CN114445240A - Big data safety monitoring management system for building wall - Google Patents
Big data safety monitoring management system for building wall Download PDFInfo
- Publication number
- CN114445240A CN114445240A CN202210125894.1A CN202210125894A CN114445240A CN 114445240 A CN114445240 A CN 114445240A CN 202210125894 A CN202210125894 A CN 202210125894A CN 114445240 A CN114445240 A CN 114445240A
- Authority
- CN
- China
- Prior art keywords
- wall body
- image
- wall
- crack
- grade
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 25
- 238000012423 maintenance Methods 0.000 claims abstract description 20
- 238000005457 optimization Methods 0.000 claims abstract description 20
- 238000007781 pre-processing Methods 0.000 claims abstract description 14
- 230000005484 gravity Effects 0.000 claims description 22
- 238000007664 blowing Methods 0.000 claims description 13
- 238000007790 scraping Methods 0.000 claims description 12
- 230000003203 everyday effect Effects 0.000 claims description 11
- 230000007613 environmental effect Effects 0.000 claims description 10
- 238000001514 detection method Methods 0.000 claims description 9
- 238000011156 evaluation Methods 0.000 claims description 8
- 238000012216 screening Methods 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 5
- 230000000694 effects Effects 0.000 claims description 3
- 230000002093 peripheral effect Effects 0.000 claims description 3
- 230000007547 defect Effects 0.000 description 4
- 239000004568 cement Substances 0.000 description 2
- 238000007792 addition Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000000919 ceramic Substances 0.000 description 1
- 238000005034 decoration Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/08—Construction
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/02—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/14—Measuring arrangements characterised by the use of optical techniques for measuring distance or clearance between spaced objects or spaced apertures
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Health & Medical Sciences (AREA)
- Economics (AREA)
- General Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Working Measures On Existing Buildindgs (AREA)
Abstract
The invention discloses a big data safety monitoring and management system for a building wall, which comprises an image acquisition module, an image preprocessing module, a parameter calling and analyzing module, a cloud database, a wall surface environment acquisition module, an optimization management module, a cloud server, an estimation module and a monitoring and early warning module. According to the invention, through the parameter calling analysis module and the wall surface environment acquisition module and combining with the cloud server, crack evolution influence coefficients of the wall body under the influence of the environment can be analyzed according to the current environment parameters and the wall body crack condition in the wall body, so that the influence degree of the environment factors in the wall body can be conveniently clarified, an evolution model of the wall body crack can be accurately and effectively constructed, reliable data support is provided for the later estimation of the service life of the wall body, the expected maintenance life of the wall body surface from the current to the wall body crack threshold under the current environment influence factors can be estimated, the expected maintenance life of the wall body under the influence of the current environment can be accurately estimated, and early warning follow-up is realized.
Description
Technical Field
The invention belongs to the technical field of wall safety management, and relates to a big data safety monitoring and management system for a building wall.
Background
Along with the development of economy and the progress of science and technology, urban high-rise buildings are more and more, and accidents that people are injured by falling objects from high altitudes frequently happen. Besides the throwing of the floor, the problems that the decoration objects such as ceramic tiles, cement and the like on the surface of the outer wall of the building are cracked and fall off along with the increase of time, and how to detect the falling of the cement and the like on the outer side of the wall in real time for preventing and monitoring in time are solved urgently for the safety of the building wall.
At present, in order to solve the problems of falling of the surface of a wall body and the like, a worker can regularly detect the wall body and maintain the wall body according to the detection condition so as to improve the attractiveness and service life of the surface of the wall body, but the defect detection is carried out on the wall body through manual work, a large amount of manpower and material resources are increased, the detection efficiency is low, the surface of the wall body cannot be monitored in real time, whether the crack defect on the surface of the wall body reaches the maintenance degree or not can not be accurately judged due to the subjective consciousness, the service life of the surface of the wall body reaching the maintenance state can not be estimated in advance according to the current environment condition of the wall body, the problem that the crack on the surface of the wall body cannot be maintained in time when the crack on the surface of the wall body reaches the falling state is avoided, and the possibility of casualties caused by falling objects and increasing personnel casualties is increased.
Disclosure of Invention
The invention aims to provide a big data safety monitoring and management system for a building wall, which solves the problems that the wall defect detection accuracy is poor, the service life of the wall cannot be estimated and the like in the prior art, and further cannot realize early warning and reminding for wall maintenance.
