A collaborative platform and method for marking damaged sections of in-service highways based on GIS-BIM

Abstract

The present invention discloses a collaborative platform and method for marking diseased sections of in-service highways based on GIS-BIM. The steps include: constructing an EBS structure tree model and importing it into the GIS system to form a GIS-BIM platform, sending the lines to be inspected and the pile number coordinate table to an unmanned aerial vehicle; the unmanned aerial vehicle takes a long image during the inspection and matches the pile number labels, and the server cuts the image and identifies the disease characteristics; the GIS-BIM platform maps the disease image to the corresponding BIM model segment, and deletes the map if there is no disease image during the next inspection. The pile number coordinate table is generated incrementally by pile number, and the disease is identified using a convolutional neural network model, distinguishing by color gradient according to severity. The platform integrates the dual-core structure tree of EBS and WBS, and realizes full-cycle collaboration through dynamic mapping of pile number intervals, improving the efficiency and accuracy of disease marking, and realizing full-process digital operation and full-cycle automated collaboration.

Description

Technical Field

[0001] The present invention relates to the field of highway digitization technology, and in particular to a collaborative platform and method for marking damaged sections of in-service highways based on GIS-BIM. Background Art

[0002] In the field of highway digitization, the traditional method for marking damaged sections on in-service highways relies primarily on manual inspections, a method with significant drawbacks. Manual inspections are inefficient, unable to quickly cover large areas of highway, and prone to missed inspections. Furthermore, manual location of damaged sections often lacks precision, leading to significant deviations and inaccurate marking, which in turn impacts subsequent repair and maintenance efforts.

[0003] Furthermore, traditional highway maintenance management models suffer from data silos between design, construction, and maintenance. Data from these various stages cannot be effectively communicated and coordinated, hindering efficient integration of processes such as defect location, task assignment, repair progress tracking, and as-built data archiving. This leads to irrational scheduling of maintenance resources and low maintenance efficiency.

[0004] Traditional methods lack intuitive visualization tools for managing and displaying disease information, making it difficult to clearly and quickly demonstrate the severity and distribution of diseases, hindering decision-making and management. Furthermore, the lack of a comprehensive traceability system for data from the entire disease treatment cycle hinders long-term assessment and management of the health of highway facilities.

[0005] In summary, existing technologies have many shortcomings in the marking, positioning, management, and full-cycle coordination of diseased sections of in-service highways. A more efficient, accurate, and coordinated method and platform are urgently needed to solve these problems. Summary of the Invention

[0006] In order to solve the above technical problems, the present invention adopts the following solutions:

[0007] A method for marking damaged sections of an in-service highway based on GIS-BIM includes a GIS-BIM platform. An EBS structure tree model is preset in the GIS-BIM platform. The EBS structure tree model divides a route into various facility sections. Each facility section includes a pile number interval of a starting pile number and an ending pile number, and a corresponding BIM model. The BIM model includes a pavement model, and the pavement model is segmented by pile number. The specific steps are as follows:

[0008] S1. The GIS-BIM platform constructs a stake coordinate table based on the inspection route and the stake numbers of each facility section along the inspection route, along with the latitude and longitude coordinates of the corresponding stake numbers, and sends the table to the inspection drone.

[0009] S2. The inspection drone inspects the route it receives and continuously captures a long-range inspection map of the entire route. While capturing the long-range inspection map, the drone's GPS-based latitude and longitude coordinates are matched with the longitude and latitude of the stake number in the stake number coordinate table. If a successful match is found, the stake number is labeled in the long-range inspection map.

[0010] S3. After the inspection drone completes its inspection of the required route, it sends a long road map of the entire route to the server. The server then segments the long road map into segmented images based on the stake number labels.

[0011] S4. The server identifies the damage features in the segmented images and marks the damage labels to obtain damage images. The server then sends the damage images with the stake number labels to the GIS-BIM platform.

[0012] S5. The GIS-BIM platform saves the damage image with the pile number label and traverses the pile number intervals of the BIM model based on the pile number label of the damage image to match the pile number intervals to obtain the matching facility road section. The platform then uses the pile number label on the damage image to find the corresponding pile number and the corresponding pavement model in the matching facility road section. The damage image is then loaded as a texture onto the pavement model in the corresponding BIM model.

[0013] S6. After the inspection route is inspected by the drone again, if the GIS-BIM platform does not receive the defect image of the corresponding BIM model, the map on the corresponding BIM model is deleted, the pavement model in the corresponding BIM model is displayed, and the corresponding defect image is deleted.

