CN116007542A - Unmanned aerial vehicle intelligent detection system based on BIM - Google Patents
Unmanned aerial vehicle intelligent detection system based on BIM Download PDFInfo
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Abstract
The invention provides a BIM-based unmanned aerial vehicle intelligent detection system, which comprises terminal equipment, front-end unmanned aerial vehicle body equipment, a middle-end controller, a BIM file importing unit and a detection scene selecting unit, wherein the terminal equipment is used for receiving BIM files; the terminal equipment or the middle-end controller acquires the BIM model imported by the BIM file importing unit, the detection scene selecting unit carries out detection scene selection based on the imported BIM model, the detection scene comprises curtain wall detection, tower crane equipment detection and cable inspection, the terminal equipment is used for carrying out curtain wall detection, tower crane equipment detection and cable inspection data analysis and storage, conventional quality defects and background data analysis are intelligently identified to carry out combined application and quality defect positioning, detection precision and efficiency are greatly improved, timeliness of maintenance and maintenance is guaranteed, labor input is reduced, safety risks brought to human bodies by high-altitude operation are reduced, and meanwhile hidden danger of equipment, buildings and electric power safety quality is reduced.
Description
Technical Field
The invention relates to the technical field of intelligent detection of buildings, in particular to an unmanned aerial vehicle intelligent detection system based on BIM.
Background
The drone is an unmanned aircraft that operates with a radio remote control device and a self-contained programming device.
At present, a conventional unmanned aerial vehicle ground station control scheme is adopted for unmanned aerial vehicle monitoring and control. The ground station is usually intelligent mobile terminal, unmanned aerial vehicle controlgear etc. that the special-purpose was established, and these ground stations are provided with the data transmission module that is used for carrying out radio communication with unmanned aerial vehicle, and equally, also be provided with data transmission module on unmanned aerial vehicle, through the data transmission module that sets up respectively on ground station and the unmanned aerial vehicle, realize the data interaction between ground station and the unmanned aerial vehicle.
However, the current unmanned aerial vehicle has fewer functions in the field of engineering detection, has single working mode, cannot be combined and operated with background data analysis, and cannot switch the detection modes according to different operation scenes.
The existing high-rise curtain wall detection mostly depends on special work hanging basket high-altitude operation for detection, high-altitude operation staff does not have the capability of quality detection, and the quality of output detection results cannot be guaranteed.
Most of existing tower crane equipment detection is carried out climbing inspection on manual sites, intelligent and convenient, and safety is not enough.
Outdoor cable detection is mostly photographed and video is artificially detected at present, and unmanned aerial vehicle equipment can't replace manual detection some quality defect problems. Meanwhile, how to solve the precision and efficiency of the power line detection is always a serious problem puzzling the power industry.
Disclosure of Invention
Aiming at the defects of the technical scheme, the invention provides the intelligent unmanned aerial vehicle detection system based on BIM, which can improve the detection precision and efficiency, ensure the timeliness of maintenance and reduce the occurrence of potential safety quality hazards.
The invention provides a BIM-based unmanned aerial vehicle intelligent detection system, which comprises terminal equipment, front-end unmanned aerial vehicle body equipment, a middle-end controller, a BIM file importing unit and a detection scene selecting unit; the terminal equipment or the middle-end controller acquires the BIM model imported by the BIM file importing unit, the detection scene selecting unit performs detection scene selection based on the imported BIM model, wherein the detection scene comprises curtain wall detection, tower crane equipment detection and cable inspection, the terminal equipment is used for performing curtain wall detection, tower crane equipment detection and cable inspection data analysis and storage, the middle-end controller is connected with a mobile device used for remotely controlling front-end unmanned aerial vehicle body equipment through a wireless network communication module, and the front-end unmanned aerial vehicle body equipment is used for performing curtain wall detection, tower crane equipment detection and cable inspection data acquisition.
In the unmanned aerial vehicle intelligent detection system based on BIM, when the terminal equipment or the middle-end controller obtains the BIM model imported by the BIM file importing unit as the BIM model of the building curtain wall detected in the curtain wall detection, the detection scene selecting unit carries out curtain wall detection based on the imported BIM model of the building curtain wall detected in the curtain wall detection, the curtain wall detection carries out facade curtain wall quality detection based on the imported BIM model of the building curtain wall detected, and detection contents comprise facade flatness, joint sealing glue holes, joint sealing glue cracking degree and profile buckling deformation.
