Intelligent roughening control method and system for concrete prefabricated bridge deck based on three-dimensional scanning

Abstract

The application discloses a concrete prefabricated bridge deck intelligent roughening control method and system based on three-dimensional scanning, and relates to the technical field of bridge engineering. Firstly, a three-dimensional scanner is used to obtain point cloud data of a concrete prefabricated bridge deck and construct a building information model. Then, the building information model is regionally divided, and the roughening parameters of each bridge deck region, including average depth and density index, are extracted. The parameters are compared with preset standards for analysis, and it is determined whether defects exist and highlighted rendering. Then, the defect categories are identified, and a notification and control instruction are generated for an operator to review and determine whether secondary processing is needed. If so, the instruction is transmitted to an automatic roughening device for execution. Through the combination of scanning modeling, defect identification and joint control repair of three modules, efficient intelligent management and control of the roughening quality of the concrete prefabricated bridge deck is realized, and the construction precision and efficiency are improved.

Description

Technical Field

[0001] This invention belongs to the field of bridge engineering technology, specifically, it relates to a method and system for intelligent roughening control of precast concrete bridge decks based on three-dimensional scanning. Background Technology

[0002] Infrastructure construction is an important pillar of economic development. Among them, bridges, as a key component of the transportation network, directly affect the smoothness of regional traffic and economic development through their construction quality and efficiency.

[0003] Current technologies for controlling the surface roughening quality of precast concrete bridge decks typically rely on manual visual inspection or simple mechanical measuring tools. This approach has several drawbacks. First, existing technologies lack high-precision three-dimensional data acquisition methods, often relying on manual visual inspection or local sampling inspections using traditional measuring instruments. This results in incomplete and inaccurate acquisition of roughening depth and density parameters, failing to cover the entire bridge deck area and easily overlooking minor defects, thus affecting the reliability of the overall quality assessment. Second, existing technologies do not integrate Building Information Modeling (BIM) technology, making it impossible to construct a visualized three-dimensional model. This makes it difficult to visually present and locate defect areas, forcing operators to rely on experience for judgment, increasing the risk of subjective errors and hindering real-time data sharing and collaborative analysis. Furthermore, existing methods heavily rely on manual operation for area division and defect identification, resulting in low efficiency, especially in large-scale bridge deck projects, where it is time-consuming and prone to misjudgment due to fatigue or negligence.

[0004] To address the aforementioned issues, this invention proposes an intelligent roughening control method and system for precast concrete bridge decks based on three-dimensional scanning. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an intelligent roughening control method and system for precast concrete bridge decks based on three-dimensional scanning, which solves the problems of inaccuracy, low efficiency, and insufficient automation in roughening quality detection in existing technologies.

[0006] The objective of this invention can be achieved through the following technical solutions: A method for intelligent roughening control of precast concrete bridge decks based on 3D scanning, the method comprising: Step 1: Identify the precast concrete bridge deck to be scanned and use a pre-built 3D scanner to scan it to obtain the 3D point cloud data associated with the precast concrete bridge deck. Combine this with BIM technology to build a building information model associated with the precast concrete bridge deck. Step 2: Divide the determined building information model into regions, determine the set of bridge deck regions, determine the texture parameters associated with each bridge deck region based on the 3D point cloud data of each bridge deck region, perform quality analysis on each bridge deck region based on the texture parameters, and highlight the bridge deck regions with texture defects. Step 3: Identify the type of roughening defects in the bridge deck area with roughening defects. Based on the results of the roughening defect type identification, generate notification and control instructions and synchronize them to the operator. The operator then determines whether the bridge deck area with roughening defects needs secondary processing and transmits the control instructions to the automated roughening equipment.

[0007] As a further aspect of the present invention, in step one, the precast concrete bridge deck to be scanned is a precast concrete bridge deck that has undergone roughening treatment, and the time between the roughening treatment and the current time does not exceed a preset time threshold.

[0008] As a further aspect of the present invention, the specific method for constructing the building information model associated with the precast concrete bridge deck in step one, using BIM technology, is as follows: The pre-built 3D scanner is used to scan the precast concrete bridge deck to obtain the 3D point cloud data associated with the precast concrete bridge deck. If the length of the precast concrete bridge deck exceeds the effective measurement range of the 3D scanner, segmented scanning is adopted and the 3D point cloud data is stitched together in the scanning order to form a set of 3D point cloud data associated with the precast concrete bridge deck. A building information model (BIM) associated with the precast concrete bridge deck is constructed using BIM technology and the associated 3D point cloud data set, denoted as H.

