CN111242945A - Pipeline defect detection system based on big data - Google Patents
Pipeline defect detection system based on big data Download PDFInfo
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Abstract
The invention discloses a pipeline defect detection system based on big data, which comprises an image acquisition module, an image screening processing module, a pipeline parameter adjustment analysis module, a management database, a pipeline environment acquisition module, a data optimization and arrangement module, a cloud server, an estimation module and a supervision prompt terminal. According to the invention, through the pipeline parameter adjusting and analyzing module, the pipeline environment collecting module and the cloud server, the crack evolution influence coefficient of the pipeline under the influence of the environment can be analyzed according to the current environmental parameters and the pipeline crack condition in the pipeline, the influence degree of the environmental factors in the pipeline can be conveniently clarified, the evolution model of the pipeline crack can be accurately and effectively constructed, the reliable data support is provided for the later estimation of the service life of the pipeline, the expected follow-up life of the pipeline from the current to the pipeline crack threshold under the current environmental influence factor can be estimated, the expected service life of the pipeline under the influence of the current environment can be accurately estimated, and the early warning follow-up is realized.
Description
Technical Field
The invention belongs to the technical field of pipeline management, and relates to a pipeline defect detection system based on big data.
Background
The drainage pipeline refers to a system consisting of a pipeline collecting and discharging sewage, wastewater and rainwater and auxiliary facilities thereof, the drainage pipeline is normal and is convenient for discharging the sewage and the wastewater, in recent years, with the rapid development of urban construction in China, the construction of the drainage pipeline is rapidly developed, but with the environmental parameters of the drainage pipeline and the rolling of road vehicles on the pipeline, the pipeline is seriously damaged and is often lower than the standard service life requirement of the design of the drainage pipeline, for example, the current pipeline often has serious problems of blockage, fracture, water seepage and the like, the service life of the pipeline is shortened, and the loss and traffic accidents caused by the pipeline problem are increased.
At present, in order to improve the problem such as pipeline jam, the staff can regularly detect the pipeline, and maintain the pipeline according to the measuring condition, in order to improve the drainage ability and the life of pipeline, however, carry out the defect detection to the pipeline through the manual work, not only increase a large amount of manpowers, material resources, and detection efficiency is low, because there is subjective consciousness, can't accurately judge the defect degree of pipeline, and can't predict the life of pipeline in advance according to the environmental aspect that the pipeline is located at present, avoid the pipeline jam that causes because of pipeline life arrives and the not enough scheduling problem of drainage, and then can't follow and trade the defect pipeline early, influence pipeline drainage and current scheduling problem.
Disclosure of Invention
According to the pipeline defect detection system based on the big data, provided by the invention, through the pipeline parameter adjusting and analyzing module, the pipeline environment collecting module, the pipeline parameter adjusting and analyzing module, the data optimizing and sorting module, the cloud server and the like, the crack evolution influence coefficient of the pipeline in the current environment can be analyzed, the expected service life of the pipeline is analyzed according to the crack evolution influence coefficient, the problems that the accuracy of pipeline defect detection is poor, the service life of the pipeline cannot be estimated and the like in the prior art are solved, and early warning reminding of pipeline replacement cannot be realized.
The purpose of the invention can be realized by the following technical scheme:
a pipeline defect detection system based on big data comprises an image acquisition module, an image screening processing module, a pipeline parameter adjusting and analyzing module, a management database, a pipeline environment acquisition module, a data optimizing and sorting module, a cloud server, an estimation and evaluation module and a supervision and prompt terminal;
the cloud server is respectively connected with the pipeline parameter adjusting and analyzing module, the management database, the data optimizing and sorting module and the estimation and evaluation module, the pipeline environment acquisition module is respectively connected with the data optimizing and sorting module and the management database, and the estimation and evaluation module is respectively connected with the supervision prompt terminal and the management database;
the image acquisition module is used for acquiring images inside the pipeline and sending the acquired images to the image screening processing module;
the image screening processing module is used for receiving the images in the drainage pipeline sent by the image acquisition module, carrying out image signal-to-noise ratio processing on the acquired images in the pipeline, screening out the images with the image signal-to-noise ratio larger than a set signal-to-noise ratio threshold value, and sending the screened images with the signal-to-noise ratio larger than the set signal-to-noise ratio threshold value to the pipeline parameter adjustment analysis module;
