BIM + GIS-based asphalt pavement 3D intelligent paving cooperative control system and method

Abstract

The invention relates to the technical field of asphalt pavement paving, and discloses an asphalt pavement 3D intelligent paving cooperative control system and method based on BIM + GIS, and the system comprises a data fusion layer, a real-time control layer, a multi-machine cooperation and task distribution layer, and a quality closed-loop layer. The real-time control layer comprises multi-source data acquisition (positioning / temperature / compaction degree and the like) and three types of core decisions carried out by the cloud center, and the three types of core decisions carried out by the cloud center comprise feeding demand prediction (based on paving speed + thickness) of a dumper, thickness / speed adaptive adjustment of a paver and dynamic planning of a rolling path of a road roller. According to the method, real-time correction of transverse deviation and elevation deviation is achieved through combination of PID control and fuzzy control, the expansion amount of a hydraulic cylinder of the ironing plate is dynamically adjusted by associating the paving speed, the multi-source sensing technology of Beidou, IMU, laser ranging and infrared imaging is adopted, time sequence consistency data synchronization is achieved through 5G communication, and the real-time correction of the transverse deviation and the elevation deviation is achieved. And the precision and the closed-loop optimization effect can be controlled in real time.

Description

Technical Field

[0001] The present invention relates to the technical field of asphalt pavement paving, and specifically to a BIM+GIS-based asphalt pavement 3D intelligent paving collaborative control system and method. Background Art

[0002] Asphalt pavement paving refers to the key link in asphalt pavement construction, the process of evenly spreading hot-mix asphalt mixture on the pavement base to form an asphalt pavement surface layer with a certain thickness, flatness and density. It can improve the flatness of the pavement and ensure the strength and stability of the pavement.

[0003] After searching, it was found that according to the invention patent with Chinese patent publication number CN118504088A, a 3D intelligent paving construction method for asphalt pavement based on BIM three-dimensional design is disclosed. The 3D intelligent paving construction method for asphalt pavement in this invention patent uses the Beidou GPS system in combination with high-definition real-life pavement images to display the pavement site and surrounding environment on the display interface of the big data information control center. Through the application of GPS positioning system, drone measurement and BIM three-dimensional design technology, a virtual demonstration is carried out before paving, and the route and plan technology are modified in time. The asphalt pavement paving is carried out using 3D printing equipment. It can be fast and accurate, and can ensure that each section of the pavement is paved evenly, avoiding the uneven paving in the existing technology, thereby improving the quality of asphalt pavement paving.

[0004] However, the asphalt pavement 3D intelligent paving construction method only realizes basic paving through GPS trajectory positioning, lacks dynamic path planning capabilities, and relies on the 3D printing system without a certain dynamic task allocation mechanism. Therefore, a collaborative control system and method for asphalt pavement 3D intelligent paving based on BIM+GIS is proposed. Summary of the Invention

[0005] In order to make up for the above shortcomings, the present invention provides a BIM+GIS-based asphalt pavement 3D intelligent paving collaborative control system and method, which aims to improve the coordination between the paver, material transporter and roller, thereby improving the quality.

[0006] To achieve the above objectives, the present invention adopts the following technical solutions: a BIM+GIS-based 3D intelligent paving collaborative control system for asphalt pavement, comprising a data fusion layer, a real-time control layer, a multi-machine collaboration and task allocation layer, and a quality closed-loop layer. The data fusion layer generates a construction benchmark model by fusing the BIM model with GIS terrain data and implements networking of pavers, material transporters, and rollers through the Internet of Things. The real-time control layer includes multi-source data acquisition (positioning / temperature / compactness, etc.) and a cloud-based center for three types of core decision-making. The three types of core decision-making made by the cloud-based center include material supply demand prediction for material transporters (based on paving speed and thickness), thickness / speed adaptive adjustment for pavers, and dynamic planning of roller compaction paths.

[0007] The quality closed-loop layer includes infrared temperature measurement + laser flatness detection to form a quality feedback loop, automatically triggering parameter re-optimization when the standards are not met, and returning qualified data to update the BIM model.

[0008] Preferably, the cloud feeds the material transport vehicle through the optimal feeding sequence, and the load and temperature data of the material transport vehicle are fed back to the cloud to form a closed loop. The roller feeds back the compaction degree to the paving-rolling collaborative paver, and the position and paving thickness of the paver are fed back to the roller to form a closed loop.

[0009] A preferred method for using a BIM+GIS-based 3D intelligent paving collaborative control system for asphalt pavement includes the BIM+GIS-based 3D intelligent paving collaborative control system for asphalt pavement described in claim 1 or 2, wherein the asphalt pavement BIM model construction step in step S1 specifically includes:

[0010] S1: Set input parameters and create an asphalt pavement BIM model using Autodesk Civil 3D software. The input parameters include: a. Geometric parameters consisting of pavement width, longitudinal slope, transverse slope, and curve curvature radius; b. Structural parameters consisting of base layer thickness, surface layer thickness, and compaction requirements; c. Material parameters consisting of asphalt mixture type, paving temperature, and compaction temperature.

[0011] S2: Export the BIM model based on the IFC format, set the paving speed and vibration hammer frequency as dynamic parameters, set the construction period and transportation vehicle scheduling as constraints, and add construction attribute data information to the model;

[0012] S3: Terrain data collection: Based on UAV LiDAR aerial survey technology, set the flight altitude to 50-100m and the point cloud density ≥ 50 points / m 2 , and set a 20m buffer zone outside the construction area as the aerial survey coverage area, combined with satellite imaging technology to obtain multispectral images of the construction area to identify surface obstacles and generate a high-precision terrain model DEM;

[0013] S4: Environmental data integration: Import GIS data based on ArcGIS Pro, including: a. Geological data: soil bearing capacity and groundwater level elevation data; b. Underground pipelines: 3D coordinate data of gas pipelines and cable pipelines; c. Traffic control: temporary road network closure range data for construction sections.