[0014] The various facility sections include tunnels, bridges, and ordinary roads, all of which contain road surfaces, and their corresponding BIM models also contain road surface models.

[0015] Further,

[0016] In said S1, the construction of the stake coordinate table includes:

[0017] Based on the pavement model of each facility section in the inspected route, the pile number interval of each facility section and all pile numbers and the longitude and latitude coordinates corresponding to all pile numbers are extracted, and a structured table is generated in ascending order of pile numbers, and the table fields include pile number, longitude, and latitude.

[0018] Further,

[0019] In S2, the latitude and longitude coordinates of the UAV GPS positioning are matched with the longitude and latitude of the stake number as follows:

[0020] The Euclidean distance between the longitude and latitude of the surveying and mapping drone and the longitude and latitude of each stake in the stake coordinate table is calculated in real time. If the Euclidean distance is less than or equal to the preset matching threshold, the match is successful and the corresponding stake label is marked in the inspection map.

[0021] Further,

[0022] In S4, the identification of disease characteristics adopts a convolutional neural network model, which supports multi-label classification and identifies types including at least one of cracks, pits, subsidence, and spalling.

[0023] Further,

[0024] The EBS and WBS are associated to form a dual-core structure tree. The EBS structure tree constructs the BIM model based on each facility section and pile number, and associates design parameters with defect information; the WBS structure tree is based on the decomposition of construction tasks, and associates the repair progress with maintenance resources. The two are dynamically mapped through pile number intervals to achieve full-cycle collaboration of defect location, task dispatch and completion data archiving.

[0025] Further,

[0026] The dynamic mapping includes:

[0027] When the defect image is loaded into the BIM model segment, the corresponding construction task node is automatically generated in the WBS structure tree and is bound to the responsible unit, estimated construction period and repair status fields; after the repair is completed, the completion attributes of the corresponding facilities and the WBS task node status in the EBS structure tree are synchronously updated.

[0028] Further,

[0029] In S6, the corresponding diseased images are deleted, and the following operations are performed simultaneously:

[0030] Mark the repaired BIM model as "accepted" and archive the original defect images, repair records and acceptance reports to the completion database of the EBS structure tree and associate them with the corresponding pile number range.

[0031] Further,

[0032] A collaborative platform for marking damaged sections of in-service highways based on GIS-BIM. The GIS-BIM platform pushes images of the damage and associated construction task information and locations to repair personnel. After inspection personnel fill in the repair progress, the task status changes in the WBS structure tree are automatically updated and real-time notifications are sent to the GIS-BIM platform.

[0033] Further,

[0034] The collaborative platform includes a readable storage medium and a processor. The computer program in the readable storage medium is executed by the processor to implement the above-mentioned GIS-BIM-based method for marking damaged sections of in-service highways.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] 1. This invention utilizes GIS-BIM platform integration and drone-based intelligent inspection technology to precisely locate and visually mark road sections with defects. By constructing an EBS structure tree model and importing it into the GIS system, this integrated platform deeply integrates the BIM model's stake coordinates with GIS spatial positioning. In an application scenario, when a drone inspects a route based on the stake coordinate table, it automatically labels the corresponding stake in the inspection map by matching the GPS coordinates with the stake's latitude and longitude (with a threshold of 50m). The server then slices the image by stake and identifies defects, ultimately accurately loading the defect images onto the BIM model segment surface as textures. This process upgrades the traditional defect location method, which relies on manual inspections, to a fully digital process encompassing "drone imaging - automatic matching - intelligent identification - model positioning." This eliminates manual misses and positioning errors, improves the efficiency and accuracy of defect marking, and uses a color gradient to differentiate between defect severity levels (red / yellow / green), enabling intuitive and hierarchical management of highway defects.