[0009] As a further aspect of the present invention, the specific method for determining the bridge deck area set in step two is as follows: Starting from the beginning of the building information model H, n textured lines are determined. The n textured lines are selected by the smallest rectangle, and the inner area of ​​the smallest rectangle is used as a bridge surface area. Here, n is the preset number of textured lines. Similarly, the bridge deck areas are continuously determined in the building information model H until the end of the building information model H. The total number of bridge deck areas is counted and denoted as m. If the number of textured stripes in the last bridge deck area is less than n, it is directly regarded as an independent bridge deck area. The beginning and end of the building information model H are determined by the operator. Based on the order from the beginning to the end in the Building Information Model H, the m bridge deck areas are summarized to form a bridge deck area set G={g1,g2,...,gm}.

[0010] As a further aspect of the present invention, the specific method for determining the texture parameters associated with each bridge surface region based on the three-dimensional point cloud data of each bridge surface region in step two is as follows: Extract any bridge surface region gk from the set of bridge surface regions G, and obtain the 3D point cloud data associated with bridge surface region gk, where k is the counting index, 1≤k≤m; Based on the n textured lines in the bridge surface region gk, the bridge surface region gk is further divided to obtain n textured regions in the bridge surface region gk. Based on the order from the first end to the last end in the building information model H, they are denoted as the textured region sequence L1, L2, ..., Ln, where any textured region Li contains only one textured line, and i is the counting index, 1≤i≤n; Using the bridge surface within the roughened area Li as a horizontal reference, determine the average depth of the roughening in the roughened area Li; Similarly, determine the average depth of the fur texture in each fur texture region of the fur texture region sequence L1, L2, ..., Ln; The average depth of the roughening in bridge surface region gk is obtained by averaging the average depths of the n roughening regions, denoted as Dk. Similarly, determine the average depth of the roughening texture in each bridge surface region of the bridge surface region set G={g1,g2,...,gm}; The width Wk of the bridge deck region gk is extracted from the 3D point cloud data of the bridge deck region gk. The texture density index ρk in the bridge deck region gk is calculated using Wk / n=ρk. The larger the texture density index ρk is, the lower the texture density is, and vice versa. Similarly, determine the texture density index of the texture in each bridge surface region in the bridge surface region set G={g1,g2,...,gm}; The average depth Dk and the texture density index ρk of the bridge deck area gk are obtained to form the texture parameters associated with the bridge deck area gk.

[0011] As a further aspect of the present invention, in step two, the specific method for performing quality analysis on each bridge surface area based on the roughening parameters and highlighting the bridge surface areas with roughening defects is as follows: Obtain the operator's preset average depth range [D_min, D_max] and the texture density index range [ρ_min, ρ_max]; Extract the average depth Dk and the texture density index ρk of any bridge surface region gk in the bridge surface region set G, and compare them with the average depth interval [D_min,D_max] and the texture density index interval [ρ_min,ρ_max], respectively; If Dk∈[D_min,D_max] and ρk∈[ρ_min,ρ_max], then it is determined that there is no roughening defect in the bridge surface area gk, and the bridge surface area gk is rendered as green in the building information model H; Conversely, if the bridge surface area gk is found to have a rough texture defect, the bridge surface area gk will be rendered red in the building information model H. Repeat the above steps to synchronize all bridge deck regions in the bridge deck region set G.

[0012] As a further aspect of the present invention, the specific method for identifying the type of roughening defects in the bridge deck area with roughening defects in step three is as follows: Determine the average depth Dk and the texture density index ρk of any bridge deck region gk; If Dk < D_min, then the bridge surface area gk is determined to have a defect of shallow texture depth. If Dk > D_max, then the bridge surface area gk is determined to have a defect of excessively deep roughening. If ρk < ρ_min, then the bridge surface area gk is determined to have a defect of excessively high texture density. If ρk>ρ_min, then the bridge deck area gk is determined to have a defect of low texture density. Similarly, all bridge surface regions in the bridge surface region set G are processed synchronously.