The purpose of the invention can be realized by the following technical scheme:
a big data safety monitoring management system for a building wall comprises an image acquisition module, an image preprocessing module, a parameter calling and analyzing module, a cloud database, a wall surface environment acquisition module, an optimization management module, a cloud server, a pre-estimation evaluation module and a monitoring and early warning module;
the image acquisition module is arranged on the outer side of the wall body and used for acquiring an image of the outer surface of the wall body to be monitored and sending the acquired image to the image preprocessing module;
the image preprocessing module is used for receiving the image of the outer surface of the wall body sent by the image acquisition module, carrying out image signal-to-noise ratio processing on the acquired image of the outer surface of the wall body, screening out an image of which the signal-to-noise ratio is greater than a set signal-to-noise ratio threshold value, and sending the screened image of which the signal-to-noise ratio is greater than the set signal-to-noise ratio threshold value to the parameter calling and analyzing module;
the parameter calling and analyzing module is used for receiving the image which is sent by the image preprocessing module and has the signal-to-noise ratio larger than a set signal-to-noise ratio threshold value, carrying out wall surface 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 wall surface 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 cloud database respectively, obtaining the crack length grade and the crack width grade in the wall surface image after the image parameter adjustment, sending the crack length grade and the crack width grade in the wall surface image to the cloud database and the cloud server respectively, and sending the wall surface image after the image parameter adjustment and the image parameters to the cloud database;
the cloud database is used for storing the wall surface image and the image parameters which are sent by the parameter calling and analyzing module and are subjected to image parameter adjustment, and the crack length grades and the crack width grades in the wall surface image, and storing the crack length grades and the crack width grades, wherein the crack length grades correspond to different crack length ranges, and the different crack width grades correspond to different crack width ranges;
the cloud database stores environment parameters such as temperature, humidity, wind speed and stress of the wall surface in the environment where the wall is located, stores a wind speed range corresponding to each wind speed grade and a stress numerical range of the wall surface corresponding to each stress grade, and receives the expected service life of the wall surface fed back by the pre-estimation module according to the previous environment factors;
the wall surface environment acquisition module comprises a plurality of wall surface environment acquisition units, and the wall surface environment acquisition units are installed in the outer surfaces of the peripheral sides of the wall body, are used for detecting the temperature, the humidity and the wind speed of the surface of the wall body and the stress borne by the surface of the wall body in real time, and respectively send the detected temperature, humidity, wind speed and the stress borne by the surface of the wall body to the cloud database and the optimization management module;
the optimization management module is used for receiving the temperature, humidity, wind speed and stress on the wall surface of the wall body sent by the wall surface environment acquisition module, acquiring the temperature, humidity, wind speed and stress on the wall surface of the wall body in the environment where the wall body is located at equal intervals t, calculating the average temperature and humidity of the wall body every day, establishing an average temperature set and an average humidity set in the previous acquisition time period and the next acquisition time period according to the number of days of image acquisition intervals of the wall body, meanwhile, comparing the wind speed in the wall body every day with the set flow speed range corresponding to each wind speed grade by the optimization management module, screening the blowing and scraping time sets of the outer surface of the wall body at different wind speed grades, comparing the stress on the wall body every day with the stress range corresponding to each stress grade, screening the stress time sets on the wall body every day, and optimizing the average temperature set, wind speed and stress on the wall body surface, Sending the average humidity set, the blowing and scraping time set and the stress time set to a cloud server;
the cloud server is used for receiving the length grade and the width grade of the crack in the wall surface image sent by the parameter calling and analyzing module, simultaneously extracting the length grade and the width grade of the crack in the wall surface image acquired in the last acquisition time period stored in the cloud database, acquiring image parameters acquired in the two times, extracting 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 blowing and scraping time set and the stress time set which are optimized and processed by the optimization management module, counting crack evolution influence coefficients of the wall surface under the influence of the environment according to crack length levels and width levels in the wall surface 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 blowing and scraping time set and the stress 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 wall surface 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 wall surface under the current environmental influence, which is sent by the cloud server, and acquires a crack length grade threshold value and a crack width grade threshold value corresponding to the wall surface, pre-estimates the expected maintenance life of the wall surface from the current to the wall crack threshold value under the current environmental influence factor according to the received wall crack evolution influence coefficient, the crack length grade threshold value, the crack width grade threshold value, the current crack length grade and the crack width grade, and respectively sends the pre-estimated expected maintenance life of the wall under the current environmental influence factor to the cloud database and the monitoring and early warning module;
the monitoring and early warning module is used for receiving the expected maintenance life of the wall body sent by the estimation module and tracking whether the service time of the surface of the wall body reaches the expected maintenance life or not in real time.
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,., li-l (i-1),. and ln-l (n-1), wherein each fracture width grade is F1, F2,. and.Fj,. and Fn, and the fracture width range corresponding to each fracture width grade is: h1-h2, h2-h3,., hj-h (j-1),. hn-h (n-1), Ei represents the ith fracture length grade, Fj represents the jth fracture width grade, li-l (i-1) represents the fracture length range corresponding to the ith fracture length grade, and hj-h (j-1) represents the maximum fracture width corresponding to the jth fracture width grade.
