CN113689416A - Building curtain wall safety nondestructive detection imaging method based on microwave imaging - Google Patents
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- 238000003384 imaging method Methods 0.000 title claims abstract description 118
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- 239000004575 stone Substances 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 238000010521 absorption reaction Methods 0.000 description 1
- 238000004026 adhesive bonding Methods 0.000 description 1
- 229910052782 aluminium Inorganic materials 0.000 description 1
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Abstract
The invention discloses a building curtain wall safety nondestructive detection imaging method based on microwave imaging, which comprises the following steps: respectively imaging the defect curtain walls and the newly-built curtain walls under different working conditions, extracting imaging characteristics of various defects, and establishing a characteristic map library as identification elements of the defects; setting detection equipment and carrying equipment, and confirming an initial acquisition position; carrying out comprehensive scanning detection on the curtain wall to be detected to realize microwave imaging; identifying a microwave imaging result, comparing the microwave imaging result with data in a feature map library, marking working conditions with the same features, determining defect types, and reminding a responsible person to further observe and confirm; and confirming the defect position and combining the defect type, and recording the defect position, type and non-conformity according to the drawing, the standard and the conformity of the characteristic diagram library. The invention realizes the complete scanning detection on the premise of not damaging the original structure of the curtain wall, avoids accidental factors brought by sampling inspection, and has more accurate detection and good detection effect.
Description
Technical Field
The invention relates to the technical field of nondestructive testing of building curtain walls, in particular to a safe nondestructive testing imaging method of a building curtain wall based on microwave imaging.
Background
The building curtain wall is an outer protective structure or a decorative structure of a building, and can be divided into a glass curtain wall, an aluminum plate curtain wall, a stone curtain wall, an artificial board curtain wall and the like according to panel materials, and the building curtain wall is rapidly developed since the building curtain wall is introduced in 1983 due to the characteristics of attractive appearance and strong designability, the storage is huge after 15 hundred million square meters, and meanwhile, the related standards of the curtain wall are gradually released in China since 1996, and the curtain wall built before the above lacks of unified standard requirements. According to the regulations of unified building structure design reliability standard GB50068-2018 and glass curtain wall engineering technical specification JG102-2003, the allowable service life of curtain wall design is 25 years, namely, the service life of the early curtain wall exceeds the required service life, and the stock increases year by year. With the gradual improvement of people's life, the requirements on building curtain walls are higher and higher, but because the curtain wall safety accidents caused by the aging of structural adhesives, the corrosion of metal parts, the falling of panels and the like are frequent, the safety life of people is seriously threatened.
At present, the mainstream detection method for the existing curtain wall safety inspection is mainly evaluated by an experienced expert through visual inspection, hand test, small tool detection and the like, and is greatly influenced by human factors; in addition, the structural adhesive bonding strength, keel corrosion, hanging piece installation condition, stone installation firmness and the like are detected according to the fact that plates need to be removed for spot inspection, so that the accidental performance exists, and secondary damage is easily caused or the original structure is easily damaged; that is, the prior art has no efficient and feasible curtain wall nondestructive testing method. Therefore, it is necessary to provide a microwave imaging-based building curtain wall safety nondestructive testing imaging method for detecting the corrosion degree of metal members, the deformation degree of frameworks, the aging degree of structural adhesives and the like of invisible parts of existing curtain walls, so as to solve some defects in the prior art.
Disclosure of Invention
The invention aims to overcome part or all of the defects in the prior art and provides a building curtain wall safety nondestructive testing imaging method based on microwave imaging.
The technical scheme of the invention is as follows:
a building curtain wall safety nondestructive detection imaging method based on microwave imaging comprises the following steps:
s1, respectively imaging the defect curtain walls and the newly-built curtain walls under different working conditions through microwave imaging equipment, and establishing a characteristic map library by comparing and extracting imaging characteristics of various defects as identification elements of the defects;
s2, setting detection equipment and carrying equipment for drawing the horizontal movement and the vertical movement of the detection equipment and confirming an initial acquisition position;
s3, setting detection parameters and carrying out comprehensive scanning detection on the curtain wall to be detected to finish microwave imaging;
s4, identifying the microwave imaging result, comparing the microwave imaging result with the data in the feature map library, marking the working conditions with the same features, determining the defect type, and reminding a responsible person to further observe and confirm;
and S5, confirming the defect position and recording the defect position, type and non-compliance according to the drawing, the standard and the compliance of the characteristic diagram library by combining the defect type in the step S4.