[0013] As a further aspect of the present invention, the specific method for generating notification and control instructions based on the results of roughness defect category identification and synchronizing them with the operator in step three is as follows: If it is determined that any bridge deck area gk has a defect of shallow texture depth, calculate the absolute value of the difference between the average depth Dk and the minimum value D_min in the average depth interval, and use the absolute value of the difference as the texture depth to be increased. If it is determined that the bridge deck area gk has a defect of shallow texture depth, then the absolute value of the difference between the average depth Dk and the maximum value D_max in the average depth range is calculated as the texture depth to be reduced. If it is determined that the bridge deck area gk has a defect of excessive texture density, then the absolute value of the difference between the texture density index ρk and the minimum value ρ_min in the texture density index interval [ρ_min, ρ_max] is calculated as the texture density index to be increased. If it is determined that the bridge deck area gk has a defect of low texture density, then the absolute value of the difference between the texture density index ρk and the maximum value ρ_max in the texture density index interval [ρ_min, ρ_max] is calculated as the texture density index to be reduced. The desired increase or decrease in texture depth and texture density index, along with the desired decrease in texture density index, are summarized as control instructions. Corresponding notification instructions are then generated and synchronized with the control instructions to the operator.

[0014] As a further aspect of the present invention, in step three, if the operator receives notification instructions and control instructions and determines that secondary processing is required, all control instructions are transmitted to the automated texturing equipment to perform secondary processing on the bridge deck area gk. The control instructions are reviewed and confirmed by the operator.

[0015] A three-dimensional scanning-based intelligent surface roughening control system for precast concrete bridge decks, the system comprising: The scanning and modeling module identifies the precast concrete bridge deck to be scanned and uses a pre-built 3D scanner to scan it, obtaining the 3D point cloud data associated with the precast concrete bridge deck. The module then combines BIM technology to construct the building information model associated with the precast concrete bridge deck. The defect identification module divides the determined building information model into regions, identifies the bridge deck region set, determines the texture parameters associated with each bridge deck region based on the 3D point cloud data of each bridge deck region, performs quality analysis, and highlights the bridge deck regions with texture defects. The joint control and repair module identifies the type of scratches in bridge deck areas with scratches. Based on the identification results, it generates notification and control commands and synchronizes them with the operators. The operators then determine whether the bridge deck areas with scratches need secondary processing and transmit the control commands to the automated scratching equipment.

[0016] The beneficial effects of this invention are: This invention achieves high-precision digital reconstruction of bridge deck roughening by integrating 3D scanning technology and BIM modeling, thereby significantly improving the accuracy and comprehensiveness of data acquisition. Secondly, by adopting automatic area division and quality analysis, roughening defects are quickly identified and highlighted, greatly reducing the subjective error and time cost of manual inspection. At the same time, based on the defect category, notifications and control instructions are intelligently generated, promoting collaborative work between operators and automated equipment. This not only improves processing efficiency but also ensures the consistency and quality control of the roughening process. This invention ensures that the surface texture of precast concrete bridge decks is in a suitable scanning state by limiting the time threshold for roughening treatment, thereby guaranteeing the accuracy and consistency of 3D point cloud data acquisition. Based on this, it employs 3D scanning technology to acquire detailed surface 3D point cloud data and innovatively overcomes the limitations of the measurement range of large components through a segmented scanning and orderly splicing strategy, ensuring the integrity and coherence of the overall 3D data. Finally, it integrates BIM technology to construct a building information model, achieving a high-fidelity mapping between the physical entity and the digital model, improving the accuracy, completeness, and engineering applicability of bridge deck quality inspection and model construction. This invention achieves automated and efficient detection of bridge deck texture quality by dividing the bridge deck into regions and extracting texture parameters in a Building Information Model (BIM). Its core advantage lies in its comprehensive coverage of the entire bridge deck, allowing for independent processing even incomplete texture in end areas, thus avoiding omissions. Simultaneously, it utilizes 3D point cloud data to accurately calculate the average depth and density index of the texture, providing objective and quantitative quality assessment standards. By automatically identifying defective areas and highlighting them within preset parameter ranges, it enhances the intuitiveness of detection and decision-making efficiency, reducing subjective human error. The combination of BIM technology and point cloud analysis forms a complete closed loop from data extraction to visualization output, improving the operability of bridge deck construction quality control. Attached Figure Description

[0017] The invention will now be further described with reference to the accompanying drawings.