Further, the specific gravity coefficients corresponding to the fracture length grades are respectively pE1, pE2,. 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,. pFn, pF1 < pF2 < pFj. < pFn, pF1+ pF2+. pFj +. pFn ═ 1, pEi is expressed as the specific gravity of the ith fracture length grade, and pFj is expressed as the specific gravity of the jth fracture width grade.
Further, wall environment acquisition unit includes temperature acquisition unit, humidity acquisition unit, wind speed acquisition unit and stress detecting element, and temperature acquisition unit is temperature sensor, installs at the wall body surface for gather the temperature that detects the wall body surface in real time, and humidity acquisition unit is humidity sensor, installs in the wall body, is used for gathering the humidity that detects the wall body in real time, and wind speed acquisition unit is wind speed sensor, is used for gathering the wind speed that detects the wall body surface in real time, and stress detecting element is used for gathering the stress numerical value that receives in the detection wall body in real time.
Further, the formula for calculating the fracture evolution influence coefficient
μMExpressed as the specific gravity coefficient corresponding to the crack width grade in the wall body image after M days, u is expressed as the specific gravity coefficient corresponding to the crack length width grade in the wall body image before M days, and muMU all belong to pE1, pE 2.., pEi.., pEn,
expressed as the specific gravity coefficient corresponding to the crack length scale in the wall surface image after M days,
expressed as the specific gravity coefficient corresponding to the crack length grade in the wall surface image before M days,
all belong to pF1, pF2, pFj, pFn, betakExpressed as the factor of the effect of the crack length on the appearance of the wall, gammacExpressed as the factor of the crack width on the appearance of the wall, Dk MExpressed as the crack length rating, D, in the wall surface image after M dayskExpressed as the crack length rating, D, in the wall surface image M days agoc MExpressed as crack width rating, D, in the wall surface image after M dayscExpressed as the crack width rating in the wall surface image M days ago, gvr is expressed as the weight coefficient of the wall at the r-th wind speed rating,
expressed as the time of the wall body on the f day under the r wind speed level, gyr is expressed as the weight coefficient of the stress on the surface of the wall body under the r stress level,
expressed as the time at which the wall surface is stressed at the r-th stress level on day f,
expressed as the average temperature of the wall on day f,
expressed as the average humidity of the wall body on the f day, alpha 1, alpha 2, alpha 3 and alpha 4 are respectively expressed as the influence factor of the wind speed on the surface life of the wall body, the influence factor of the stress on the surface of the wall body on the surface life of the wall body, the influence factor of the temperature on the surface life of the wall body and the influence factor of the humidity on the surface life of the wall body, and alpha 2 is more than alpha 1 and more than alpha 3 is more than alpha 4,
the larger the crack evolution influence coefficient of the wall surface under the influence of the environment is, the faster the evolution and damage speed of the crack in the wall surface is shown,
further, the expected service life of the wall surface from the current to the wall fracture threshold under the current environmental impact factors
Expressed as the crack evolution influence coefficient of the wall under the influence of the current environment, Dk thresholdExpressed as crack length level threshold in the wall, Dc thresholdExpressed as crack width level threshold in the wall.
The invention has the beneficial effects that:
according to the big data safety monitoring management system for the building wall, provided by the invention, the length and the width of the crack in the wall image are analyzed through the parameter calling and analyzing module so as to obtain the length grade and the width grade of the crack in the wall image, and the change total amount of the wall crack in the previous and next acquisition time periods can be clearly known through the change of the length grade and the width grade of the crack in the image acquired twice, so that the length and the width of the crack in the wall can be qualitatively and conveniently sorted, and the change amount of the wall defect can be visually displayed.
The temperature, the humidity, the wind speed and the stress on the wall surface of the wall body are obtained through a wall surface environment acquisition module, the temperature, the humidity, the wind speed and the stress on the wall body surface of the wall body are optimized through an optimization management module to obtain an average temperature set, an average humidity set, a blowing and scraping time set and a stress time set in the wall body, meanwhile, a cloud server is combined, the cloud server counts crack evolution influence coefficients of the wall body under the influence of the environment according to the length grade and the width grade of cracks in wall body images respectively corresponding to the previous acquisition and the next acquisition, and the average temperature set, the average humidity set, the blowing and scraping time set and the stress time set corresponding to the previous acquisition and the next acquisition in two acquisition time periods, so as to obtain the influence degree of the cracks in the wall body under the influence of the environment factors in the wall body in the previous acquisition and next acquisition time periods, and an evolution model of the wall crack can be accurately and effectively constructed, and reliable data support is provided for the later-stage estimation of the service life of the wall.