the pipeline parameter adjusting and analyzing module is used for receiving the image which is sent by the image screening and processing module and has the signal-to-noise ratio larger than a set signal-to-noise ratio threshold value, carrying out pipeline image parameter adjustment on the image which has the signal-to-noise ratio larger than the set signal-to-noise ratio threshold value so as to adjust the image to a set standard image parameter, extracting cracks in the pipeline image after the image parameter adjustment, comparing the extracted cracks with the crack length corresponding to each crack length grade and the crack width corresponding to each crack width grade stored in the management database respectively, obtaining the crack length grade and the crack width grade in the pipeline image after the image parameter adjustment, sending the crack length grade and the crack width grade in the pipeline image to the management database and the cloud server respectively, and sending the pipeline image after the image parameter adjustment and the image parameter to the management database;
the management database is used for storing the pipeline image and the image parameters which are sent by the pipeline parameter adjusting and analyzing module and are subjected to image parameter adjustment, and the length grade and the width grade of the crack in the pipeline image, and storing each length grade of the crack and each width grade of the crack, wherein each length grade of the crack corresponds to different length ranges of the crack, and different width grades of the crack correspond to different width ranges of the crack; the management database stores the temperature, humidity, water flow speed and pressure environment parameters at the upper end of the inner wall of the pipeline in each drainage pipeline, stores the water flow speed range corresponding to each water flow grade and the pressure range applied to the upper end of the inner wall of the pipeline corresponding to each pressure grade, and receives the expected follow-up change service life of the pipeline fed back by the estimation module according to the previous environment factors;
the pipeline environment acquisition module comprises a plurality of pipeline environment acquisition units, and the pipeline environment acquisition units are arranged in the pipeline and used for detecting the temperature, the humidity and the water flow speed in the pipeline and the pressure applied to the upper end of the inner wall of the pipeline in real time and respectively sending the detected temperature, humidity, water flow speed and the pressure applied to the upper end of the inner wall of the pipeline to the management database and the data optimization and arrangement module;
the data optimization and arrangement module is used for receiving the temperature, the humidity and the water flow speed in the drainage pipeline and the pressure applied to the upper end of the inner wall of the pipeline, which are sent by the pipeline environment acquisition module, optimizing the received temperature, humidity, water flow speed and pressure on the upper end of the inner wall of the pipeline in the drainage pipeline to obtain an average temperature set and an average humidity set in the previous and next acquisition time periods, and comparing the water flow speed in the pipeline every day with the set flow speed range corresponding to each water flow grade, screening out the flowing time set of the water flow speed in the pipeline under different water flow grades, comparing the pressure born by the pipeline every day with the pressure range corresponding to each pressure grade, screening out a pressure time set suffered by the pipeline every day, and sending the average temperature set, the average humidity set, the flowing time set and the pressure time set which are subjected to optimization processing to a cloud server;
the cloud server is used for receiving the length grade and the width grade of the crack in the pipeline image sent by the pipeline parameter adjusting and analyzing module, extracting the length grade and the width grade of the crack in the pipeline image acquired in the last acquisition time period stored in the management database, acquiring image parameters acquired in the previous acquisition time period and the next two times, and extracting image acquisition time in the image parameters;
the cloud server is further used for receiving the average temperature set, the average humidity set, the flowing time set and the pressure time set which are optimized and processed by the data optimizing and sorting module, counting crack evolution influence coefficients of the pipeline under the influence of the environment according to crack length levels and width levels in the pipeline image respectively corresponding to the interval days M acquired in the previous and next two times and the average temperature set, the average humidity set, the flowing time set and the pressure time set corresponding to the interval days M acquired in the previous and next two times, and sending the crack evolution influence coefficients of the pipeline under the influence of the current environment to the pre-estimation and evaluation module;
the pre-estimation evaluation module receives a crack evolution influence coefficient of the pipeline under the influence of the current environment, which is sent by the cloud server, acquires a crack length grade threshold value and a crack width grade threshold value corresponding to the pipeline, and pre-estimates the expected follow-up change service life of the pipeline from the current to the pipeline crack threshold value under the influence factors of the current environment according to the received pipeline crack evolution influence coefficient, the crack length grade threshold value, the crack width grade threshold value, the current crack length grade and the current crack width grade
Expressed as the crack evolution impact coefficient of the pipeline under the current environmental impact, Dk thresholdExpressed as a fracture length level threshold in the pipe, Dc thresholdThe estimated expected replacement life of the pipeline under the current environmental influence factors is respectively sent to a management database and a supervision prompt terminal;
and the supervision prompting terminal is used for receiving the expected replacement life of the pipeline sent by the estimation module and tracking whether the service time of the pipeline reaches the expected replacement life or not in real time so as to prompt a road manager to replace the pipeline reaching the expected replacement life.