[0018] Figure 1 This is a schematic diagram of the system described in this invention; Figure 2 This is a flowchart illustrating the method described in Embodiment 2 of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1 Intelligent roughening control system for precast concrete bridge decks based on 3D scanning, such as Figure 1 As shown, this system includes the following: This system, a 3D scanning-based intelligent surface roughening control system for precast concrete bridge decks, is essentially a modern construction management system that integrates high-precision 3D perception, intelligent data analysis, real-time human-computer interaction, and closed-loop control. It aims to change the current situation of traditional concrete bridge deck roughening operations, which rely on manual experience, have large quality fluctuations, and are inefficient, and to realize the digitalization, intelligence, and visualization of the construction process. This system mainly consists of three modules: a scanning and modeling module, a defect identification module, and a joint control and repair module. The scanning and modeling module first identifies the precast concrete bridge deck to be scanned and then uses a pre-built 3D scanner to scan it, obtaining the associated 3D point cloud data. This data is then combined with BIM technology to construct a building information model (BIM) of the precast concrete bridge deck. Specifically: The pre-built 3D scanner is a combination of LiDAR and structured light 3D scanner. It emits laser beams or specific light spot patterns onto the precast concrete bridge deck and captures its reflected signals. Based on the time-of-flight principle or triangulation principle, it calculates the precise 3D coordinates of each point on the bridge deck from the scanner. By summing up the precise 3D coordinates of all points, it obtains the 3D point cloud data associated with the precast concrete bridge deck. The 3D scanner includes three deployment methods: Firstly, it is integrated into the exit end or specific station of an automated texturing production line or automated texturing equipment (such as roller brush type or milling equipment), and the components are automatically scanned as they pass through. Secondly, the operator scans the component after roughening, which is suitable for large or non-assembly line prefabricated components; Third, it is installed on an automated guide rail or robotic arm to perform a systematic and thorough scan of the entire precast concrete bridge deck to be processed; It is important to note that the precast concrete bridge deck to be scanned must be a precast concrete bridge deck that has undergone roughening treatment, and the time since the roughening treatment is completed must not exceed a preset time threshold to ensure that even if anomalies or defects are detected, secondary processing is still possible.

[0021] The BIM technology creates a 1:1 three-dimensional model of the precast concrete bridge deck by modeling the three-dimensional point cloud data associated with the precast concrete bridge deck. This model, or building information model, reflects the actual size, flatness, and initial, unprocessed surface texture of the precast concrete bridge deck. It should be noted that if the length of the precast concrete bridge deck exceeds the effective measurement range of the pre-purchased Aquino 3D scanner, segmented scanning will be adopted and the 3D point cloud data will be stitched together in the scanning order (from the first end to the last end of the precast concrete bridge deck) to form a set of 3D point cloud data associated with this precast concrete bridge deck. Using BIM technology and the three-dimensional point cloud data set associated with the precast concrete bridge deck, a building information model associated with the precast concrete bridge deck is constructed, denoted as H.

[0022] In the defect identification module, the determined building information model is divided into regions to identify a set of bridge deck regions. Based on the 3D point cloud data of each bridge deck region, the associated texture parameters are determined, and quality analysis is performed. Bridge deck regions with texture defects are highlighted and rendered. Specifically: The system divides the complete building information model into a set of logical bridge surface areas according to the zoning rules preset by the operator based on the actual situation. For example, if there is a building information model with a length of 1000 meters and a width of 10 meters, then the length of 10 meters is used as the zoning rule to divide the building information model with a length of 1000 meters into 100 bridge surface areas, and the 100 bridge surface areas form a set of bridge surface areas.

[0023] Based on the three-dimensional point cloud data of each bridge deck area, each textured surface can be identified, and by determining the depth of the textured surface, the average depth of the textured surface in each bridge deck area can be determined. Similarly, the texture density index of each bridge surface area can be determined, and the texture parameters of each bridge surface area are composed of the texture density index and the average depth. By analyzing the texture parameters of each bridge deck area, the bridge deck areas with texture defects were identified and highlighted in the building information model, making the texture defects immediately apparent and providing operators with an intuitive monitoring method.

[0024] In the joint control and repair module, firstly, based on the standard preset by the operator and the roughness parameters of the bridge deck area with roughness defects, the roughness defect category of the bridge deck area with roughness defects is identified. Then, based on the result of the roughness defect category identification, notification instructions and control instructions are generated and transmitted to the operator at the same time, so that the operator can determine whether the bridge deck area with roughness defects needs to be processed again. If not needed, no human deterioration treatment will be performed; if needed, the control commands will be synchronized to the automated texturing equipment.