The crack evolution influence coefficient of the wall body under the influence of the environment is obtained through the statistics of the cloud server, the wall body crack length grade and the wall body crack width grade of the wall body in the later image acquisition are estimated, the expected service life of the wall body under the influence of the current environment can be accurately estimated, the wall body maintenance early warning reminding can be carried out before falling as soon as possible or on the surface of the wall body, the wall body surface to be fallen is maintained on the premise of ensuring the service life of the wall body, and people are prevented from being injured by falling.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to 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.
The big data safety monitoring and management system for the building wall comprises an image acquisition module, an image preprocessing module, a parameter calling and analyzing module, a cloud database, a wall surface environment acquisition module, an optimization management module, a cloud server, an estimation module and a monitoring and early warning module.
The image acquisition module is arranged on the outer side of the wall body and used for acquiring an image of the outer surface of the wall body to be monitored and sending the acquired image to the image preprocessing module;
the image preprocessing module is used for receiving the image of the outer surface of the wall body sent by the image acquisition module, carrying out image signal-to-noise ratio processing on the acquired image of the outer surface of the wall body, screening out an image of which the image signal-to-noise ratio is greater than a set signal-to-noise ratio threshold value, sending the screened image of which the signal-to-noise ratio is greater than the set signal-to-noise ratio threshold value to the parameter calling and analyzing module, and screening out a wall body surface image with high quality through the image preprocessing module;
the parameter calling and analyzing module is used for receiving the image with the signal-to-noise ratio larger than the set signal-to-noise ratio threshold value sent by the image preprocessing module, adjusting the wall surface image parameters of the image with the signal-to-noise ratio larger than the set signal-to-noise ratio threshold value to adjust to the set standard image parameters, extracting cracks in the wall surface image after the image parameter adjustment, comparing the extracted cracks with the crack lengths corresponding to the crack length grades and the crack widths corresponding to the crack width grades stored in the cloud database respectively, obtaining the crack length grades and the crack width grades in the wall surface image after the image parameter adjustment, sending the crack length grades and the crack width grades in the wall surface image to the cloud database and the cloud server respectively, and sending the wall surface image after the image parameter adjustment and the image parameters to the cloud database, wherein, the image parameters include resolution, brightness, image size and time of image acquisition, etc.
The cloud database is used for storing the wall surface image and the image parameters which are sent by the parameter calling and analyzing module and are subjected to image parameter adjustment, and crack length grades and width grades in the wall surface image, and storing each crack length grade and each crack width grade, wherein each crack length grade corresponds to different crack length ranges, and different crack width grades correspond to different crack width ranges, and each crack length grade is respectively E1, E2, E. l1-l2, l2-l3,., li-l (i-1),. and ln-l (n-1), wherein each fracture width grade is F1, F2,. and.Fj,. and Fn, and the fracture width range corresponding to each fracture width grade is: h1-h2, h2-h3, a., hj-h (j-1),. hn-h (n-1), and each crack length grade is larger, which indicates that the length of the wall surface crack is longer, each crack width grade is larger, which indicates that the maximum width in the wall surface crack is larger, and each crack length grade corresponds to a specific gravity coefficient of pE1, pE2,.., pEi, a.., pEn, pE 387 1 < pE 6. < pEi. < pEn, pE1+ pE 2. +. 6855. + pEn @ 1, each crack width grade corresponds to a specific gravity coefficient of pF 4,. 685.6854.,. pEn, pF pEn < pEn. + pEn. f, p pEn. + pEn. f-p. + pEn. pEn, p. + pEn. indicates the length of the pF pEn + pEn +,6854, pEi, pFj, li-l (i-1), hj-h (j-1), and hj-h (j-1), wherein li-l (i-1) represents the crack length range corresponding to the ith crack length grade, hj-h (j-1) represents the maximum crack width corresponding to the jth crack width grade, namely when the crack widths in the wall surface images are not consistent, the maximum width in the cracks is extracted to screen the width grades corresponding to the cracks.
The cloud database stores environment parameters such as temperature, humidity, wind speed and stress of the wall surface in the environment where the wall body is located, stores a wind speed range corresponding to each wind speed grade and a stress numerical range of the wall surface corresponding to each stress grade, and meanwhile receives the expected service life of the wall surface fed back by the pre-estimation module according to the previous environment factors.