Further, the specific implementation manner of step S1 is: firstly, imaging preset defect curtain walls containing different defect types through the microwave imaging equipment, and extracting a defect part image as a defect signal; then, imaging a newly-built curtain wall which is preset and comprises the same structural type as the defect curtain wall through the microwave imaging equipment, and extracting a defect-free position image which has the same structure as the defect position of the defect curtain wall as a reference signal; and finally, respectively introducing the defect signals, the reference signals and the imaging images under different working conditions into an upper computer, comparing the introduced defect signals, the reference signals and the imaging images through the upper computer, and extracting the echo signal change characteristics and the imaging characteristics as identification factors of the defects to establish a characteristic map library.
Further, in the step S1, the upper computer further performs signal change comparison and different imaging comparison on the introduced defect signal and the reference signal, extracts corresponding features where the same defect repeatedly appears in a deep learning manner, compares the corresponding features with the actual sample image on site, identifies and marks the positions where the same features exist, and records the imaging features of the defects under different working conditions to form a feature library.
Further, the specific implementation manner of step S2 is: and connecting the detection equipment with the carrying equipment, erecting the whole body of the detection equipment connected with the carrying equipment on the roof of the curtain wall to be detected, and taking the set point of the detection equipment as an initial acquisition position.
Further, the carrying device comprises a horizontal movement mechanism and a vertical movement mechanism arranged on the horizontal movement mechanism; the horizontal movement mechanism and the vertical movement mechanism are both driven by a motor with an encoder, the horizontal movement mechanism is movably arranged on the roof of the curtain wall to be detected, the vertical movement mechanism is fixed on the horizontal movement mechanism, and the detection equipment is connected with the vertical movement mechanism; during detection, the encoder records the operation angle and the number of turns of the motor to confirm the horizontal and vertical distances between the image acquisition position and the initial acquisition position.
Further, the specific implementation manner of step S3 is: setting detection parameters including a unilateral overlapping rate, a horizontal stepping interval and a vertical moving speed according to the actual condition of the curtain wall to be detected, dragging the detection equipment to move vertically through the carrying equipment according to the set vertical moving speed to enable the detection equipment to carry out scanning detection on a vertical area to be detected of the curtain wall to be detected, and dragging the detection equipment to move horizontally through the carrying equipment according to the set horizontal stepping interval to change the detection position so as to realize comprehensive scanning detection of the curtain wall to be detected and finish microwave imaging.
Further, the specific implementation manner of the microwave imaging in step S3 is as follows: the control unit of the detection device controls a transmitting antenna of the detection device to transmit microwaves to the curtain wall to be detected according to a set mode, a receiving antenna of the detection device receives echo signals containing amplitude and phase information after being reflected by structures under different working conditions, the echo signals are transmitted to an upper computer through a wireless transmission module of the detection device, the upper computer processes the echo signals in a synthetic aperture radar imaging mode to form gray level images, and microwave imaging is completed.
Further, the specific implementation manner of step S4 is: and the upper computer automatically identifies and extracts position images of different working conditions according to the microwave imaging result of the step S3, compares the images with the same working conditions in the feature map library to identify whether the same imaging features exist, marks the features with high matching degree to confirm the defect types, and reminds a responsible person to further observe and confirm.
Further, the specific implementation manner of step S5 is: and confirming the defect position according to the horizontal and vertical distances between the image acquisition position and the initial acquisition position, finding the corresponding drawing and the standard of the relevant requirement by combining the defect type confirmed in the step S4, describing the conformity of the defect position, the defect type and the characteristic diagram library, the drawing and the standard according to the found drawing and standard, and recording the defect position, the defect type and the nonconformity.