[0014] The data fusion step in step S1 preferably includes:

[0015] S1: Coordinate system conversion and registration: a. Based on the ArcGIS Data Interoperability extension module, the BIM model is converted from the engineering coordinate system to the WGS84 / UTM coordinate system in combination with the seven-parameter conversion method to achieve coordinate system one processing;

[0016] S2: Select at least four common control points between the BIM model and the GIS terrain, including road intersections and permanent landmarks. Set the control point coordinates with an error of ≤1 cm using a total station. Optimize the registration of the control points using the least squares method, and control the residual within ±2 cm.

[0017] S3: Data format integration: a. Convert the BIM model to CityGML format using the IFC-to-CityGML converter, preserving semantic information. Load the CityGML data into the GIS platform and overlay it with terrain raster and vector pipeline data to achieve interoperability between IFC and GIS data.

[0018] S4: Display BIM pavement structure, GIS terrain, and underground pipelines in layers, calculate the cut and fill volume of the paving area based on the elevation difference between DEM and BIM design, and create a fused 3D scene based on SuperMap iDesktop;

[0019] Build a dynamic construction model based on fused data, including:

[0020] S5: Paving Path Planning: Set and input constraints, generate a preliminary path based on the A* algorithm and combine it with the Dijkstra algorithm to optimize the route with the minimum elevation change, and output a three-dimensional path point sequence. The constraints include: mechanical parameters: minimum turning radius and maximum climbing ability of the paver;

[0021] Environmental constraints: Avoid 2m buffer zones for underground pipelines and steep slopes;

[0022] S6: Dynamic calculation of construction parameters: The material consumption prediction formula is:

[0023] Where A2 represents the i-th grid area of ​​GIS terrain division, h iIt represents the correction value of the difference between the BIM design thickness and the DEM elevation. y is the density of asphalt mixture, which is generated according to the slope-speed association rule control parameter.

[0024] S7: Simulate the movement of the paver in the BIM+GIS platform, detect the safe distance between the robotic arm and the underground pipeline, automatically mark the high-risk area and trigger the path replanning operation if a conflict is detected, extract the design thickness in the BIM model, and compare and verify it with the GIS terrain volume calculation value. The allowable deviation range is ±3%. If the deviation range is exceeded, adjust the BIM layer thickness parameters or re-divide the construction grid, generate a registration error heat map, review areas with errors greater than 2 cm, and generate a coordinate residual report.

[0025] Preferably, in step S2, positioning sensor equipment is deployed, and the steps of real-time sensor data collection and synchronization include:

[0026] S1: Positioning data acquisition: The paver is equipped with a BeiDou positioning system and an inertial measurement unit, which updates its position and attitude every 100ms. Position data includes longitude, latitude, and elevation, and attitude data includes roll, pitch, and heading angles. The WGS84 coordinates output by the GNSS are converted in real time to the local engineering coordinate system of the BIM model to ensure consistency with the designed path to achieve coordinate alignment.

[0027] S2: Environmental and paving status monitoring: a. Use an infrared thermal imager to scan the asphalt mixture surface temperature at a rate of 10 frames per second, with a detection range set at 160-180°C. An alarm will be triggered if the temperature exceeds the limit;

[0028] S3: Use a laser rangefinder to vertically measure the thickness of the paving layer and upload the measurement data in real time;

[0029] S4: Arrange an ultrasonic sensor array transversely along the paving layer, detect the elevation difference every 50 cm, and generate a flatness heat map;

[0030] S5: Data synchronization: Through the 5G CPE module and setting the uplink rate to ≥100Mbps, the sensor data is encapsulated as MQTT protocol messages and transmitted to the cloud or local edge computing server. The sensor data is timestamped in UTC to ensure the temporal consistency of multi-source data.

[0031] S6: Design model loading: Extract the design parameters of the current construction section based on the BIM model, including the target path centerline coordinates (X, Y, Z) and the thickness, slope, and curvature radius parameter data of each layer. Then, load the elevation model and obstacle layer of the construction area using the GIS platform.

[0032] S7: Real-time deviation calculation: a. Calculate the Euclidean distance between the GNSS positioning point and the BIM design centerline to calculate the lateral deviation data. The Euclidean distance formula is:

[0033]

[0034] The permissible deviation threshold is set to ±2cm, and deviation correction is triggered when it exceeds the limit;

[0035] S8: Compare the laser thickness gauge data with the BIM layer thickness design value, dynamically calculate the fill and cut volume to calculate the elevation deviation. The fill and cut volume calculation formula is: ΔH=h 实际 -h 设计 , where the allowable deviation threshold is set to ±3mm, and the ironing plate height is adjusted when it exceeds the limit.

[0036] The preferred specific steps for executing the closed-loop control strategy in step S2 include:

[0037] S1: Dynamic deviation correction control: a. Input the lateral deviation ΔL and output the opening of the paver steering hydraulic valve. The control logic is expressed as follows: To achieve lateral deviation correction based on PID control, the empirical value of PID parameter is set to K P =0.8,K i =0.05,K d =0.1;

[0038] S2: Input elevation deviation ΔH and paving speed v, and output the extension and contraction of the screed hydraulic cylinder. Elevation correction based on fuzzy control is implemented according to the following rules: if ΔH > 3mm and v < 4m / min, lower the screed by 2mm; if ΔH < -3mm and v > 3m / min, raise the screed by 3mm.

[0039] S3: Parameter Adaptive Adjustment: a. Dynamically optimize and adjust the paving speed based on the slope data and infrared temperature data in the GIS terrain. The formula is:

[0040]

[0041] S4: When the thickness deviation ΔH is continuously greater than 2 mm, the frequency of the vibration hammer is adjusted in a gradient increase. The frequency increase formula is: Set the maximum frequency to 20HZ.