[0037] 2. A full-cycle collaboration mechanism based on a dual-core structure (EBS and WBS) connects the entire data chain for defect management, from discovery to remediation. The platform integrates the EBS (Facility Breakdown Structure) and the WBS (Work Breakdown Structure), seamlessly linking design parameters, defect information, and construction tasks through dynamic mapping of pile number intervals. In practical applications, when a defect image is loaded into a BIM model segment, the system automatically generates a construction task node in the WBS, binding the responsible unit, construction period, and remediation status. Upon completion of the remediation, the facility's as-built attributes and WBS task status are simultaneously updated in the EBS, and the original defect image and remediation records are archived in the as-built database. This mechanism breaks down the data silos between design, construction, and maintenance in traditional maintenance, enabling automated collaboration throughout the entire lifecycle of "defect location - task assignment - progress tracking - acceptance archiving." This reduces manual errors, improves the efficiency of maintenance resource scheduling, and provides a comprehensive data traceability system for highway lifecycle management. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a schematic diagram of a flow chart of the present invention;

[0039] Figure 2 This is a diagram of the EBS structure tree;

[0040] Figure 3 It is a schematic diagram of line information;

[0041] Figure 4 It is the intention of the stake coordinate;

[0042] Figure 5 This is a schematic diagram of the matching range of the stake number longitude and latitude coordinates;

[0043] Figure 6 Modeling sections for BIM.

[0044] Figure 1: UAV, 2: Stake number, 3: Pavement model, 4: Facility section, 5: Stake number interval, 6: Guardrail model, 7: Foundation middle layer model, 8: Foundation model, 9: Road centerline, 10: Stake number latitude and longitude coordinate point, 11: Matching threshold. DETAILED DESCRIPTION

[0045] The present invention will be further described in detail below with reference to the embodiments and the accompanying drawings, but the embodiments of the present invention are not limited thereto.

[0046] In the description of the present invention, it should be noted that the terms "center", "up", "down", "left", "right", "vertical", "longitudinal", "lateral", "horizontal", "inside", "outside", "front", "back", "top", "bottom", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, or are the orientation or position relationship in which the inventive product is usually placed when used. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation to the present invention.

[0047] In the description of the present invention, it should also be noted that, unless otherwise expressly specified or limited, the terms "disposed," "having," "installed," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal connections between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention in specific contexts. Example

[0048] A method for marking damaged sections of an in-service highway based on GIS-BIM includes a GIS-BIM platform. An EBS structure tree model is preset in the GIS-BIM platform. The EBS structure tree model divides a route into various facility sections. Each facility section includes a pile number interval of a starting pile number and an ending pile number, and a corresponding BIM model. The BIM model includes a pavement model, and the pavement model is segmented by pile number. The specific steps are as follows:

[0049] S1. The GIS-BIM platform constructs a stake coordinate table based on the inspection route and the stake numbers of each facility section along the inspection route, along with the latitude and longitude coordinates of the corresponding stake numbers, and sends the table to the inspection drone.

[0050] S2. The inspection drone inspects the route it receives and continuously captures a long-range inspection map of the entire route. While capturing the long-range inspection map, the drone's GPS-based latitude and longitude coordinates are matched with the longitude and latitude of the stake number in the stake number coordinate table. If a successful match is found, the stake number is labeled in the long-range inspection map.

[0051] S3. After the inspection drone completes its inspection of the required route, it sends a long road map of the entire route to the server. The server then segments the long road map into segmented images based on the stake number labels.

[0052] S4. The server identifies the damage features in the segmented images and marks the damage labels to obtain damage images. The server then sends the damage images with the stake number labels to the GIS-BIM platform.

[0053] S5. The GIS-BIM platform saves the damage image with the pile number label and traverses the pile number intervals of the BIM model based on the pile number label of the damage image to match the pile number intervals to obtain the matching facility road section. The platform then uses the pile number label on the damage image to find the corresponding pile number and the corresponding pavement model in the matching facility road section. The damage image is then loaded as a texture onto the pavement model in the corresponding BIM model.

[0054] S6. After the inspection route is inspected by the drone again, if the GIS-BIM platform does not receive the defect image of the corresponding BIM model, the map on the corresponding BIM model is deleted, the pavement model in the corresponding BIM model is displayed, and the corresponding defect image is deleted.

[0055] By importing different BIM models into the GIS system according to the hierarchical relationships within the EBS structure tree, a GIS-BIM platform is formed, integrating hierarchical management and spatial positioning of highway facilities. The EBS structure tree, constructed based on facility-type BIM models, clearly expresses the hierarchical relationships between facilities (such as pavement, bridges, and tunnels), sub-facilities (such as roadbeds, beams, and tunnel surfaces), and components (such as guardrails and lighting structures), facilitating refined management. Combined with the spatial analysis capabilities of the GIS system, the BIM model segments corresponding to defects can be quickly located, improving data collaboration efficiency.

[0056] When building a GIS-BIM platform for a particular highway, BIM models for facilities such as pavement, bridges, and tunnels were first imported into the GIS system hierarchically according to the EBS structure tree. For example, bridge facilities were divided into sub-facilities such as "beam," "cap," and "pier," and the beam sub-facilities were further subdivided into components such as "T-beam" and "diaphragm." Each BIM model included a stake number and corresponding longitude and latitude coordinates. For example, the stake numbers for bridge beam components ranged from K00+200 to K10+500, with the longitude and latitude coordinates accurately matching the locations.

[0057] The EBS tree structure is as follows Figure 2 As shown, the design drawings in the relevant information of the EBS structure tree represent the BIM model selected and set in the EBS structure tree.

[0058] In one embodiment, the various facility sections include tunnels, bridges, and ordinary roads, and their BIM model cross-sectional structures are as follows: Figure 6 As shown in the figure, the BIM models corresponding to tunnels, bridges, and ordinary roads all include road surface models, and the segmentation and route information of each facility section in the inspection route are as follows: Figure 3 shown.

[0059] Further,

[0060] In said S1, the construction of the stake coordinate table includes:

[0061] Based on the pavement model of each facility section in the inspected route, the pile number interval of each facility section and all pile numbers and the longitude and latitude coordinates corresponding to all pile numbers are extracted, and a structured table is generated in ascending order of pile numbers, and the table fields include pile number, longitude, and latitude.

[0062] Extract the stake numbers and longitude and latitude coordinates of the BIM models of the facilities along the route to be inspected. Generate a structured table containing "stake number, longitude, and latitude" fields in ascending order of stake numbers, providing an accurate coordinate matching benchmark for drone inspections. Structured data avoids coordinate confusion, and sorting by stake number facilitates rapid, sequential search and matching by drone during flight, improving inspection efficiency and positioning accuracy.

[0063] In the embodiment, the stake coordinate table is as follows: Figure 4 As shown, all stake numbers (such as K0+000, K00+100…K21+000) and their corresponding longitude and latitude coordinates are extracted from the BIM model. A stake coordinate table is generated, sorted by stake number from smallest to largest. When a drone inspects the tunnel route, the stake coordinate table is matched in real time with the drone's GPS coordinates, ensuring that images taken at different stake locations are accurately associated with the corresponding BIM model.

[0064] Further,

[0065] In S2, the latitude and longitude coordinates of the UAV GPS positioning are matched with the longitude and latitude of the stake number as follows:

[0066] The Euclidean distance between the longitude and latitude of the surveying and mapping drone and the longitude and latitude of each stake in the stake coordinate table is calculated in real time. If the Euclidean distance is less than or equal to the preset matching threshold, the match is successful and the corresponding stake label is marked in the inspection map.

[0067] In the embodiment, the pile number generally refers to the longitude and latitude coordinates, which refer to the longitude and latitude coordinates of the center line of the road corresponding to the pile number. The longitude and latitude coordinates of the drone are matched with the longitude and latitude of the pile number. Figure 5 As shown, the drone flies along the centerline of the inspection road, calculating the Euclidean distance between the drone's longitude and latitude and that of the stake in real time. This distance is then compared with the matching threshold to determine the drone's trigger range, ensuring that the drone dynamically and accurately tags stakes during flight. This avoids computational redundancy in global coordinate matching and only calculates distances for adjacent stakes, improving matching efficiency. The threshold setting balances positioning accuracy with drone flight errors, ensuring the reliability of stake labeling.

[0068] If in the inspection route, if the stake number refers to the edge line or isolation center line of the inspection route, then the longitude and latitude coordinates of the stake number are converted into longitude and latitude according to the distance from the stake number to the center line of the road, and the longitude and latitude of the corresponding center line of the road are calculated based on the longitude and latitude of the stake number itself, and then compared with the matching threshold.

[0069] For example, when a drone inspects a bridge, it obtains its GPS coordinates in real time (e.g., longitude 118.001, latitude 30.002) and calculates the Euclidean distance with the adjacent stake number in the stake number coordinate table (e.g., K15+000, longitude 118.000, 30.003). If the distance is 0.5m (less than the matching threshold of 1m), the match is considered successful and the K15+000 stake number is marked in the inspection image, providing an accurate basis for subsequent segmentation of the image by stake number.

[0070] Further,

[0071] In S4, the identification of disease characteristics adopts a convolutional neural network model, which supports multi-label classification and identifies types including at least one of cracks, pits, subsidence, and spalling.

[0072] By leveraging the convolutional neural network model's ability to support multi-label classification, it can simultaneously identify multiple types of defects such as cracks, pits, subsidence, and spalling, and automatically associate severity levels such as high risk, medium risk, and low risk, avoiding the subjectivity and missed detections of manual identification and improving recognition efficiency.

[0073] After the server receives an image of a road segment, the convolutional neural network model automatically identifies two types of defects: "cracks (high risk)" and "peeling (low risk)." The system automatically generates a list of defects based on the identification results. High-risk cracks trigger the maintenance process first, and low-risk peeling is included in the daily inspection plan to achieve differentiated management of defects.

[0074] Further,

[0075] The EBS and WBS are associated to form a dual-core structure tree. The EBS structure tree constructs the BIM model based on each facility section and pile number, and associates design parameters with defect information; the WBS structure tree is based on the decomposition of construction tasks, and associates the repair progress with maintenance resources. The two are dynamically mapped through pile number intervals to achieve full-cycle collaboration of defect location, task dispatch and completion data archiving.

[0076] The EBS structure tree focuses on facility entities (such as pavements and bridges) and their defect information, while the WBS structure tree focuses on construction tasks and resource scheduling. Dynamic mapping between these two through pile number intervals enables full-cycle collaboration: defect location, task dispatch, repair progress, and completion archiving. This dual-core architecture breaks down data silos across design, construction, and maintenance, ensuring data consistency across all business processes and improving collaborative efficiency and management precision in highway maintenance.

[0077] When the EBS structure tree locates a damaged section of pavement (pile numbers K00+000-K00+500), the system automatically creates a "Pavement Repair Project" task node in the WBS structure tree, binding fields such as the responsible unit and estimated duration. During the repair process, the WBS updates the task status (e.g., "Under Construction," "Accepted") in real time, simultaneously triggering an update to the corresponding facility's completion attribute in the EBS structure tree (e.g., "Repair Completed") and archiving the repair record to the EBS completion database, ensuring data integration throughout the entire process.

[0078] Further,

[0079] The dynamic mapping includes:

[0080] When the defect image is loaded into the BIM model segment, the corresponding construction task node is automatically generated in the WBS structure tree and is bound to the responsible unit, estimated construction period and repair status fields; after the repair is completed, the completion attributes of the corresponding facilities and the WBS task node status in the EBS structure tree are synchronously updated.

[0081] When defect images are loaded onto the pavement model surface within the BIM model segment, construction task nodes are automatically generated within the WBS structure tree, reducing manual errors and improving task dispatch efficiency. Upon completion of repairs, EBS and WBS statuses are simultaneously updated to ensure real-time data consistency and avoid information lags. This mechanism automates the connection between defect discovery and repair tasks, ensuring the timeliness and standardization of the maintenance process.

[0082] Further,

[0083] In S6, the corresponding diseased images are deleted, and the following operations are performed simultaneously:

[0084] Mark the repaired BIM model as "accepted" and archive the original defect images, repair records and acceptance reports to the completion database of the EBS structure tree and associate them with the corresponding pile number range.

[0085] When deleting a defect image, the BIM model segment is marked as "accepted" and the original defect image, repair records, and acceptance report are archived in the EBS as-built database to ensure traceability of the defect treatment process. This operation forms a complete maintenance management closed loop, providing data support for subsequent facility health assessments and historical problem tracing.

[0086] After confirming that a bridge defect has been repaired during the next inspection, the GIS-BIM platform automatically deletes the defect map of the BIM model section of the bridge, marks it as "accepted", and archives the original defect image, construction log during the repair process, material inspection report, acceptance form, etc. to the completion database of the EBS structure tree and associates it with the corresponding pile number range.

[0087] Further,

[0088] A collaborative platform for marking damaged sections of in-service highways based on GIS-BIM. The GIS-BIM platform pushes images of the damage and associated construction task information and locations to repair personnel. After inspection personnel fill in the repair progress, the task status changes in the WBS structure tree are automatically updated and real-time notifications are sent to the GIS-BIM platform.

[0089] Further,

[0090] The collaborative platform includes a readable storage medium and a processor. The computer program in the readable storage medium is executed by the processor to implement the above-mentioned GIS-BIM-based method for marking damaged sections of in-service highways.