In the unmanned aerial vehicle intelligent detection system based on BIM, BIM files of a building curtain wall to be detected at present are imported into terminal equipment during curtain wall detection, and then detection items are selected through a middle-end controller, wherein the selectable items include flatness detection of the curtain wall, detection of a curtain wall seam sealant hole, detection of a seam sealant cracking gap size and detection of section bar warp deformation.
In the unmanned aerial vehicle intelligent detection system based on BIM, flatness detection of the curtain wall is carried out by randomly extracting 50% of curtain wall surfaces in BIM files for detection after front-end unmanned aerial vehicle body equipment scans in an infrared mode; the infrared rays emitted by the front-end unmanned aerial vehicle body equipment are emitted to the glass curtain wall, the outermost edge infrared rays emitted by the front-end unmanned aerial vehicle body equipment are taken as references, and the vertical distance between the front-end unmanned aerial vehicle body equipment and the curtain wall is calculated through a trigonometric function according to the contact distance between the front-end unmanned aerial vehicle body equipment and the infrared rays and the included angle between the emitted infrared rays and the curtain wall; calculating the vertical distance between an infrared region emitted by the front-end unmanned aerial vehicle body equipment and the infrared rays and the curtain wall by taking the obtained vertical distance as a reference; a step of measuring and calculating the height difference of the detection area; and obtaining the difference values M1, M2 and M3 … … between all the vertical distances and the reference vertical distance, wherein the height difference=max (M1, M2 and M3 … …) of the detection area, so as to measure the flatness in the detection range and the height difference of the adjacent panel seam.
In the unmanned aerial vehicle intelligent detection system based on BIM, the detection of the hole of the curtain wall joint seal and the detection of the size of the crack gap of the joint seal are used for subsequent image recognition comparison analysis through the photo material sealed by the standard seal imported in advance in the curtain wall detection; according to the detection points listed in the BIM file of the building curtain wall which is imported and detected currently, taking high-definition photos by adopting a device photographing port, automatically positioning the taken photos into a BIM three-dimensional image for positioning, recording and numbering, and transmitting the numbered photos into terminal equipment for image recognition; the image identification identifies the generated hole photo through system data comparison according to the Harris corner feature extraction method, meanwhile, the photo finds out the problem of forming a three-dimensional image by the corresponding BIM three-dimensional image according to the number taking position and records the problem, and the recording result is fed back to the terminal equipment and finally the hole is maintained, detected and repaired through final positioning.
In the unmanned aerial vehicle intelligent detection system based on BIM, the profile buckling deformation degree is detected by adopting an infrared mode to scan through front-end unmanned aerial vehicle body equipment and randomly extracting 50% of curtain wall profile surfaces in BIM files; the method comprises the steps that infrared rays emitted by front-end unmanned aerial vehicle body equipment are emitted onto curtain wall sections, the outermost edge infrared rays emitted by the front-end unmanned aerial vehicle body equipment are used as references, the distance between the infrared rays and infrared contact points on the curtain wall sections and the included angle between the emitted infrared rays and the curtain wall sections are used as the reference, and the vertical distance between the front-end unmanned aerial vehicle body equipment and the curtain wall sections is calculated through trigonometric functions; calculating the vertical distance between an infrared region emitted by the front-end unmanned aerial vehicle body equipment and the curtain wall section by taking the obtained vertical distance as a reference; the difference M1, M2, M3, … … between all the vertical distances and the reference vertical distance is obtained, and the height difference=max (M1, M2, M3, … …) of the detection area is obtained, so that the profile height difference in the detection range is measured.
In the unmanned aerial vehicle intelligent detection system based on BIM, when the terminal equipment or the middle-end controller obtains that the BIM model imported by the BIM file importing unit is a BIM model detected by tower crane equipment, the detection scene selecting unit performs image recognition based on the BIM model detected by the imported tower crane equipment.