By adopting the scheme, the invention has the following beneficial effects:
1. only partial contact with the surface of the curtain wall is realized, so that comprehensive scanning detection is completed on the premise of not damaging the original structure of the curtain wall, accidental factors caused by sampling inspection are avoided, the detection is more accurate, and the detection effect is good;
2. defects of the existing curtain wall are visually displayed in an imaging mode, and the defects are identified in a machine deep learning mode, so that the requirement of detection on professional ability of personnel is lowered;
3. the high-altitude operation of personnel is reduced, and semi-automatic detection can be realized after the route is predetermine, improves personnel's security, has richened the detection means of existing curtain, increases the safety in utilization of existing curtain.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the structures shown in the drawings without creative efforts.
FIG. 1 is a flowchart illustrating steps of a method for non-destructive testing and imaging of building curtain wall safety based on microwave imaging according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a field test of a microwave imaging-based building curtain wall safety nondestructive testing imaging method according to an embodiment of the present invention;
fig. 3 is a device layout diagram of a microwave imaging-based building curtain wall safety nondestructive testing imaging method according to an embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
The invention is described in detail below with reference to the figures and the specific embodiments.
Referring to fig. 1 to 3, the invention provides a building curtain wall safety nondestructive detection imaging method based on microwave imaging, which performs nondestructive detection based on the principle that curtain wall materials, distribution positions and materials have different microwave reflection absorption coefficients, receives reflection signals after a penetrating panel, sealant and the like touch metal through externally emitting low-frequency-band microwaves at the same side, analyzes and visually displays and marks related defects after imaging, and comprises the following steps:
s1, respectively imaging the defect curtain walls and the newly-built curtain walls under different working conditions through microwave imaging equipment, and establishing a characteristic map library by comparing and extracting imaging characteristics of various defects as identification elements of the defects; further, the specific implementation manner of step S1 is: firstly, imaging the preset defect curtain walls containing different defect types (namely different working conditions) by the microwave imaging equipment to finish the imaging operation of the defects of hanging-off of curtain wall hanging pieces, aging and hardening of structural adhesive, corrosion of metal and the like in different forms, and extracting a defect part image as a defect signal; then, imaging a newly-built curtain wall which is preset and comprises the same structural type as the defect curtain wall through the microwave imaging equipment, and extracting a defect-free position image which has the same structure as the defect position of the defect curtain wall as a reference signal; finally, respectively introducing the defect signals and the reference signals of different working conditions and imaging images (the imaging images are images formed by imaging the curtain wall through microwave imaging equipment) into an upper computer 5, wherein the upper computer 5 can be a computer, and then comparing the introduced defect signals, the reference signals and the imaging images through the upper computer 5 and extracting echo signal change characteristics and imaging characteristics as identification elements of the defects to establish a characteristic map library, so that the establishment work of the characteristic map library is completed, and data reference is provided for subsequent detection; more specifically, in the step S1, the upper computer 5 performs signal change comparison and imaging-different comparison on the introduced defect signal and the reference signal, extracts corresponding features of the same kind of defects appearing repeatedly and imaging-contrast identification with an on-site actual template (the on-site actual template includes a defect curtain wall and a newly-built curtain wall) in a deep learning manner, marks positions having the same features, and records defect imaging features of different working conditions to form a feature map library; it is worth mentioning that, if the conditions are not allowed, for convenience, the same working condition positions can be compared, images with unique imaging characteristic positions are extracted and identified, and manual identification can be performed;
s2, setting a detection device 1 and a carrying device 2 for drawing the detection device 1 to move horizontally and vertically and confirming an initial acquisition position; further, the specific implementation manner of step S2 is: connecting the detection equipment 1 with carrying equipment 2 for drawing the detection equipment 1 to move horizontally and vertically, erecting the whole body formed by connecting the detection equipment 1 with the carrying equipment 2 on the roof of a curtain wall 3 to be detected, setting a balancing weight on the carrying equipment 2 to balance weight for ensuring safety, and using a set point confirmed by the detection equipment 1 based on an initial set point of the carrying equipment 2 as an initial collection position so as to confirm the initial collection position; wherein, the carrying device 2 of step S2 includes a horizontal motion mechanism and a vertical motion mechanism disposed on the horizontal motion mechanism, the horizontal motion mechanism is preferably a motor-driven moving platform, the vertical motion mechanism is preferably a motor-driven sling mechanism, the detection device 1 is preferably a low-frequency microwave imaging device, that is, the moving platform is movably disposed on the roof of the curtain wall 3 