[0025] Example 2 This embodiment is a refinement of Embodiment 1, explaining the defect identification module in Embodiment 1, such as... Figure 2 As shown, it specifically includes the following: Based on the content described in Example 1, the building information model associated with the precast concrete bridge deck can be obtained. For ease of subsequent operation, it is abbreviated as building information model H. As described in Example 1, the beginning and end of the Building Information Model H have been determined. Starting from the beginning of the Building Information Model H, n textured lines are continuously determined, and the n textured lines are selected by using a minimum rectangle. It should be noted that other textured lines should not be disturbed. In this way, a partitioning operation is completed. The inner area of ​​the minimum rectangle is a bridge surface area. Here, n is the number of textured lines preset by the operator, which needs to be determined according to the actual situation.

[0026] As mentioned above, by further partitioning the Building Information Model H, we can divide the Building Information Model H into several bridge deck areas. The total number of bridge deck areas is counted and denoted as m. It should be noted that after partitioning, there may be cases where the number of textured stripes in the last bridge deck area is less than n. If this is the case, it will be directly treated as an independent bridge deck area. Even if the number of textured stripes is less than n, the following processing can still be performed to ensure that 100% of the area of ​​the precast concrete bridge deck is included in the analysis.

[0027] Next, based on the determined m bridge deck areas, the m bridge deck areas are arranged and summarized in the order from the first end to the last end in the building information model H to obtain a set of bridge deck areas G, denoted as G={g1,g2,...,gm}.

[0028] Then, any bridge surface region gk is extracted from the determined set of bridge surface regions G={g1,g2,...,gm} for example processing. It should be noted that all bridge surface regions in the set of bridge surface regions G={g1,g2,...,gm} are processed synchronously in the same way as bridge surface region gk, where k is the counting index, 1≤k≤m.

[0029] First, obtain the 3D point cloud data associated with the bridge deck area gk as a backup; Then, the bridge deck area gk is divided again. This time, the division is based on a single textured line. That is, the bridge deck area gk is divided into n textured areas according to n textured lines. Then, the n textured areas are arranged in the order from the first end to the last end in the building information model H to obtain the textured area sequence associated with the bridge deck area gk, represented as: L1, L2, ..., Ln; In the sequence of textured regions L1, L2, ..., Ln, any textured region Li contains exactly one textured line, and i is the counting index, 1 ≤ i ≤ n.

[0030] Next, the height of the bridge deck can be directly obtained from the 3D point cloud data associated with the spare bridge deck area gk, and used as the horizontal plane reference. Then, based on the distance difference between the height of the bridge deck and the depth of the textured surface (similar to the peaks and troughs, the bottom of the textured surface is the trough, and the height of the bridge deck is the peak), the average depth of a single textured surface is obtained by calculating the average vertical distance from the valley bottom point cloud to the horizontal plane reference. In this way, the average depth of any textured surface area Li in the textured surface area sequence L1, L2, ..., Ln can be determined. By repeating the above steps, the average depth of the textured areas in all textured regions of the textured region sequence L1, L2, ..., Ln can be determined, for a total of n average depths.

[0031] Next, by averaging the n average depths of the n textured areas, we can obtain the average depth of the textured surface associated with the bridge surface area gk, and denote it as Dk, representing the average depth of the bridge surface area gk.

[0032] Similarly, the average depth of the roughening pattern in all bridge surface regions of the bridge surface region set G={g1,g2,...,gm} is determined by the method of determining the average depth Dk of the roughening pattern associated with the bridge surface region gk.

[0033] Next, the width of the bridge surface region gk is obtained based on the three-dimensional point cloud data of the bridge surface region gk, denoted as Wk. Then, the texture density index ρk in the bridge surface region gk is determined by using the calculation method Wk / n=ρk. It should be noted that the larger the texture density index ρk is, the lower the texture density is, and the smaller the texture density index ρk is, the higher the texture density is.

[0034] This can be understood as follows: the larger the texture density index ρk, the wider the spacing between textures, and the fewer textures per unit area, which means the lower the actual density. The smaller the texture density index ρk, the more tightly the texture is arranged, and the more textures are in a unit area, meaning the higher the actual density.