In the unmanned aerial vehicle intelligent detection system based on BIM, the tower crane equipment detects and guides in advance the standard section photo of the tower crane equipment and the photo materials fixed by bolts according to the terminal equipment or the middle-end control, and is used for subsequent image recognition, comparison and analysis, the high-definition photo is taken through the detection point position listed in the imported BIM file, the taken photo is automatically positioned in the BIM three-dimensional image to carry out positioning record number, the numbered photo is transmitted into the equipment end to carry out image recognition, the image recognition is identified according to the Harris corner feature extraction method, the high-definition photo is taken through the detection point position listed in the imported tower crane equipment BIM file, the taken photo is automatically positioned in the BIM three-dimensional image to carry out image recognition, the image recognition is carried out through the image recognition system, the corresponding BIM three-dimensional image is found according to the position of the taken number, the three-dimensional image is recorded, and finally the recording result is fed back to the terminal equipment to carry out positioning record number and replacement in time.
In the unmanned aerial vehicle intelligent detection system based on BIM, when the terminal equipment or the middle-end controller obtains that the BIM model imported by the BIM file importing unit is a BIM model for cable inspection, the detection scene selecting unit detects an outdoor long-distance cable according to an image recognition system and a Beidou positioning system based on the imported BIM model for cable inspection.
In the intelligent detection system of the unmanned aerial vehicle based on BIM, the cable inspection is conducted into a cable line diagram through a terminal equipment system or is conducted and transmitted into the terminal equipment system through a middle-end controller, wherein the cable line diagram comprises the geographic coordinates and the serial numbers of each cable rod and the serial numbers of each cable, after the cable line diagram is conducted, a front-end unmanned aerial vehicle body is started through the middle-end controller and automatically inspected according to the serial numbers of the cable rods, and when a broken or broken part occurs to a cable, the front-end unmanned aerial vehicle body is sent back to the terminal equipment system through an image recognition system to be distinguished; the image recognition is carried out according to the Harris angular point feature extraction method, the problem is fed back to the front-end unmanned aerial vehicle body after the identification terminal equipment recognizes, and the front-end unmanned aerial vehicle body adopts a Beidou system to carry out geographic position positioning and match with the cable line diagram position in the system; the front-end unmanned aerial vehicle body transmits the data back to the terminal equipment to form an alarm signal; and after the terminal equipment gives an alarm, the cable maintenance staff performs maintenance treatment according to the positioning to the site.
According to the BIM-based unmanned aerial vehicle intelligent detection system, the unmanned aerial vehicle intelligent detection equipment is used for performing multifunctional scene application, applied to intelligent detection of curtain walls, tower crane equipment, outdoor cables and the like, and used for performing combined application and quality defect positioning through intelligent recognition of conventional quality defects and background data analysis, so that the detection precision and efficiency are greatly improved, the timeliness of maintenance and maintenance is ensured, the investment of labor force is reduced, the safety risk brought to human bodies by high-altitude operation is reduced, and meanwhile, the occurrence of potential safety hazards of equipment, buildings and electric power quality is reduced.
Drawings
Fig. 1 is a schematic flow chart of an embodiment of a BIM-based unmanned aerial vehicle intelligent detection system of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Fig. 1 is a schematic flow chart of an embodiment of a BIM-based unmanned aerial vehicle intelligent detection system according to the present invention. The unmanned aerial vehicle intelligent detection system based on BIM comprises terminal equipment, front-end unmanned aerial vehicle body equipment, a middle-end controller, a BIM file importing unit and a detection scene selecting unit; the terminal equipment or the middle-end controller acquires a BIM model imported by the BIM file importing unit, the detection scene selecting unit carries out detection scene selection based on the imported BIM model, the detection scene comprises curtain wall detection, tower crane equipment detection and cable inspection, the terminal equipment is used for carrying out curtain wall detection, tower crane equipment detection and cable inspection data analysis and storage, the middle-end controller is connected with a mobile device used for carrying out remote control on front-end unmanned aerial vehicle body equipment through a wireless network communication module, and the front-end unmanned aerial vehicle body equipment is used for carrying out curtain wall detection, tower crane equipment detection and cable inspection data acquisition. Wherein BIM is a shorthand for building information model (Building Information Modeling).
In an embodiment, when the terminal device or the middle-end controller obtains that the BIM model imported by the BIM file importing unit is the BIM model of the building curtain wall detected in the curtain wall detection, the detection scene selecting unit performs curtain wall detection based on the BIM model of the building curtain wall detected in the imported curtain wall detection, the curtain wall detection performs facade curtain wall quality detection based on the BIM model of the building curtain wall imported and detected, and detection contents comprise facade flatness, joint sealant holes, joint sealant cracking degree and profile buckling deformation.