to be detected, the sling mechanism is fixed on the moving platform, the detection device 1 is connected with the sling of the sling mechanism, the detection device 1 is drawn by the sling mechanism to move vertically during detection, and the sling mechanism is drawn by the moving platform to move horizontally, and the detection device 1 moves along with the horizontal movement of the sling mechanism in the same direction, so that the detection device 1 can realize horizontal movement and vertical movement (i.e. up-down lifting), the detection equipment 1 can perform comprehensive scanning detection on the curtain wall 3 to be detected; more specifically, the motors of the moving platform and the sling mechanism are provided with encoders, that is, the horizontal movement mechanism and the vertical movement mechanism are driven by the motors provided with the encoders, a coordinate system is established by taking the horizontal distance direction and the height position direction of the image acquisition position from the initial acquisition position as coordinate axes during actual detection, and the horizontal and vertical distances (that is, the horizontal and vertical distances of the detection device 1 from the initial acquisition position) of the image acquisition position from the initial acquisition position are confirmed by recording the operation angles and the number of turns of the motors through the encoders, so that the region position of the curtain wall corresponding to imaging can be obtained, and the defect position feedback is facilitated;
s3, setting detection parameters and carrying out comprehensive scanning detection on the curtain wall 3 to be detected to finish microwave imaging; further, the specific implementation manner of step S3 is: setting detection parameters including a unilateral overlapping rate, a horizontal stepping interval and a vertical moving speed (the vertical moving speed is the running speed of the sling mechanism when the detection equipment 1 is pulled to ascend and descend) according to the actual condition of the curtain wall 3 to be detected, pulling the detection equipment 1 to vertically move through the carrying equipment 2 according to the set vertical moving speed, enabling the detection equipment 1 to carry out scanning detection on a vertical region 4 to be detected of the curtain wall 3 to be detected, and then pulling the detection equipment 1 to horizontally move through the carrying equipment 2 according to the set horizontal stepping interval to carry out detection position replacement, so that the detection equipment 1 carries out two-dimensional movement parallel to the wall surface of the curtain wall 3 to be detected, and further realizing comprehensive scanning detection of the curtain wall 3 to be detected to finish microwave imaging of the whole curtain wall; more specifically, the microwave imaging in step S3 is implemented in a specific manner: because the transmitting antennas and the receiving antennas of the detection device 1 are distributed in parallel in two rows and are aligned to the curtain wall 3 to be detected on one side, the transmitting antennas of the detection device 1 are controlled by the control unit of the detection device 1 to transmit microwaves to the curtain wall 3 to be detected according to a set mode, echo signals containing amplitude and phase information after being reflected by different working condition structures are received by the receiving antennas of the detection device 1, the echo signals are transmitted to the upper computer 5 through the wireless transmission module of the detection device 1, the echo signals are processed by the upper computer 5 in a synthetic aperture radar imaging mode to form a gray image, and microwave imaging is completed; as a preferred embodiment, the single-side overlapping rate is set to be more than or equal to 10%, the horizontal stepping interval is set to be 0.9m, and the moving speed in the vertical direction is set to be 1m/s, so that the detection equipment 1 finishes the scanning detection of the vertical to-be-detected area 4 of the curtain wall 3 to be detected with the width of 1m through the vertical movement of the carrying equipment 2, the detection equipment 1 is changed in the detection position through the horizontal movement of the carrying equipment 2, and by doing so, the comprehensive scanning detection of the curtain wall by the detection equipment 1 can be realized, and the microwave imaging can be realized by matching with the operation of the upper computer 5;
s4, identifying the microwave imaging result, comparing the microwave imaging result with the data in the feature map library, marking the working conditions with the same features, determining the defect type, and reminding a responsible person to further observe and confirm; further, the specific implementation manner of step S4 is: the upper computer 5 automatically identifies and extracts position images under different working conditions according to the microwave imaging result of the step S3, compares the images with the same working conditions in the feature map library to identify whether the same imaging features exist, marks the features with high matching degree to confirm the defect types, and reminds a responsible person to further observe and confirm;
s5, confirming the defect position and recording the defect position, type and non-conformity according to the drawing, standard and conformity of the characteristic diagram library by combining the defect type in the step S4; further, the specific implementation manner of step S5 is: and confirming the defect position according to the horizontal and vertical distances between the image acquisition position and the initial acquisition position, finding the corresponding drawing and the standard of the relevant requirement by combining the defect type confirmed in the step S4, describing the conformity of the defect position, the defect type and the characteristic diagram library, the drawing and the standard according to the found drawing and standard, and recording the defect position, the defect type and the nonconformity, thereby providing a basis for maintenance and defect judgment.