[0091] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Based on the technical essence of the present invention and within the spirit and principles of the present invention, any simple modification, equivalent replacement and improvement of the above embodiment shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A method for marking damaged sections of in-service highways based on GIS-BIM, characterized in that: The GIS-BIM platform includes an EBS structure tree model preset in the GIS-BIM platform. The EBS structure tree model divides the route into various facility sections. Each facility section includes a pile number interval of a starting pile number and an ending pile number, and a corresponding BIM model. The BIM model includes a road surface model, and the road surface model is segmented by pile number. The specific steps are as follows: S1. The GIS-BIM platform constructs a stake coordinate table based on the inspection route and the stake numbers of each facility section along the inspection route, along with the latitude and longitude coordinates of the corresponding stake numbers, and sends the table to the inspection drone. S2. The inspection drone inspects the route it receives and continuously captures a long-range inspection map of the entire route. While capturing the long-range inspection map, the drone's GPS-based latitude and longitude coordinates are matched with the longitude and latitude of the stake number in the stake number coordinate table. If a successful match is found, the stake number is labeled in the long-range inspection map. S3. After the inspection drone completes its inspection of the required route, it sends a long road map of the entire route to the server. The server then segments the long road map into segmented images based on the stake number labels. S4. The server identifies the damage features in the segmented images and marks the damage labels to obtain damage images. The server then sends the damage images with the stake number labels to the GIS-BIM platform. S5. The GIS-BIM platform saves the damage image with the pile number label and traverses the pile number intervals of the BIM model based on the pile number label of the damage image to match the pile number intervals to obtain the matching facility road section. The platform then uses the pile number label on the damage image to find the corresponding pile number and the corresponding pavement model in the matching facility road section. The damage image is then loaded as a texture onto the pavement model in the corresponding BIM model. S6. After the inspection route is inspected by the drone again, if the GIS-BIM platform does not receive the defect image of the corresponding BIM model, the map on the corresponding BIM model is deleted and the pavement model in the corresponding BIM model is displayed, and the corresponding defect image is deleted.

2. The method for marking damaged sections of in-service highways based on GIS-BIM according to claim 1 is characterized in that: In said S1, the construction of the stake coordinate table includes: Based on the pavement model of each facility section in the inspected route, the pile number interval of each facility section and all pile numbers and the longitude and latitude coordinates corresponding to all pile numbers are extracted, and a structured table is generated in ascending order of pile numbers, and the table fields include pile number, longitude, and latitude.

3. The method for marking damaged sections of in-service highways based on GIS-BIM according to claim 1 is characterized in that: In S2, the latitude and longitude coordinates of the UAV GPS positioning are matched with the longitude and latitude of the stake number as follows: The Euclidean distance between the longitude and latitude of the surveying and mapping drone and the longitude and latitude of each stake in the stake coordinate table is calculated in real time. If the Euclidean distance is less than or equal to the preset matching threshold, the match is successful and the corresponding stake label is marked in the inspection map.

4. The method for marking damaged sections of in-service highways based on GIS-BIM according to claim 1 is characterized in that: In S4, the identification of disease characteristics adopts a convolutional neural network model, which supports multi-label classification and identifies types including at least one of cracks, pits, subsidence, and spalling.

5. The method for marking damaged sections of in-service highways based on GIS-BIM according to claim 1 is characterized in that: The EBS and WBS are associated to form a dual-core structure tree. The EBS structure tree constructs the BIM model based on each facility section and pile number, and associates design parameters with defect information; the WBS structure tree is based on the decomposition of construction tasks, and associates the repair progress with maintenance resources. The two are dynamically mapped through pile number intervals to achieve full-cycle collaboration of defect location, task dispatch and completion data archiving.

6. The method for marking damaged sections of in-service highways based on GIS-BIM according to claim 5 is characterized in that: The dynamic mapping includes: When the defect image is loaded into the BIM model segment, the corresponding construction task node is automatically generated in the WBS structure tree and is bound to the responsible unit, estimated construction period and repair status fields; after the repair is completed, the completion attributes of the corresponding facilities and the WBS task node status in the EBS structure tree are synchronously updated.

7. The method for marking damaged sections of in-service highways based on GIS-BIM according to claim 1 is characterized in that: In S6, the corresponding diseased images are deleted, and the following operations are performed simultaneously: Mark the repaired BIM model as "Accepted" and archive the original defect images, repair records, and acceptance reports to the as-built database in the EBS structure tree, linking them to the corresponding pile number range.

8. A collaborative platform for marking damaged sections of in-service highways based on GIS-BIM, comprising a readable storage medium and a processor, characterized in that: The computer program in the readable storage medium is executed by the processor to implement the method for marking damaged sections of in-service highways based on GIS-BIM as described in any one of claims 1 to 7.

Images