monitoring point position in each sub-region in the tunnel internal top layer region to be checked and accepted to the grayscale image processing module;
the grayscale image processing module is used for receiving grayscale images at the positions of monitoring points in each sub-region in the tunnel internal top layer region to be checked and accepted, which are sent by the grayscale image acquisition module, normalizing the grayscale images at the positions of the monitoring points in each sub-region in the tunnel internal top layer region to be checked and accepted, converting the normalized grayscale images into grayscale images in a fixed standard form, filtering and denoising the converted grayscale images and enhancing the images, counting the grayscale enhanced images at the positions of the monitoring points in each sub-region in the tunnel internal top layer region to be checked and accepted, and sending the grayscale enhanced images at the positions of the monitoring points in each sub-region in the tunnel internal top layer region to be checked and accepted to the layer thickness acquisition module;
the layer thickness acquisition module is used for receiving the gray level enhanced images at the positions of the monitoring points in the sub-regions of the tunnel internal top layer region to be checked and received, which are sent by the gray level image processing module, acquiring the thickness of each layer of the gray level enhanced images in the sub-regions of the tunnel internal top layer region to be checked and received, counting the thickness of each layer of the gray level enhanced images in the sub-regions of the tunnel internal top layer region to be checked and received, and forming a layer thickness set D of the gray level enhanced images in the sub-regions of the tunnel internal top layer region to be checked and receivediX(d1 ix,d2 ix,...,dj ix,...,dm ix)，dj ix is expressed as the thickness of the x-th layer of the j-th gray level enhanced image in the ith sub-region in the tunnel inner top layer region to be checked and accepted, and x is x1,x2,x3,x4,x5，x1,x2,x3,x4,x5Respectively representing a reinforced concrete base layer surface, a leveling layer surface, a waterproof protection layer surface and a facing layer surface in the top layer inside the tunnel, and sending the thickness set of each layer surface of each gray level enhancement image in each sub-area in the top layer area inside the tunnel to be checked to a layer surface thickness analysis module;
the layer thickness analysis module is used for receiving the top layer in the tunnel to be checked and accepted sent by the layer thickness acquisition moduleCollecting the thickness of each layer of each gray level enhanced image in each sub-area in the area, calculating the average thickness of each layer in each sub-area in the tunnel internal top layer area to be checked and accepted, counting the average thickness of each layer in each sub-area in the tunnel internal top layer area to be checked and accepted, and forming the average thickness collection of each layer in each sub-area in the tunnel internal top layer area to be checked and accepted
The average thickness of the xth layer surface in the ith sub-area in the tunnel internal top layer area to be checked and accepted is represented, and the average thickness set of each layer surface in each sub-area in the tunnel internal top layer area to be checked and accepted is sent to the analysis server;
the analysis server is used for receiving an average thickness set of each layer in each sub-area in the tunnel internal top layer area to be checked and accepted, which is sent by the layer thickness analysis module, extracting a proportional coefficient of preset gray image data and actual data stored in a storage database, a standard arc length of the tunnel internal top layer and a standard tunnel length, calculating the volume of each layer in the tunnel internal top layer area to be checked and accepted, counting the volume of each layer in the tunnel internal top layer area to be checked and accepted, and sending the volume of each layer in the tunnel internal top layer area to be checked and accepted to the cloud management center;
the cloud management center is used for receiving standard deviation of distance difference values between central arc lines of all sub-areas in the tunnel internal top layer area to be checked and accepted and all layer volumes in the tunnel internal top layer area to be checked and accepted, which are sent by the analysis server, extracting plan standard volumes of all layers in the tunnel internal top layer area and quality influence proportional coefficients of all layers in the tunnel internal top layer area, which are stored in the storage database, calculating comprehensive quality influence coefficients of the tunnel internal top layer to be checked and accepted, and sending the comprehensive quality influence coefficients of the tunnel internal top layer to be checked and accepted to the display terminal;
the display terminal is used for receiving the comprehensive quality influence coefficient of the top layer in the tunnel to be checked and received, which is sent by the cloud management center, and displaying the comprehensive quality influence coefficient;
the storage database is used for storing the standard radius of the central arc line of the top layer in the tunnel from the center of the circle, and simultaneously storing the proportional coefficient mu of the preset gray image data and the actual data and the standard arc length L of the top layer in the tunnelSign boardAnd standard tunnel length kSign boardAnd storing the planned standard volume of each layer in the top layer area inside the tunnel and the quality influence proportionality coefficient of each layer in the top layer area inside the tunnel.