Further, the fracture length grades are E1, E2, E, Ei, E, En, and the fracture length ranges corresponding to the fracture length grades are: l1-l2, l2-l3, a., li-l (i-1), a., ln-l (n-1), wherein each fracture width grade is F1, F2, a., Fj, a., Fn, and the fracture width range corresponding to each fracture width grade is: h1-h2, h2-h 3., hj-h (j-1),. hn-h (n-1), Ei represents the ith fracture length level, Fj represents the jth fracture width level, li-l (i-1) represents the fracture length range corresponding to the ith fracture length level, and hj-h (j-1) represents the maximum fracture width corresponding to the jth fracture width level.
Further, the specific gravity coefficients corresponding to the fracture length grades are respectively pE1, pE2, pEi, pEn, pE1 < pE2 > < pEi > < pEn, pE1+ pE2+. + pEi +. + pEn ═ 1, and the specific gravity coefficients corresponding to the fracture width grades are respectively pF1, pF2,. pFj,. pFn, pF1 < pF2 < pFj > pFn, pF1+ pF2+. + pFj +. pFn @ 1, and the specific gravity coefficient corresponding to the fracture length grade is expressed as the specific gravity of the ith pEi grade, and pFj is expressed as the specific gravity of the jth fracture width grade.
Further, the pipeline environment acquisition unit includes temperature acquisition unit, humidity acquisition unit, velocity of water acquisition unit and pressure measurement unit, temperature acquisition unit is temperature sensor, install in pipeline inner wall upper end, a temperature for in the detection pipeline is gathered in real time, humidity acquisition unit is humidity sensor, install in pipeline inner wall upper end, a humidity for in the detection pipeline is gathered in real time, velocity of water acquisition unit is velocity of flow sensor, a velocity of water for in the detection pipeline is gathered in real time, pressure measurement unit is pressure sensor, a velocity of water for in the detection pipeline inner wall upper end is gathered in real time.
Further, the pipeline is influenced by crack evolution influence coefficient under the influence of environment
μMExpressed as the specific gravity coefficient corresponding to the crack width level in the pipeline image after M days, u is expressed as the specific gravity coefficient corresponding to the crack length width level in the pipeline image before M days, muMU all belong to pE1, pE 2.., pEi.., pEn,
expressed as the specific gravity coefficient corresponding to the crack length scale in the pipeline image after M days,
expressed as the specific gravity coefficient corresponding to the crack length grade in the pipeline image M days ago,
all belong to pF1, pF 2.., pFj.., pFn, βkExpressed as the factor of influence of the fracture length on the pipe use, γcExpressed as the factor of the crack width on the pipe usage, Dk MExpressed as the crack length scale in the pipeline image after M days, DkExpressed as the crack length scale in the image of the pipe M days ago, Dc MExpressed as the crack width rating, D, in the pipe image after M dayscExpressed as the crack width level in the pipe image M days ago, gvr is expressed as the weight coefficient of the pipe water flow at the r-th water flow level,
expressed as the time of the pipeline water flow at the r water flow grade on the f day, gyr is expressed as the weight coefficient of the pressure applied to the upper end of the inner wall of the pipeline at the r pressure grade,
expressed as the time at which the upper end of the inner wall of the pipeline is subjected to pressure at the r pressure level on the f day,
expressed as the average temperature of the pipe on day f,
expressed as the average humidity of the pipeline on the f day, α 1, α 2, α 3 and α 4 are expressed as the influence factor of water flow on the service life of the pipeline, the influence factor of pressure on the upper end of the inner wall of the pipeline on the service life of the pipeline, the influence factor of temperature in the pipeline on the service life of the pipeline and the influence factor of humidity in the pipeline on the pipeline respectively, and α 2 is more than α 1 and more than α 3 and more than α 4,
the invention has the beneficial effects that:
according to the pipeline defect detection system based on the big data, provided by the invention, the length and the width of the crack in the pipeline image are analyzed through the pipeline parameter adjusting and analyzing module so as to obtain the length grade and the width grade of the crack in the pipeline image, and the change total amount of the pipeline crack in the previous and next acquisition time periods can be clearly known through acquiring the change of the length grade and the width grade of the crack in the image twice, so that the length and the width of the crack in the pipeline can be qualitatively and conveniently sorted, and the change amount of the pipeline defect can be visually displayed.