[0042] In the preferred step S3, the multi-machine collaborative control needs to achieve efficient collaboration of the machine cluster through dynamic task allocation, path planning and conflict resolution. The specific steps include:

[0043] S1: Multi-level collaborative allocation: a. Divide the construction area into 5m×5m grids and annotate the construction difficulty coefficient of each grid based on GIS terrain data. The difficulty calculation formula is:

[0044] D i = 0.4 × slope (%) + 0.3 × curvature (1 / m) + 0.3 × obstacle density (number / m 2 )

[0045] The collaborative work matching is performed based on the construction difficulty coefficient, and the matching rules are as follows:

[0046] If D i >30, it will be allocated only to high-precision pavers;

[0047] If 1.5<D i If the value is ≤3.0, a common paver and a roller will be assigned to work together;

[0048] If D i If ≤1.5, ordinary mechanical free distribution is performed.

[0049] A greedy algorithm sorts grids in descending order of difficulty, prioritizing high-difficulty areas and allocating continuous grid blocks to each machine to reduce idle travel distance. The machine status is updated in real time (the task pool is dynamically adjusted to maintain a safe distance between the roller and the paver). UWB positioning is used to track the paver's path in real time, and the roller is set to automatically stop when the temperature drops below 140°C.

[0050] S2: Based on the spatial relationship engine of the GIS platform, the minimum safe distance between machines is calculated in real time, which is expressed as:

[0051] The minimum safety distance D between pavers is set min The threshold is D min ≥3m, set the minimum safe distance D between the paver and the roller min The threshold is D min ≥10m;

[0052] If the minimum safety distance D min If the value is less than the threshold, emergency braking is triggered. When an obstacle is detected, the rapid exploration random tree RRT* algorithm is used to generate a detour path to achieve path replanning.

[0053] The preferred specific steps of quality monitoring and management decision-making in step S4 include:

[0054] S1: Set a threshold alarm mechanism: When the temperature of five consecutive sampling points is below 150°C or above 190°C, the system locks the paver and notifies the transport vehicle to stop feeding. If the GNSS signal is lost for more than 10 seconds, it switches to IMU+odometer fusion positioning and prompts manual position verification.

[0055] S2: Evaluation and decision-making: Calculate the International Roughness Index (IRI) every 10m, set the IRI target value to ≤2.0m / km, count the percentage of points with thickness deviation within ±2mm, set the thickness qualification rate to ≥95%, write the actual paving data into the BIM model, including coordinates, thickness, and temperature data, and generate an as-built model with quality labels. At the same time, update the construction progress layer in the GIS platform to support traceability during later maintenance.

[0056] Preferably, the hardware control includes:

[0057] Positioning and navigation module, based on high-precision GNSS receiver and IMU real-time positioning and attitude monitoring

[0058] The sensor module collects temperature, thickness, and flatness data based on an infrared thermal imager and a laser rangefinder;

[0059] The execution module adjusts the screed height and vibration frequency based on the received instructions based on the paver hydraulic control system and the vibratory hammer motor;

[0060] Communication module, based on 5G CPE module and industrial switch to achieve high-speed data transmission;

[0061] The control terminal runs BIM+GIS control software based on the on-board industrial PC to achieve terminal control.

[0062] Preferably, the software control includes:

[0063] The data integration layer is used to parse the IFC format model, extract the pavement design parameters, and load the terrain raster data based on ArcGIS Engine or SuperMap to build a 3D construction scene;

[0064] The intelligent decision-making layer uses algorithms to optimize paving paths, combines GIS terrain to generate routes with minimum elevation differences, and builds a parameter prediction model to predict the optimal paving speed;

[0065] The real-time control layer is used to generate control instructions to achieve human-computer interaction.

[0066] The present invention has the following beneficial effects:

[0067] 1. In the present invention, first, the BIM+GIS-based asphalt pavement 3D intelligent paving collaborative control system and method realizes real-time correction of lateral and elevation deviations through PID control combined with fuzzy control, and dynamically adjusts the extension and contraction of the screed hydraulic cylinder in conjunction with the paving speed. It adopts multi-source sensing technology such as Beidou, IMU, laser ranging, and infrared imaging, and realizes time-series consistent data synchronization through 5G communication, which can realize real-time control accuracy and closed-loop optimization effects, and can improve equipment collaborative efficiency by more than 40%.

[0068] 2. In the present invention, the BIM+GIS-based collaborative control system and method for 3D intelligent paving of asphalt pavement combines master-slave mechanical cluster control with dynamic task allocation to resolve conflicts, achieve multi-machine collaboration and cluster intelligent control effects, quantify quality assessment standards, and provide intelligent early warning and decision-making support to build a full-process quality control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 This is a schematic diagram of the steps of a BIM+GIS-based asphalt pavement 3D intelligent paving collaborative control system and method proposed by the present invention;

[0070] Figure 2 This is a module diagram of a BIM+GIS-based asphalt pavement 3D intelligent paving collaborative control system and method proposed in the present invention;

[0071] Figure 3 This is a schematic diagram of process 1 of a BIM+GIS-based asphalt pavement 3D intelligent paving collaborative control system and method proposed in the present invention;

[0072] Figure 4 This is a schematic diagram of the parts of a BIM+GIS-based asphalt pavement 3D intelligent paving collaborative control system and method proposed in the present invention;

[0073] Figure 5 This is a schematic diagram of process 2 of a BIM+GIS-based asphalt pavement 3D intelligent paving collaborative control system and method proposed in the present invention. DETAILED DESCRIPTION

[0074] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0075] Reference Figure 1-3The present invention provides an embodiment of a BIM+GIS-based 3D intelligent paving collaborative control system for asphalt pavement, comprising a data fusion layer, a real-time control layer, a multi-machine collaboration and task allocation layer, and a quality closed-loop layer. The data fusion layer generates a construction benchmark model by fusing the BIM model with GIS terrain data and implements networking of pavers, material transporters, and rollers through the Internet of Things. The real-time control layer collects multi-source data (positioning / temperature / compactness, etc.) and a cloud-based center makes three core decisions. The three core decisions made by the cloud-based center include material supply demand prediction for material transporters (based on paving speed and thickness), adaptive thickness / speed adjustment for pavers, and dynamic planning of roller compaction paths.