In one embodiment, during curtain wall detection, BIM files of a building curtain wall to be detected at present are imported into terminal equipment, and then detection items are selected through a middle-end controller, wherein the selectable items are flatness detection of the curtain wall, curtain wall seam sealant hole detection, seam sealant cracking gap size detection and profile buckling deformation detection.
In one embodiment, flatness detection of the curtain wall is performed by randomly extracting 50% of curtain wall surfaces in the BIM file for detection after the front-end unmanned aerial vehicle body equipment scans in an infrared mode; the infrared rays emitted by the front-end unmanned aerial vehicle body equipment are emitted to the glass curtain wall, the outermost edge infrared rays emitted by the front-end unmanned aerial vehicle body equipment are taken as references, and the vertical distance between the front-end unmanned aerial vehicle body equipment and the curtain wall is calculated through a trigonometric function according to the contact distance between the front-end unmanned aerial vehicle body equipment and the infrared rays and the included angle between the emitted infrared rays and the curtain wall; calculating the vertical distance between an infrared region emitted by the front-end unmanned aerial vehicle body equipment and the infrared rays and the curtain wall by taking the obtained vertical distance as a reference; a step of measuring and calculating the height difference of the detection area; and obtaining the difference values M1, M2 and M3 … … between all the vertical distances and the reference vertical distance, wherein the height difference=max (M1, M2 and M3 … …) of the detection area, so as to measure the flatness in the detection range and the height difference of the adjacent panel seam.
In one embodiment, the curtain wall joint sealant hole detection and the joint sealant cracking gap size detection are used for subsequent image recognition comparison analysis through the photo materials sealed by the standard sealant imported in advance in the curtain wall detection; according to the detection points listed in the BIM file of the building curtain wall which is imported and detected currently, taking high-definition photos by adopting a device photographing port, automatically positioning the taken photos into a BIM three-dimensional image for positioning, recording and numbering, and transmitting the numbered photos into terminal equipment for image recognition; the image identification is carried out according to a Harris angular point feature extraction method, namely, the similarity of an image block in a certain local window and an image block in a window after tiny movement in all directions is carried out, different images are different in gray scale, obvious edges are arranged at the boundary, the image is segmented by utilizing the feature, the edges are not equivalent to the boundary between objects, the edges refer to the places where the values of pixels in the image have abrupt changes, the boundary between objects refers to the boundary between objects in a real scene, and the parts with the obvious edges are identified by an image identification system; and identifying the generated hole photo through system data comparison, finding out a corresponding BIM three-dimensional image according to the position of the shooting number to form a three-dimensional image problem, recording, feeding back the recording result to terminal equipment, and carrying out hole maintenance, detection and repair through final positioning.
In one embodiment, the profile buckling deformation degree detection is performed by adopting an infrared mode to scan through front-end unmanned aerial vehicle body equipment and randomly extracting 50% of curtain wall profile surfaces in BIM files for detection; the method comprises the steps that infrared rays emitted by front-end unmanned aerial vehicle body equipment are emitted onto curtain wall sections, the outermost edge infrared rays emitted by the front-end unmanned aerial vehicle body equipment are used as references, the distance between the infrared rays and infrared contact points on the curtain wall sections and the included angle between the emitted infrared rays and the curtain wall sections are used as the reference, and the vertical distance between the front-end unmanned aerial vehicle body equipment and the curtain wall sections is calculated through trigonometric functions; calculating the vertical distance between an infrared region emitted by the front-end unmanned aerial vehicle body equipment and the curtain wall section by taking the obtained vertical distance as a reference; the difference M1, M2, M3, … … between all the vertical distances and the reference vertical distance is obtained, and the height difference=max (M1, M2, M3, … …) of the detection area is obtained, so that the profile height difference in the detection range is measured.
In an embodiment, when the terminal device or the middle-end controller obtains that the BIM model imported by the BIM file importing unit is a BIM model detected by the tower crane device, the detection scene selecting unit performs image recognition based on the BIM model detected by the imported tower crane device.