Acquiring the temperature, the humidity, the water flow velocity and the pressure on the upper end of the inner wall of a pipeline in a drainage pipeline through a pipeline environment acquisition module, optimizing the temperature, the humidity, the water flow velocity and the pressure on the upper end of the inner wall of the pipeline through a data optimization and arrangement module to acquire an average temperature set, an average humidity set, a flowing time set and a pressure time set in the pipeline, meanwhile, combining a cloud server, counting crack evolution influence coefficients of the pipeline under the influence of the environment according to the length level and the width level of cracks in pipeline images respectively corresponding to the acquisition of the front and back times and the average temperature set, the average humidity set, the flowing time set and the pressure time set corresponding to the acquisition of the front and back times, so as to acquire the influence degree of the cracks in the pipeline under the influence of the environment factors in the front and back acquisition time periods, and an evolution model of the pipeline crack can be accurately and effectively constructed, and reliable data support is provided for the later-stage estimation of the service life of the pipeline.
The method comprises the steps of obtaining crack evolution influence coefficients of a pipeline under the influence of an environment through cloud server statistics, predicting the expected follow-up service life of the pipeline from the current to a pipeline crack threshold value under the influence factor of the current environment, accurately evaluating the expected service life of the pipeline under the influence of the current environment, facilitating early warning reminding of pipeline follow-up or warning that the pipeline cannot be normally carried out, improving the speed of pipeline follow-up on the premise of guaranteeing the service life of the pipeline, improving the drainage efficiency of the pipeline, and further avoiding the problems that the road surface is influenced by unsmooth drainage and the like.
Drawings
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 a pipeline defect detection system based on big data according to 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, a pipeline defect detection system based on big data includes an image acquisition module, an image screening processing module, a pipeline parameter adjustment analysis module, a management database, a pipeline environment acquisition module, a data optimization and arrangement module, a cloud server, an estimation module, and a supervision prompt terminal.
The image acquisition module is connected with the image screening processing module, the image screening processing module is connected with the pipeline parameter adjusting and analyzing module, the cloud server is respectively connected with the pipeline parameter adjusting and analyzing module, the management database, the data optimizing and sorting module and the estimation and evaluation module, the pipeline environment acquisition module is respectively connected with the data optimizing and sorting module and the management database, and the estimation and evaluation module is respectively connected with the supervision prompt terminal and the management database.
The image acquisition module is a high-definition camera and is arranged on the pipeline crawling vehicle, the crawling vehicle walks in the drainage pipeline, the camera arranged on the crawling vehicle acquires images inside the pipeline and sends the acquired images to the image screening processing module, and an auxiliary shooting light source is arranged on the pipeline crawling vehicle;
the image screening processing module is used for receiving the images in the drainage pipeline sent by the image acquisition module, carrying out image signal-to-noise ratio processing on the acquired images in the pipeline, screening out the images with the image signal-to-noise ratio larger than a set signal-to-noise ratio threshold value, sending the screened images with the signal-to-noise ratio larger than the set signal-to-noise ratio threshold value to the pipeline parameter adjusting and analyzing module, and screening out high-quality pipeline images through the image screening processing module;
the pipeline parameter adjusting and analyzing module is used for receiving the image which is sent by the image screening and processing module and has the signal-to-noise ratio larger than a set signal-to-noise ratio threshold value, carrying out pipeline image parameter adjustment on the image which has the signal-to-noise ratio larger than the set signal-to-noise ratio threshold value so as to adjust the image to a set standard image parameter, extracting cracks in the pipeline image after the image parameter adjustment, comparing the extracted cracks with the crack length corresponding to each crack length grade and the crack width corresponding to each crack width grade stored in the management database respectively, obtaining the crack length grade and the crack width grade in the pipeline image after the image parameter adjustment, sending the crack length grade and the crack width grade in the pipeline image to the management database and the cloud server respectively, and sending the pipeline image after the image parameter adjustment and the image parameter to the management database simultaneously, wherein the image parameter comprises resolution ratio, Brightness, image size, and time of image acquisition, etc.