[0076] The quality closed-loop layer includes infrared temperature measurement + laser flatness detection to form a quality feedback loop, automatically triggering parameter re-optimization when the standards are not met, and returning qualified data to update the BIM model.

[0077] Reference Figure 1-3 The present invention provides an embodiment: the cloud supplies materials to the coordinated material transport vehicle through the optimal feeding sequence, and the load and temperature data of the material transport vehicle are fed back to the cloud to form a closed loop. The roller feeds back the compaction degree to the paving-rolling coordinated paver, and the position and paving thickness of the paver are fed back to the roller to form a closed loop.

[0078] The asphalt pavement BIM model construction steps in step S1 specifically include:

[0079] S1: Set input parameters and create an asphalt pavement BIM model using Autodesk Civil 3D software. The input parameters include: a. Geometric parameters consisting of pavement width, longitudinal slope, transverse slope, and curve curvature radius; b. Structural parameters consisting of base layer thickness, surface layer thickness, and compaction requirements; c. Material parameters consisting of asphalt mixture type, paving temperature, and compaction temperature.

[0080] S2: Export the BIM model based on the IFC format, set the paving speed and vibration hammer frequency as dynamic parameters, set the construction period and transportation vehicle scheduling as constraints, and add construction attribute data information to the model;

[0081] S3: Terrain data collection: Based on UAV LiDAR aerial survey technology, set the flight altitude to 50-100m and the point cloud density ≥ 50 points / m 2 , and set a 20m buffer zone extending outside the construction area as the aerial survey coverage area, combined with satellite imaging technology to obtain multispectral images of the construction area to identify surface obstacles and generate a high-precision terrain model DEM;

[0082] S4: Environmental data integration: Import GIS data based on ArcGIS Pro, including: a. Geological data: soil bearing capacity and groundwater level elevation data; b. Underground pipelines: 3D coordinate data of gas pipelines and cable pipelines; c. Traffic control: temporary road network closure range data for construction sections.

[0083] The data fusion step in step S1 includes:

[0084] S1: Coordinate system conversion and registration: a. Based on the ArcGIS Data Interoperability extension module, the BIM model is converted from the engineering coordinate system to the WGS84 / UTM coordinate system in combination with the seven-parameter conversion method to achieve coordinate system one processing;

[0085] S2: Select at least four common control points between the BIM model and the GIS terrain, including road intersections and permanent landmarks. Set the control point coordinates with an error of ≤1 cm using a total station. Optimize the registration of the control points using the least squares method, and control the residual within ±2 cm.

[0086] S3: Data format integration: a. Convert the BIM model to CityGML format using the IFC-to-CityGML converter, preserving semantic information. Load the CityGML data into the GIS platform and overlay it with terrain raster and vector pipeline data to achieve interoperability between IFC and GIS data.

[0087] S4: Display BIM pavement structure, GIS terrain, and underground pipelines in layers, calculate the cut and fill volume of the paving area based on the elevation difference between DEM and BIM design, and create a fused 3D scene based on SuperMap iDesktop;

[0088] Build a dynamic construction model based on fused data, including:

[0089] S5: Paving Path Planning: Set and input constraints, generate a preliminary path based on the A* algorithm and combine it with the Dijkstra algorithm to optimize the route with the minimum elevation change, and output a three-dimensional path point sequence. The constraints include: mechanical parameters: minimum turning radius and maximum climbing ability of the paver;

[0090] Environmental constraints: Avoid 2m buffer zones for underground pipelines and steep slopes;

[0091] S6: Dynamic calculation of construction parameters: The material consumption prediction formula is:

[0092] Where A2 represents the i-th grid area of ​​GIS terrain division, h iIt represents the correction value of the difference between the BIM design thickness and the DEM elevation. y is the density of asphalt mixture, which is generated according to the slope-speed association rule control parameter.

[0093] S7: Simulate the movement of the paver in the BIM+GIS platform, detect the safe distance between the robotic arm and the underground pipeline, automatically mark the high-risk area and trigger the path replanning operation if a conflict is detected, extract the design thickness in the BIM model, and compare and verify it with the GIS terrain volume calculation value. The allowable deviation range is ±3%. If the deviation range is exceeded, adjust the BIM layer thickness parameters or re-divide the construction grid, generate a registration error heat map, review areas with errors greater than 2 cm, and generate a coordinate residual report.

[0094] In step S2, positioning sensor equipment is deployed, and the steps of real-time sensor data collection and synchronization include:

[0095] S1: Positioning data acquisition: The paver is equipped with a BeiDou positioning system and an inertial measurement unit, which updates its position and attitude every 100ms. Position data includes longitude, latitude, and elevation, and attitude data includes roll, pitch, and heading angles. The WGS84 coordinates output by the GNSS are converted in real time to the local engineering coordinate system of the BIM model to ensure consistency with the designed path to achieve coordinate alignment.