Compared with the prior art, the invention has the following beneficial effects:
1. only partial contact with the surface of the curtain wall is realized, so that comprehensive scanning detection is completed on the premise of not damaging the original structure of the curtain wall, accidental factors caused by sampling inspection are avoided, the detection is more accurate, and the detection effect is good;
2. defects of the existing curtain wall are visually displayed in an imaging mode, and the defects are identified in a machine deep learning mode, so that the requirement of detection on professional ability of personnel is lowered;
3. the high-altitude operation of personnel is reduced, and semi-automatic detection can be realized after the route is predetermine, improves personnel's security, has richened the detection means of existing curtain, increases the safety in utilization of existing curtain.
The present invention is not limited to the above preferred embodiments, and any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. A building curtain wall safety nondestructive detection imaging method based on microwave imaging is characterized by comprising the following steps:
s1, respectively imaging the defect curtain walls and the newly-built curtain walls under different working conditions through microwave imaging equipment, and establishing a characteristic map library by comparing and extracting imaging characteristics of various defects as identification elements of the defects;
s2, setting detection equipment and carrying equipment for drawing the horizontal movement and the vertical movement of the detection equipment and confirming an initial acquisition position;
s3, setting detection parameters and carrying out comprehensive scanning detection on the curtain wall to be detected to finish microwave imaging;
s4, identifying the microwave imaging result, comparing the microwave imaging result with the data in the feature map library, marking the working conditions with the same features, determining the defect type, and reminding a responsible person to further observe and confirm;
and S5, confirming the defect position and recording the defect position, type and non-compliance according to the drawing, the standard and the compliance of the characteristic diagram library by combining the defect type in the step S4.
2. The microwave imaging-based building curtain wall safety nondestructive testing imaging method as claimed in claim 1, wherein the specific implementation manner of step S1 is as follows: firstly, imaging preset defect curtain walls containing different defect types through the microwave imaging equipment, and extracting a defect part image as a defect signal; then, imaging a newly-built curtain wall which is preset and comprises the same structural type as the defect curtain wall through the microwave imaging equipment, and extracting a defect-free position image which has the same structure as the defect position of the defect curtain wall as a reference signal; and finally, respectively introducing the defect signals, the reference signals and the imaging images under different working conditions into an upper computer, comparing the introduced defect signals, the reference signals and the imaging images through the upper computer, and extracting the echo signal change characteristics and the imaging characteristics as identification factors of the defects to establish a characteristic map library.
3. The microwave imaging-based building curtain wall safety nondestructive detection imaging method as claimed in claim 2, wherein in step S1, the upper computer further performs signal change comparison and imaging difference comparison on the introduced defect signal and the reference signal, extracts corresponding features of the same kind of defects appearing repeatedly in a deep learning manner, compares the corresponding features with the actual sample plate image on site, identifies and marks the positions where the same features exist, and records the imaging features of the defects under different conditions to form a feature map library.
4. The microwave imaging-based building curtain wall safety nondestructive testing imaging method as claimed in claim 1, wherein the specific implementation manner of step S2 is as follows: and connecting the detection equipment with the carrying equipment, erecting the whole body of the detection equipment connected with the carrying equipment on the roof of the curtain wall to be detected, and taking the set point of the detection equipment as an initial acquisition position.
