Intelligent construction method for building thermal insulation wall

Abstract

The invention discloses an intelligent construction method for a building thermal insulation wall, which comprises the following steps: step 1, three-dimensional scanning and digital modeling of a base layer: obtaining point cloud data of a base layer wall surface through a three-dimensional scanner, and generating a three-dimensional digital model containing a wall surface flatness deviation value delta; step 2, self-adaptive spraying of the binder: a mobile spraying robot regulates and controls the spraying flow Q, the moving speed V and the spraying pressure P in real time according to the three-dimensional digital model and the flatness deviation value delta of the current spraying area, so that the wet film thickness Dtarget of the binder is matched with the fluctuation of the wall surface; step 3, automatically pasting and intelligently compacting the insulation board; fourthly, slab joint treatment and anchoring part installation are conducted, specifically, the slab joints are slotted, and anchoring nails are implanted; 5, intelligent construction of a plastering layer; and step 6, carrying out quality digital evaluation and delivery. The method has the beneficial effects that the bonding safety of the thermal insulation system is improved, the bonding quality is effectively improved, and the common quality problem of cracking of a plastering layer is effectively inhibited.

Description

Technical Field

[0001] This invention relates to the field of building construction technology, and in particular to an intelligent construction method for building insulation walls. Background Technology

[0002] External wall insulation systems are currently the mainstream technology for building energy conservation, with the most widely used process being the application of adhesive insulation boards (such as EPS boards, XPS boards, and rock wool boards) combined with a fiberglass mesh plaster layer. Chinese patent CN104018591A discloses a mechanized construction method for wall insulation. Although it achieves mechanized spraying of adhesive mortar and plaster mortar, the following technical problems still exist in practical applications: First, the flatness error of the base wall directly affects the thickness and fullness of the adhesive layer. Existing spraying equipment cannot adjust the spray flow and pressure in real time according to the wall surface undulations, resulting in localized gaps or weak adhesion after the insulation boards are pasted, posing a risk of detachment later. Second, the kneading and leveling process during insulation board pasting relies entirely on worker experience. Uneven kneading pressure can easily cause board warping or adhesive overflow, and poor control of board joints can easily lead to cold bridges. Third, the embedding position of the mesh and the thickness of the plaster mortar are difficult to control precisely during plaster layer construction, leading to a decrease in crack resistance. Although existing technologies include robots that perform removal operations through image recognition, as disclosed in Chinese patent CN118815225A, and construction methods involving BIM modeling for positioning, there is still a lack of effective intelligent solutions for the core challenges of "adaptive control of the bonding layer" and "real-time feedback of compaction" in thin plastering systems. Summary of the Invention

[0003] This invention aims to provide an intelligent construction method for building insulation walls, in order to solve the technical problems existing in the prior art, such as the inability to adaptively control the thickness of adhesive spraying, the unknown degree of insulation board bonding and compaction, and the inaccurate embedding position of the mesh cloth, so as to achieve online controllability and traceability of the construction quality of the insulation layer.

[0004] The technical solution adopted by this invention to solve the technical problem is as follows: According to one aspect of the present invention, an intelligent construction method for building insulation walls is designed, comprising the following steps: Step 1: Basic 3D Scanning and Digital Modeling A 3D laser scanner is set up around the base wall to be constructed to scan the wall from all directions, obtain the original point cloud data of the wall, and import the point cloud data into the industrial control computer. After processing, a 3D digital model of the base wall is generated. The 3D digital model contains quantitative data of the wall undulation, that is, the average flatness deviation value δ of the wall and the coordinates of local high and low points are calculated.

[0005] Step 2: Adaptive spraying of adhesive The mobile spraying robot is moved to the starting position of the construction. The mobile spraying robot includes a six-axis robotic arm, a spray head connected to the end of the robotic arm, a material supply system connected to the spray head, and a vision sensor. The movement of the six-axis robotic arm and the material supply and spraying of the material supply system are controlled by an industrial control computer. The vision sensor is used to identify the spraying situation and feed it back to the industrial control computer. The vision sensor is connected to one side of the end of the six-axis robotic arm through a connecting rod.