The wall surface environment acquisition module comprises a plurality of wall surface environment acquisition units, and the wall surface environment acquisition units are installed in the outer surfaces of the peripheral sides of the wall body, are used for detecting the temperature, the humidity and the wind speed of the surface of the wall body and the stress borne by the surface of the wall body in real time, and respectively send the detected temperature, humidity, wind speed and the stress borne by the surface of the wall body to the cloud database and the optimization management module;
the wall surface environment acquisition unit comprises a temperature acquisition unit, a humidity acquisition unit, a wind speed acquisition unit and a stress detection unit, wherein the temperature acquisition unit is a temperature sensor and is arranged on the outer surface of the wall body and used for acquiring the temperature of the outer surface of the wall body in real time, the humidity acquisition unit is a humidity sensor and is arranged in the wall body and used for acquiring the humidity of the wall body in real time, the wind speed acquisition unit is a wind speed sensor and used for acquiring the wind speed of the outer surface of the wall body in real time, and the stress detection unit is used for acquiring the stress value received in the wall body in real time.
The optimization management module is used for receiving the temperature, humidity, wind speed and stress on the surface of the wall body sent by the wall surface environment acquisition module, acquiring the temperature, humidity, wind speed and stress on the surface of the wall body of the environment where the wall body is located at equal intervals t (the interval t is 4h, 6h or 8h), calculating the average temperature and humidity of the wall body every day, and establishing an average temperature set in the acquisition time period before and after the acquisition according to the number of days between the image acquisition of the wall body
Mean humidity ensemble
Wherein the content of the first and second substances,
expressed as the average temperature of the wall surface on day f,
the average humidity of the wall body on the f day is represented, and meanwhile, the optimization management module compares the wind speed in the wall body on each day with the set flow speed range corresponding to each wind speed grade to screen out the wall bodyBlowing and scraping time set of outer surface at different wind speed grades
Comparing the stress applied to the wall every day with the stress range corresponding to each stress level to screen out the stress time set applied to the wall every day
Expressed as the time of wall wind speed at the r wind speed level on day f,
expressed as the time at which the wall surface is stressed at the r-th stress level on day f,
where q is represented as the number of levels of the wind speed level,
and q is the level number of the stress level, and the optimization management module sends the average temperature set, the average humidity set, the blowing time set and the stress time set after optimization processing to the cloud server.
The cloud server is used for receiving the crack length grade and the width grade in the wall surface image sent by the parameter calling and analyzing module, extracting the crack length grade and the width grade in the wall surface image acquired in the last acquisition time period stored in the cloud database and acquiring image parameters acquired in the 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 blowing time set and the stress time set which are subjected to optimization processing by the optimization management module, and the cloud server respectively performs comparison according to the interval days M acquired in two times before and afterAccording to the length grade and the width grade of the crack in the wall surface image, and corresponding average temperature set, average humidity set, blowing and scraping time set and stress time set within the interval days M of two previous and next acquisitions, the crack evolution influence coefficient of the wall surface under the influence of the environment is counted
μMExpressed as the specific gravity coefficient corresponding to the crack width grade in the wall body image after M days, u is expressed as the specific gravity coefficient corresponding to the crack length width grade in the wall body image before M days, and muMU all belong to pE1, pE 2.., pEi.., pEn,
expressed as the specific gravity coefficient corresponding to the crack length scale in the wall surface image after M days,
expressed as the specific gravity coefficient corresponding to the crack length grade in the wall surface image before M days,
all belong to pF1, pF2, pFj, pFn, βkExpressed as the factor of the effect of the crack length on the appearance of the wall, gammacExpressed as the factor of the crack width on the appearance of the wall, Dk MExpressed as the crack length rating, D, in the wall surface image after M dayskExpressed as the crack length rating, D, in the wall surface image M days agoc MExpressed as the crack width rating, D, in the wall surface image after M dayscExpressed as the crack width rating in the wall surface image M days ago, gvr is expressed as the weight coefficient of the wall at the r-th wind speed rating,
expressed as the time of the wall body on the f day under the r wind speed level, gyr is expressed as the weight coefficient of the stress on the surface of the wall body under the r stress level,
expressed as the time at which the wall surface is stressed at the r-th stress level on day f,
expressed as the average temperature of the wall on day f,
expressed as the average humidity of the wall body on the f day, alpha 1, alpha 2, alpha 3 and alpha 4 are respectively expressed as the influence factor of the wind speed on the surface life of the wall body, the influence factor of the stress on the surface of the wall body on the surface life of the wall body, the influence factor of the temperature on the surface life of the wall body and the influence factor of the humidity on the surface life of the wall body, and alpha 2 is more than alpha 1 and more than alpha 3 is more than alpha 4,
the larger the crack evolution influence coefficient of the wall surface under the influence of the environment is, the higher the evolution damage speed of the crack in the wall surface is, and the cloud server sends the crack evolution influence coefficient of the wall surface 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 wall surface 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 wall surface, and pre-estimates the expected maintenance life of the wall surface from the current to the wall crack threshold under the current environmental influence factors according to the received wall crack evolution influence coefficient, the crack length grade threshold, the crack width grade threshold, the current crack length grade and the crack width grade
Expressed as the crack evolution influence coefficient of the wall under the influence of the current environment, Dk thresholdExpressed as crack length level threshold in the wall, Dc thresholdThe service life of the wall body is predicted to be the threshold value of the crack width grade in the wall body, the predicted expected maintenance life of the wall body under the influence factors of the current environment is respectively sent to the cloud database and the monitoring and early warning module, the expected maintenance life of the wall body is shorter than the actual usable life of the wall body, the surface of the wall body is further guaranteed to be maintained in advance before the usable life of the wall body, and meanwhile the appearance of the outer surface of the wall body can be beautified to the greatest extent according to the service life of the wall body.