5. The microwave imaging-based building curtain wall safety nondestructive testing imaging method as claimed in claim 4, wherein the carrying device comprises a horizontal movement mechanism and a vertical movement mechanism disposed on the horizontal movement mechanism; the horizontal movement mechanism and the vertical movement mechanism are both driven by a motor with an encoder, the horizontal movement mechanism is movably arranged on the roof of the curtain wall to be detected, the vertical movement mechanism is fixed on the horizontal movement mechanism, and the detection equipment is connected with the vertical movement mechanism; during detection, the encoder records the operation angle and the number of turns of the motor to confirm the horizontal and vertical distances between the image acquisition position and the initial acquisition position.
6. The microwave imaging-based building curtain wall safety nondestructive testing imaging method as claimed in claim 1, wherein the specific implementation manner of step S3 is as follows: setting detection parameters including a unilateral overlapping rate, a horizontal stepping interval and a vertical moving speed according to the actual condition of the curtain wall to be detected, dragging the detection equipment to move vertically through the carrying equipment according to the set vertical moving speed to enable the detection equipment to carry out scanning detection on a vertical area to be detected of the curtain wall to be detected, and dragging the detection equipment to move horizontally through the carrying equipment according to the set horizontal stepping interval to change the detection position so as to realize comprehensive scanning detection of the curtain wall to be detected and finish microwave imaging.
7. The microwave imaging-based building curtain wall safety nondestructive testing imaging method according to claim 6, wherein the microwave imaging in step S3 is realized in a specific manner as follows: the control unit of the detection device controls a transmitting antenna of the detection device to transmit microwaves to the curtain wall to be detected according to a set mode, a receiving antenna of the detection device receives echo signals containing amplitude and phase information after being reflected by structures under different working conditions, the echo signals are transmitted to an upper computer through a wireless transmission module of the detection device, the upper computer processes the echo signals in a synthetic aperture radar imaging mode to form gray level images, and microwave imaging is completed.
8. The microwave imaging-based building curtain wall safety nondestructive testing imaging method as claimed in claim 1, wherein the specific implementation manner of step S4 is as follows: and the upper computer automatically identifies and extracts position images of different working conditions according to the microwave imaging result of the step S3, compares the images with the same working conditions in the feature map library to identify whether the same imaging features exist, marks the features with high matching degree to confirm the defect types, and reminds a responsible person to further observe and confirm.
9. The microwave imaging-based building curtain wall safety nondestructive testing imaging method as claimed in claim 1, wherein the specific implementation manner of step S5 is as follows: and confirming the defect position according to the horizontal and vertical distances between the image acquisition position and the initial acquisition position, finding the corresponding drawing and the standard of the relevant requirement by combining the defect type confirmed in the step S4, describing the conformity of the defect position, the defect type and the characteristic diagram library, the drawing and the standard according to the found drawing and standard, and recording the defect position, the defect type and the nonconformity.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115063345A (en) * | 2022-05-11 | 2022-09-16 | 水利部交通运输部国家能源局南京水利科学研究院 | Electromagnetic wave standard map-based dam hidden danger identification method |
| CN115131518A (en) * | 2022-07-15 | 2022-09-30 | 中建深圳装饰有限公司 | Three-dimensional display method for millimeter wave near-field imaging perspective detection of building curtain wall |