[0006] The industrial control computer generates the spraying path based on the three-dimensional digital model obtained in step one, and adjusts the spraying parameters in real time according to the flatness deviation value δ of each area of ​​the wall: When the vision sensor identifies a local high point δ>+4mm in the current spraying area, the industrial control computer controls the material supply system to reduce the spraying flow rate Q, and at the same time controls the spraying head to increase the moving speed V, so that the wet film adhesive thickness D_target in this area is controlled at 3-5mm. When the vision sensor identifies a local low point δ<-4mm in the current spraying area, the industrial control computer controls the material supply system to increase the spraying flow rate Q, and at the same time controls the spraying head to reduce the moving speed V, so that the wet film adhesive thickness D_target in this area is controlled at 8-12mm. When the vision sensor identifies the current spraying area as a flat area -4mm ≤δ≤ 4mm, the industrial control computer controls the normal spraying flow rate Q of the material supply system, and at the same time controls the normal moving speed V of the spraying head, so that the adhesive thickness D_target in this area is 6-8mm. The spray head remains perpendicular to the normal direction of the substrate wall during the spraying process. The spraying pressure P is linearly adjusted between 0.4MPa and 0.8MPa according to the flow rate Q to ensure the atomization effect and uniform spreading of the adhesive. The smoothness of the adhesive surface after spraying is less than 3mm.

[0007] Step 3: Automatic Adhesion and Intelligent Compaction of Insulation Boards Before the adhesive initially sets, the insulation board installation robot performs the pasting operation. The insulation board installation robot is a conventional board installation robot, which includes an omnidirectional moving chassis, a six-axis manipulator mounted on the omnidirectional moving chassis, and a compaction plate connected to the end of the six-axis manipulator. The compaction plate is equipped with vacuum suction holes and an array of pressure sensors. The pressure sensors feed back pressure information to the industrial control computer. The vacuum suction holes are connected to the vacuum system through pipelines. The six-axis manipulator and the vacuum system are controlled by the industrial control computer.

[0008] The specific steps are as follows: S31, a six-axis robotic arm drives a compactor to pick up insulation boards from a silo; S32. The insulation board installation robot moves the insulation board above the target pasting area according to the model data in step one, so that the board seam is aligned with the preset ink line or the board seam line projected by the laser projector. S33. The insulation board installation robot controls a six-axis manipulator to drive the compaction plate to place the insulation board flat on the base layer coated with adhesive, and then drives the compaction plate to adhere to the surface of the insulation board. S34. Driven by a six-axis robotic arm, the compaction plate performs a "kneading-translation" composite motion on the insulation board along a set trajectory. During this process, an array of pressure sensors detects the pressure value F of the compaction plate on the board surface in real time and feeds the signal back to the industrial control computer. The industrial control computer adjusts the downward pressure of the robotic arm in real time according to the preset ideal compaction pressure threshold (such as 150N-200N) to ensure that the adhesive is in full contact with the back of the insulation board and the fullness reaches more than 90%. Step 4: Joint Treatment and Anchor Installation After the insulation board is pasted and compacted, an intelligent cutting robot cuts grooves along the board seams. The width and depth of the grooves are automatically controlled by the industrial control computer according to the preset anchor dimensions. The dust generated during grooving is collected by a dust collection device. Subsequently, an automated drilling robot, based on the 3D model from step one and the actual bonding position of the insulation board, automatically positions the drill hole, avoiding board seams and hollow areas, and inserts anchor nails. The drilling depth is precisely controlled by a limit sensor on the drill rod.

[0009] Step 5: Intelligent Construction of the Finishing Layer After the anchors are installed, proceed with the finishing layer application: S51. First coat of finishing mortar spraying: The spraying parameters are finely adjusted according to the flatness of the base layer (i.e. the surface of the insulation board) to ensure that the thickness of the mortar layer is controlled at 2-3mm. S52. Automatic laying and pre-embedding of mesh fabric: The mesh fabric laying robot pulls out the rolled fiberglass mesh fabric and uses a flattening roller to flatten the mesh fabric onto the wet plastering mortar. The infrared heating device at the front end of the mesh fabric laying robot preheats the mesh fabric to a temperature of 30-50℃, making it soft and conformable to irregular parts such as corners. S53. Intelligent Scraping and Thickness Control: The scraper robot drives the scraper to scrape and press the mesh fabric onto the mortar surface, embedding the mesh fabric into the mortar. The scraper is equipped with a laser thickness sensor to measure the total thickness of the mortar layer in real time. When a local thickness of less than 4mm is detected, the scraper robot stops moving forward and sends a feedback signal, allowing the replenishment system to perform targeted replenishment spraying in that area. When a local thickness of more than 6mm is detected, the scraper increases its downward pressure to scrape off and recycle the excess mortar. S54. Second coat of finishing mortar: After the first coat of mortar has initially set, repeat the spraying and smoothing steps to make the total thickness reach the design requirements, and then finish the surface.