[0035] By repeating the above steps, the texture density index of the texture in all bridge surface regions in the bridge surface region set G={g1,g2,...,gm} can be determined.

[0036] Finally, the average depth Dk and texture density index ρk associated with the bridge deck area gk are obtained and combined to form the texture parameters associated with the bridge deck area gk. Other bridge deck areas are processed simultaneously.

[0037] After determining the texture parameters associated with the bridge deck area gk, the operator's preset average depth range [D_min, D_max] and texture density index range [ρ_min, ρ_max] are then obtained. The texture parameters of the bridge deck area gk are judged based on the average depth interval [D_min,D_max] and the texture density index interval [ρ_min,ρ_max] to determine whether there are defects in the bridge deck area gk. The average depth Dk and the texture density index ρk of the bridge deck region gk are extracted and compared with the average depth interval [D_min, D_max] and the texture density index interval [ρ_min, ρ_max], respectively. If the average depth Dk∈[D_min,D_max] and the texture density index ρk∈[ρ_min,ρ_max], then it is determined that there is no texture defect in the bridge surface area gk, and the bridge surface area gk is rendered as a green highlight in the building information model H. If the average depth Dk does not belong to [D_min,D_max] or the texture density index ρk does not belong to [ρ_min,ρ_max], then it is determined that the bridge surface area gk has a texture defect, and the bridge surface area gk is rendered as a red highlight in the building information model H.

[0038] By repeating the above steps, all bridge surface areas in the bridge surface area set G are synchronously processed for defect confirmation and highlight rendering.

[0039] Example 3 This embodiment is a further supplement to Embodiment 2. It identifies the type of scratches in the bridge deck area where scratches are present and generates notification and control commands, specifically including the following: As described in Examples 2 and 1, after determining that any bridge deck region gk in the bridge deck region set G has a roughening defect, it is also necessary to identify the type of roughening defect, as follows: The average depth Dk and the roughening density index ρk of the bridge deck area gk are obtained again, and the following judgments are made: If the average depth Dk is less than the minimum value D_min in the average depth interval [D_min, D_max], it is considered that the roughening depth of the bridge surface area gk is insufficient and cannot form effective adhesion. It is determined that the roughening depth of the bridge surface area gk is too shallow. If the average depth Dk is greater than the maximum value D_max in the average depth interval [D_min, D_max], then the roughening depth of the bridge deck area gk is considered to be too deep, which may damage the structural layer of the bridge deck. Therefore, it is determined that the roughening depth of the bridge deck area gk is too deep. If the texture density index ρk is less than the minimum value ρ_min in the texture density index range [ρ_min, ρ_max], it is considered that the actual texture is too dense, which may lead to the asphalt pavement layer not being properly embedded or wasting construction resources. Therefore, it is determined that the bridge deck area gk has a defect of excessive texture density. If the texture density index ρk is greater than the maximum value ρ_max in the texture density index interval [ρ_min, ρ_max], then the actual texture is considered to be too sparse, and the bridge surface area gk is judged to have a defect of low texture density.

[0040] Repeat the above steps to simultaneously process all bridge deck areas with scratches in the bridge deck area set G, and determine the type of scratches.

[0041] If it is determined that any bridge deck area gk has a defect of shallow texture depth, then the absolute value of the difference between the average depth Dk and the minimum value D_min in the average depth interval [D_min, D_max] is calculated, and then the calculated absolute value of the difference is used as the texture depth to be increased. If it is determined that any bridge deck area gk has a defect of shallow texture depth, then the absolute value of the difference between the average depth Dk and the maximum value D_max in the average depth interval [D_min, D_max] is calculated, and the calculated absolute value of the difference is used as the texture depth to be reduced. If it is determined that any bridge deck area gk has a defect of excessive texture density, then the absolute value of the difference between the texture density index ρk and the minimum value ρ_min in the texture density index interval [ρ_min, ρ_max] is calculated, and the calculated absolute value of the difference is used as the texture density index to be increased. If it is determined that any bridge deck area gk has a defect of low texture density, then the absolute value of the difference between the texture density index ρk and the maximum value ρ_max in the texture density index interval [ρ_min, ρ_max] is calculated, and the calculated absolute value of the difference is used as the texture density index to be reduced. Finally, the parameters to be increased or decreased in the texture depth and the texture density index to be decreased are summarized. The summarized parameters are used as control commands, and corresponding notification commands are generated and sent to the operators simultaneously along with the control commands.