The monitoring and early warning module is used for receiving the expected maintenance life of the wall body sent by the estimation module and tracking whether the service time of the wall body surface reaches the expected maintenance life or not in real time so as to prompt wall surface management personnel to maintain the wall body surface reaching the expected maintenance life.
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 (6)
1. The utility model provides a big data safety monitoring management system to building wall which characterized in that: the system comprises an image acquisition module, an image preprocessing module, a parameter calling and analyzing module, a cloud database, a wall surface environment acquisition module, an optimization management module, a cloud server, a pre-estimation evaluation module and a monitoring and early warning module;
the image acquisition module is arranged on the outer side of the wall body and used for acquiring an image of the outer surface of the wall body to be monitored and sending the acquired image to the image preprocessing module;
the image preprocessing module is used for receiving the image of the outer surface of the wall body sent by the image acquisition module, carrying out image signal-to-noise ratio processing on the acquired image of the outer surface of the wall body, screening out an image of which the signal-to-noise ratio is greater than a set signal-to-noise ratio threshold value, and sending the screened image of which the signal-to-noise ratio is greater than the set signal-to-noise ratio threshold value to the parameter calling and analyzing module;
the parameter calling and analyzing module is used for receiving the image which is sent by the image preprocessing module and has the signal-to-noise ratio larger than a set signal-to-noise ratio threshold value, carrying out wall surface 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 wall surface 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 cloud database respectively, obtaining the crack length grade and the crack width grade in the wall surface image after the image parameter adjustment, sending the crack length grade and the crack width grade in the wall surface image to the cloud database and the cloud server respectively, and sending the wall surface image after the image parameter adjustment and the image parameters to the cloud database;
the cloud database is used for storing the wall surface image and the image parameters which are sent by the parameter calling and analyzing module and are subjected to image parameter adjustment, and the crack length grades and the crack width grades in the wall surface image, and storing the crack length grades and the crack width grades, wherein the crack length grades correspond to different crack length ranges, and the different crack width grades correspond to different crack width ranges;
the cloud database stores environment parameters such as temperature, humidity, wind speed and stress of the wall surface in the environment where the wall is located, stores a wind speed range corresponding to each wind speed grade and a stress numerical range of the wall surface corresponding to each stress grade, and receives the expected service life of the wall surface fed back by the pre-estimation module according to the previous environment factors;
the wall surface environment acquisition module comprises a plurality of wall surface environment acquisition units, and the wall surface environment acquisition units are installed in the outer surfaces of the peripheral sides of the wall body, are used for detecting the temperature, the humidity and the wind speed of the surface of the wall body and the stress borne by the surface of the wall body in real time, and respectively send the detected temperature, humidity, wind speed and the stress borne by the surface of the wall body to the cloud database and the optimization management module;
the optimization management module is used for receiving the temperature, humidity, wind speed and stress on the wall surface, which are sent by the wall surface environment acquisition module, acquiring the temperature, humidity, wind speed and stress on the wall surface of the wall body in the environment where the wall body is located at equal intervals t, calculating the average temperature and humidity of the wall body every day, establishing an average temperature set and an average humidity set in the previous acquisition time period and the next acquisition time period according to the number of days of image acquisition intervals of the wall body, comparing the wind speed in the wall body every day with the set flow speed range corresponding to each wind speed grade by the optimization management module to screen out the blowing and scraping time sets of the outer surface of the wall body at different wind speed grades, comparing the stress on the wall body every day with the stress ranges corresponding to each stress grade to screen out the stress time sets on the wall body every day, and carrying out the optimized average temperature sets, the optimized temperature sets, the average humidity sets and the humidity sets, the optimized temperature sets, the average temperature sets and the humidity sets, the stress on the wall body surface are acquired according to the number t of the wall body, Sending the average humidity set, the blowing and scraping time set and the stress time set to a cloud server;
the cloud server is used for receiving the length grade and the width grade of the crack in the wall surface image sent by the parameter calling and analyzing module, simultaneously extracting the length grade and the width grade of the crack in the wall surface image acquired in the last acquisition time period stored in the cloud database, acquiring image parameters acquired in the two times, extracting 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 blowing and scraping time set and the stress time set which are optimized and processed by the optimization management module, counting crack evolution influence coefficients of the wall surface under the influence of the environment according to crack length levels and width levels in the wall surface 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 blowing and scraping time set and the stress 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 wall surface 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 wall surface under the current environmental influence, which is sent by the cloud server, and acquires a crack length grade threshold value and a crack width grade threshold value corresponding to the wall surface, pre-estimates the expected maintenance life of the wall surface from the current to the wall crack threshold value under the current environmental influence factor according to the received wall crack evolution influence coefficient, the crack length grade threshold value, the crack width grade threshold value, the current crack length grade and the crack width grade, and respectively sends the pre-estimated expected maintenance life of the wall under the current environmental influence factor to the cloud database and the monitoring and early warning module;
the monitoring and early warning module is used for receiving the expected maintenance life of the wall body sent by the estimation module and tracking whether the service time of the surface of the wall body reaches the expected maintenance life or not in real time.