The management database is used for storing the pipeline image and the image parameters which are sent by the pipeline parameter adjustment analysis module and are subjected to image parameter adjustment, and fracture length grades and width grades in the pipeline image, and storing each fracture length grade and each fracture width grade, wherein each fracture length grade corresponds to different fracture length ranges, and different fracture width grades correspond to different fracture width ranges, and each fracture length grade is respectively E1, E2, E. l1-l2, l2-l3, a., li-l (i-1), a., ln-l (n-1), wherein each fracture width grade is F1, F2, a., Fj, a., Fn, and the fracture width range corresponding to each fracture width grade is: h1-h2, h2-h 3., hj-h (j-1),. hn-h (n-1), and the larger each fracture length level indicates the longer the length of the pipe fracture, the larger each fracture width level indicates the larger the maximum width in the pipe fracture, the higher the specific gravity coefficient corresponding to each fracture length level is pE1, pE 2.,. pEi.., pEn, pE1 < pE 2. < pEi. < pEn, pE1+ pE 1. +. 1. + 1, the specific gravity coefficient corresponding to each fracture width level is pF1,. pF 72,. 1. + the specific gravity coefficient representing the length of the first fracture length, pF + 1 +. 1, and the second equivalent pF1+ 1. + the length level indicates the first pF-h + (7. + 1, pF-1. + 1, the length level indicates the length of the first and pF 72 +),72, pFj, i.e. the proportion of the jth crack width grade, li-l (i-1) is the crack length range corresponding to the ith crack length grade, hj-h (j-1) is the maximum crack width corresponding to the jth crack width grade, i.e. when the crack widths in the pipeline images are not consistent, the maximum width in the crack is extracted to screen the width grade corresponding to the crack.
The management database stores environmental parameters such as temperature, humidity, water flow speed and pressure at the upper end of the inner wall of the pipeline in each drainage pipeline, stores water flow speed ranges corresponding to water flow grades and pressure ranges borne by the upper end of the inner wall of the pipeline corresponding to pressure grades, and receives expected follow-up life of the pipeline fed back by the estimation module according to previous environmental factors.
The pipeline environment acquisition module comprises a plurality of pipeline environment acquisition units, and the pipeline environment acquisition units are installed in the pipeline and used for detecting the temperature, the humidity and the water flow speed in the pipeline and the pressure applied to the upper end of the inner wall of the pipeline in real time and respectively sending the detected temperature, the detected humidity, the detected water flow speed and the pressure applied to the upper end of the inner wall of the pipeline to the management database and the data optimization and arrangement module;
the pipeline environment acquisition unit comprises a temperature acquisition unit, a humidity acquisition unit, a water velocity acquisition unit and a pressure detection unit, wherein the temperature acquisition unit is a temperature sensor, the temperature acquisition unit is installed on the upper end of the inner wall of the pipeline and used for acquiring the temperature in the detection pipeline in real time, the humidity acquisition unit is a humidity sensor and installed on the upper end of the inner wall of the pipeline and used for acquiring the humidity in the detection pipeline in real time, the water velocity acquisition unit is a flow velocity sensor and used for acquiring the water velocity in the detection pipeline in real time, and the pressure detection unit is a pressure sensor and used for acquiring the water velocity on the upper end of the inner wall.
Data optimization arrangementThe module is used for receiving the temperature, the humidity, the water flow speed and the pressure applied to the upper end of the inner wall of the pipeline in the drainage pipeline sent by the pipeline environment acquisition module, acquiring the temperature, the humidity, the water flow speed and the pressure applied to the upper end of the inner wall of the pipeline at equal intervals t (the interval t is 4h, 6h or 8h), calculating the average temperature and the humidity in the pipeline every day, and establishing an average temperature set in the front and back acquisition time periods according to the number of days between image acquisition on the pipeline crawling vehicle
Mean humidity ensemble
Wherein the content of the first and second substances,
expressed as the average temperature of the pipe on day f,
the average humidity of the pipeline on the f day is represented, and meanwhile, the data optimization and arrangement module compares the water flow speed in the pipeline every day with the set flow speed range corresponding to each water flow grade so as to screen out the flowing time set of the water flow speed in the pipeline under different water flow grades
Comparing the pressure applied to the pipeline every day with the pressure range corresponding to each pressure grade to screen out the pressure time set applied to the pipeline every day
Expressed as the time of pipeline water flow at the r water flow level on day f,
expressed as the time at which the upper end of the inner wall of the pipeline is subjected to pressure at the r pressure level on the f day,
where q is expressed as the number of levels of the water flow level,
and q is the level number of the pressure grade, and the data optimization sorting module sends the average temperature set, the average humidity set, the flowing time set and the pressure time set which are subjected to optimization processing to the cloud server.
The cloud server is used for receiving the length grade and the width grade of the crack in the pipeline image sent by the pipeline parameter adjusting and analyzing module, extracting the length grade and the width grade of the crack in the pipeline image acquired in the last acquisition time period stored in the management database, acquiring image parameters acquired in two times, extracting the image acquisition time in the image parameters, and acquiring the interval days M between the two times of acquisition according to the image parameters acquired in the two times.