[0042] If it is determined that the operator received the notification and control instructions but did not perform any feedback action, then ignore this control instruction; If the operator determines that secondary processing is required, all control commands will be transmitted to the automated texturing equipment to perform secondary processing on the bridge deck area. It should be noted that the control commands need to be reviewed by the operator. If they are not suitable for the current situation, the operator needs to manually modify and confirm them.

[0043] All data in the formulas described above are numerical calculations performed with dimensions removed. Furthermore, any content not described in detail in this specification is existing technology known to those skilled in the art.

[0044] The above description is merely an example and illustration of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

[0045] It should be stated that all user data collected in this application was collected with the user's consent and authorization. Furthermore, the uses of user data are legal and compliant, and the use and processing of user data comply with the relevant laws, regulations, and standards of the relevant regions.

Claims

1. A method for intelligent roughening control of precast concrete bridge decks based on three-dimensional scanning, characterized in that, The method includes: Step 1: Identify the precast concrete bridge deck to be scanned and use a pre-built 3D scanner to scan it to obtain the 3D point cloud data associated with the precast concrete bridge deck. Combine this with BIM technology to construct the building information model H associated with the precast concrete bridge deck. Step two involves dividing the determined building information model into regions to define the bridge deck region set, specifically; Starting from the beginning of the building information model H, n textured lines are determined. The n textured lines are selected by the smallest rectangle, and the inner area of ​​the smallest rectangle is used as a bridge surface area. Here, n is the preset number of textured lines. Similarly, the bridge deck areas are continuously determined in the building information model H until the end of the building information model H. The total number of bridge deck areas is counted and denoted as m. If the number of textured stripes in the last bridge deck area is less than n, it is directly regarded as an independent bridge deck area. The beginning and end of the building information model H are determined by the operator. Based on the order from the beginning to the end in the Building Information Model H, the m bridge deck regions are summarized to form a bridge deck region set G={g1,g2,...,gm}; The texture parameters associated with each bridge deck area are determined based on the 3D point cloud data of each area, specifically: Extract any bridge surface region gk from the set of bridge surface regions G, and obtain the 3D point cloud data associated with bridge surface region gk, where k is the counting index, 1≤k≤m; Based on the n textured lines in the bridge surface region gk, the bridge surface region gk is further divided to obtain n textured regions in the bridge surface region gk. Based on the order from the first end to the last end in the building information model H, they are denoted as the textured region sequence L1, L2, ..., Ln, where any textured region Li contains only one textured line, and i is the counting index, 1≤i≤n; Using the bridge surface within the roughened area Li as a horizontal reference, determine the average depth of the roughening in the roughened area Li; Similarly, determine the average depth of the fur texture in each fur texture region of the fur texture region sequence L1, L2, ..., Ln; The average depth of the roughening in bridge surface region gk is obtained by averaging the average depths of the n roughening regions, denoted as Dk. Similarly, determine the average depth of the roughening texture in each bridge surface region of the bridge surface region set G={g1,g2,...,gm}; The width Wk of the bridge deck region gk is extracted from the 3D point cloud data of the bridge deck region gk. The texture density index ρk in the bridge deck region gk is calculated using Wk / n=ρk. The larger the texture density index ρk is, the lower the texture density is, and vice versa. Similarly, determine the texture density index of the texture in each bridge surface region in the bridge surface region set G={g1,g2,...,gm}; The average depth Dk and the texture density index ρk of the bridge deck area gk are obtained to form the texture parameters associated with the bridge deck area gk. Based on the texture parameters, a quality analysis was performed on each bridge surface area. Areas with texture defects were then highlighted and rendered. Specifically: Obtain the operator's preset average depth range [D_min, D_max] and the texture density index range [ρ_min, ρ_max]; Extract the average depth Dk and the texture density index ρk of any bridge surface region gk in the bridge surface region set G, and compare them with the average depth interval [D_min,D_max] and the texture density index interval [ρ_min,ρ_max], respectively; If Dk∈[D_min,D_max] and ρk∈[ρ_min,ρ_max], then it is determined that there is no roughening defect in the bridge surface area gk, and the bridge surface area gk is rendered as green in the building information model H; Conversely, if the bridge surface area gk is found to have a rough texture defect, the bridge surface area gk will be rendered red in the building information model H. Repeat the above steps to synchronize all bridge deck regions in the bridge deck region set G; Step 3: Identify the type of roughening defects in the bridge deck area with roughening defects. Based on the results of the roughening defect type identification, generate notification and control instructions and synchronize them to the operator. The operator then determines whether the bridge deck area with roughening defects needs secondary processing and transmits the control instructions to the automated roughening equipment.