| CN116203031A (en) * | 2023-03-24 | 2023-06-02 | 苏州电光波工业智能科技有限公司 | Industrial product defect intelligent detection system based on microwave and machine vision technology |
| CN116819529A (en) * | 2023-07-13 | 2023-09-29 | 中建深圳装饰有限公司 | Stone curtain wall metal structure perspective imaging method and device based on millimeter waves |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103886602A (en) * | 2014-03-28 | 2014-06-25 | 重庆大学 | Radial image deflect detecting method based on veins |
| CN107202793A (en) * | 2017-05-16 | 2017-09-26 | 镇江市建科工程质量检测中心有限公司 | A kind of detecting system and method for detecting external wall mass defect |
| CN109363816A (en) * | 2018-10-10 | 2019-02-22 | 孙瑞悒 | A kind of reduction of the fracture traction device and application method based on 3D scanning technique |
| CN109443428A (en) * | 2018-10-25 | 2019-03-08 | 南京工大建设工程技术有限公司 | A kind of external wall waterproof engineering construction quality Testing and appraisal method |
| CN110596123A (en) * | 2019-09-27 | 2019-12-20 | 北京奥博泰科技有限公司 | Device and method for detecting impurities and defects of toughened glass curtain wall on site |
| CN110632593A (en) * | 2015-12-25 | 2019-12-31 | 华讯方舟科技有限公司 | Human body security check system and method based on millimeter wave holographic three-dimensional imaging |
| CN111879779A (en) * | 2020-08-10 | 2020-11-03 | 济南大学 | Micro-damage building curtain wall detection method |
| CN113075296A (en) * | 2021-04-01 | 2021-07-06 | 湖南翰坤实业有限公司 | Method and device for detecting safety of outer wall structure based on sound wave detection and BIM model |
| CN113239806A (en) * | 2021-05-14 | 2021-08-10 | 中建深圳装饰有限公司 | Curtain wall plate identification method and system based on image identification |
-
2021
- 2021-08-30 CN CN202111006384.4A patent/CN113689416A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103886602A (en) * | 2014-03-28 | 2014-06-25 | 重庆大学 | Radial image deflect detecting method based on veins |
| CN110632593A (en) * | 2015-12-25 | 2019-12-31 | 华讯方舟科技有限公司 | Human body security check system and method based on millimeter wave holographic three-dimensional imaging |
| CN107202793A (en) * | 2017-05-16 | 2017-09-26 | 镇江市建科工程质量检测中心有限公司 | A kind of detecting system and method for detecting external wall mass defect |
| CN109363816A (en) * | 2018-10-10 | 2019-02-22 | 孙瑞悒 | A kind of reduction of the fracture traction device and application method based on 3D scanning technique |
| CN109443428A (en) * | 2018-10-25 | 2019-03-08 | 南京工大建设工程技术有限公司 | A kind of external wall waterproof engineering construction quality Testing and appraisal method |
| CN110596123A (en) * | 2019-09-27 | 2019-12-20 | 北京奥博泰科技有限公司 | Device and method for detecting impurities and defects of toughened glass curtain wall on site |
| CN111879779A (en) * | 2020-08-10 | 2020-11-03 | 济南大学 | Micro-damage building curtain wall detection method |
| CN113075296A (en) * | 2021-04-01 | 2021-07-06 | 湖南翰坤实业有限公司 | Method and device for detecting safety of outer wall structure based on sound wave detection and BIM model |
| CN113239806A (en) * | 2021-05-14 | 2021-08-10 | 中建深圳装饰有限公司 | Curtain wall plate identification method and system based on image identification |
Non-Patent Citations (1)
| Title |
|---|
| 赵建华;杨放;曹旷;王文博;: "微波测湿技术在ALC外墙板渗漏检测中的应用实例" * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115063345A (en) * | 2022-05-11 | 2022-09-16 | 水利部交通运输部国家能源局南京水利科学研究院 | Electromagnetic wave standard map-based dam hidden danger identification method |
| CN115131518A (en) * | 2022-07-15 | 2022-09-30 | 中建深圳装饰有限公司 | Three-dimensional display method for millimeter wave near-field imaging perspective detection of building curtain wall |
| CN115131518B (en) * | 2022-07-15 | 2024-03-01 | 中建深圳装饰有限公司 | Three-dimensional display method for building curtain wall millimeter wave near-field imaging perspective detection |
| CN116203031A (en) * | 2023-03-24 | 2023-06-02 | 苏州电光波工业智能科技有限公司 | Industrial product defect intelligent detection system based on microwave and machine vision technology |
| CN116819529A (en) * | 2023-07-13 | 2023-09-29 | 中建深圳装饰有限公司 | Stone curtain wall metal structure perspective imaging method and device based on millimeter waves |
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