In addition, the cloud server is used for receiving the average temperature set, the average humidity set, the flowing time set and the pressure time set which are optimized and processed by the data optimizing and sorting module, and counting crack evolution influence coefficients of the pipeline under the influence of the environment according to the crack length level and the crack width level in the pipeline image respectively corresponding to the interval days M acquired in the previous and next two times, and the average temperature set, the average humidity set, the flowing time set and the pressure time set corresponding to the interval days M acquired in the previous and next two times
μMExpressed as the specific gravity coefficient corresponding to the crack width level in the pipeline image after M days, u is expressed as the specific gravity coefficient corresponding to the crack length width level in the pipeline image before M days, muMU all belong to pE1, pE 2.., pEi.., pEn,
expressed as the specific gravity coefficient corresponding to the crack length scale in the pipeline image after M days,
is shown asThe specific gravity coefficient corresponding to the crack length grade in the pipeline image M days before,
all belong to pF1, pF 2.., pFj.., pFn, βkExpressed as the factor of influence of the fracture length on the pipe use, γcExpressed as the factor of the crack width on the pipe usage, Dk MExpressed as the crack length scale in the pipeline image after M days, DkExpressed as the crack length scale in the image of the pipe M days ago, Dc MExpressed as the crack width rating, D, in the pipe image after M dayscExpressed as the crack width level in the pipe image M days ago, gvr is expressed as the weight coefficient of the pipe water flow at the r-th water flow level,
expressed as the time of the pipeline water flow at the r water flow grade on the f day, gyr is expressed as the weight coefficient of the pressure applied to the upper end of the inner wall of the pipeline at the r pressure grade,
expressed as the time at which the upper end of the inner wall of the pipeline is subjected to pressure at the r pressure level on the f day,
expressed as the average temperature of the pipe on day f,
expressed as the average humidity of the pipeline on the f day, α 1, α 2, α 3 and α 4 are expressed as the influence factor of water flow on the service life of the pipeline, the influence factor of pressure on the upper end of the inner wall of the pipeline on the service life of the pipeline, the influence factor of temperature in the pipeline on the service life of the pipeline and the influence factor of humidity in the pipeline on the pipeline respectively, and α 2 is more than α 1 and more than α 3 and more than α 4,
the greater the coefficient of influence of the development of a crack in a pipe under the influence of the environment, the greater the crack development in the pipeThe faster the evolution damage speed is, the cloud server sends the crack evolution influence coefficient of the pipeline under the influence of the current environment to the estimation and evaluation module.
The pre-estimation evaluation module receives a crack evolution influence coefficient of the pipeline under the current environmental influence sent by the cloud server, acquires a crack length grade threshold and a crack width grade threshold corresponding to the pipeline, and pre-estimates the expected follow-up change service life of the pipeline from the current to the pipeline crack threshold under the current environmental influence factors according to the received pipeline crack evolution influence coefficient, the crack length grade threshold, the crack width grade threshold, the current crack length grade and the current crack width grade
Expressed as the crack evolution impact coefficient of the pipeline under the current environmental impact, Dk thresholdExpressed as a fracture length level threshold in the pipe, Dc thresholdThe method is characterized in that the method is expressed as a crack width grade threshold value in the pipeline, and the estimated expected replacement life of the pipeline under the current environmental influence factors is respectively sent to a management database and a supervision prompt terminal, wherein the expected replacement life of the pipeline is shorter than the actual usable life of the pipeline, so that the pipeline is ensured to be replaced in advance before the usable life of the pipeline, meanwhile, the service life of the pipeline can be utilized to the maximum extent, and the replacement frequency of the drainage pipeline in each area is reduced.
The supervision prompt terminal is used for receiving the expected service life of the pipeline to be replaced sent by the estimation module, and tracking whether the service time of the pipeline reaches the expected service life of the pipeline to prompt road management personnel to replace the pipeline reaching the expected service life of the pipeline to facilitate drainage pipeline damage and incapability of drainage, so that the problem that road surface water accumulation is influenced due to unsmooth drainage is caused.