[0010] Step Six: Digital Quality Assessment and Delivery After construction is completed, the completed insulation wall surface is scanned again using a 3D scanner to generate an as-built model. The as-built model is then overlaid and compared with the base model from step one and the design BIM model to automatically generate a quality inspection report, including the estimated value of adhesive fullness, insulation board flatness, joint width, and plaster layer thickness distribution. This report is then uploaded to the cloud server via the Internet of Things.

[0011] Preferably, the spray head in step two includes a rotating spray cup or a fan-shaped nozzle, with a laser range sensor on the edge of the nozzle for real-time measurement of the distance between the spray head and the wall, ensuring that the spraying distance is constant at 300-500mm.

[0012] Preferably, the path of the "kneading-translation" composite motion in step three is "S"-shaped or "Z"-shaped, the kneading frequency is 2-5Hz, and the translation speed is 0.1-0.3m / s. This motion trajectory is beneficial for expelling air bubbles between the adhesive and the insulation board.

[0013] The beneficial effects of this invention are as follows: This invention, by introducing 3D scanning and digital modeling of the substrate, enables on-demand control of adhesive spraying thickness, fundamentally solving the problem of weak adhesion or hollow areas caused by uneven substrates, and improving the bonding safety of the insulation system. Through an intelligent compaction plate with pressure feedback, the bonding process of the insulation board is controlled in real-time in a closed loop, ensuring the full compaction of the adhesive and avoiding the subjectivity and lag of traditional manual "tapping and listening" to judge hollow areas, effectively improving bonding quality. This invention integrates mesh preheating and laser thickness measurement closed-loop control in the plastering layer construction, ensuring the uniformity of the protective layer thickness and the correct position of the mesh, effectively suppressing the common quality problem of plastering layer cracking. Furthermore, through full-process digital modeling and as-built comparison, the construction process is visualized and quality is traceable, providing a reliable data foundation for intelligent construction and the operation and maintenance delivery of Building Information Modeling (BIM). Attached Figure Description

[0014] Figure 1 A structural schematic diagram of an intelligent construction method for building insulation walls according to one embodiment of the present invention; Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described below with reference to specific implementation examples, but the invention is not limited to the following embodiments.

[0016] refer to Figure 1 As shown, the present invention provides an intelligent construction method for building insulation walls, comprising the following steps: Step 1: Basic 3D Scanning and Digital Modeling A 3D laser scanner is set up around the base wall to be constructed to scan the wall from all directions, obtain the original point cloud data of the wall, and import the point cloud data into the industrial control computer. After processing, a 3D digital model of the base wall is generated. The 3D digital model contains quantitative data of the wall undulation, that is, the average flatness deviation value δ of the wall and the coordinates of local high and low points are calculated.

[0017] Step 2: Adaptive spraying of adhesive The mobile spraying robot is moved to the starting position of the construction. The mobile spraying robot includes a six-axis robotic arm, a spray head connected to the end of the robotic arm, a material supply system connected to the spray head, and a vision sensor. The movement of the six-axis robotic arm and the material supply and spraying of the material supply system are controlled by an industrial control computer. The vision sensor is used to identify the spraying situation and feed it back to the industrial control computer. The vision sensor is connected to one side of the end of the six-axis robotic arm through a connecting rod.