In an embodiment, the tower crane equipment detects and guides in advance the standard section photo and the bolt fixed photo material of the tower crane equipment according to the terminal equipment or the middle end control, and is used for subsequent image recognition comparison analysis, the high-definition photo is taken by adopting the equipment photographing port, the taken photo is automatically positioned to the boundary between the objects in the BIM three-dimensional map for positioning and recording the number, the numbered photo is transmitted to the equipment end for image recognition, the image recognition is identified according to the Harris corner feature extraction method, namely, the similarity between the image block in a certain local window and the image block in the window after the small movement in all directions is similar to a local slice comparison method, wherein different image gray scales are different, the boundary is divided by utilizing the feature, the boundary between the edge and the object is not equal, the edge refers to the position where the pixel value in the image is suddenly changed, the boundary between the objects refers to the boundary between the objects in the real scene, the position where the obvious edge is identified by the image recognition system, the image recognition system is used for positioning and recording the photo by adopting the BIM three-dimensional map, the image is automatically positioned by adopting the detection port of the tower crane equipment after the photo is slightly moved in all directions, the three-dimensional map is simultaneously recorded, and the image recognition system is correspondingly recorded by the three-dimensional map, and the image recognition system is obtained after the three-dimensional map is correspondingly positioned to the photo is obtained.
In an embodiment, when the terminal device or the middle-end controller obtains that the BIM model imported by the BIM file importing unit is a BIM model for cable inspection, the detection scene selecting unit detects the outdoor long-distance cable according to the image recognition system and the beidou positioning system based on the imported BIM model for cable inspection.
In an embodiment, the cable inspection is conducted into the terminal equipment system by leading in a cable line diagram or leading in and transmitting the cable line diagram to the terminal equipment system by a middle-end controller, wherein the cable line diagram comprises the geographic coordinates and the serial numbers of each cable rod and the serial numbers of each cable, after the cable line diagram is led in, a front-end unmanned aerial vehicle body is started by the middle-end controller and automatically inspected according to the serial numbers of the cable rods, and when a damaged or broken part occurs in the cable, the front-end unmanned aerial vehicle body is sent back to the terminal equipment system for screening by an image recognition system; the image recognition is carried out according to a Harris angular point feature extraction method, namely, the similarity of an image block in a certain local window and an image block in a window after micro-movement in all directions is different in gray level of different images, obvious edges are arranged at the boundary, the image is segmented by utilizing the feature, wherein the boundary between the edges and objects is not identical, the edges refer to the places where the values of pixels in the image have abrupt changes, the boundary between the objects refers to the boundary between the objects in a real scene, the positions with the obvious edges are identified by an image recognition system, the terminal equipment feeds back to a front-end unmanned aerial vehicle body after identifying the problems, and the front-end unmanned aerial vehicle body adopts a Beidou system to carry out geographic position positioning and is matched with the cable line graph position in the system; the front-end unmanned aerial vehicle body transmits the data back to the terminal equipment to form an alarm signal; and after the terminal equipment gives an alarm, the cable maintenance staff performs maintenance treatment according to the positioning to the site. The transmission flow of the front-end unmanned aerial vehicle body for transmitting the data back to the terminal equipment approximately works for 10-15 seconds.
Specifically, when the flatness of the curtain wall is detected, the normal index parameter is set to be that the deviation in the measuring range of the plane 2m is less than or equal to 2.5mm, and the height difference of the edge joints of two adjacent panels is less than or equal to 1.0mm. After the BIM file of the building curtain wall is imported, the standard parameter range of the profile is set in the equipment system, the front-end unmanned aerial vehicle body scans in an infrared mode, and the height difference between 2m straight lines of the detection area is measured through the distance and the angle, so that the warp deformation of the profile in the detection range is measured. When the aerial flight detection is carried out according to the selected detection items, if deviation occurs according to normal parameters in the BIM file, the system automatically records the position of the three-dimensional map in the BIM according to the problem position.
It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present invention is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present invention. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required for the present invention.
From the description of the above embodiments, it will be clear to a person skilled in the art that the method according to the above embodiments may be implemented by means of software plus the necessary general hardware platform, but of course also by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present invention.
Therefore, the above description is only a preferred embodiment of the present invention, and the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention should be covered by the scope of the present invention, which is defined by the claims.
Claims (10)
1. The unmanned aerial vehicle intelligent detection system based on BIM is characterized by comprising terminal equipment, front-end unmanned aerial vehicle body equipment, a middle-end controller, a BIM file importing unit and a detection scene selecting unit; the terminal equipment or the middle-end controller acquires the BIM model imported by the BIM file importing unit, the detection scene selecting unit performs detection scene selection based on the imported BIM model, wherein the detection scene comprises curtain wall detection, tower crane equipment detection and cable inspection, the terminal equipment is used for performing curtain wall detection, tower crane equipment detection and cable inspection data analysis and storage, the middle-end controller is connected with a mobile device used for remotely controlling front-end unmanned aerial vehicle body equipment through a wireless network communication module, and the front-end unmanned aerial vehicle body equipment is used for performing curtain wall detection, tower crane equipment detection and cable inspection data acquisition.
2. The intelligent detection system of a unmanned aerial vehicle based on BIM according to claim 1, wherein when the terminal equipment or the middle-end controller obtains the BIM model imported by the BIM file importing unit as the BIM model of the building curtain wall detected in the curtain wall detection, the detection scene selecting unit performs curtain wall detection based on the imported BIM model of the building curtain wall detected in the curtain wall detection, the curtain wall detection performs facade curtain wall quality detection based on the imported BIM model of the building curtain wall detected, and detection contents comprise facade flatness, joint seal holes, joint seal cracking degree and profile buckling deformation.
3. The intelligent detection system of the unmanned aerial vehicle based on BIM according to claim 2, wherein the BIM file of the building curtain wall to be detected at present is imported into terminal equipment during detection of the curtain wall, and then selection of detection items is carried out through a middle-end controller, wherein the selectable items are flatness detection of the curtain wall, detection of a curtain wall seam sealant hole, detection of a seam sealant cracking gap size and detection of profile buckling deformation.
4. The intelligent detection system of unmanned aerial vehicle based on BIM according to claim 3, wherein flatness detection of the curtain wall is carried out by randomly extracting 50% of curtain wall surfaces in BIM files for detection after the front-end unmanned aerial vehicle body equipment adopts an infrared mode for scanning; the infrared rays emitted by the front-end unmanned aerial vehicle body equipment are emitted to the glass curtain wall, the outermost edge infrared rays emitted by the front-end unmanned aerial vehicle body equipment are taken as references, and the vertical distance between the front-end unmanned aerial vehicle body equipment and the curtain wall is calculated through a trigonometric function according to the contact distance between the front-end unmanned aerial vehicle body equipment and the infrared rays and the included angle between the emitted infrared rays and the curtain wall; calculating the vertical distance between an infrared region emitted by the front-end unmanned aerial vehicle body equipment and the infrared rays and the curtain wall by taking the obtained vertical distance as a reference; a step of measuring and calculating the height difference of the detection area; and obtaining the difference values M1, M2 and M3 … … between all the vertical distances and the reference vertical distance, wherein the height difference=max (M1, M2 and M3 … …) of the detection area, so as to measure the flatness in the detection range and the height difference of the adjacent panel seam.
5. The BIM-based unmanned aerial vehicle intelligent detection system of claim 4, wherein the curtain wall splice joint sealant hole detection and the splice joint sealant crack gap size detection are used for subsequent image recognition comparison analysis by the photo material sealed by the standard sealant imported in advance in the curtain wall detection; according to the detection points listed in the BIM file of the building curtain wall which is imported and detected currently, taking high-definition photos by adopting a device photographing port, automatically positioning the taken photos into a BIM three-dimensional image for positioning, recording and numbering, and transmitting the numbered photos into terminal equipment for image recognition; the image identification identifies the generated hole photo through system data comparison according to the Harris corner feature extraction method, meanwhile, the photo finds out the problem of forming a three-dimensional image by the corresponding BIM three-dimensional image according to the number taking position and records the problem, and the recording result is fed back to the terminal equipment and finally the hole is maintained, detected and repaired through final positioning.
6. The intelligent BIM-based unmanned aerial vehicle detection system according to claim 5, wherein the profile buckling deformation degree detection is performed by scanning front-end unmanned aerial vehicle body equipment in an infrared mode and randomly extracting 50% of curtain wall profile surfaces in BIM files; the method comprises the steps that infrared rays emitted by front-end unmanned aerial vehicle body equipment are emitted onto curtain wall sections, the outermost edge infrared rays emitted by the front-end unmanned aerial vehicle body equipment are used as references, the distance between the infrared rays and infrared contact points on the curtain wall sections and the included angle between the emitted infrared rays and the curtain wall sections are used as the reference, and the vertical distance between the front-end unmanned aerial vehicle body equipment and the curtain wall sections is calculated through trigonometric functions; calculating the vertical distance between an infrared region emitted by the front-end unmanned aerial vehicle body equipment and the curtain wall section by taking the obtained vertical distance as a reference; the difference M1, M2, M3, … … between all the vertical distances and the reference vertical distance is obtained, and the height difference=max (M1, M2, M3, … …) of the detection area is obtained, so that the profile height difference in the detection range is measured.
7. The intelligent detection system of a unmanned aerial vehicle based on BIM according to claim 6, wherein when the terminal device or the middle-end controller obtains that the BIM model imported by the BIM file importing unit is a BIM model detected by a tower crane device, the detection scene selecting unit performs image recognition based on the BIM model detected by the imported tower crane device.
8. The intelligent unmanned aerial vehicle detection system based on BIM according to claim 7, wherein the tower crane equipment detects and guides in advance the standard section photo of the tower crane equipment and the photo material fixed by bolts according to the terminal equipment or the middle-end control, and is used for subsequent image recognition comparison analysis, the high-definition photo is taken by adopting the equipment photographing port through the detection point listed in the imported BIM file, the taken photo is automatically positioned in the BIM three-dimensional graph to carry out positioning record number, the numbered photo is transmitted into the equipment end to carry out image recognition, the image recognition is identified according to the Harris corner feature extraction method, the high-definition photo is taken by adopting the equipment photographing port, the taken photo is automatically positioned in the BIM three-dimensional graph to carry out positioning record number, the image recognition adopts the image recognition system, the photo is simultaneously found according to the taking number position to form a three-dimensional graph after the corresponding to the BIM three-dimensional graph, the three-dimensional graph is recorded, the final positioning record is fed back to the terminal equipment, and the final maintenance result is timely replaced through the terminal maintenance result.
9. The intelligent detection system of a unmanned aerial vehicle based on BIM according to claim 8, wherein when the terminal device or the middle-end controller obtains that the BIM model imported by the BIM file importing unit is a BIM model for cable inspection, the detection scene selecting unit detects the outdoor long-distance cable according to the image recognition system and the Beidou positioning system based on the imported BIM model for cable inspection.
10. The intelligent detection system of a BIM-based unmanned aerial vehicle according to claim 9, wherein the cable inspection is transmitted to the terminal equipment system by leading a cable line diagram into the terminal equipment system or leading the cable line diagram into the terminal equipment system through a middle-end controller, wherein the cable line diagram comprises the geographical coordinates and the serial numbers of each cable pole and the serial numbers of each cable, after the cable line diagram is led in, a front-end unmanned aerial vehicle body is started through the middle-end controller and automatically inspected according to the serial numbers of the cable poles, and when a broken or broken part occurs in the cable, the front-end unmanned aerial vehicle body sends back to the terminal equipment system through an image recognition system for discrimination; the image recognition is carried out according to the Harris angular point feature extraction method, the problem is fed back to the front-end unmanned aerial vehicle body after the identification terminal equipment recognizes, and the front-end unmanned aerial vehicle body adopts a Beidou system to carry out geographic position positioning and match with the cable line diagram position in the system; the front-end unmanned aerial vehicle body transmits the data back to the terminal equipment to form an alarm signal; and after the terminal equipment gives an alarm, the cable maintenance staff performs maintenance treatment according to the positioning to the site.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116838114A (en) * | 2023-07-06 | 2023-10-03 | 同创华建集团有限公司 | Steel construction and curtain intelligent monitoring system based on data analysis |
| CN117689276A (en) * | 2024-02-04 | 2024-03-12 | 济宁久邦工程机械设备有限公司 | Machine vision-based production quality analysis method for folding arm of overhead working truck |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116838114A (en) * | 2023-07-06 | 2023-10-03 | 同创华建集团有限公司 | Steel construction and curtain intelligent monitoring system based on data analysis |
| CN116838114B (en) * | 2023-07-06 | 2024-01-23 | 同创华建集团有限公司 | Steel construction and curtain intelligent monitoring system based on data analysis |
| CN117689276A (en) * | 2024-02-04 | 2024-03-12 | 济宁久邦工程机械设备有限公司 | Machine vision-based production quality analysis method for folding arm of overhead working truck |
| CN117689276B (en) * | 2024-02-04 | 2024-04-19 | 济宁久邦工程机械设备有限公司 | Machine vision-based production quality analysis method for folding arm of overhead working truck |
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