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 (5)
1. The utility model provides a pipeline defect detecting system based on big data which characterized in that: the system comprises an image acquisition module, an image screening processing module, a pipeline parameter adjusting and analyzing module, a management database, a pipeline environment acquisition module, a data optimizing and sorting module, a cloud server, an estimation and evaluation module and a supervision prompt terminal;
the cloud server is respectively connected with the pipeline parameter adjusting and analyzing module, the management database, the data optimizing and sorting module and the estimation and evaluation module, the pipeline environment acquisition module is respectively connected with the data optimizing and sorting module and the management database, and the estimation and evaluation module is respectively connected with the supervision prompt terminal and the management database;
the image acquisition module is used for acquiring images inside the pipeline and sending the acquired images to the image screening processing module;
the image screening processing module is used for receiving the images in the drainage pipeline sent by the image acquisition module, carrying out image signal-to-noise ratio processing on the acquired images in the pipeline, screening out the images with the image signal-to-noise ratio larger than a set signal-to-noise ratio threshold value, and sending the screened images with the signal-to-noise ratio larger than the set signal-to-noise ratio threshold value to the pipeline parameter adjustment analysis module;
the pipeline parameter adjusting and analyzing module is used for receiving the image which is sent by the image screening and processing module and has the signal-to-noise ratio larger than a set signal-to-noise ratio threshold value, carrying out pipeline image parameter adjustment on the image which has the signal-to-noise ratio larger than the set signal-to-noise ratio threshold value so as to adjust the image to a set standard image parameter, extracting cracks in the pipeline image after the image parameter adjustment, comparing the extracted cracks with the crack length corresponding to each crack length grade and the crack width corresponding to each crack width grade stored in the management database respectively, obtaining the crack length grade and the crack width grade in the pipeline image after the image parameter adjustment, sending the crack length grade and the crack width grade in the pipeline image to the management database and the cloud server respectively, and sending the pipeline image after the image parameter adjustment and the image parameter to the management database;
the management database is used for storing the pipeline image and the image parameters which are sent by the pipeline parameter adjusting and analyzing module and are subjected to image parameter adjustment, and the length grade and the width grade of the crack in the pipeline image, and storing each length grade of the crack and each width grade of the crack, wherein each length grade of the crack corresponds to different length ranges of the crack, and different width grades of the crack correspond to different width ranges of the crack; the management database stores the temperature, humidity, water flow speed and pressure environment parameters at the upper end of the inner wall of the pipeline in each drainage pipeline, stores the water flow speed range corresponding to each water flow grade and the pressure range applied to the upper end of the inner wall of the pipeline corresponding to each pressure grade, and receives the expected follow-up change service life of the pipeline fed back by the estimation module according to the previous environment factors;
the pipeline environment acquisition module comprises a plurality of pipeline environment acquisition units, and the pipeline environment acquisition units are arranged in the pipeline and used for detecting the temperature, the humidity and the water flow speed in the pipeline and the pressure applied to the upper end of the inner wall of the pipeline in real time and respectively sending the detected temperature, humidity, water flow speed and the pressure applied to the upper end of the inner wall of the pipeline to the management database and the data optimization and arrangement module;
the data optimization and arrangement module is used for receiving the temperature, the humidity and the water flow speed in the drainage pipeline and the pressure applied to the upper end of the inner wall of the pipeline, which are sent by the pipeline environment acquisition module, optimizing the received temperature, humidity, water flow speed and pressure on the upper end of the inner wall of the pipeline in the drainage pipeline to obtain an average temperature set and an average humidity set in the previous and next acquisition time periods, and comparing the water flow speed in the pipeline every day with the set flow speed range corresponding to each water flow grade, screening out the flowing time set of the water flow speed in the pipeline under different water flow grades, comparing the pressure born by the pipeline every day with the pressure range corresponding to each pressure grade, screening out a pressure time set suffered by the pipeline every day, and sending the average temperature set, the average humidity set, the flowing time set and the pressure time set which are subjected to optimization processing to a cloud server;
the cloud server is used for receiving the length grade and the width grade of the crack in the pipeline image sent by the pipeline parameter adjusting and analyzing module, extracting the length grade and the width grade of the crack in the pipeline image acquired in the last acquisition time period stored in the management database, acquiring image parameters acquired in the previous acquisition time period and the next two times, and extracting image acquisition time in the image parameters;
the cloud server is further used for receiving the average temperature set, the average humidity set, the flowing time set and the pressure time set which are optimized and processed by the data optimizing and sorting module, counting crack evolution influence coefficients of the pipeline under the influence of the environment according to crack length levels and width levels in the pipeline image respectively corresponding to the interval days M acquired in the previous and next two times and the average temperature set, the average humidity set, the flowing time set and the pressure time set corresponding to the interval days M acquired in the previous and next two times, and sending the crack evolution influence coefficients of the pipeline under the influence of the current environment to the pre-estimation and evaluation module;
the pre-estimation evaluation module receives a crack evolution influence coefficient of the pipeline under the influence of the current environment, which is sent by the cloud server, acquires a crack length grade threshold value and a crack width grade threshold value corresponding to the pipeline, and pre-estimates the expected follow-up change service life of the pipeline from the current to the pipeline crack threshold value under the influence factors of the current environment according to the received pipeline crack evolution influence coefficient, the crack length grade threshold value, the crack width grade threshold value, the current crack length grade and the current crack width grade
Expressed as the crack evolution impact coefficient of the pipeline under the current environmental impact, Dk thresholdExpressed as a fracture length level threshold in the pipe, Dc thresholdExpressed as a crack width level threshold in the pipeline and accounts for the estimated pipeline's current environmental impactThe expected replacement life under the condition is respectively sent to a management database and a supervision prompt terminal;
and the supervision prompting terminal is used for receiving the expected replacement life of the pipeline sent by the estimation module and tracking whether the service time of the pipeline reaches the expected replacement life or not in real time so as to prompt a road manager to replace the pipeline reaching the expected replacement life.