2. The intelligent supervision and management system for project quality staged acceptance inspection based on big data and artificial intelligence as claimed in claim 1, wherein: the monitoring point distribution module is used for distributing a plurality of monitoring points on the central arc line of each subregion in an evenly distributed mode, wherein the arc length distances among the monitoring point positions in each subregion are equal, and the number of the monitoring points distributed on the central arc line of each subregion is the same.
3. The intelligent supervision and management system for project quality staged acceptance inspection based on big data and artificial intelligence as claimed in claim 1, wherein: the ultrasonic ranging sensor is used for transmitting a beam of ultrasonic pulse to a monitoring point, receiving echo reflected by the monitoring point by an electronic element, converting the echo into an electric signal, counting the time from transmitting to receiving of ultrasonic waves, and analyzing the distance between the monitoring point and the center of a circle corresponding to the central arc line according to the known sound velocity.
4. The intelligent supervision and management system for project quality staged acceptance inspection based on big data and artificial intelligence as claimed in claim 1, wherein: the standard deviation calculation formula of the distance difference value between the center circular arc line of each subarea in the tunnel inner top layer area to be checked and accepted and the circle center is
σiDenoted as i-th in the region of the tunnel's internal roof to be checked outStandard deviation of difference between central arc line of sub-region and circle center, delta riajAnd the comparison difference value between the circle center distance between the jth monitoring point position in the ith sub-area in the tunnel inner top layer area to be checked and accepted and the corresponding central circular arc line and the standard radius is expressed, and m is the number of the monitoring points distributed on the central circular arc line of each sub-area.
5. The intelligent supervision and management system for project quality staged acceptance inspection based on big data and artificial intelligence as claimed in claim 1, wherein: the calculation formula of the average thickness of each layer in each sub-area of the inner top layer area of the tunnel to be checked and accepted is
Expressed as the average thickness of the x-th layer in the ith sub-area in the tunnel inner top layer area to be checked, x is x1,x2,x3,x4,x5，dj iAnd x is expressed as the thickness of the x-th layer of the j-th gray level enhanced image in the ith sub-region in the tunnel inner top layer region to be checked and accepted, and m is expressed as the number of monitoring points distributed on the central circular arc line of each sub-region.
6. The intelligent supervision and management system for project quality staged acceptance inspection based on big data and artificial intelligence as claimed in claim 1, wherein: the volume calculation formula of each layer in the top layer area in the tunnel to be checked and accepted is
VxExpressed as the x-th floor volume in the inner top floor area of the tunnel to be checked, x ═ x1,x2,x3,x4,x5，LSign boardExpressed as the standard arc length of the top layer inside the tunnel, and mu expressed as the preset gray image data and the actual dataThe ratio coefficient of the ratio is,
expressed as the average thickness of the x-th layer surface in the ith sub-area in the tunnel inner top layer area to be checked, n is expressed as the number of sub-areas divided by the tunnel inner top layer area to be checked, kSign boardExpressed as the standard tunnel length of the top layer inside the tunnel.
7. The intelligent supervision and management system for project quality staged acceptance inspection based on big data and artificial intelligence as claimed in claim 1, wherein: the calculation formula of the comprehensive quality influence coefficient of the top layer in the tunnel to be checked is
Xi is expressed as the comprehensive quality influence coefficient of the top layer in the tunnel to be checked and received, lambdaxExpressed as the mass influence proportionality coefficient of the x-th layer in the tunnel inner top layer area, wherein x is x1,x2,x3,x4,x5，VxExpressed as the x-th layer volume, V ', in the inner top layer area of the tunnel to be inspected'xExpressed as the planned standard volume of the x-th level in the top layer area inside the tunnel, e is expressed as a natural number and is equal to 2.718, n is expressed as the number of sub-areas divided by the top layer area inside the tunnel to be checked and accepted, and sigma is expressed as the number of sub-areas divided by the top layer area inside the tunneliAnd expressing the standard deviation of the distance difference between the central arc line of the ith sub-area in the tunnel inner top layer area to be checked and accepted and the circle center.
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