2. The method according to claim 1, characterized in that, In step one, the precast concrete bridge deck to be scanned is a precast concrete bridge deck that has undergone roughening treatment, and the time between the roughening treatment and the current time does not exceed a preset time threshold.

3. The method according to claim 1, characterized in that, In step one, the specific method for constructing the building information model associated with the precast concrete bridge deck using BIM technology is as follows: The pre-built 3D scanner is used to scan the precast concrete bridge deck to obtain the 3D point cloud data associated with the precast concrete bridge deck. If the length of the precast concrete bridge deck exceeds the effective measurement range of the 3D scanner, segmented scanning is adopted and the 3D point cloud data is stitched together in the scanning order to form a set of 3D point cloud data associated with the precast concrete bridge deck. Using BIM technology and the three-dimensional point cloud data set associated with the precast concrete bridge deck, a building information model associated with the precast concrete bridge deck is constructed, denoted as H.

4. The method according to claim 3, characterized in that, In step three, the specific method for identifying the type of roughening defects in the bridge deck area is as follows: Determine the average depth Dk and the texture density index ρk of any bridge deck region gk; If Dk < D_min, then the bridge surface area gk is determined to have a defect of shallow texture depth. If Dk > D_max, then the bridge surface area gk is determined to have a defect of excessively deep roughening. If ρk < ρ_min, then the bridge surface area gk is determined to have a defect of excessively high texture density. If ρk>ρ_min, then the bridge deck area gk is determined to have a defect of low texture density. Similarly, all bridge surface regions in the bridge surface region set G are processed synchronously.

5. The method according to claim 4, characterized in that, In step three, the specific method for generating notification and control instructions based on the results of roughness defect category identification and synchronizing them with the operator is as follows: If it is determined that any bridge deck area gk has a defect of shallow texture depth, calculate the absolute value of the difference between the average depth Dk and the minimum value D_min in the average depth interval, and use the absolute value of the difference as the texture depth to be increased. If it is determined that the bridge deck area gk has a defect of shallow texture depth, then the absolute value of the difference between the average depth Dk and the maximum value D_max in the average depth range is calculated as the texture depth to be reduced. If it is determined that the bridge deck area gk has a defect of excessive texture density, then the absolute value of the difference between the texture density index ρk and the minimum value ρ_min in the texture density index interval [ρ_min, ρ_max] is calculated as the texture density index to be increased. If it is determined that the bridge deck area gk has a defect of low texture density, then the absolute value of the difference between the texture density index ρk and the maximum value ρ_max in the texture density index interval [ρ_min, ρ_max] is calculated as the texture density index to be reduced. The desired increase or decrease in texture depth and texture density index, along with the desired decrease in texture density index, are summarized as control instructions. Corresponding notification instructions are then generated and synchronized with the control instructions to the operator.

6. The method according to claim 5, characterized in that, In step three, if the operator receives notification and control instructions and determines that secondary processing is required, all control instructions are transmitted to the automated texturing equipment to perform secondary processing on the bridge deck area gk. The control instructions are reviewed and confirmed by the operator.

7. A three-dimensional scanning-based intelligent roughening control system for precast concrete bridge decks, used to execute the method described in any one of claims 1 to 6, characterized in that, The system includes: The scanning and modeling module identifies the precast concrete bridge deck to be scanned and uses a pre-built 3D scanner to scan it, obtaining the 3D point cloud data associated with the precast concrete bridge deck. The module then combines BIM technology to construct the building information model associated with the precast concrete bridge deck. The defect identification module divides the determined building information model into regions, identifies the bridge deck region set, determines the texture parameters associated with each bridge deck region based on the 3D point cloud data of each bridge deck region, performs quality analysis, and highlights the bridge deck regions with texture defects. The joint control and repair module identifies the type of scratches in bridge deck areas with scratches. Based on the identification results, it generates notification and control commands and synchronizes them with the operators. The operators then determine whether the bridge deck areas with scratches need secondary processing and transmit the control commands to the automated scratching equipment.

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