[0018] The industrial control computer generates the spraying path based on the three-dimensional digital model obtained in step one, and adjusts the spraying parameters in real time according to the flatness deviation value δ of each area of ​​the wall: When the vision sensor identifies a local high point δ>+4mm in the current spraying area, the industrial control computer controls the material supply system to reduce the spraying flow rate Q, and at the same time controls the spraying head to increase the moving speed V, so that the wet film adhesive thickness D_target in this area is controlled at 3-5mm. When the vision sensor identifies a local low point δ<-4mm in the current spraying area, the industrial control computer controls the material supply system to increase the spraying flow rate Q, and at the same time controls the spraying head to reduce the moving speed V, so that the wet film adhesive thickness D_target in this area is controlled at 8-12mm. When the vision sensor identifies the current spraying area as a flat area -4mm ≤δ≤ 4mm, the industrial control computer controls the normal spraying flow rate Q of the material supply system, and at the same time controls the normal moving speed V of the spraying head, so that the adhesive thickness D_target in this area is 6-8mm. The spray head remains perpendicular to the normal direction of the substrate wall during the spraying process. The spraying pressure P is linearly adjusted between 0.4MPa and 0.8MPa according to the flow rate Q to ensure the atomization effect and uniform spreading of the adhesive. The smoothness of the adhesive surface after spraying is less than 3mm.

[0019] Step 3: Automatic Adhesion and Intelligent Compaction of Insulation Boards Before the adhesive initially sets, the insulation board installation robot performs the pasting operation. The insulation board installation robot is a conventional board installation robot, which includes an omnidirectional moving chassis, a six-axis manipulator mounted on the omnidirectional moving chassis, and a compaction plate connected to the end of the six-axis manipulator. The compaction plate is equipped with vacuum suction holes and an array of pressure sensors. The pressure sensors feed back pressure information to the industrial control computer. The vacuum suction holes are connected to the vacuum system through pipelines. The six-axis manipulator and the vacuum system are controlled by the industrial control computer.

[0020] The specific steps are as follows: S31, a six-axis robotic arm drives a compactor to pick up insulation boards from a silo; S32. The insulation board installation robot moves the insulation board above the target pasting area according to the model data in step one, so that the board seam is aligned with the preset ink line or the board seam line projected by the laser projector. S33. The insulation board installation robot controls a six-axis manipulator to drive the compaction plate to place the insulation board flat on the base layer coated with adhesive, and then drives the compaction plate to adhere to the surface of the insulation board. S34. Driven by a six-axis robotic arm, the compaction plate performs a "kneading-translation" composite motion on the insulation board along a set trajectory. During this process, an array of pressure sensors detects the pressure value F of the compaction plate on the board surface in real time and feeds the signal back to the industrial control computer. The industrial control computer adjusts the downward pressure of the robotic arm in real time according to the preset ideal compaction pressure threshold (such as 150N-200N) to ensure that the adhesive is in full contact with the back of the insulation board and the fullness reaches more than 90%. Step 4: Joint Treatment and Anchor Installation After the insulation board is pasted and compacted, an intelligent cutting robot cuts grooves along the board seams. The width and depth of the grooves are automatically controlled by the industrial control computer according to the preset anchor dimensions. The dust generated during grooving is collected by a dust collection device. Subsequently, an automated drilling robot, based on the 3D model from step one and the actual bonding position of the insulation board, automatically positions the drill hole, avoiding board seams and hollow areas, and inserts anchor nails. The drilling depth is precisely controlled by a limit sensor on the drill rod.

[0021] Step 5: Intelligent Construction of the Finishing Layer After the anchors are installed, proceed with the finishing layer application: S51. First coat of finishing mortar spraying: The spraying parameters are finely adjusted according to the flatness of the base layer (i.e. the surface of the insulation board) to ensure that the thickness of the mortar layer is controlled at 2-3mm. S52. Automatic laying and pre-embedding of mesh fabric: The mesh fabric laying robot pulls out the rolled fiberglass mesh fabric and uses a flattening roller to flatten the mesh fabric onto the wet plastering mortar. The infrared heating device at the front end of the mesh fabric laying robot preheats the mesh fabric to a temperature of 30-50℃, making it soft and conformable to irregular parts such as corners. S53. Intelligent Scraping and Thickness Control: The scraper robot drives the scraper to scrape and press the mesh fabric onto the mortar surface, embedding the mesh fabric into the mortar. The scraper is equipped with a laser thickness sensor to measure the total thickness of the mortar layer in real time. When a local thickness of less than 4mm is detected, the scraper robot stops moving forward and sends a feedback signal, allowing the replenishment system to perform targeted replenishment spraying in that area. When a local thickness of more than 6mm is detected, the scraper increases its downward pressure to scrape off and recycle the excess mortar. S54. Second coat of finishing mortar: After the first coat of mortar has initially set, repeat the spraying and smoothing steps to make the total thickness reach the design requirements, and then finish the surface.