2. The big-data based pipeline defect detection system according to claim 1, wherein: the fracture length grades are respectively E1, E2, a. l1-l2, l2-l3, a., li-l (i-1), a., ln-l (n-1), wherein each fracture width grade is F1, F2, a., Fj, a., Fn, and the fracture width range corresponding to each fracture width grade is: h1-h2, h2-h 3., hj-h (j-1),. hn-h (n-1), Ei represents the ith fracture length level, Fj represents the jth fracture width level, li-l (i-1) represents the fracture length range corresponding to the ith fracture length level, and hj-h (j-1) represents the maximum fracture width corresponding to the jth fracture width level.
3. The big-data based pipeline defect detection system according to claim 2, wherein: the specific gravity coefficients corresponding to the fracture length grades are respectively pE1, pE2,. 3, pEi,. pEn, pE1 < pE 2. < pEi. < pEn, pE1+ pE2+. + pEi +. + pEn ═ 1, and the specific gravity coefficients corresponding to the fracture width grades are respectively pF1, pF2,. pFj,. once, pFn, pF1 < pF 2. < pFj. < pFn, pF1+ pF2+. 2. + 2 · + 1, 2 is expressed as the specific gravity of the ith fracture length grade, and 2 is expressed as the specific gravity of the jth fracture width grade.
4. The big-data based pipeline defect detection system according to claim 1, wherein: the pipeline environment acquisition unit comprises a temperature acquisition unit, a humidity acquisition unit, a water velocity acquisition unit and a pressure detection unit, wherein the temperature acquisition unit is a temperature sensor and is arranged at the upper end of the inner wall of the pipeline for acquiring the temperature in the detection pipeline in real time, the humidity acquisition unit is a humidity sensor and is arranged at the upper end of the inner wall of the pipeline for acquiring the humidity in the detection pipeline in real time, the water velocity acquisition unit is a flow velocity sensor and is used for acquiring the water velocity in the detection pipeline in real time, and the pressure detection unit is a pressure sensor and is used for acquiring the water velocity at the upper end of the inner wall of the detection pipeline in real.
5. The big-data based pipeline defect detection system according to claim 4, wherein: crack evolution influence coefficient of pipeline under influence of environment
μMExpressed as the specific gravity coefficient corresponding to the crack width level in the pipeline image after M days, u is expressed as the specific gravity coefficient corresponding to the crack length width level in the pipeline image before M days, muMU all belong to pE1, pE 2.., pEi.., pEn,
expressed as the specific gravity coefficient corresponding to the crack length scale in the pipeline image after M days,
expressed as the specific gravity coefficient corresponding to the crack length grade in the pipeline image M days ago,
all belong to pF1, pF 2.., pFj.., pFn, βkExpressed as the factor of influence of the fracture length on the pipe use, γcExpressed as the factor of the crack width on the pipe usage, Dk MExpressed as the crack length scale in the pipeline image after M days, DkExpressed as the crack length scale in the image of the pipe M days ago, Dc MExpressed as the crack width rating, D, in the pipe image after M dayscExpressed as the crack width rating in the channel image M days ago, gvr expressed as the channel current in the r-th currentThe weight coefficient at the level of the weight coefficient,
expressed as the time of the pipeline water flow at the r water flow grade on the f day, gyr is expressed as the weight coefficient of the pressure applied to the upper end of the inner wall of the pipeline at the r pressure grade,
expressed as the time at which the upper end of the inner wall of the pipeline is subjected to pressure at the r pressure level on the f day,
expressed as the average temperature of the pipe on day f,
expressed as the average humidity of the pipeline on the f day, α 1, α 2, α 3 and α 4 are expressed as the influence factor of water flow on the service life of the pipeline, the influence factor of pressure on the upper end of the inner wall of the pipeline on the service life of the pipeline, the influence factor of temperature in the pipeline on the service life of the pipeline and the influence factor of humidity in the pipeline on the pipeline respectively, and α 2 is more than α 1 and more than α 3 and more than α 4,
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