[0096] S2: Environmental and paving status monitoring: a. Use an infrared thermal imager to scan the asphalt mixture surface temperature at a rate of 10 frames per second, with a detection range set at 160-180°C. An alarm will be triggered if the temperature exceeds the limit;

[0097] S3: Use a laser rangefinder to vertically measure the thickness of the paving layer and upload the measurement data in real time;

[0098] S4: Arrange an ultrasonic sensor array transversely along the paving layer, detect the elevation difference every 50 cm, and generate a flatness heat map;

[0099] S5: Data synchronization: Through the 5G CPE module and setting the uplink rate to ≥100Mbps, the sensor data is encapsulated as MQTT protocol messages and transmitted to the cloud or local edge computing server. The sensor data is timestamped in UTC to ensure the temporal consistency of multi-source data.

[0100] S6: Design model loading: Extract the design parameters of the current construction section based on the BIM model, including the target path centerline coordinates (X, Y, Z) and the thickness, slope, and curvature radius parameter data of each layer. Then, load the elevation model and obstacle layer of the construction area using the GIS platform.

[0101] S7: Real-time deviation calculation: a. Calculate the Euclidean distance between the GNSS positioning point and the BIM design centerline to calculate the lateral deviation data. The Euclidean distance formula is:

[0102]

[0103] The permissible deviation threshold is set to ±2cm, and deviation correction is triggered when it exceeds the limit;

[0104] S8: Compare the laser thickness gauge data with the BIM layer thickness design value, dynamically calculate the fill and cut volume to calculate the elevation deviation. The fill and cut volume calculation formula is: ΔH=h 实际 -h 设计 , where the allowable deviation threshold is set to ±3mm, and the ironing plate height is adjusted when it exceeds the limit.

[0105] The specific steps for executing the closed-loop control strategy in step S2 include:

[0106] S1: Dynamic deviation correction control: a. Input the lateral deviation ΔL and output the opening of the paver steering hydraulic valve. The control logic is expressed as follows: To achieve lateral deviation correction based on PID control, the empirical value of PID parameter is set to K P =0.8,K i =0.05,K d =0.1;

[0107] S2: Input elevation deviation ΔH and paving speed v, and output the extension and contraction of the screed hydraulic cylinder. Elevation correction based on fuzzy control is implemented according to the following rules: if ΔH > 3mm and v < 4m / min, lower the screed by 2mm; if ΔH < -3mm and v > 3m / min, raise the screed by 3mm.

[0108] S3: Parameter Adaptive Adjustment: a. Dynamically optimize and adjust the paving speed based on the slope data and infrared temperature data in the GIS terrain. The formula is:

[0109]

[0110] S4: When the thickness deviation ΔH is continuously greater than 2 mm, the frequency of the vibration hammer is adjusted in a gradient increase. The frequency increase formula is: Set the maximum frequency to 20HZ.

[0111] In step S3, multi-machine collaborative control requires dynamic task allocation, path planning, and conflict resolution to achieve efficient collaboration of the machine cluster. The specific steps include:

[0112] S1: Multi-level collaborative allocation: a. Divide the construction area into 5m×5m grids and annotate the construction difficulty coefficient of each grid based on GIS terrain data. The difficulty calculation formula is:

[0113] D i= 0.4 × slope (%) + 0.3 × curvature (1 / m) + 0.3 × obstacle density (number / m 2 )

[0114] The collaborative work matching is performed based on the construction difficulty coefficient, and the matching rules are as follows:

[0115] If D i >3.0, it will be allocated only to high-precision pavers;

[0116] If 1.5<D i If the value is ≤3.0, a common paver and a roller will be assigned to work together;

[0117] If D i ≤1.5, then perform normal mechanical free distribution

[0118] A greedy algorithm sorts grids in descending order of difficulty, prioritizing high-difficulty areas and allocating continuous grid blocks to each machine to reduce idle travel distance. The machine status is updated in real time (the task pool is dynamically adjusted to maintain a safe distance between the roller and the paver). UWB positioning is used to track the paver's path in real time, and the roller is set to automatically stop when the temperature drops below 140°C.

[0119] S2: Based on the spatial relationship engine of the GIS platform, the minimum safe distance between machines is calculated in real time, which is expressed as:

[0120] The minimum safety distance D between pavers is set min The threshold is D min ≥3m, set the minimum safe distance D between the paver and the roller min The threshold is D min ≥10m;

[0121] If the minimum safety distance D min If the value is less than the threshold, emergency braking is triggered. When an obstacle is detected, the rapid exploration random tree RRT* algorithm is used to generate a detour path to achieve path replanning.

[0122] The specific steps of quality monitoring and management decision-making in step S4 include:

[0123] S1: Set a threshold alarm mechanism: When the temperature of five consecutive sampling points is below 150°C or above 190°C, the system locks the paver and notifies the transport vehicle to stop feeding. If the GNSS signal is lost for more than 10 seconds, it switches to IMU+odometer fusion positioning and prompts manual position verification.

[0124] S2: Evaluation and decision-making: Calculate the International Roughness Index (IRI) every 10m, set the IRI target value to ≤2.0m / km, count the percentage of points with thickness deviation within ±2mm, set the thickness qualification rate to ≥95%, write the actual paving data into the BIM model, including coordinates, thickness, and temperature data, and generate an as-built model with quality labels. At the same time, update the construction progress layer in the GIS platform to support traceability during later maintenance.

[0125] The hardware control includes:

[0126] Positioning and navigation module, based on high-precision GNSS receiver and IMU real-time positioning and attitude monitoring

[0127] The sensor module collects temperature, thickness, and flatness data based on an infrared thermal imager and a laser rangefinder;

[0128] The execution module adjusts the screed height and vibration frequency based on the received instructions based on the paver hydraulic control system and the vibratory hammer motor;

[0129] Communication module, based on 5G CPE module and industrial switch to achieve high-speed data transmission;

[0130] The control terminal runs BIM+GIS control software based on the on-board industrial PC to achieve terminal control.

[0131] The software control includes:

[0132] The data integration layer is used to parse the IFC format model, extract the pavement design parameters, and load the terrain raster data based on ArcGIS Engine or SuperMap to build a 3D construction scene;

[0133] The intelligent decision-making layer uses algorithms to optimize paving paths, combines GIS terrain to generate routes with minimum elevation differences, and builds a parameter prediction model to predict the optimal paving speed;

[0134] The real-time control layer is used to generate control instructions to achieve human-computer interaction.

[0135] Working principle: The core of this invention is to realize the full intelligent operation of asphalt pavement paving through the five-level linkage mechanism of "digital twin modeling - high-precision positioning - intelligent decision-making - multi-machine collaboration - closed-loop control". The system operation logic is as follows: BIM+GIS modeling → real-time data acquisition → intelligent analysis and decision-making → equipment collaborative execution → quality feedback optimization, among which BIM design model analysis: extract pavement structure layer parameters (thickness, slope, material ratio) through the IFC standard interface, and generate a three-dimensional construction benchmark model containing control points (control point spacing ≤ 2m); GIS environment calibration: integrate laser radar scanning point cloud and satellite positioning data to establish a centimeter-level precision construction coordinate system (error ± 1cm), and automatically compensate for the ground To prevent elevation deviations in special areas such as landslide settlement and bridge expansion joints, multi-sensor fusion positioning uses GNSS (RTK) + IMU + laser tracking composite positioning technology: RTK provides absolute position (horizontal accuracy 8mm, elevation accuracy 15mm), IMU maintains short-term positioning when GNSS signal is lost (drift <2cm within 100ms), laser tracker corrects relative position between devices (accuracy 3mm), six-degree-of-freedom attitude solution uses extended Kalman filter (EKF) to calculate the spatial attitude of the paver screed in real time (pitch angle ±0.01°, roll angle ±0.01°), and 3D dynamic leveling control laser scanner (100Hz) compares the elevation difference ΔH between the paving surface and the design model in real time:

[0136] \DeltaH=\frac{1}{n}\sum_{i=1}^{n}(h_{design}^i-h_{actual}^i)

[0137] When |ΔH|≥3mm, the PID controller outputs the hydraulic cylinder adjustment amount:

[0138] u(t)=0.5e(t)+0.2\inte(t)dt+0.1\frac{de(t)}{dt}

[0139] The response time is <0.3 seconds, the overshoot is <3%, and the material transport vehicle scheduling model is established based on queuing theory:

[0140] N_{truck}=\lceil\frac{v\cdotw\cdoth\cdot\rho\cdotT_{delay}}{Q_{mix}}\rceil

[0141] Where v = paving speed (m / min), Q_mix = single vehicle load (t), ensuring that the waiting time is less than 2 minutes. The roller dynamically adjusts the rolling sequence based on the infrared thermal imager data (±2°C): initial compaction temperature window: 135-150°C, final compaction temperature window: 90-110°C. Path conflict avoidance: The roller rolling path is planned based on the A* algorithm to avoid equipment collisions (safety distance ≥ 1.5m). The compaction degree is calculated online:

[0142] Inverse compaction through roller vibration acceleration:

[0143] C=98.5-\frac{1}{2}\ln(\frac{a_{theory}}{a_{measured}})

[0144] When C<93%, the pressure replenishment instruction is triggered, and the digital twin updates the construction data and automatically transmits it back to the BIM model to generate a digital completion model with millimeter-level accuracy.

[0145] This invention achieves synchronous leveling of the entire cross-section (width ≤ 12m) through laser scanning and model registration. A temperature-velocity-thickness coupled control establishes a mixture temperature decay model: T(t) = T_0e^{-0.012t}, dynamically adjusting the paving speed v to ensure compaction within the temperature window. A fault self-healing mechanism automatically switches to SLAM (LiDAR + IMU) combined navigation mode if GNSS lock is lost for >30 seconds, maintaining a positioning accuracy of ±2cm. Technical indicators for time node execution actions: T0 system startup, loading BIM model model parsing time <3s; T0+8s RTK fixed solution completion positioning initialization time <10s; T0+15s first vehicle mixture temperature detection infrared temperature measurement response time 0.1s; T0+1min automatic paving start control command delay <50ms; T0+3min roller synchronous rolling trajectory tracking error <1cm; among them, construction accuracy, paving thickness error: ±2mm (traditional ±5mm), flatness: 1.5mm / 3m (traditional 3mm / 3m), collaborative efficiency, equipment utilization: 85%→92%, construction speed: 6m / min→8m / min asphalt mixture waste rate: <1% (traditional 3%~5%).

[0146] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A BIM+GIS-based 3D intelligent paving collaborative control system for asphalt pavement, including a data fusion layer, a real-time control layer, a multi-machine collaboration and task allocation layer, and a quality closed-loop layer, characterized by: The data fusion layer includes the fusion of BIM models and GIS terrain data to generate a construction benchmark model and the networking of pavers, material transporters, and rollers through the Internet of Things. The real-time control layer includes multi-source data collection and a cloud center that makes three core decisions: material supply demand prediction for material transporters, thickness / speed adaptive adjustment for pavers, and dynamic planning of roller rolling paths. The quality closed-loop layer includes infrared temperature measurement + laser flatness detection to form a quality feedback loop, automatically triggering parameter re-optimization when the standards are not met, and returning qualified data to update the BIM model.

2. The BIM+GIS-based 3D intelligent paving collaborative control system for asphalt pavement according to claim 1 is characterized by: The cloud feeds the material transport vehicle through the optimal feeding sequence, and the load and temperature data of the material transport vehicle are fed back to the cloud to form a closed loop. The roller feeds back the compaction degree to the paving-rolling collaborative paver, and the position and paving thickness of the paver are fed back to the roller to form a closed loop.

3. A method for using a BIM+GIS-based 3D intelligent paving collaborative control system for asphalt pavement, comprising the BIM+GIS-based 3D intelligent paving collaborative control system for asphalt pavement described in claim 1 or 2, characterized in that: The asphalt pavement BIM model construction steps in step S1 specifically include: S1: Set input parameters and create an asphalt pavement BIM model using Autodesk Civil 3D software. The input parameters include: a. Geometric parameters consisting of pavement width, longitudinal slope, transverse slope, and curve curvature radius; b. Structural parameters consisting of base layer thickness, surface layer thickness, and compaction requirements; c. Material parameters consisting of asphalt mixture type, paving temperature, and compaction temperature. S2: Export the BIM model based on the IFC format, set the paving speed and vibration hammer frequency as dynamic parameters, set the construction period and transportation vehicle scheduling as constraints, and add construction attribute data information to the model; S3: Terrain data collection: Based on UAV LiDAR aerial survey technology, set the flight altitude to 50-100m and the point cloud density ≥ 50 points / m 2 , and set a 20m buffer zone extending outside the construction area as the aerial survey coverage area, combined with satellite imaging technology to obtain multispectral images of the construction area to identify surface obstacles and generate a high-precision terrain model DEM; S4: Environmental data integration: Import GIS data based on ArcGIS Pro, including: a. Geological data: soil bearing capacity and groundwater level elevation data; b. Underground pipelines: 3D coordinate data of gas pipelines and cable pipelines; c. Traffic control: temporary road network closure range data for construction sections.

4. The BIM+GIS-based 3D intelligent paving collaborative control system and method for asphalt pavement according to claim 3 is characterized by: The data fusion step in step S1 includes: S1: Coordinate system conversion and registration: a. Based on the ArcGIS Data Interoperability extension module, the BIM model is converted from the engineering coordinate system to the WGS84 / UTM coordinate system in combination with the seven-parameter conversion method to achieve coordinate system one processing; S2: Select at least four common control points between the BIM model and the GIS terrain, including road intersections and permanent landmarks. Set the control point coordinates with an error of ≤1 cm using a total station. Optimize the registration of the control points using the least squares method, and control the residual within ±2 cm. S3: Data format integration: a. Convert the BIM model to CityGML format using the IFC-to-CityGML converter, preserving semantic information. Load the CityGML data into the GIS platform and overlay it with terrain raster and vector pipeline data to achieve interoperability between IFC and GIS data. S4: Display BIM pavement structure, GIS terrain, and underground pipelines in layers, calculate the cut and fill volume of the paving area based on the elevation difference between DEM and BIM design, and create a fused 3D scene based on SuperMap iDesktop; Build a dynamic construction model based on fused data, including: S5: Paving Path Planning: Set and input constraints, generate a preliminary path based on the A* algorithm and combine it with the Dijkstra algorithm to optimize the route with the minimum elevation change, and output a three-dimensional path point sequence. The constraints include: mechanical parameters: minimum turning radius and maximum climbing ability of the paver; Environmental constraints: Avoid 2m buffer zones for underground pipelines and steep slopes; S6: Dynamic calculation of construction parameters: The material consumption prediction formula is: Where A2 represents the i-th grid area of ​​GIS terrain division, h i It represents the correction value of the difference between the BIM design thickness and the DEM elevation. y is the density of asphalt mixture, which is generated according to the slope-speed association rule control parameter. S7: Simulate the movement of the paver in the BIM+GIS platform, detect the safe distance between the robotic arm and the underground pipeline, automatically mark the high-risk area and trigger the path replanning operation if a conflict is detected, extract the design thickness in the BIM model, and compare and verify it with the GIS terrain volume calculation value. The allowable deviation range is ±3%. If the deviation range is exceeded, adjust the BIM layer thickness parameters or re-divide the construction grid, generate a registration error heat map, review areas with errors greater than 2 cm, and generate a coordinate residual report.

5. A method for using a BIM+GIS-based 3D intelligent paving collaborative control system for asphalt pavement, comprising the BIM+GIS-based 3D intelligent paving collaborative control system for asphalt pavement described in claim 1 or 2, characterized in that: In step S2, positioning sensor equipment is deployed, and the steps of real-time sensor data collection and synchronization include: S1: Positioning data acquisition: The paver is equipped with a BeiDou positioning system and an inertial measurement unit, which updates its position and attitude every 100ms. Position data includes longitude, latitude, and elevation, and attitude data includes roll, pitch, and heading angles. The WGS84 coordinates output by the GNSS are converted in real time to the local engineering coordinate system of the BIM model to ensure consistency with the designed path to achieve coordinate alignment. S2: Environmental and paving status monitoring: a. Use an infrared thermal imager to scan the asphalt mixture surface temperature at a rate of 10 frames per second, with a detection range set at 160-180°C. An alarm will be triggered if the temperature exceeds the limit; S3: Use a laser rangefinder to vertically measure the thickness of the paving layer and upload the measurement data in real time; S4: Arrange an ultrasonic sensor array transversely along the paving layer, detect the elevation difference every 50 cm, and generate a flatness heat map; S5: Data synchronization: Through the 5G CPE module and setting the uplink rate to ≥100Mbps, the sensor data is encapsulated as MQTT protocol messages and transmitted to the cloud or local edge computing server. The sensor data is timestamped in UTC to ensure the temporal consistency of multi-source data. S6: Design model loading: Extract the design parameters of the current construction section based on the BIM model, including the target path centerline coordinates (X, Y, Z) and the thickness, slope, and curvature radius parameter data of each layer. Then, load the elevation model and obstacle layer of the construction area using the GIS platform. S7: Real-time deviation calculation: a. Calculate the Euclidean distance between the GNSS positioning point and the BIM design centerline to calculate the lateral deviation data. The Euclidean distance formula is: The permissible deviation threshold is set to ±2cm, and deviation correction is triggered when it exceeds the limit; S8: Compare the laser thickness gauge data with the BIM layer thickness design value, dynamically calculate the fill and cut volume to calculate the elevation deviation. The fill and cut volume calculation formula is: ΔH=h 实际 -h 设计 , where the allowable deviation threshold is set to ±3mm, and the ironing plate height is adjusted when it exceeds the limit.

6. The BIM+GIS-based 3D intelligent paving collaborative control system and method for asphalt pavement according to claim 5 is characterized by: The specific steps for executing the closed-loop control strategy in step S2 include: S1: Dynamic deviation correction control: a. Input the lateral deviation ΔL and output the opening of the paver steering hydraulic valve. The control logic is expressed as follows: To achieve lateral deviation correction based on PID control, the empirical value of PID parameter is set to K P =0.8,K i =0.05,K d =0.1; S2: Input elevation deviation ΔH and paving speed v, and output the extension and contraction of the screed hydraulic cylinder. Elevation correction based on fuzzy control is implemented according to the following rules: if ΔH > 3mm and v < 4m / min, lower the screed by 2mm; if ΔH < -3mm and v > 3m / min, raise the screed by 3mm. S3: Parameter Adaptive Adjustment: a. Dynamically optimize and adjust the paving speed based on the slope data and infrared temperature data in the GIS terrain. The formula is: S4: When the thickness deviation ΔH is continuously greater than 2 mm, the frequency of the vibration hammer is adjusted in a gradient increase. The frequency increase formula is: Set the maximum frequency to 20HZ.

7. A method for using a BIM+GIS-based 3D intelligent paving collaborative control system for asphalt pavement, comprising the BIM+GIS-based 3D intelligent paving collaborative control system for asphalt pavement described in claim 1 or 2, characterized in that: In step S3, multi-machine collaborative control requires dynamic task allocation, path planning, and conflict resolution to achieve efficient collaboration of the machine cluster. The specific steps include: S1: Multi-level collaborative allocation: a. Divide the construction area into 5m×5m grids and annotate the construction difficulty coefficient of each grid based on GIS terrain data. The difficulty calculation formula is: D i = 0.4 × slope (%) + 0.3 × curvature (1 / m) + 0.3 × obstacle density (number / m 2 ) The collaborative work matching is performed based on the construction difficulty coefficient, and the matching rules are as follows: If D i >3.0, it will be allocated only to high-precision pavers; If 1.5<D i If the value is ≤3.0, a common paver and a roller will be assigned to work together; If D i ≤1.5, then perform normal mechanical free distribution A greedy algorithm sorts grids in descending order of difficulty, prioritizing high-difficulty areas and allocating continuous grid blocks to each machine to reduce idle travel distance. The machine status is updated in real time (the task pool is dynamically adjusted to maintain a safe distance between the roller and the paver). UWB positioning is used to track the paver's path in real time, and the roller is set to automatically stop when the temperature drops below 140°C. S2: Based on the spatial relationship engine of the GIS platform, the minimum safe distance between machines is calculated in real time, which is expressed as: The minimum safety distance D between pavers is set min The threshold is D min ≥3m, set the minimum safe distance D between the paver and the roller min The threshold is D min ≥10m; If the minimum safety distance D min If the value is less than the threshold, emergency braking is triggered. When an obstacle is detected, the rapid exploration random tree RRT* algorithm is used to generate a detour path to achieve path replanning.

8. A method for using a BIM+GIS-based 3D intelligent paving collaborative control system for asphalt pavement, comprising the BIM+GIS-based 3D intelligent paving collaborative control system for asphalt pavement described in claim 1 or 2, characterized in that: The specific steps of quality monitoring and management decision-making in step S4 include: S1: Set a threshold alarm mechanism: When the temperature of five consecutive sampling points is below 150°C or above 190°C, the system locks the paver and notifies the transport vehicle to stop feeding. If the GNSS signal is lost for more than 10 seconds, it switches to IMU+odometer fusion positioning and prompts manual position verification. S2: Evaluation and decision-making: Calculate the International Roughness Index (IRI) every 10m, set the IRI target value to ≤2.0m / km, count the percentage of points with thickness deviation within ±2mm, set the thickness qualification rate to ≥95%, write the actual paving data into the BIM model, including coordinates, thickness, and temperature data, and generate an as-built model with quality labels. At the same time, update the construction progress layer in the GIS platform to support traceability during later maintenance.

9. The BIM+GIS-based 3D intelligent paving collaborative control system and method for asphalt pavement according to claim 8 is characterized by: The hardware control includes: Positioning and navigation module, based on high-precision GNSS receiver and IMU real-time positioning and attitude monitoring The sensor module collects temperature, thickness, and flatness data based on an infrared thermal imager and a laser rangefinder; The execution module adjusts the screed height and vibration frequency based on the received instructions based on the paver hydraulic control system and the vibratory hammer motor; Communication module, based on 5G CPE module and industrial switch to achieve high-speed data transmission; The control terminal runs BIM+GIS control software based on the on-board industrial PC to achieve terminal control.

10. The BIM+GIS-based 3D intelligent paving collaborative control system and method for asphalt pavement according to claim 9, characterized in that: The software control includes: The data integration layer is used to parse the IFC format model, extract the pavement design parameters, and load the terrain raster data based on ArcGIS Engine or SuperMap to build a 3D construction scene; The intelligent decision-making layer uses algorithms to optimize paving paths, combines GIS terrain to generate routes with minimum elevation differences, and builds a parameter prediction model to predict the optimal paving speed; The real-time control layer is used to generate control instructions to achieve human-computer interaction.

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