2. The big data security monitoring and management system for the building wall body as claimed in claim 1, wherein: the fracture length grades are respectively E1, E2, a. l1-l2, l2-l3,., li-l (i-1),. and ln-l (n-1), wherein each fracture width grade is F1, F2,. and.Fj,. and Fn, and the fracture width range corresponding to each fracture width grade is: h1-h2, h2-h3,., hj-h (j-1),. hn-h (n-1), Ei represents the ith fracture length grade, Fj represents the jth fracture width grade, li-l (i-1) represents the fracture length range corresponding to the ith fracture length grade, and hj-h (j-1) represents the maximum fracture width corresponding to the jth fracture width grade.
3. The big data security monitoring and management system for the building wall body as claimed in claim 2, wherein: the specific gravity coefficients corresponding to the fracture length grades are respectively pE1, pE2,. so, 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, pF 2.., pFj,. so, 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 security monitoring and management system for the building wall body as claimed in claim 3, wherein: the wall surface environment acquisition unit comprises a temperature acquisition unit, a humidity acquisition unit, a wind speed acquisition unit and a stress detection unit, wherein the temperature acquisition unit is a temperature sensor and is arranged on the outer surface of the wall body and used for acquiring the temperature of the outer surface of the wall body in real time, the humidity acquisition unit is a humidity sensor and is arranged in the wall body and used for acquiring the humidity of the wall body in real time, the wind speed acquisition unit is a wind speed sensor and used for acquiring the wind speed of the outer surface of the wall body in real time, and the stress detection unit is used for acquiring the stress value received in the wall body in real time.
5. The big data security monitoring and management system for the building wall body as claimed in claim 4, wherein: formula for calculating fracture evolution influence coefficient
μMExpressed as the specific gravity coefficient corresponding to the crack width grade in the wall body image after M days, u is expressed as the specific gravity coefficient corresponding to the crack length width grade in the wall body image before M days, and muMU all belong to pE1, pE 2.., pEi.., pEn,
expressed as the specific gravity coefficient corresponding to the crack length scale in the wall surface image after M days,
expressed as the specific gravity coefficient corresponding to the crack length grade in the wall surface image before M days,
all belonging to pF1, pF 2., pFj., pFn,. betakExpressed as the factor of the effect of the crack length on the appearance of the wall, gammacExpressed as the factor of the crack width on the appearance of the wall, Dk MExpressed as the crack length rating, D, in the wall surface image after M dayskExpressed as the crack length rating, D, in the wall surface image M days agoc MExpressed as crack width rating, D, in the wall surface image after M dayscExpressed as the crack width rating in the wall surface image M days ago, gvr is expressed as the weight coefficient of the wall at the r-th wind speed rating,
expressed as the time of the wall body on the f day under the r wind speed level, gyr is expressed as the weight coefficient of the stress on the surface of the wall body under the r stress level,
expressed as the time at which the wall surface is stressed at the r-th stress level on day f,
expressed as the average temperature of the wall on day f,
expressed as the average humidity of the wall body on the f day, alpha 1, alpha 2, alpha 3 and alpha 4 are respectively expressed as the influence factor of the wind speed on the surface life of the wall body, the influence factor of the stress on the surface of the wall body on the surface life of the wall body, the influence factor of the temperature on the surface life of the wall body and the influence factor of the humidity on the surface life of the wall body, and alpha 2 is more than alpha 1 and more than alpha 3 is more than alpha 4,
the larger the crack evolution influence coefficient of the wall surface under the influence of the environment is, the faster the crack evolution damage speed in the wall surface is.