[0022] Step Six: Digital Quality Assessment and Delivery After construction is completed, the completed insulation wall surface is scanned again using a 3D scanner to generate an as-built model. The as-built model is then overlaid and compared with the base model from step one and the design BIM model to automatically generate a quality inspection report, including the estimated value of adhesive fullness, insulation board flatness, joint width, and plaster layer thickness distribution. This report is then uploaded to the cloud server via the Internet of Things.

[0023] Example

[0024] This invention provides an intelligent construction method for building insulation walls. Taking the external insulation construction of a high-rise residential project as an example, the wall type is a reinforced concrete shear wall, the insulation material is a 60mm thick EPS board, the bonding area is required to be no less than 50%, and the total thickness of the plaster layer is 5mm.

[0025] First, in step one, operators set up a 3D laser scanner (such as a FARO FocusS series 3D laser scanner) on the construction floor to scan the cleaned base wall surface. The scanning resolution is 1 / 4, quality is 4x, and point cloud data is acquired. The point cloud is preprocessed using supporting software (such as Scene software) to remove noise, and then imported into the modeling software on the industrial control computer. The generated 3D model shows that the overall flatness deviation of the wall surface is between -8mm and +12mm, with multiple local high points at concrete joints.

[0026] Step two involves activating the mobile painting robot. This robot uses a tracked chassis and is equipped with a six-axis robotic arm. The end of the robotic arm is fitted with a Graco pneumatic spray gun, which integrates a laser displacement sensor (Keyence LR-W series laser displacement sensor). The industrial control computer guides the six-axis robotic arm's movement according to the path planned by the 3D model. When the spray reaches a local high point area δ=+10mm, the industrial control computer sets the target spray thickness of the PID controller to 4mm. By adjusting the speed of the variable frequency motor in the feeding system, the flow rate is reduced from 6L / min to 3L / min, while the end-effector speed of the six-axis robotic arm is increased from 0.3m / s to 0.5m / s, and the spraying pressure is adjusted from 0.6MPa to 0.45MPa to reduce the output and thin the coating. After spraying, the measured wet film thickness at this high point is 4.2mm, which is within the set range.

[0027] In step three, the insulation board installation robot uses visual navigation to automatically pick up a 600mm x 1200mm EPS board. The robot moves to the pre-sprayed area and positions the insulation board according to the laser-projected seam lines. The robot then drives a compacted board (500mm x 1000mm, using a soft polyurethane padding) to press down. The compacted board contains 12 thin-film pressure sensors (FSR402) arranged in a 3x4 array. The robotic arm on the robot drives the compacted board in a zigzag motion at a frequency of 3Hz. When the thin-film pressure sensors detect that the pressure in the lower left corner of the board is only 80N (below the lower threshold of 150N), the industrial control computer determines that the area is insufficiently compacted. It immediately adjusts the posture of the robotic arm on the insulation board installation robot, increasing the local downward pressure in that area to 180N and extending the kneading time by 0.5 seconds. After the board is pasted, a visual inspection confirms that the seam alignment accuracy meets the requirements.

[0028] Step 4: The intelligent slitting robot moves along the board seam and cuts grooves at the preset anchoring positions. Then, the automatic drilling robot, based on the model positioning, drills φ8mm, 55mm deep holes in non-board seam locations and inserts anchoring nails.

[0029] Step 5: Finishing Layer Construction. First, a spraying robot applies the first coat of finishing mortar, with a thickness controlled at 3mm. Then, a mesh laying robot pulls out an alkali-resistant fiberglass mesh, preheats it using a preheating roller (temperature controlled at 40℃), and lays it on the mortar. Next, a scraper robot operates. Equipped with a laser displacement sensor on its scraper, the scraper calculates the finishing layer thickness by measuring the distance from the bottom of the scraper to the surface of the insulation board. When the designed total thickness is 5mm, and the sensor shows a thickness of 5.8mm at a certain point, the industrial control computer controls the scraper robot to increase the downward pressure of the scraper, scraping excess mortar to an adjacent area, which is then collected through the suction port. After smoothing, the actual thickness of this area is measured at 5.1mm, meeting the acceptance standards. Finally, a second coat of mortar is sprayed and the surface is smoothed.

[0030] Step Six: As-built Scan. The completed wall surface is scanned again to generate a point cloud model, which is then imported into the BIM comparison software. The software automatically calculates and generates a report: the estimated adhesive fullness is 95% (based on the spraying volume and compaction displacement), the maximum deviation of the insulation board surface flatness is 3mm, the joint width pass rate is 99%, and the average thickness of the plaster layer is 5.2mm with a standard deviation of 0.3mm. All data is packaged and uploaded to the cloud server, completing the delivery.

[0031] The above embodiments are merely preferred embodiments of the present invention, describing in detail the technical concept and operation process of the present invention, but should not be construed as limiting the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A smart construction method for building insulation walls, characterized in that, Includes the following steps: Step 1: 3D scanning and digital modeling of the base layer: The point cloud data of the base wall surface is obtained by a 3D scanner, and a 3D digital model containing the wall surface flatness deviation value δ is generated. Step 2, Adhesive Adaptive Spraying: The mobile spraying robot adjusts the spraying flow rate Q, moving speed V, and spraying pressure P in real time according to the three-dimensional digital model and the flatness deviation value δ of the current spraying area, so that the adhesive wet film thickness D_target matches the wall surface undulations. Step 3: Automatic pasting and intelligent compaction of insulation boards: The insulation board installation robot places the insulation board on the adhesive and uses a compaction plate with an array of pressure sensors to press the insulation board, monitor the pressure value F in real time and adjust the downward pressure accordingly. Step 4, Joint Treatment and Anchor Installation: Grooving the joints and inserting anchor nails; Step 5, Intelligent construction of the finishing layer: The first layer of finishing mortar is sprayed, the mesh cloth is laid and pressed in, and the second layer of finishing mortar is applied in sequence. During the scraping and pressing process, the thickness of the finishing layer is monitored in real time by a laser thickness sensor, and the material is added or scraped off at fixed points. Step Six: Digital Quality Assessment and Delivery: After construction, a completion scan is performed to generate an as-built model, which is then compared with the design model to output a quality inspection report.

2. The intelligent construction method for building insulation walls according to claim 1, characterized in that, In step two, the control threshold for the flatness deviation value δ is as follows: when δ > +4mm, control D_target to 3-5mm; when δ < -4mm, control D_target to 8-12mm; when -4mm ≤ δ ≤ 4mm, control D_target to 6-8mm.

3. The intelligent construction method for building insulation walls according to claim 1, characterized in that, The insulation board installation robot includes a six-axis manipulator and a compaction plate connected to the end of the six-axis manipulator. The compaction plate is equipped with vacuum suction holes and an array of pressure sensors. The vacuum suction holes are connected to a vacuum system through pipelines.

4. The intelligent construction method for building insulation walls according to claim 1, characterized in that, Before laying the mesh fabric in step five, the mesh fabric is preheated using an infrared heating device at a temperature of 30-50℃.

5. The intelligent construction method for building insulation walls according to claim 1, characterized in that, In step five, the laser thickness sensor monitors the thickness of the plaster layer in real time. When the thickness is less than 4mm, it performs spot-addition spraying. When the thickness is greater than 6mm, it increases the downward pressure of the scraper to remove excess mortar.

6. The intelligent construction method for building insulation walls according to claim 1, characterized in that, In step two, a laser rangefinder is installed on the edge of the spray head to keep the distance between the spray head and the wall at 300-500mm in real time, and the spray head is always perpendicular to the normal direction of the base wall.

7. The intelligent construction method for building insulation walls according to claim 1, characterized in that, In step four, the slot width and depth are automatically controlled according to the preset anchor size.

8. The intelligent construction method for building insulation walls according to claim 1, characterized in that, The quality inspection report in step six includes the estimated value of adhesive fullness, insulation board flatness, joint width, and plaster layer thickness distribution parameters, and is uploaded to the cloud server via the Internet of Things.

9. A smart construction method for building insulation walls according to any one of claims 1-8, characterized in that, All data generated in steps one, two, three, four, five, and six are integrated into the Building Information Model (BIM) to achieve digital traceability of the entire construction process.

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