6. The big data security monitoring and management system for the building wall body as claimed in claim 5, wherein: from the current to the wall fracture threshold of the wall surface under the current environmental influence factorsExpected service life
Expressed as the crack evolution influence coefficient of the wall under the influence of the current environment, Dk thresholdExpressed as crack length level threshold in the wall, Dc thresholdExpressed as crack width level threshold in the wall.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210125894.1A CN114445240A (en) | 2022-02-10 | 2022-02-10 | Big data safety monitoring management system for building wall |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210125894.1A CN114445240A (en) | 2022-02-10 | 2022-02-10 | Big data safety monitoring management system for building wall |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114445240A true CN114445240A (en) | 2022-05-06 |
Family
ID=81372077
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210125894.1A Pending CN114445240A (en) | 2022-02-10 | 2022-02-10 | Big data safety monitoring management system for building wall |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114445240A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114676861A (en) * | 2022-05-27 | 2022-06-28 | 石家庄星海高科非金属矿业材料有限责任公司 | Energy-saving and environment-friendly maintenance method and system for outer vertical surface of building |
| CN116486572A (en) * | 2023-04-17 | 2023-07-25 | 广东诚誉工程咨询监理有限公司 | Safety risk early warning and monitoring system and method based on power grid engineering project |
-
2022
- 2022-02-10 CN CN202210125894.1A patent/CN114445240A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114676861A (en) * | 2022-05-27 | 2022-06-28 | 石家庄星海高科非金属矿业材料有限责任公司 | Energy-saving and environment-friendly maintenance method and system for outer vertical surface of building |
| CN114676861B (en) * | 2022-05-27 | 2022-08-02 | 石家庄星海高科非金属矿业材料有限责任公司 | Energy-saving and environment-friendly maintenance method and system for outer vertical surface of building |
| CN116486572A (en) * | 2023-04-17 | 2023-07-25 | 广东诚誉工程咨询监理有限公司 | Safety risk early warning and monitoring system and method based on power grid engineering project |
| CN116486572B (en) * | 2023-04-17 | 2024-01-12 | 广东诚誉工程咨询监理有限公司 | Safety risk early warning and monitoring system and method based on power grid engineering project |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114445240A (en) | Big data safety monitoring management system for building wall | |
| Ding et al. | Atmospheric microplastics in the Northwestern Pacific Ocean: Distribution, source, and deposition | |
| CN111505010B (en) | Bridge safety detection system based on cloud platform | |
| CN111539630B (en) | Building engineering construction operation environment intelligent monitoring system based on big data | |
| Tesche et al. | Optical and microphysical properties of smoke over Cape Verde inferred from multiwavelength lidar measurements | |
| CN112816380A (en) | Building engineering construction site construction environment online monitoring method based on big data analysis and monitoring cloud platform | |
| CN109028233A (en) | Kitchen fume concentration division methods and oil smoke image identification system and kitchen ventilator | |
| Camino et al. | An empirical equation to estimate mineral dust concentrations from visibility observations in Northern Africa | |
| CN110569248B (en) | Improved SPC (selective pressure control) residential water supply leakage monitoring and early warning method | |
| CN112216071A (en) | Intelligent chemical monitoring and early warning platform based on big data | |
| CN112579975A (en) | Environmental parameter monitoring management system based on big data visual analysis | |
| CN110704805B (en) | Pre-stressed concrete beam bridge cracking early warning method based on live load strain | |
| CN108732649A (en) | A kind of Hydrologic Drought prediction and warning method transmitted based on meteorological drought | |
| CN109000714A (en) | It is a kind of based on the pollution source monitoring system acquired in real time | |
| CN112668837A (en) | Cloud computing platform for real-time monitoring and dynamic analysis of building engineering quality safety based on big data | |
| CN109656974A (en) | A kind of big data processing method for realizing haze on-line monitoring | |
| CN115372571A (en) | Intelligent water environment monitoring system | |
| Pereira et al. | Scattering properties and mass concentration of local and long-range transported aerosols over the South Western Iberia Peninsula | |
| CN108593006A (en) | It is a kind of based on the pollution source management system acquired in real time | |
| CN116840732A (en) | Distributed power supply device monitoring system for wind power plant | |
| Brázdil et al. | Wind-stilling in the light of wind speed measurements: the Czech experience | |
| CN110716978A (en) | Mountain reservoir type water source intelligent management method | |
| CN114299489A (en) | Smart museum management method based on big data and cloud management platform | |
| CN111242945A (en) | Pipeline defect detection system based on big data | |
| CN115293940A (en) | Environmental monitoring system based on industrial internet |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |