Construction condition digital twin construction method and system for high-face rockfill dam construction towards intelligent construction

Abstract

The present application relates to the technical field of high face slab rockfill dam intelligent construction and construction condition digital twinning, especially to a high face slab rockfill dam construction condition digital twinning construction method and system for intelligent construction. The method obtains material partition condition, dam body grading construction procedure and warehouse coordinate data, grids the construction warehouse surface, associates the building information model engineering model and the three-dimensional finite element model, generates the warehouse surface grid corresponding relationship; matches the unloading, paving and rolling information to the corresponding grid, generates the paving monitoring result and the rolling parameter, and generates the compaction quality monitoring result in combination with the material source parameter, the continuous compaction index and the test pit test result; when new construction information appears in the corresponding grid, the compaction quality monitoring result is regenerated according to the construction sequence, and the digital twinning model of the corresponding grid and the finite element node is updated according to the knowledge graph. The present application improves the accuracy of construction quality evaluation and model updating.

Description

Technical Field

[0001] This invention relates to the field of digital twin construction of high-face rockfill dams and construction conditions, and particularly to a method and system for constructing digital twins of construction conditions for high-face rockfill dams for intelligent construction. Background Technology

[0002] In the field of digital twin construction of high-face rockfill dams, existing solutions typically establish the construction site based on material zoning, dam body graded construction procedures, and construction coordinate data. They then use dump truck unloading information, bulldozer real-time spatial positioning information, roller positioning data, vibration force data, and test pit results for paving monitoring, compaction parameter statistics, and compaction quality evaluation, respectively. Some solutions also link the construction site to a three-dimensional finite element model and update the digital twin model based on construction monitoring results.

[0003] Existing methods often receive and process data separately for each construction stage. The construction sequence relationship between paving monitoring results, compaction parameters, and compaction quality monitoring results is not clearly defined. Once compaction quality monitoring results have been generated for a corresponding grid, if new dump truck unloading information, paving process information, or compaction parameters appear, the original monitoring results may still be used as the basis for the current quality evaluation, or they may be directly overwritten by the new monitoring results. As a result, the test pit test results may precede the last target movement trajectory, and the number of compaction passes may also include movement trajectories before paving completion, where the running speed does not conform to the compaction plan, or the excitation force output state does not correspond to the vibration state, leading to inconsistencies between the current compaction quality monitoring results and the actual construction conditions of the corresponding grid.

[0004] In the staged construction of high-face rockfill dams, new dump truck unloading information, paving process information, and compaction parameters correspond to different construction stages. Existing solutions typically lack a process for regenerating the scope according to the construction sequence, making it difficult to separately handle the sequential relationship between paving monitoring results, compaction parameters, test pit test results, and compaction quality monitoring results. Furthermore, there is a lack of a correspondence between the original and current monitoring results, established according to grid, material zoning, dam construction stages, and construction time, which easily leads to the misuse of historical data and current quality evaluation criteria.

[0005] Existing technologies generally lack a continuous correspondence when jointly processing construction surfaces, grids, material zoning, dam body graded construction procedures, dump truck unloading information, paving process information, compaction parameters, test pit results, and finite element nodes. When new construction information emerges, it is difficult to accurately define the affected corresponding grids and finite element nodes, and digital twin model updates can easily extend to areas where no construction changes have occurred. When the calculation results of the 3D finite element model are inconsistent with sensor monitoring data, existing solutions also struggle to trace back the dump truck unloading information, paving process information, compaction parameters, and test pit results of the corresponding grids according to the construction sequence, thus making it difficult to maintain consistency between compaction quality monitoring results, corresponding grids, and finite element nodes. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a method for constructing a digital twin of the construction conditions of a high-face rockfill dam for intelligent construction, comprising: S100: Obtain material zoning information, dam body graded construction procedures and construction coordinate data, grid the construction surface and associate it with the BIM engineering model and three-dimensional finite element model to generate the corresponding relationship of the surface grid. S200. Based on the grid correspondence of the storage surface, the dump truck unloading information, bulldozer real-time spatial positioning information and paving process information are matched to the corresponding grid, and paving monitoring results are generated according to the paving thickness, flatness and quality control criteria. S300. Based on the paving monitoring results, the roller positioning data and excitation force data are matched to the corresponding grid, the target motion trajectory is selected and the number of rolling passes is calculated to generate rolling parameters; the rolling parameters, material source parameters, continuous compaction index and test pit test results are input into the compaction quality evaluation model to generate compaction quality monitoring results. S400. Based on the compaction quality monitoring results, when new dump truck unloading information, paving process information, or compaction parameters appear in the corresponding grid, the original monitoring results are no longer used as the basis for the current quality evaluation according to the construction sequence, and the compaction quality monitoring results are regenerated; the digital twin model of the corresponding grid and finite element nodes is updated according to the regenerated compaction quality monitoring results and knowledge graph.

[0007] The key innovations of this invention include: (1) Based on the grid correspondence of the slab surface, the dump truck unloading information, bulldozer real-time spatial positioning information, paving process information, roller positioning data and excitation force data are matched to the corresponding grid; the target motion trajectory is selected according to the paving monitoring results and the number of rolling passes is calculated, and the rolling parameters, material source parameters, continuous compaction index and test pit test results are input into the compaction quality evaluation model.

[0008] (2) When new dump truck unloading information, paving process information or compaction parameters appear in the corresponding grid, the original monitoring results shall no longer be used as the basis for current quality evaluation according to the construction sequence, and the paving monitoring results, compaction parameters and compaction quality monitoring results shall be regenerated respectively.

[0009] (3) Establish the corresponding relationships between construction site, grid, material zoning, dam body graded construction procedure, dump truck unloading information, paving process information, compaction parameters, test pit test results and finite element nodes through knowledge graph; update the digital twin model of the corresponding grid and finite element nodes according to the regenerated compaction quality monitoring results, and compare the calculation results of the three-dimensional finite element model with the sensor monitoring data.

[0010] The following are its main beneficial effects: (1) By mapping unloading, paving, rolling and test pit tests to the same grid, and limiting the target movement trajectory and rolling passes by the paving monitoring results, the compaction quality monitoring results correspond to the material zoning, paving status and rolling status of the current construction site, reducing the entry of roller positioning data from non-current construction conditions into the compaction quality evaluation model.

[0011] (2) By processing new dump truck unloading information, paving process information or compaction parameters according to the construction sequence, the original monitoring results are no longer used as the basis for current quality evaluation, and the corresponding compaction quality monitoring results are regenerated to avoid mixing historical data with the basis for current quality evaluation.

[0012] (3) The new construction information is matched to the corresponding grid and finite element node through the knowledge graph, so that the update range of the digital twin model corresponds to the dump truck unloading information, paving process information or compaction parameters, and maintains the correspondence between the compaction quality monitoring results, grid and finite element node.

[0013] (4) By inputting the compaction parameters, material source parameters, continuous compaction index and test pit test results into the compaction quality evaluation model, the compaction quality monitoring results can simultaneously reflect the dam material, layer thickness, target movement trajectory, number of compaction passes and test pit test results.

[0014] (5) By comparing the calculation results of the three-dimensional finite element model with the sensor monitoring data, and rereading the dump truck unloading information, paving process information, rolling parameters and test pit test results of the corresponding grid when the monitoring requirements are not met, the newly generated compaction quality monitoring results can continue to be used for updating the digital twin model of the corresponding grid and finite element nodes. Attached Figure Description

[0015] Figure 1 A flowchart illustrating the digital twin construction method for high panel rockfill dam construction conditions for intelligent construction provided in this application embodiment; Figure 2 The structural block diagram of the digital twin construction system for the construction conditions of high panel rockfill dams for intelligent construction provided in the embodiments of this application. Detailed Implementation

[0016] Example 1: Refer to Figure 1 This is a flowchart illustrating the method for constructing a digital twin of the construction conditions of a high-face rockfill dam for intelligent construction, provided by an embodiment of the present invention. The process may include at least steps S100-S400: S100: Obtain material zoning information, dam body graded construction procedures and construction coordinate data, grid the construction surface and associate it with the BIM engineering model and three-dimensional finite element model to generate the corresponding relationship of the surface grid. S200. Based on the grid correspondence of the storage surface, the dump truck unloading information, bulldozer real-time spatial positioning information and paving process information are matched to the corresponding grid, and paving monitoring results are generated according to the paving thickness, flatness and quality control criteria. S300. Based on the paving monitoring results, the roller positioning data and excitation force data are matched to the corresponding grid, the target motion trajectory is selected and the number of rolling passes is calculated to generate rolling parameters; the rolling parameters, material source parameters, continuous compaction index and test pit test results are input into the compaction quality evaluation model to generate compaction quality monitoring results. S400. Based on the compaction quality monitoring results, when new dump truck unloading information, paving process information, or compaction parameters appear in the corresponding grid, the original monitoring results are no longer used as the basis for the current quality evaluation according to the construction sequence, and the compaction quality monitoring results are regenerated; the digital twin model of the corresponding grid and finite element nodes is updated according to the regenerated compaction quality monitoring results and knowledge graph.

[0017] S100: Obtain material zoning information, dam body graded construction procedures and construction coordinate data, grid the construction surface and associate it with the BIM engineering model and three-dimensional finite element model to generate the corresponding relationship of the surface grid. Specifically, step S100 is executed by the server after receiving the design drawings of each section of the rockfill dam, the material zoning information, the dam body graded construction procedure, and the construction coordinate data. The material zoning information refers to the spatial range and adjacent boundaries of the rockfill sections, cushion layers, transition materials, and panel components in the 3D model of the rockfill dam. The dam body graded construction procedure refers to the construction area, construction time, and sequential construction order corresponding to each construction elevation. The construction coordinate data refers to the spatial coordinates collected by the BeiDou measuring rod at each turning point of the construction area boundary. The server initiates the initial information processing of the construction area in this step when it receives new construction coordinate data or when the current construction elevation changes according to the dam body graded construction procedure.

[0018] The Building Information Modeling (BIM) engineering model consists of a 3D geometric model of the rockfill dam, material zoning, and construction sites. The 3D finite element model is established based on the design drawings of each section of the rockfill dam, material zoning, and the dam's graded construction procedure. The model internally includes rockfill zoning, bedding layers, transition materials, panel components, and finite element nodes. The BIM engineering model records the 3D geometric location and material zoning of the construction sites. The 3D finite element model records the corresponding material zoning, construction grade, and finite element nodes for each construction site. Both models use spatial coordinates from the 3D model of the rockfill dam. The server performs coordinate processing on the construction site coordinate data before mapping the mesh to the model area.

[0019] Specifically, the construction coordinate data is collected by Beidou measuring rods positioned at various turning points along the boundary of the construction site. These Beidou measuring rods transmit the planar coordinates and elevation of each turning point to a sub-control station. The sub-control station checks the collection sequence and construction time of adjacent turning points and sends the checked construction coordinate data to the server. If the coordinates of a turning point are temporarily missing, the sub-control station retains the already received construction coordinate data and records that the data collection for that turning point is incomplete, without generating a closed construction site boundary. After the turning point is remeasured, the sub-control station writes the remeasured coordinates into the same construction site coordinate data according to the construction time, and the server re-executes the boundary connection. If the lines connecting adjacent turning points intersect, or if a turning point is located outside the material zoning area, the server stops generating the current construction site boundary and returns the corresponding turning point to the sub-control station for remeasurement.

[0020] The server connects adjacent turning points according to the acquisition sequence of the construction coordinate data to obtain the construction site boundary. The construction site boundary includes a planar range and a construction elevation. The planar range is enclosed by the boundary lines between each turning point, and the construction elevation comes from the elevation acquired by the BeiDou surveying pole and the current construction elevation in the dam body grading construction program. The server spatially matches the construction site boundary with the material partitioning. When the entire construction site boundary is within the same material partition, the server writes that material partition into the material partitioning field of the current construction site. When the construction site boundary crosses adjacent boundaries of the rockfill partition, cushion layer, or transition material, the server segments the construction site boundary along the material partition boundary, forming construction sites for the corresponding material partitions. The segmented construction sites use the same construction elevation and are then entered into the subsequent mesh generation process.

[0021] Furthermore, the server divides the grid according to the compactor's operating range and positioning accuracy. The compactor's operating range refers to the actual coverage width of the rollers on the construction site surface during compactor operation. The positioning accuracy refers to the positional error range of the compactor's positioning data within the construction site surface. The grid is continuously arranged in the direction of the dam axis and perpendicular to the dam axis, with adjacent grids sharing a boundary. The grid width is no greater than the compactor's operating range, and the intersection of the grid boundary and the construction site surface boundary is trimmed according to the construction site surface boundary. Grids formed at the edge of the construction site surface still record their actual spatial range and are not incorporated into adjacent material zones. The server records the dynamic position coordinates of each grid, which consist of the spatial coordinates of each turning point of the grid boundary and the internal position coordinates of the grid.

[0022] Understandably, the mesh generation process first reads the material zones and construction elevation of the current construction site, then reads the compactor's effective range and positioning accuracy. The server generates the first row of meshes along one side of the construction site boundary and continuously generates subsequent meshes according to the same effective range. When a subsequent mesh exceeds the construction site boundary, the server trims the excess portion according to the construction site boundary. When a mesh crosses a material zone boundary, the server divides that mesh into two meshes according to the material zone boundary and records the material zones separately. This process does not change the construction elevation of the construction site or the dam construction grade. After mesh generation is complete, the server checks for overlaps and gaps in the mesh spatial range. If two meshes overlap, the server re-trims them according to their common boundary. If there are unmeshed spaces within the construction site, the server adds meshes to those spaces and re-records the dynamic position coordinates.

[0023] The dam body tiered construction procedure consists of construction elevation, material zoning, and construction time. The server compares the construction elevation of the current construction zone with the construction elevation in the dam body tiered construction procedure and reads the corresponding construction tier. When the construction elevation of the current construction zone is between two adjacent construction tiers, the server reads the construction tier corresponding to the construction time in the zone coordinate data. If the construction time in the zone coordinate data is earlier than the start time of the current construction tier or later than the start time of the next construction tier, the server does not write the dam body construction tier and returns to the sub-control station to check the construction time and the current construction elevation. After both the construction elevation and construction time correspond to the same construction tier, the server writes the construction tier into the dam body construction tier field of each grid.

[0024] When associating with the BIM engineering model, the server inputs the dynamic position coordinates of the grid into the BIM engineering model's coordinate processing. The server first reads the 3D model region corresponding to the current construction elevation in the BIM engineering model, and then retrieves intersecting regions according to the spatial coordinates of each turning point of the grid boundary. When the grid is completely within a certain BIM engineering model region, the server records that region as the corresponding region of the BIM engineering model. When the grid intersects with multiple BIM engineering model regions simultaneously, the server trims the grid again based on the material partition boundaries until each grid corresponds to only one material partition and one BIM engineering model region. If there is no region in the BIM engineering model that intersects with the grid's dynamic position coordinates, the server retains the grid and construction coordinate data, records that the BIM engineering model is not matched, and does not output the grid to S200. After the corresponding region is added to the BIM engineering model, the server re-executes spatial matching.

[0025] When associating a 3D finite element model, the server reads the material partitions, dam construction levels, and dynamic position coordinates of the mesh, and retrieves finite element nodes within the same material partition and construction level in the 3D finite element model. When a finite element node is within the mesh space, the server writes it into the corresponding finite element node field of the mesh. When a finite element node is on a mesh boundary, the server records it in the mesh with the smaller distance from its internal position coordinates; if the distances are the same, it records it in both adjacent meshes and writes it to the common boundary. If no finite element node exists within a mesh, the server reads the 3D finite element model region intersecting with that mesh and records the finite element node within that region that is closest to its internal position coordinates. When a finite element node falls into meshes of different material partitions simultaneously, the server re-examines the finite element node and mesh according to the material partitions in the 3D finite element model, without directly establishing a correspondence.

[0026] Furthermore, the server performs cross-checks on the corresponding areas and finite element nodes of the BIM engineering model. The cross-check reads the dynamic position coordinates of the mesh, material partitions, dam construction classifications, the corresponding areas of the BIM engineering model, and the finite element nodes. When the material partitions of the corresponding areas in the BIM engineering model are the same as those in the 3D finite element model, the server retains the correspondence. When they are different, the server first checks whether the mesh crosses the material partition boundary, and then checks the material partitions in both the BIM engineering model and the 3D finite element model. If the mesh crosses the material partition boundary, the server re-trimmes the mesh. If the material partitions in the model are inconsistent, the server records a model mismatch and stops the mesh from entering S200. After the material partitions in the model are made consistent, the server regenerates the correspondence.

[0027] The grid correspondence of the construction site consists of the construction site, construction site boundary, grid, dynamic position coordinates, material zoning, dam construction grade, corresponding area in the BIM engineering model, material zoning in the 3D finite element model, finite element nodes, and construction time. The construction site field records the current construction area. The construction site boundary field records the spatial coordinates of each turning point collected by the Beidou surveying rod. The grid field records the spatial range of each grid within the construction site. The dynamic position coordinate field records the grid boundary and the position within the grid. The material zoning field records the rockfill zoning, subbase, or transition material. The dam construction grade field records the dam grade construction procedure corresponding to the current grid. The corresponding area field in the BIM engineering model records the position of the grid in the 3D model. The material zoning and finite element node fields in the 3D finite element model record the corresponding position of the grid in the 3D finite element model. The construction time field records the acquisition time of the construction coordinate data and the start time of the current construction grade.

[0028] The core parameter set for the grid correspondence of the dam surface consists of the construction dam surface boundary, grid dynamic position coordinates, material zoning, dam construction grade, corresponding area of ​​the BIM engineering model, and finite element nodes. The construction dam surface boundary and grid dynamic position coordinates are used in S200 to match dump truck unloading information, bulldozer real-time spatial positioning information, and paving process information to the corresponding grid. Material zoning is used in S200 to check the correspondence between dam material names and material zoning, and in S300 to call the corresponding material source parameters and quality control criteria. Dam construction grade, corresponding area of ​​the BIM engineering model, and finite element nodes are used in S400 to update the digital twin model of the corresponding grid and finite element nodes. Preferably, the grid correspondence also records construction time and common boundaries. Construction time is used to distinguish different construction grades and different dam construction processes, and common boundaries are used for subsequent grid re-division and finite element node matching.

[0029] This step automatically generates the grid correspondence for the construction site upon receiving a complete set of construction coordinate data for the first time. When the current construction site boundary, material zoning, or dam grading construction procedure changes, the server regenerates the grid correspondence affected by the change. Grids unaffected by the change retain their original correspondence. The grid correspondence before regeneration is saved as historical data according to construction time. The regenerated grid correspondence is written to the current construction site and made available for use by S200. If the server detects mismatches between the construction coordinate data, BIM engineering model, and 3D finite element model, it retains the original data and mismatched locations, and does not output the corresponding grid to S200 until the relevant information is completed.

[0030] In one engineering embodiment, the current construction elevation corresponds to the adjacent areas of the transition material and riprap sections. A Beidou measuring rod collects spatial coordinates at each turning point of the construction site boundary and transmits them to the server via a sub-control station. The server connects adjacent turning points to form the construction site boundary. The construction site boundary spans the adjacent boundaries of the transition material and riprap sections. The server divides the construction site into two construction sites along the material section boundaries and records the transition material and riprap sections respectively. The server divides the grid according to the compactor's operating range and positioning accuracy, trims the grid at the construction site edge according to the boundary, and records adjacent grids at the material section boundaries. Subsequently, the server matches the dynamic position coordinates of each grid to the corresponding area in the BIM engineering model and retrieves finite element nodes within the same material section and construction level in the 3D finite element model. After completing the cross-check, the server generates the site grid correspondence. After receiving the site grid correspondence, S200 matches the dump truck unloading information, bulldozer real-time spatial positioning information, and paving process information to the corresponding grids according to the construction site boundary and the grid dynamic position coordinates.

[0031] In Technical Solution 1, all construction sites are located within the same material partition. The server directly and continuously meshes within the boundary of the construction site and establishes a correspondence between each mesh and the corresponding area in the BIM engineering model, the material partition in the 3D finite element model, and the finite element nodes. In Technical Solution 2, the construction site spans adjacent material partitions. The server first segments the construction site along the material partition boundary, then meshes and establishes the model correspondence separately. In Technical Solution 3, there are positional differences between the material partition boundaries of the BIM engineering model and the 3D finite element model. The server retains the unmatched mesh and its corresponding spatial coordinates, stops the mesh from entering S200, and regenerates the site mesh correspondence after the material partitions in the two models have been consistent. All three technical solutions output site mesh correspondences with the same field names, and the input structure of S200 remains unchanged.

[0032] The technical effects of this step can be summarized as follows: S100 integrates the construction coordinate data, material zoning, and dam body graded construction procedures into the construction site grid, and establishes a correspondence between the grid and the BIM engineering model and the 3D finite element model. This grid correspondence provides S200 with unified spatial location, material zoning, and construction graded inputs. When the construction site boundary or model area changes, only the grid correspondence affected by the change is regenerated; the original correspondence is saved as historical data according to the construction time.

[0033] S200. Based on the grid correspondence of the storage surface, the dump truck unloading information, bulldozer real-time spatial positioning information and paving process information are matched to the corresponding grid, and paving monitoring results are generated according to the paving thickness, flatness and quality control criteria. Specifically, step S200 is executed by the paving monitoring module on the server. The paving monitoring module receives the grid correspondence relationship of the paving surface output from step S100. This grid correspondence relationship includes the construction paving surface boundary, dynamic grid position coordinates, material zoning, dam construction grade, corresponding area in the BIM engineering model, finite element nodes, and construction time. The construction paving surface boundary defines the spatial range of this round of paving operations. The dynamic grid position coordinates are used for spatial matching of dump truck unloading information, bulldozer real-time spatial positioning information, and paving process information. The material zoning is used to verify the dam material name. The dam construction grade and construction time are used to verify the construction sequence of this round of paving operations. After the paving surface grid correspondence relationship is written to the server, the paving monitoring module enters a waiting state and initiates this round of processing when the Beidou command terminal sends new dump truck unloading information, the bulldozer monitoring terminal sends new bulldozer real-time spatial positioning information, or the sub-control station sends new initial paving surface information.

[0034] The dump truck unloading information is generated by a dump truck unloading positioning device installed on the dump truck. This device includes an onboard BeiDou positioning device and an unloading sensor. The onboard BeiDou positioning device is installed on the roof of the dump truck to collect its positioning information. The unloading sensor is installed on the beam below the dump truck hopper to sense changes in the distance between the bottom of the hopper and the beam, and outputs an unloading signal when the distance change corresponds to the unloading state. The onboard BeiDou positioning device integrates the dump truck positioning information, the unloading signal, the construction time, and the name of the dam material into dump truck unloading information, which is then sent to the server via the BeiDou command unit. When the unloading signal is not present, the dump truck positioning information is only stored as vehicle operation information and is not written into the current paving process information.

[0035] The server reads the unloading location, construction time, and dam material name from the dump truck's unloading information and matches them item by item against the grid correspondence on the dam surface. The server first checks if the unloading location is within the boundary of the construction dam surface. If the unloading location is outside the boundary, the server records the dump truck's unloading information and location, but does not update the grid within the construction dam surface. If the unloading location is within the boundary, the server retrieves the grid containing the unloading location based on the grid's dynamic position coordinates and records that grid as the unloading grid. If the unloading location falls on the common boundary of adjacent grids, the server combines the dump truck's travel direction and the positioning information during the duration of the unloading signal to match the unloading location to the grid with the larger distribution range of the positioning information; if the distribution range of the positioning information is the same in adjacent grids, the server records both adjacent grids as the unloading grid.

[0036] Furthermore, the server compares the dam material name with the material partition corresponding to the unloading grid. When the dam material name and material partition correspond, the server writes the dump truck unloading information into the unloading grid. When the dam material name and material partition do not correspond, the server retains the dump truck unloading information, records the unloading grid as a mismatch between dam material and material partition, and does not generate a paving completion result for that grid. When the construction time in the dump truck unloading information is earlier than the construction time in the corresponding grid of the slab surface, the server saves this information as historical data and does not write it into the current construction slab surface. When the construction time in the dump truck unloading information belongs to the current dam body construction level, the server writes this information into the current unloading grid and records the unloading location and soil pile location.

[0037] Understandably, the location of the soil pile is generated jointly by the unloading location and the dump truck positioning information during the duration of the unloading signal. The server projects the dump truck positioning information from the start to the end of the unloading signal onto the construction site and reads the covered grid. When only one grid is covered during the duration of the unloading signal, that grid constitutes the soil pile location. When the duration of the unloading signal spans adjacent grids, the unloading grid and the adjacent grids affected by the unloading together constitute the soil pile location. Grids affected by the unloading are limited to those sharing boundaries with the unloading grid and located in the same material zone. Adjacent grids that cross material zone boundaries are not written into the same soil pile location.

[0038] The real-time spatial positioning information of the bulldozer is generated by the bulldozer monitoring terminal. The bulldozer monitoring terminal includes a bulldozer disc antenna, a bulldozer differential antenna, a bulldozer satellite positioning receiver, a bulldozer controller, a bulldozer data transmission radio, and a bulldozer data transmission antenna. The differential base station transmits differential positioning information. The bulldozer controller performs differential calculations on the differential positioning information and the satellite positioning information received by the bulldozer disc antenna to obtain the bulldozer's real-time spatial positioning information. This real-time spatial positioning information includes plane coordinates, elevation, direction of travel, and construction time, and is transmitted to the server via a relay station. After receiving the bulldozer's real-time spatial positioning information, the server first checks whether the construction time belongs to the current dam construction level, and then retrieves the grid where the bulldozer is located based on the plane coordinates.

[0039] The server generates the bulldozer's movement trajectory within the construction area based on the continuous changes in the bulldozer's real-time spatial positioning information. When the bulldozer moves from one grid to an adjacent grid, the server records the entry time, exit time, and trajectory coverage area. If the bulldozer's real-time spatial positioning information falls outside the boundary of the construction area, this positioning information is not included in the current paving process information. If the bulldozer's real-time spatial positioning information is temporarily missing, the server retains the positioning information before and after the missing information and records the corresponding trajectory segment as missing positioning information. Before the positioning information is replenished, this trajectory segment is not used to determine whether the unloading grid has been paved.

[0040] The server spatially matches the bulldozer's movement trajectory with the location of the soil pile. When the bulldozer's trajectory enters the soil pile location, the server records the bulldozer's operating status as bulldozing and activates the paving process information for the corresponding grid at the soil pile location. When the bulldozer's trajectory does not enter the soil pile location, the server records the bulldozer's operating status as a transfer, without updating the layer thickness and smoothness of the unloading grid. When the bulldozer's trajectory leaves one soil pile location and enters another, the server ends the current bulldozing record for the previous soil pile location and starts the bulldozing record for the next soil pile location. The paving process information consists of dump truck unloading information, bulldozer real-time spatial positioning information, soil pile location, bulldozer operating status, grid paving elevation, layer thickness, smoothness, and construction time.

[0041] Specifically, the server calculates the grid paving elevation based on the initial information of the work surface and the real-time spatial positioning information of the bulldozer. The initial information of the work surface includes the initial elevation of each location on the work surface. The server reads the elevation of the bulldozer as it passes through the grid and matches it with the initial elevation of that grid. When multiple bulldozer elevations exist within a grid, the server arranges these elevations according to construction time and updates the grid paving elevation using the elevation formed by the last consecutive passage. When only one bulldozer elevation exists within a grid, the server records this elevation as the current grid paving elevation and retains the record status pending subsequent positioning information. When the spatial position of the bulldozer elevation does not correspond to the initial elevation of the grid, the server does not calculate the ply thickness at that location.

[0042] The ply thickness refers to the difference between the grid paving elevation and the grid initial elevation. Let the paving elevation at the i-th location within the same grid be Zi, and the initial elevation be Z0i. Then the ply thickness at that location is hi = Zi - Z0i. When a grid contains n valid locations, the grid ply thickness is the average of the ply thicknesses at each location. The server simultaneously records the maximum and minimum ply thicknesses within the grid. A valid location refers to a location where both the bulldozer's real-time spatial positioning information and the initial ply information correspond to an elevation. If a location lacks an initial elevation or a bulldozer elevation, that location is not included in the grid ply thickness calculation and is recorded as having missing elevation information.

[0043] The smoothness refers to the undulation of the paving elevation of the grid and adjacent grids. The server reads the paving elevation of each valid location within the current grid and calculates the difference between the maximum and minimum paving elevation to obtain the internal smoothness of the grid. The server then reads the paving elevation of adjacent grids that share a boundary with the current grid and belong to the same material zone, calculates the difference between the average paving elevation of the current grid and the average paving elevation of each adjacent grid, and obtains the grid boundary smoothness. The larger value between the internal smoothness of the grid and the grid boundary smoothness is recorded as the current grid smoothness. When there is no paving elevation for adjacent grids, the server first records the internal smoothness of the grid and updates the grid boundary smoothness after the adjacent grids have formed paving elevations.

[0044] The quality control criteria are read from the construction process requirements of the material zone corresponding to the current grid, including the specified ply thickness, allowable ply thickness deviation, and allowable flatness value. The server compares the grid ply thickness with the specified ply thickness and compares the absolute value of the difference with the allowable ply thickness deviation. If the absolute value of the difference is not greater than the allowable ply thickness deviation, and the flatness of the current grid is not greater than the allowable flatness value, the server records that the ply thickness and flatness meet the quality control criteria. If either comparison result is not met, the server records that the corresponding grid continues to be paved. When the material zones are different, the server calls the quality control criteria corresponding to each material zone separately, and does not share the specified ply thickness and allowable flatness values ​​between different material zones.

[0045] Furthermore, the coverage of the bulldozer's real-time spatial positioning information with the grid corresponding to the dump truck's unloading information is determined by the coverage range of the movement trajectory. The server overlays the bulldozer's movement trajectory coverage range with the spatial range of the unloading grid. When the movement trajectory coverage range covers the recorded soil pile positions within the unloading grid, the server records that the bulldozer's real-time spatial positioning information covers the grid corresponding to the dump truck's unloading information. When the movement trajectory only passes through the edge of the unloading grid, or when there are still parts of the soil pile position not covered by the movement trajectory, the server records the corresponding grid and continues paving. This coverage determination is performed in the same processing round as the layer thickness and flatness determination.

[0046] When the bulldozer's real-time spatial positioning information covers the grid corresponding to the dump truck's unloading information, and the pavement thickness and smoothness meet the quality control criteria, the server marks the corresponding grid in the paving monitoring results to receive compaction parameters. If the corresponding grid does not meet the coverage, pavement thickness, or smoothness conditions, the server marks the corresponding grid in the paving monitoring results to continue paving and writes the unmet requirements into the paving process information. During continued paving, the server continuously receives new real-time spatial positioning information from the bulldozer and recalculates the trajectory coverage, grid paving elevation, pavement thickness, and smoothness. When the dump truck unloads again into the same grid during continued paving, the server updates the soil pile position based on the new dump truck unloading information and re-records the paving process information for that grid from the new unloading construction time.

[0047] The paving monitoring results consist of grid data, material zoning, dam construction grading, dump truck unloading information, soil pile location, bulldozer real-time spatial positioning information, bulldozer operating status, movement trajectory coverage, pavement thickness, smoothness, quality control criteria, grid markers, and construction time. The grid markers indicate whether to receive compaction parameters or continue paving. The server sends the grids that receive compaction parameters, along with their dynamic coordinates, material zoning, pavement thickness, smoothness, and construction time, to S300. S300 only matches the compactor positioning data and excitation force data to the grids marked as receiving compaction parameters in the paving monitoring results. Grids marked as continuing paving are retained in S200 and updated with new bulldozer real-time spatial positioning information.

[0048] Understandably, the minimum set of core parameters in this step includes the construction site boundary, grid dynamic position coordinates, material zoning, dump truck positioning information, unloading signal, dam material name, bulldozer real-time spatial positioning information, initial site information, pavement thickness, flatness, and quality control criteria. This minimum set of core parameters is used to complete unloading information matching, bulldozer operating range matching, and paving result determination. Preferably, the paving monitoring results also record the soil pile location, bulldozer operating status, movement trajectory coverage, and construction time. These preferred contents are used for reprocessing upon the arrival of new unloading information and new paving process information, and are also used by the S400 to read the original monitoring results.

[0049] This step is automatically triggered under four conditions: arrival of new dump truck unloading information, bulldozer entering the unloading grid, bulldozer leaving the unloading grid, and changes in the initial information of the storage area. When new dump truck unloading information arrives, the server updates the unloading grid and the position of the soil pile. When the bulldozer enters the unloading grid, the server initiates the paving process information update for that grid. When the bulldozer leaves the unloading grid, the server calculates the ply thickness and smoothness for that round. When the initial information of the storage area changes, the server recalculates the ply thickness and smoothness of the affected grids based on the new initial elevation. The paving monitoring results before recalculation are saved as historical data according to the construction time; grids unaffected by changes in the initial information of the storage area retain their original paving monitoring results.

[0050] In one engineering embodiment, the current construction site belongs to the transition material zone. The grid correspondence output by S100 has recorded the construction site boundary, the dynamic position coordinates of each grid, the transition material zone, the dam construction grade, and the construction time. After the dump truck enters the construction site, the dump truck unloading positioning device collects the dump truck's positioning information. The unloading sensor outputs an unloading signal when the hopper is raised. The vehicle-mounted Beidou positioning device integrates the dump truck's positioning information, unloading signal, transition material name, and construction time into dump truck unloading information, and sends it to the server via the Beidou command unit. The server matches the unloading position to the target grid and checks the correspondence between the transition material name and the target grid material zone. After successful matching, the server records the unloading grid and the soil pile position.

[0051] Subsequently, the bulldozer monitoring terminal sends the bulldozer's real-time spatial positioning information to the server via a relay station. The server determines whether the bulldozer has transitioned from a transfer state to a bulldozing state based on its movement trajectory and updates the paving process information of the target grid according to the location of the soil pile. After the bulldozer leaves the target grid, the server reads the initial elevation of the target grid and the bulldozer's elevation, and calculates the pavement thickness. Simultaneously, the server calculates the paving elevation difference within the target grid and its difference from the average paving elevation of adjacent grids to obtain the smoothness. When the bulldozer's movement trajectory covers the soil pile location, and both the pavement thickness and smoothness meet the quality control criteria for transition material zoning, the paving monitoring result marks the target grid as receiving compaction parameters. After receiving this paving monitoring result, the S300 matches the subsequent compactor positioning data and excitation force data to the target grid.

[0052] In Technical Solution 1, each unloading action falls into a single grid. The server calculates the pavement thickness and smoothness within that grid and marks it as accepting compaction parameters only after meeting the quality control criteria. In Technical Solution 2, an unloading action spans multiple adjacent grids. The server records the bulldozer's movement trajectory, pavement thickness, and smoothness for each grid. Only grids meeting the quality control criteria are marked as accepting compaction parameters. In Technical Solution 3, the unloading location and dam material name correspond to different material zones. The server retains the dump truck unloading information, records the corresponding grid as mismatched dam material, and stops that grid from entering S300. All three technical solutions generate paving monitoring results with the same field structure.

[0053] The technical effects of this step can be summarized as follows: S200 maps dump truck unloading information, bulldozer real-time spatial positioning information, and initial paving information to the grid generated by S100, forming the paving thickness, smoothness, and trajectory coverage within the same grid. The quality control criteria and bulldozer coverage jointly control whether the grid receives the compaction parameters from S300; grids that have not completed paving are not processed for compaction parameters. When new dump truck unloading information or initial paving information appears, the affected grids regenerate paving monitoring results.

[0054] S300. Based on the paving monitoring results, the roller positioning data and excitation force data are matched to the corresponding grid, the target motion trajectory is selected and the number of rolling passes is calculated to generate rolling parameters; the rolling parameters, material source parameters, continuous compaction index and test pit test results are input into the compaction quality evaluation model to generate compaction quality monitoring results. Specifically, step S300 is executed by the compaction quality evaluation module in the server. The compaction quality evaluation module receives the paving monitoring results output by step S200. These results include the grid, grid dynamic position coordinates, material zoning, dam construction grading, dump truck unloading information, paving process information, layer thickness, smoothness, quality control criteria, grid markers, and construction time. The grid markers include receiving compaction parameters and continuing paving. The compaction quality evaluation module only initiates compaction process monitoring for grids marked as receiving compaction parameters. Grids marked as continuing paving are still processed by step S200, and the target movement trajectory and number of compaction passes collected in this step are not received.

[0055] The compaction quality evaluation module includes a positioning data receiving section, a positioning data checking section, a target motion trajectory processing section, a compaction parameter processing section, a continuous compaction index acquisition section, a test pit test result processing section, and a compaction quality evaluation model. The positioning data receiving section receives the roller positioning data and excitation force data. The positioning data checking section checks the positioning quality index and accuracy attenuation factor of the roller positioning data. The target motion trajectory processing section arranges the roller positioning data that meets the positioning accuracy requirements according to the construction time and matches it to the corresponding grid. The compaction parameter processing section generates compaction parameters based on the target motion trajectory, running time, running speed, driving direction, vibration state, pavement thickness, and excitation frequency. The continuous compaction index acquisition section obtains the compaction wave velocity and unit volume compaction work from the roller vibration signal. The test pit test result processing section matches the test pit coordinates, test pit time, dam material name, compaction degree, dry density, or porosity to the corresponding grid. The compaction quality evaluation model receives the above-mentioned rolling parameters, material source parameters, continuous compaction index and test pit test results, and outputs the compaction quality monitoring results of the corresponding grid.

[0056] The S300 module is activated when the grid for receiving compaction parameters first appears in the paving monitoring results. When compactor positioning data or excitation force data arrives at the server, the compaction quality evaluation module performs real-time processing on that grid. When new material source parameters arrive at the server, the compaction quality evaluation module updates the material source parameters for the corresponding material zone. When new continuous compaction indices arrive at the server, the compaction quality evaluation module updates the continuous compaction indices for the corresponding grid. When new test pit results arrive at the server, the compaction quality evaluation module compares the test pit time with the target trajectory time of the last time the corresponding grid was included in the compaction pass count, and calls the compaction quality evaluation model based on the comparison result.

[0057] Furthermore, the compactor positioning data is collected by a compactor monitoring terminal installed on the compactor. The compactor monitoring terminal includes a satellite antenna, a differential signal receiving antenna, a Global Navigation Satellite System (GNSS) receiver, a ZIGBEE data transmission device, a ZIGBEE data transmission antenna, and vibration sensors. The satellite antenna and differential signal receiving antenna are respectively connected to the GNSS receiver. The GNSS receiver receives differential information sent by the positioning reference station and corrects the compactor's current position using Real-Time Kinematic (RTK) positioning. Vibration sensors are installed on the compactor rollers to collect excitation force data during compactor operation. The ZIGBEE data transmission device integrates the GNSS positioning data and excitation force data and transmits them to the server via the ZIGBEE dam area wireless network. The compactor positioning data includes the compactor number, plane coordinates, elevation, running time, running speed, direction of travel, positioning quality indicators, and accuracy attenuation factor. The excitation force data includes the compactor number, acquisition time, excitation force output status, and excitation frequency.

[0058] After receiving the roller compactor positioning data and excitation force data, the server first pairs them according to the roller compactor number and acquisition time. If the roller compactor positioning data and excitation force data for the same roller compactor number are acquired within the same acquisition interval, the server writes both types of data into the same roller compactor operation record. If excitation force data is missing, the server retains the roller compactor positioning data and marks the corresponding roller compactor operation record as missing excitation force data. If roller compactor positioning data is missing, the server retains the excitation force data but does not match it to the grid. Subsequently supplemented roller compactor positioning data and excitation force data are re-paired according to the roller compactor number and acquisition time.

[0059] The positioning quality index characterizes the current positioning status of the GNSS receiver. The Dilution of Precision (DOP) characterizes the accuracy of the GNSS positioning data affected by the spatial distribution of satellites. The server reads the positioning quality index and DOP output by the GNSS receiver. When the positioning quality index indicates fixed RTK calculation and the DOP is less than 4.0, the server records the GNSS positioning data as meeting the positioning accuracy requirements. When the positioning quality index does not indicate fixed RTK calculation, or the DOP is not less than 4.0, the server records the GNSS positioning data as not meeting the positioning accuracy requirements and checks whether the compactor has been tracked by the intelligent total station.

[0060] A 360° omnidirectional prism is installed on top of the compactor. Each compactor corresponds to a unique 360° omnidirectional prism number. The positioning compensation station is equipped with an intelligent total station, a Zigbee data transmission device, and a data transmission antenna. The server maintains the correspondence between each compactor number and the 360° omnidirectional prism number. When the GNSS positioning data of a compactor does not meet the positioning accuracy requirements, the server reads the corresponding 360° omnidirectional prism number and sends a search command to the untracked intelligent total station. After the intelligent total station searches for and locks onto the corresponding 360° omnidirectional prism, it continuously collects the current position of the compactor at set time intervals, generating intelligent total station positioning data. The intelligent total station positioning data is sent to the server via the positioning compensation station, replacing the GNSS positioning data that does not meet the positioning accuracy requirements within the same time interval.

[0061] When the intelligent total station is tracking a compaction roller, and the subsequent GNSS positioning data for that roller is restored to RTK fixed solution with an accuracy attenuation factor of less than 4.0, the server sends a stop tracking command to the intelligent total station and re-adopts GNSS positioning data. If the 360° omnidirectional prism is obstructed, locking is interrupted, or positioning data is missing during the intelligent total station's tracking, the server retains the excitation force data for that time interval and records the corresponding trajectory segment as missing positioning data. The missing positioning data segment does not participate in grid matching and compaction pass calculation. After the intelligent total station re-locks the 360° omnidirectional prism, the server begins processing the target motion trajectory from the new intelligent total station positioning data. The intelligent total station positioning compensation process and the 1-second interval continuous tracking method can be implemented according to the layout of the real-time monitoring device for dam compaction construction quality.

[0062] After completing the positioning accuracy check, the server arranges the positioning data of the roller compactors that meet the accuracy requirements according to their running time. When adjacent roller compactor positioning data belong to the same roller compactor number and the acquisition time is continuous, the server connects the adjacent position coordinates to form the roller compactor's movement trajectory. If the acquisition time of adjacent position coordinates is discontinuous, or if there is a gap in positioning data, the server interrupts the current movement trajectory and reconstructs the trajectory from the next roller compactor positioning data that meets the accuracy requirements. The movement trajectory records the roller compactor number, position coordinates, running time, running speed, and direction of travel.

[0063] The server reads the grids that receive compaction parameters and their dynamic position coordinates from the paving monitoring results, and spatially matches the roller's movement trajectory with each grid. When the movement trajectory lies within the grid's spatial range, the server matches that segment of the trajectory to the corresponding grid. When the movement trajectory lies on the common boundary of adjacent grids, the server forms the roller's coverage area based on the roller's roller action range, and calculates the overlap between the roller's coverage area and adjacent grids. Grids with larger overlap areas record that segment of the trajectory. When the overlap areas are the same, both adjacent grids record that segment of the trajectory and calculate their respective compaction passes.

[0064] Furthermore, the server compares the running time of the target trajectory with the construction time in the corresponding grid paving process information. If the running time of the target trajectory is later than the paving completion time corresponding to the paving process information, the server continues to check the running speed and excitation force output status. If the running time of the target trajectory is earlier than or equal to the paving process information, the server saves the trajectory as construction machinery operation data and does not use it as the target trajectory for the current grid. When the paving process information is updated during compaction, the server re-selects target trajectories starting from the updated construction time; target trajectories formed before the update are retained in the historical data corresponding to the original paving monitoring results.

[0065] The operating speed is directly collected by the compactor monitoring terminal, or calculated from the position change and time difference of adjacent compactor positioning data. The server reads the operating speed range corresponding to the current material zone from the compaction plan. When the operating speed is within the specified range, the server records that the operating speed conforms to the compaction plan. When the operating speed is below the specified range, the server checks whether the trajectory segment corresponds to a stop or a turn. When the operating speed is above the specified range, the server records the trajectory segment as not conforming to the compaction plan. Trajectory segments that are stopped, turned around, or whose speed does not conform to the compaction plan are not counted in the compaction pass count.

[0066] The excitation force output state is generated by the excitation force data collected by the vibration sensor. When the excitation force data recording system of the compactor is in operation, the server records the corresponding trajectory segment as a vibration state. When the excitation force data recording system of the compactor is not in operation, the server records the corresponding trajectory segment as a non-vibration state. The compaction scheme specifies that when the current grid uses vibratory compaction, only the trajectory segment corresponding to the vibration state is retained as the target motion trajectory. When the compaction scheme specifies that the current grid uses static compaction, the server records the target motion trajectory according to the corresponding compaction scheme, without using the excitation force output state as an exclusion condition. When the excitation force output state changes within the same trajectory segment, the server divides the trajectory segment according to the time of change and records the vibration state and non-vibration state separately.

[0067] Furthermore, the target motion trajectory refers to the motion trajectory located within the grid where the paving monitoring result marker receives the compaction parameters, whose running time is later than the paving completion time corresponding to the paving process information, whose running speed conforms to the compaction scheme, whose excitation force output state conforms to the compaction scheme, and whose positioning data meets the accuracy requirements. The server writes the trajectory segments that meet the above conditions into the target motion trajectory. If any condition is not met, the server retains the original roller running record and writes the corresponding content that was not included in the compaction pass count. The original roller running record does not include the compaction parameters in the current grid.

[0068] The server calculates the number of compaction passes based on the target's trajectory and the effective range of the rollers. The server projects the area covered by the roller body corresponding to the target's trajectory onto a grid. When the roller body continuously passes through a grid, the server records one coverage. When the target's trajectory continuously passes through the same grid in the same direction of travel, continuous coverage is recorded as one pass. When the roller leaves and re-enters the grid, or when its direction of travel changes and it covers the grid again, the server records another pass. Repeated coverage caused by the roller stopping, making short round trips, or turning around within the grid does not individually increase the compaction pass count. The method for counting the number of times the grid is covered by the roller body after the compaction area is gridded corresponds to the method for collecting compaction pass counts in the earth-rock dam material compaction quality evaluation method.

[0069] When multiple compaction rollers are operating within a grid, the server generates target motion trajectories for each roller according to its roller number, and then merges the effective coverage counts of each roller according to the running time. If the coverage areas of multiple rollers overlap within the same time period, the server records the coverage separately according to their respective target motion trajectories, without repeatedly recording overlapping areas in the compaction pass count of the same roller. When the positioning data of a roller is switched from GNSS positioning data to intelligent total station positioning data, the server retains the same roller number and connects the target motion trajectories before and after the switch according to the running time, without re-increasing the compaction pass count.

[0070] The compaction parameters include the number of compaction passes, vehicle speed, vehicle direction, pavement thickness, excitation frequency, excitation force output status, running time, and dynamic position coordinates. The number of compaction passes is derived from the effective coverage of the grid by the target trajectory. Vehicle speed and direction are derived from the compactor positioning data. Pavement thickness is derived from the paving monitoring results output by the S200. Excitation frequency and excitation force output status are derived from the excitation force data. Running time records the time when the target trajectory enters and leaves the grid for each instance. Dynamic position coordinates record the position of the target trajectory within the grid. The server encapsulates the above information into compaction parameters according to grid, material zoning, and dam construction grading.

[0071] If any of the following parameters are missing: number of compaction passes, vehicle speed, vehicle direction, pavement thickness, or excitation frequency, the server retains the acquired compaction parameters and records the corresponding mesh as having incomplete compaction parameters. Mesh with incomplete compaction parameters is not included in the compaction quality evaluation model. After the missing parameters are filled in, the server repackages the compaction parameters. As the roller continues operating on the current mesh and forms a new target trajectory, the server updates the number of compaction passes and the time of the last target trajectory, and inputs the updated compaction parameters into the subsequent compaction quality evaluation process.

[0072] Furthermore, the material source parameters are derived from the particle size analysis of the dam material from the stockyard. These parameters include P5 content, uniformity coefficient, curvature coefficient, and maximum particle size. The P5 content is calculated based on the particle size analysis results corresponding to a 5 mm sieve. The uniformity coefficient reflects the particle size distribution range of the dam material. The curvature coefficient reflects the continuity of the dam material gradation curve. The maximum particle size refers to the largest particle size recorded in the dam material particle size analysis. Particle size analysis of the stockyard dam material is performed using a sieving method. Dam material with a particle size of 5 mm and larger is sieved outdoors, with sieved particle sizes including 200 mm, 100 mm, 60 mm, 40 mm, 20 mm, 10 mm, and 5 mm. Gravel larger than 200 mm is directly measured. Soil samples smaller than 5 mm undergo indoor particle size analysis. The server matches the material source parameters to the corresponding grid according to the dam material name and material zone.

[0073] When the dam material name in the dump truck unloading information matches the dam material name in the material source parameters, the server writes the corresponding material source parameters into the grid. If the dam material name is the same but the material yard test time is later than the unloading time of this grid, the server checks whether the material source parameters come from the same batch of dam material. If they belong to the same batch, the server updates the grid's material source parameters. If they are not from the same batch, the server retains the raw material source parameters and does not replace them with the later-arriving material source parameters. When the dam material name in the dump truck unloading information does not correspond to the material zone, the server uses the mismatched dam material record generated by S200, and this grid is not included in the compaction quality evaluation model.

[0074] The continuous compaction parameters include rolling wave velocity and compaction work per unit volume. A first Integrated Electronics Piezo-Electric (IEPE) accelerometer is installed on the frame next to the vibratory wheel of the compactor, and a second IEPE accelerometer is installed under the drive rear axle next to the rear rubber tire of the compactor. The two IEPE accelerometers are connected to a dynamic signal testing and analysis system. The dynamic signal testing and analysis system acquires vibration signals from both locations in real time and sends the vibration signals, acquisition time, and compactor number to a server.

[0075] The server matches vibration signals from two IEPE accelerometers within the same operating time and calculates the phase difference between the two signals. The server then calculates the compaction wave velocity based on the installation distance between the two IEPE accelerometers and the corresponding time difference between the vibration signals. If a vibration signal from one IEPE accelerometer is missing, or if the acquisition times of the two vibration signals do not correspond, the server does not generate the compaction wave velocity for that time interval. After the vibration signal is recovered, the server recalculates the compaction wave velocity from the recovery time.

[0076] The compaction work per unit volume is generated based on the work done by the vibrating roller of the compactor, its operating speed, the compactor's operating range, and the ply thickness. The server reads the excitation frequency, operating speed, compactor's operating range, and the ply thickness output by S200, and distributes the work done corresponding to the same target motion trajectory to the volume of dam material covered by the compactor body to obtain the compaction work per unit volume. If the target motion trajectory does not meet the conditions for counting compaction passes, the corresponding work is not written into the compaction work per unit volume of the current grid. When there are multiple target motion trajectories within a grid, the server accumulates the compaction work per unit volume corresponding to each target motion trajectory according to the running time. The acquisition methods of the compaction wave velocity and compaction work per unit volume correspond to the acquisition methods of continuous compaction indexes in the comparison file.

[0077] Furthermore, the test pit results were collected by a Personal Digital Assistant (PDA) device with General Packet Radio Service (GPRS) capability. The test pit results included the coordinates of the test pit location, the name of the dam material, the test date, moisture content, gradation, compaction degree, and dry density or porosity. The test pit locations were set within the compacted grid. After the on-site test pit excavation was completed, the volume of the test pit was measured using the water filling method, and the wet density and dry density were calculated based on the mass of the dam material within the pit, the moisture content, and the test pit volume. The compaction degree was generated based on the measured dry density and the corresponding design dry density of the dam material. The porosity was generated based on the density and dry density of the dam material. The PDA device sent the test pit results and the coordinates of the test pit location to the server.

[0078] The server retrieves the corresponding grid from the grid mapping based on the test pit coordinates and checks the dam material name and grid material zone corresponding to the test pit. If the dam material name and material zone correspond, the server continues to compare the test pit time with the target trajectory time of the last time the corresponding grid was included in the compaction pass count. If the test pit time is later than the last target trajectory time, and no new compaction parameters appear after the test pit, the server records the test pit result as the test result of the current grid. If the test pit time is earlier than the last target trajectory time, the server saves the test pit result as historical data and does not use it as the final basis for the current compaction quality evaluation.

[0079] After the test pit is completed, when a new target trajectory appears in the corresponding grid and new compaction parameters are generated, the server retains the original test pit results and the original test pit time, and records the test pit results as earlier than the last target trajectory. When new test pit results are subsequently generated for that grid, the server re-compares the new test pit time with the last target trajectory time. If the new test pit time satisfies the temporal sequence, the compaction quality evaluation model accepts the new test pit results. This time processing method ensures that the test pit results correspond to the last compaction state of the current grid.

[0080] The compaction quality evaluation model establishes the correspondence between material source parameters, rolling parameters, continuous compaction indices, and dry density. When establishing the compaction quality evaluation model, the server collects the material source parameters, rolling parameters, and continuous compaction indices corresponding to each test pit, and uses the measured dry density of the test pits as output data. The server performs correlation analysis on P5 content, uniformity coefficient, curvature coefficient, maximum particle size, number of rolling passes, vehicle speed, vehicle direction, pavement thickness, excitation frequency, rolling wave velocity, and compaction work per unit volume with dry density. After the correlation analysis is completed, the parameters related to dry density are used as input parameters for the compaction quality evaluation model.

[0081] The compaction quality evaluation model is built using an artificial neural network. The server divides the sample data into training data and test data. The training data is used to establish the correspondence between material source parameters, rolling parameters, continuous compaction indices, and dry density. The test data is used to compare the measured and predicted dry density values. After the compaction quality evaluation model completes training, the server saves the model parameters. When the current grid generates complete rolling parameters, material source parameters, continuous compaction indices, and test pit test results that meet the time requirements, the server inputs the above data into the compaction quality evaluation model to obtain the predicted dry density value for the current grid. The input composition and dry density output relationship of the compaction quality evaluation model can be expressed in the form ρd=f(Pms, Prp, Pcc), where ρd is the dry density, Pms is the material source parameter, Prp is the rolling parameter, and Pcc is the continuous compaction indices.

[0082] When the test pit results include compaction degree, the server writes the compaction degree into the compaction quality monitoring results. When the test pit results include dry density, the server writes both the measured and predicted dry density values ​​into the compaction quality monitoring results. When the test pit results include porosity, the server writes the porosity into the compaction quality monitoring results. After the compaction quality evaluation model outputs the predicted dry density value, the server compares the predicted dry density value with the quality control criteria for the current material partition. If the predicted dry density value meets the quality control criteria, the server records the corresponding grid as a quality-qualified area. If the predicted dry density value does not meet the quality control criteria, the server records the corresponding grid as a quality-unqualified area.

[0083] When missing values ​​exist in the input data of the compaction quality evaluation model, the server does not directly use the missing data for evaluation and records the corresponding grid as incomplete data. When duplicate values ​​exist in the input data, the server retains one corresponding data point based on the grid, roller number, acquisition time, and dynamic position coordinates. When the input data significantly deviates from other data in the same material zone, the server retains the original data and records the abnormal data, without directly deleting the corresponding construction record. After the abnormal data has been checked on-site, the server re-invokes the compaction quality evaluation model based on the checked material source parameters, compaction parameters, continuous compaction index, or test pit test results.

[0084] The compaction quality monitoring results include grid, material zoning, dam construction grading, paving process information, target movement trajectory, time of the last target movement trajectory, number of compaction passes, vehicle speed, vehicle direction, layer thickness, excitation frequency, excitation force output status, material source parameters, compaction wave velocity, compaction work per unit volume, test pit test results, compaction quality, and construction time. The compaction quality includes degree of compaction, dry density, or porosity. The server writes qualified or unqualified areas into the corresponding grid and uses this compaction quality monitoring result as direct input to the S400.

[0085] Understandably, the minimum set of core parameters in this step includes: grid markers, grid dynamic position coordinates, material zoning, pavement thickness, and paving process information from the paving monitoring results; running time, running speed, driving direction, positioning quality indicators, and accuracy attenuation factor from the roller positioning data; excitation force output state and excitation frequency from the excitation force data; P5 content, non-uniformity coefficient, curvature coefficient, and maximum particle size from the material source parameters; rolling wave velocity and compaction work per unit volume from the continuous compaction indicators; and test pit coordinates, test pit time, degree of compaction, dry density, or porosity from the test pit test results. These minimum sets of core parameters collectively generate the target motion trajectory, number of rolling passes, rolling parameters, and compaction quality monitoring results. Preferably, the compaction quality monitoring results also record the roller number, intelligent total station positioning data, moisture content, gradation, and abnormal input data records.

[0086] This step is automatically triggered based on five conditions: arrival of compactor positioning data, arrival of excitation force data, departure of the target trajectory from the grid, arrival of test pit results, and update of material source parameters. When compactor positioning data and excitation force data arrive, the server updates the target trajectory. When the target trajectory leaves the grid, the server updates the number of compaction passes and compaction parameters. When test pit results arrive, the server compares the test pit time with the time of the last target trajectory. When material source parameters are updated, the server checks the dam material name and material zone corresponding to the material source parameters. After the above data is matched, the server calls the compaction quality evaluation model to generate compaction quality monitoring results.

[0087] In one engineering embodiment, the paving monitoring results output by S200 mark the target grid in the transition material zone as receiving compaction parameters. After the compactor enters the target grid, the compactor monitoring terminal collects GNSS positioning data and excitation force data at 1-second intervals and sends them to the server via the ZIGBEE dam area wireless network. The server reads the positioning quality index and accuracy attenuation factor. The positioning quality index indicates that when the RTK is fixed and the accuracy attenuation factor is less than 4.0, the server uses GNSS positioning data. After the compactor enters the valley obstruction area, if the accuracy attenuation factor is not less than 4.0, the server controls the intelligent total station to search for and lock the 360° omnidirectional prism on the compactor, and continues to form the motion trajectory using the intelligent total station positioning data.

[0088] The server matches the motion trajectory to the target grid and compares the running time with the paving process information. If the running time is later than the paving completion time corresponding to the paving process information, the running speed conforms to the transition material compaction scheme, and the excitation force output state corresponds to the vibration state, the server writes the corresponding trajectory segment into the target motion trajectory. After the compactor body continuously passes through the target grid, the server records one compaction pass. After the compactor changes its direction and passes through the target grid again, the server records the next compaction pass. After the target grid reaches the number of compaction passes specified in the compaction scheme, the server encapsulates the number of compaction passes, vehicle speed, vehicle direction, pavement thickness, and excitation frequency into compaction parameters.

[0089] The material yard screening test generates the P5 content, uniformity coefficient, curvature coefficient, and maximum particle size of the transition material. Two IEPE accelerometers on the roller compactor generate vibration signals. The server generates the compaction wave velocity based on the phase difference between the two vibration signals and generates the unit volume compaction work based on the work performed on the target motion trajectory and the dam material volume. After the target grid completes compaction, a test pit test is conducted. The PDA device sends the test pit coordinates, test pit time, and dry density to the server. The test pit time is later than the time of the last target motion trajectory of the target grid. The server inputs the compaction parameters, material source parameters, continuous compaction index, and test pit test results into the compaction quality evaluation model to generate the compaction quality monitoring results of the target grid. The compaction quality monitoring results are sent to S400.

[0090] In Technical Solution 1, the GNSS positioning data meets the requirements of fixed RTK calculation and an accuracy attenuation factor of less than 4.0. The server directly uses the GNSS positioning data to generate the target motion trajectory. In Technical Solution 2, the GNSS positioning data does not meet the accuracy requirements. An intelligent total station tracks a 360° omnidirectional prism and provides intelligent total station positioning data. The server uses this positioning data to generate the target motion trajectory. In Technical Solution 3, after the test pit test is completed, a new target motion trajectory appears on the target grid. The server retains the original test pit test results but does not use them as the final basis for the current compaction quality evaluation, and waits for the new test pit test results. All three technical solutions generate compaction quality monitoring results with the same field structure.

[0091] The technical effects of this step can be summarized as follows: S300 only includes the grid that receives the compaction parameters from the paving monitoring results into the compaction process monitoring, and uses running time, running speed, positioning accuracy, and vibration status together for target trajectory screening. The compaction parameters generated by the target trajectory, along with material source parameters, continuous compaction indices, and test pit results from times later than the last compaction, are jointly entered into the compaction quality evaluation model. The compaction quality monitoring results retain the correspondence between the grid, construction time, target trajectory, and test pit time, and serve as input for S400 to determine whether the original monitoring results should continue to be used as the basis for the current quality evaluation.

[0092] S400. Based on the compaction quality monitoring results, when new dump truck unloading information, paving process information, or compaction parameters appear in the corresponding grid, the original monitoring results are no longer used as the current quality evaluation basis according to the construction sequence, and the compaction quality monitoring results are regenerated; the digital twin model of the corresponding grid and finite element nodes is updated according to the regenerated compaction quality monitoring results and knowledge graph. Specifically, S400 is executed by the digital twin model update module in the server. This module receives the compaction quality monitoring results output by S300 and continuously receives dump truck unloading information and paving process information output by S200, as well as compaction parameters output by S300. The compaction quality monitoring results include grid, material zoning, dam construction grading, paving process information, target movement trajectory, time of the last target movement trajectory, number of compaction passes, vehicle speed, vehicle direction, pavement thickness, excitation frequency, excitation force output status, material source parameters, compaction wave velocity, compaction work per unit volume, test pit test results, compaction quality, and construction time. The digital twin model update module also reads the grid correspondence relationship generated by S100. This grid correspondence relationship includes the construction site, grid, material zoning, dam grading construction procedure, corresponding area of ​​the BIM engineering model, material zoning of the three-dimensional finite element model, and finite element nodes. The above inputs are matched within the same construction site and the same dam construction grading.

[0093] The digital twin model update module includes a construction time comparison section, an original monitoring result processing section, a compaction quality monitoring result regeneration section, a knowledge graph processing section, a three-dimensional finite element model processing section, and a sensor monitoring data comparison section. The construction time comparison section compares the new dump truck unloading information, paving process information, or compaction parameters with the construction time in the current compaction quality monitoring results. The original monitoring result processing section reads the paving monitoring results, compaction parameters, test pit results, and compaction quality monitoring results from the corresponding grid, based on the construction stage to which the new construction information belongs. The compaction quality monitoring result regeneration section calls the processing procedures already formed in S200 and S300. The knowledge graph processing section reads the correspondence between the construction site, grid, material zoning, dam body graded construction procedure, dump truck unloading information, paving process information, compaction parameters, test pit results, and finite element nodes. The three-dimensional finite element model processing section updates the finite element nodes within the matching area. The sensor monitoring data comparison section reads settlement, horizontal displacement, panel stress, or panel deflection and compares it with the calculation results of the three-dimensional finite element model.

[0094] The S400 is automatically triggered under three conditions. The first condition is that the corresponding grid receives new dump truck unloading information. The second condition is that the corresponding grid receives new paving process information. The third condition is that the corresponding grid forms new compaction parameters. The server checks for new information based on the grid's dynamic location coordinates, material zones, dam construction grade, and construction time. If the new information is located in other construction areas, other material zones, or other dam construction grades, it does not change the current grid's compaction quality monitoring results. If the new information corresponds to the current grid, current material zone, and current dam construction grade, and its construction time is later than the construction time in the current compaction quality monitoring results, the server initiates a regeneration process. If the construction time of the new information is earlier than the construction time in the current compaction quality monitoring results, the server saves this information as historical data, without changing the current quality evaluation criteria.

[0095] Specifically, new dump truck unloading information is sent to the server by the dump truck unloading positioning device. The server reads the unloading location, dam material name, unloading signal, and construction time from the new dump truck unloading information, and retrieves the grid corresponding to the unloading location from the grid correspondence of the work area. If the unloading location is within the boundary of the work area, the dam material name corresponds to the material zone, and the construction time is later than the construction time in the current compaction quality monitoring result, the server records this information as new unloading information for the current grid. If the unloading location is outside the boundary of the work area, the server saves this information and does not trigger the current grid regeneration process. If the dam material name does not correspond to the material zone, the server retains this information and records that the dam material does not correspond; the current grid does not enter the new compaction quality monitoring result generation process.

[0096] After new dump truck unloading information is written to the current grid, the server reads paving process information, compaction parameters, test pit results, and compaction quality monitoring results from earlier unloading times. The paving process information corresponds to the original pavement thickness and smoothness. The compaction parameters correspond to the original target trajectory, number of compaction passes, vehicle speed, vehicle direction, excitation frequency, and excitation force output state. The test pit results correspond to the original compaction degree, dry density, or porosity. The server saves the above original monitoring results as historical data according to the original construction time and marks them in the current grid as no longer being used as the basis for current quality evaluation. The original monitoring results retain their grid, material zoning, dam construction classification, and construction time, and are not merged with the new construction information.

[0097] Further, the server sends the new dump truck unloading information to the paving monitoring process corresponding to S200. S200 rereads the grid correspondence of the soil pile and matches the new dump truck unloading information to the current grid, re-recording the soil pile position. After the bulldozer's real-time spatial positioning information covers the new soil pile position, S200 recalculates the pavement thickness and smoothness. When the pavement thickness and smoothness meet the quality control criteria, S200 regenerates the paving monitoring results and marks the current grid as receiving compaction parameters. Subsequently, S300 only receives compactor positioning data and excitation force data that are later than the new paving process information, re-filters the target motion trajectory, recalculates the number of compaction passes, and regenerates the compaction parameters. After the new test pit results arrive, S300 compares the test pit time with the last target motion trajectory time, and inputs the test pit results that meet the time sequence into the compaction quality evaluation model, regenerating the compaction quality monitoring results.

[0098] Understandably, the regeneration range corresponding to the new dump truck unloading information starts from the paving process. The original pavement thickness, original smoothness, original target trajectory, original number of compaction passes, and original test pit results are not directly incorporated into the new compaction quality evaluation model. The server only uses paving process information, compaction parameters, continuous compaction indices, and test pit results generated after the new unloading construction time. Raw material source parameters still need to be checked for dam material name and material zone. When the new dam material belongs to the original dam material name and raw material zone, the server continues to use the material source parameters corresponding to the same batch of dam material. When the new dam material name changes, the server reads the P5 content, uniformity coefficient, curvature coefficient, and maximum particle size corresponding to the new dam material name.

[0099] Specifically, new paving process information is generated by S200 after the bulldozer enters or leaves the corresponding grid, the initial surface information changes, or the ply thickness and smoothness are recalculated. The server reads the grid, ply thickness, smoothness, bulldozer real-time spatial positioning information, and construction time from the new paving process information. When the new paving process information corresponds to the current grid and the construction time is later than the last target movement trajectory time in the current compaction quality monitoring results, the server records this information as the new paving process information for the current grid. When the new paving process information only supplements the smoothness of adjacent grid boundaries and does not change the ply thickness and smoothness of the current grid, the server updates the smoothness record in the paving monitoring results and does not directly regenerate the compaction parameters. When the new paving process information changes the ply thickness, smoothness, or bulldozer movement trajectory coverage of the current grid, the server initiates regeneration processing.

[0100] After the new paving process information is written to the current grid, the server reads the compaction parameters, test pit results, and compaction quality monitoring results from paving construction times earlier than the current paving construction time, and saves the original monitoring results as historical data according to the original construction time. The server marks the original monitoring results as no longer valid as the basis for current quality evaluation. Dump truck unloading information from times earlier than the new paving construction time remains in the current grid. The server calls S200 to re-compare the pavement thickness, smoothness, and quality control criteria. After the current grid is re-marked to receive compaction parameters, S300 re-receives the roller positioning data and excitation force data from times later than the new paving construction time, and regenerates the compaction parameters and compaction quality monitoring results.

[0101] The regeneration range corresponding to the new paving process information starts from the paving monitoring results. If the new paving process information does not meet the quality control criteria, the current grid is marked as continuing paving, and the server does not send the grid to S300. If the new paving process information meets the quality control criteria, the current grid re-enters S300. When S300 filters target movement trajectories, movement trajectories earlier than the new paving construction time are saved as historical data and are not included in the new compaction pass count. If the test pit time is earlier than the time of the last regenerated target movement trajectory, the test pit test results are not used as the final basis for the current compaction quality evaluation.

[0102] Specifically, the new compaction parameters are generated by the S300 after forming a new target motion trajectory in the corresponding grid, increasing the number of compaction passes, changing the vehicle speed, changing the vehicle direction, changing the excitation frequency, or changing the excitation force output state. The server reads the grid, target motion trajectory, running time, number of compaction passes, vehicle speed, vehicle direction, pavement thickness, excitation frequency, and excitation force output state from the new compaction parameters. If the new compaction parameters correspond to the current grid and the running time is later than the current test pit time, the server records the new compaction parameters as the new compaction parameters for the current grid. If the new compaction parameters run earlier than the current test pit time and are already included in the original compaction quality monitoring results, the server does not trigger a regeneration process.

[0103] After the new compaction parameters are written to the current grid, the server reads the test pit results and compaction quality monitoring results from earlier times than the new compaction parameters, and saves them as historical data according to their original construction times. The server marks the original test pit results as earlier than the last target trajectory time, and marks the original compaction quality monitoring results as no longer valid as the basis for current quality evaluation. The original paving monitoring results and the original dump truck unloading information remain valid. The server re-accumulates the number of compaction passes, compaction wave velocity, and compaction work per unit volume starting from the new compaction parameters, and waits for the test pit results from later times than the last target trajectory time. After the new test pit results arrive, S300 calls the compaction quality evaluation model again and regenerates the compaction quality monitoring results.

[0104] The regeneration range corresponding to the new compaction parameters begins from the compaction quality evaluation. When the new target trajectory only increases the number of compaction passes, and the vehicle speed, pavement thickness, excitation frequency, and excitation force output state remain unchanged, the server updates the number of compaction passes, the time of the last target trajectory, and the compaction work per unit volume. When the new target trajectory is accompanied by changes in vehicle speed, vehicle direction, excitation frequency, or excitation force output state, the server repackages the complete compaction parameters. If a newly added trajectory segment does not meet the requirements for operating speed, vibration state, or positioning accuracy, this trajectory segment will not be included in the new compaction parameters, and the current compaction quality monitoring result will not change due to this trajectory segment.

[0105] Furthermore, the server controls the regeneration order based on the stage to which the construction information belongs. New dump truck unloading information first enters S200, where paving monitoring results, compaction parameters, and compaction quality monitoring results are regenerated sequentially. New paving process information first regenerates paving monitoring results in S200, then enters S300 to regenerate compaction parameters and compaction quality monitoring results. New compaction parameters directly enter the compaction quality evaluation process in S300, regenerating compaction quality monitoring results. The server does not skip previous construction stages and directly uses subsequent data. When the current grid is still marked as continuing paving, new compaction parameters are only saved as construction machinery operation data and are not included in the current quality evaluation criteria.

[0106] The historical data consists of paving monitoring results, compaction parameters, test pit test results, and compaction quality monitoring results prior to regeneration. The server stores historical data according to grid, material zoning, dam construction grade, and construction time. Each regeneration process retains the original monitoring results and does not overwrite the original records with new monitoring results. When the server reads historical data, it first reads the grid and construction time, and then reads the monitoring results for the corresponding construction stage. The current quality evaluation only references the last complete generation of paving monitoring results, compaction parameters, test pit test results, and compaction quality monitoring results in the construction sequence.

[0107] Furthermore, the knowledge graph consists of construction site areas, grids, material zones, dam body graded construction procedures, dump truck unloading information, paving process information, compaction parameters, test pit test results, and finite element nodes. Spatial inclusion relationships are recorded between the construction site areas and the grids. Material zone correspondences are recorded between the grids and the material zones. Construction grade correspondences are recorded between the grids and the dam body graded construction procedures. Unloading location correspondences are recorded between the dump truck unloading information and the grids. Paving process information correspondences are recorded between the grids and the layer thickness, smoothness, and construction time. Compaction parameters correspond between the grids and the target movement trajectory, number of compaction passes, and running time. Test pit test results correspond between the grids and the test pit coordinates and test pit time. The grids correspond between the construction site areas and the finite element nodes.

[0108] When generating the grid correspondence in S100, the server establishes the correspondence between the construction surface, grid, material zones, dam body graded construction procedures, and finite element nodes. In S200, when generating paving monitoring results, the server supplements the correspondence between dump truck unloading information, paving process information, and grids. In S300, when generating compaction quality monitoring results, the server supplements the correspondence between compaction parameters, test pit test results, and grids. Upon receiving new dump truck unloading information, paving process information, or compaction parameters in S400, the server first retrieves the corresponding grid in the knowledge graph, and then reads the associated material zones, dam body construction grades, and finite element nodes.

[0109] When new dump truck unloading information is matched to a single grid, the server records that grid as a direct matching area. When the unloading signal spans adjacent grids during its duration, the server records the grid corresponding to the unloading location and adjacent grids within the same material zone affected by the unloading as a matching area. When new paving process information spans multiple adjacent grids, the server records the matching area based on the bulldozer's real-time spatial positioning information and the coverage area of ​​its movement trajectory. When new compaction parameters span multiple grids, the server records the matching area based on the target movement trajectory and the compactor's effective range. Grids that do not correspond to new information are not included in this round of digital twin model updates.

[0110] The digital twin model includes the construction site, grid, material zoning, ply thickness, smoothness, compaction parameters, and compaction quality monitoring results from the BIM engineering model, as well as the material zoning, dam construction classification, and finite element nodes from the 3D finite element model. The server updates the ply thickness, smoothness, number of compaction passes, compaction degree, dry density, or porosity of the matching area in the BIM engineering model based on the regenerated compaction quality monitoring results. The server reads the finite element nodes corresponding to the matching area based on the knowledge graph and writes the regenerated compaction quality monitoring results into the corresponding material zoning and dam construction classification of that finite element node.

[0111] The server only updates meshes and finite element nodes in the knowledge graph that correspond to the matching region. If a corresponding mesh belongs to the same material zone and has a correspondence with the new information regarding unloading location, bulldozer trajectory, or target trajectory, the server updates that mesh. Similarly, if a finite element node is located within the matching region or has a corresponding mesh surface relationship with a mesh in the matching region, the server updates that finite element node. Other meshes and finite element nodes retain their original digital twin model content. When a finite element node corresponds to two adjacent meshes, the server reads the compaction quality monitoring results from each adjacent mesh and selects the current compaction quality monitoring result based on the material zone and construction time of the finite element node.

[0112] Furthermore, the server updates the 3D finite element model based on the material zoning, dam construction grading, dam material properties, compaction quality, and construction time corresponding to the quality-compliant areas. Material zoning is derived from the mesh correspondence of the work area. Dam construction grading is derived from the dam grading construction program. Dam material properties are derived from material source parameters. Compaction quality is derived from the regenerated compaction quality monitoring results. Construction time is used to correspond to the construction grading in the 3D finite element model. Areas with substandard quality are not included in subsequent construction gradings but are retained in the digital twin model corresponding to the current construction work area.

[0113] After the 3D finite element model is updated, the server performs nonlinear stress-deformation analysis to obtain the 3D finite element model calculation results for settlement, horizontal displacement, panel stress, or panel deflection. The sensor monitoring data is collected by monitors installed at corresponding locations on the high panel rockfill dam. Settlement monitoring data corresponds to dam settlement. Horizontal displacement monitoring data corresponds to dam horizontal deformation. Panel stress monitoring data corresponds to panel stress. Panel deflection monitoring data corresponds to panel deflection. The server matches the sensor monitoring data to the corresponding finite element nodes according to the correspondence between monitoring points and finite element nodes.

[0114] The server compares the calculation results of the 3D finite element model with the sensor monitoring data. If both meet the monitoring requirements, the server retains the regenerated compaction quality monitoring results and writes them into the current digital twin model of the corresponding mesh and finite element nodes. If neither meets the monitoring requirements, the server does not directly replace the sensor monitoring data with the new 3D finite element model calculation results, nor does it directly change all material partitions. Instead, the server first re-reads the dump truck unloading information, paving process information, compaction parameters, and test pit test results of the corresponding mesh from the knowledge graph.

[0115] Specifically, the server first checks the correspondence between monitoring points and finite element nodes, and then checks the correspondence between the mesh and material partitions. When these correspondences are consistent, the server compares the construction time of dump truck unloading information, paving process information, compaction parameters, and test pit results. If the test pit time is earlier than the time of the last target movement trajectory, the server waits for new test pit results. If the paving process information is later than the current compaction parameters, the server regenerates the compaction parameters. If the dump truck unloading information is later than the current paving process information, the server regenerates the paving monitoring results, compaction parameters, and compaction quality monitoring results. If the construction time relationship is correct but the 3D finite element model calculation results and sensor monitoring data still do not meet the monitoring requirements, the server retains the comparison record and rereads the input data of the material source parameters, continuous compaction index, and compaction quality evaluation model.

[0116] After the compaction quality monitoring results are regenerated, the server updates the mesh and finite element nodes of the matching region again based on the knowledge graph, and re-compares the 3D finite element model calculation results with the sensor monitoring data. When new construction information reappears, the server restarts the corresponding processing according to the construction stage to which the new construction information belongs. This process continues until the current mesh does not contain dump truck unloading information, paving process information, or compaction parameters later than the current compaction quality monitoring results, and the 3D finite element model calculation results and sensor monitoring data meet the monitoring requirements.

[0117] Understandably, the minimum set of core parameters in this step includes the grid, material zoning, dam construction grading, paving process information, last target trajectory time, compaction parameters, test pit results, compaction quality, and construction time from the compaction quality monitoring results; dump truck unloading information, paving process information, or compaction parameters from newly arrived information; and the correspondence between the grid and material zoning, the grid and dam grading construction procedure, and the grid and finite element nodes in the knowledge graph. This minimum set of core parameters is used to compare the construction sequence, control the current applicable scope of the original monitoring results, regenerate the compaction quality monitoring results, and update the digital twin model of the matching area. Preferably, the digital twin model update module also reads the target trajectory, compaction wave velocity, unit volume compaction work, settlement, horizontal displacement, panel stress, and panel deflection.

[0118] In one engineering embodiment, the target grid in the transition material zone has completed paving, compaction, and test pit testing. The compaction quality monitoring results generated by S300 record the grid as a quality qualified area and record the last target movement trajectory time and test pit time. Subsequently, the dump truck unloading positioning device detects a new unloading signal in the same grid. The new unloading construction time is later than the original test pit time. The server saves the original paving monitoring results, original compaction parameters, original test pit test results, and original compaction quality monitoring results as historical data according to the original construction time, and marks the above original monitoring results as no longer being used as the basis for current quality evaluation.

[0119] The server sends the new dump truck unloading information to S200. S200 re-records the soil pile position and recalculates the pavement thickness and smoothness based on the new bulldozer's real-time spatial positioning information. After the target mesh meets the quality control criteria again, S300 generates the target motion trajectory using only the roller positioning data and excitation force data that are later than the new paving process information, recalculates the number of compaction passes, and waits for the new test pit test results that are later than the last target motion trajectory time. After the new test pit test results arrive, S300 regenerates the compaction quality monitoring results. The server reads the finite element nodes corresponding to the target mesh according to the knowledge graph and only updates the digital twin model of the target mesh and the corresponding finite element nodes. Other meshes and finite element nodes retain their original content.

[0120] In Technical Solution 1, the newly arrived information is the dump truck unloading information. The server regenerates the paving monitoring results, compaction parameters, and compaction quality monitoring results from the start of the paving process. In Technical Solution 2, the newly arrived information is the paving process information. The server retains the corresponding dump truck unloading information and regenerates the compaction parameters and compaction quality monitoring results from the start of the paving monitoring results. In Technical Solution 3, the newly arrived information is the compaction parameters. The server retains the paving monitoring results and regenerates the compaction quality monitoring results from the start of the compaction quality evaluation. All three technical solutions save the monitoring results before regeneration according to the construction time and update the mesh and finite element nodes of the matching area based on the knowledge graph.

[0121] The output of S400 is an updated digital twin model. This updated digital twin model includes the regenerated compaction quality monitoring results corresponding to the current mesh, matching regions, material zones, dam construction grading, finite element nodes, 3D finite element model calculation results, and sensor monitoring data comparison results. Monitoring results before regeneration are saved as historical data according to construction time. The current digital twin model only references the last fully generated compaction quality monitoring results in the construction sequence. When subsequent construction sections begin construction, the server reads the current digital twin model and historical data of the mesh corresponding to the previous dam construction grading, and associates the new construction section through the section mesh correspondence.

[0122] The technical effects of this step can be summarized as follows: S400 controls the regeneration range of paving monitoring results, compaction parameters, and compaction quality monitoring results according to the new dump truck unloading information, paving process information, or the construction stage of the compaction parameters. Monitoring results before regeneration are saved according to construction time; the current digital twin model only references the last fully generated compaction quality monitoring result in the construction sequence. The knowledge graph maps new construction information to grids and finite element nodes. Digital twin model updates are limited to the matching area and are verified by the calculation results of the 3D finite element model and sensor monitoring data.

[0123] Example 2: Figure 2 This diagram illustrates a structural block diagram of a digital twin construction method and system for high-face rockfill dams under intelligent construction conditions, according to an embodiment of the present invention. Figure 2 As shown, the structure may include: The initial information generation module 01 for the paving surface is used to acquire material zoning information, dam body graded construction procedures, and paving coordinate data. It meshes the paving surface and associates it with the building information model (BIM) engineering model and the 3D finite element model, generating a mesh correspondence relationship for the paving surface. This mesh correspondence relationship is then sent to the paving monitoring module. Specifically, the initial information generation module receives material zoning information and dam body graded construction procedures from the design drawings of each section of the rockfill dam, and receives paving coordinate data collected by the Beidou measuring rod. The paving coordinate data includes the plane coordinates, elevation, and construction time of each turning point of the paving surface boundary. The initial information generation module connects adjacent coordinates according to the acquisition order of each turning point to form the paving surface boundary. When adjacent boundaries intersect or paving coordinate data is missing, the received paving coordinate data is retained, and supplementary data is awaited. After the paving surface boundary is formed, it is divided into meshes according to the compactor's operating range and positioning accuracy. Mesh crossing material zoning boundaries is recorded separately according to the material zoning boundaries. The initial information generation module for the paving surface associates each mesh with the corresponding region in the building information model (BIM) engineering model, the material partition of the 3D finite element model, and the finite element node, and records the dynamic position coordinates of the mesh, the material partition, the dam construction grade, and the construction time. Once the mesh mapping relationship is established, it is sent to the paving monitoring module. Mesh that have not completed model association are retained in the initial information generation module and will be reprocessed after being supplemented by the BIM engineering model or the 3D finite element model.

[0124] The paving monitoring module 02 receives the grid correspondence of the work area, matches the dump truck unloading information, bulldozer real-time spatial positioning information, and paving process information to the corresponding grid, generates paving monitoring results based on the layer thickness, smoothness, and quality control criteria, and sends the paving monitoring results to the compaction quality evaluation module. Specifically, the paving monitoring module receives the grid correspondence of the work area and receives the dump truck unloading information generated by the dump truck unloading positioning device and the bulldozer real-time spatial positioning information generated by the bulldozer monitoring terminal. The dump truck unloading information includes the unloading location, material name, unloading signal, and construction time. The paving monitoring module first checks whether the unloading location is within the boundary of the work area, and then compares the material name with the material zoning. When both conditions are met, the dump truck unloading information is written into the corresponding grid, and the soil pile position is recorded. The paving monitoring module forms the bulldozer movement trajectory according to the bulldozer real-time spatial positioning information and records the trajectory entering the soil pile position as paving process information. The paving monitoring module reads the initial elevation from the initial information of the slab surface, compares the elevation of the bulldozer when passing through the grid with the initial elevation to obtain the pavement thickness. Simultaneously, it compares the paving elevation within the grid with the paving elevation of adjacent grids to obtain the smoothness. When the bulldozer's real-time spatial positioning information covers the grid corresponding to the dump truck unloading information, and the pavement thickness and smoothness meet the quality control criteria, the paving monitoring result marks the corresponding grid to receive compaction parameters. If the criteria are not met, the paving monitoring result marks the corresponding grid to continue paving. The paving monitoring result includes the grid, material zoning, paving process information, pavement thickness, smoothness, grid marking, and construction time, and is sent to the compaction quality evaluation module. When new dump truck unloading information or new paving process information arrives, the paving monitoring module updates the affected grid and saves the paving monitoring results before the update as historical data according to the construction time.

[0125] The compaction quality evaluation module 03 receives the paving monitoring results, matches the roller positioning data and excitation force data to the corresponding grid, filters the target movement trajectory and calculates the number of compaction passes, and generates compaction parameters. It inputs the compaction parameters, material source parameters, continuous compaction index, and test pit results into the compaction quality evaluation model to generate compaction quality monitoring results, and sends these results to the digital twin model update module. Specifically, the compaction quality evaluation module only receives the grids marked as receiving compaction parameters in the paving monitoring results, and receives the roller positioning data and excitation force data sent by the roller monitoring terminal. The compaction quality evaluation module pairs the two types of data according to the roller number and acquisition time, and then completes grid matching based on the grid's dynamic position coordinates. When the positioning data does not meet the positioning accuracy requirements or the acquisition time is discontinuous, the corresponding trajectory segment is not included in the number of compaction passes. The compaction quality evaluation module compares the trajectory running time with the construction time of the paving process information and checks the running speed, excitation force output status, and vibration status. When the running time is later than the paving completion time corresponding to the paving process information, the running speed conforms to the compaction scheme, and the excitation force output state corresponds to the vibration state, the corresponding trajectory is recorded as the target motion trajectory. The compaction quality evaluation module calculates the number of compaction passes based on the effective coverage of the grid by the target motion trajectory, and records the number of compaction passes, vehicle speed, vehicle direction, pavement thickness, excitation frequency, excitation force output state, running time, and dynamic position coordinates as compaction parameters. The material source parameters are matched according to the dam material name and material zone. The continuous compaction index is matched according to the roller number, running time, and grid. The test pit test results are matched according to the test pit point coordinates and test pit time. When the test pit time is later than the time of the last target motion trajectory of the corresponding grid, the compaction quality evaluation model receives the compaction parameters, material source parameters, continuous compaction index, and test pit test results, and generates compaction quality monitoring results. The compaction quality monitoring results include the grid, material zone, dam construction grade, target motion trajectory, compaction parameters, test pit test results, compaction quality, and construction time, and are sent to the digital twin model update module.

[0126] The digital twin model update module 04 receives the compaction quality monitoring results. When new dump truck unloading information, paving process information, or compaction parameters appear in the corresponding grid, it regenerates the compaction quality monitoring results according to the construction sequence, removing the original monitoring results from the current quality evaluation basis. The monitoring results before regeneration are saved as historical data according to the construction time. The module matches the corresponding grid and finite element nodes based on the knowledge graph, updates the digital twin model of the matched area, and compares the calculation results of the three-dimensional finite element model with the sensor monitoring data. Specifically, the digital twin model update module receives the compaction quality monitoring results and continuously reads the dump truck unloading information and paving process information sent by the paving monitoring module, as well as the compaction parameters generated by the compaction quality evaluation module. When new information corresponds to the current grid, material zone, and dam construction grade, and the construction time is later than the current compaction quality monitoring results, the digital twin model update module initiates reprocessing according to the construction stage to which the new information belongs. When new dump truck unloading information appears, the paving monitoring module is invoked again to generate paving monitoring results, and then the compaction quality evaluation module is invoked to generate compaction parameters and compaction quality monitoring results. When new paving process information appears, the paving monitoring module is invoked again to generate paving monitoring results, and then the compaction quality evaluation module is invoked to generate compaction parameters and compaction quality monitoring results. When new compaction parameters appear, the compaction quality evaluation module is invoked again to generate compaction quality monitoring results. The digital twin model update module saves the paving monitoring results, compaction parameters, test pit test results, and compaction quality monitoring results before regeneration as historical data according to the construction time, and prevents the corresponding original monitoring results from being used as the basis for current quality evaluation. The knowledge graph records the correspondence between the construction site and the grid, the grid and material zoning, the grid and the dam body graded construction procedure, the dump truck unloading information and the grid, the paving process information and the grid, the compaction parameters and the grid, the test pit test results and the grid, and the grid and finite element nodes. The digital twin model update module reads the matching region and finite element nodes based on the knowledge graph, writes the regenerated compaction quality monitoring results into the corresponding mesh and finite element nodes, and updates the digital twin model. After the three-dimensional finite element model calculation results are formed, the digital twin model update module compares the three-dimensional finite element model calculation results with the sensor monitoring data. If the monitoring requirements are not met, the module rereads the dump truck unloading information, paving process information, compaction parameters, and test pit test results of the corresponding mesh, and regenerates the compaction quality monitoring results.

Claims

1. A method for constructing a digital twin of the construction conditions of a high-face rockfill dam for intelligent construction, characterized in that, include: S100: Obtain material zoning information, dam body graded construction procedures and construction coordinate data, grid the construction surface and associate it with the BIM engineering model and three-dimensional finite element model to generate the corresponding relationship of the surface grid. S200. Based on the grid correspondence of the storage surface, the dump truck unloading information, bulldozer real-time spatial positioning information and paving process information are matched to the corresponding grid, and paving monitoring results are generated according to the paving thickness, flatness and quality control criteria. S300. Based on the paving monitoring results, the roller positioning data and excitation force data are matched to the corresponding grid, the target motion trajectory is filtered and the number of rolling passes is calculated to generate rolling parameters. The rolling parameters, material source parameters, continuous compaction index and test pit test results are input into the compaction quality evaluation model to generate compaction quality monitoring results. S400. Based on the compaction quality monitoring results, when new dump truck unloading information, paving process information, or compaction parameters appear in the corresponding grid, the original monitoring results are no longer used as the basis for the current quality evaluation according to the construction sequence, and the compaction quality monitoring results are regenerated; the digital twin model of the corresponding grid and finite element nodes is updated according to the regenerated compaction quality monitoring results and knowledge graph.

2. The method according to claim 1, characterized in that, S100 includes: generating the construction site boundary based on the construction coordinate data, dividing the grid according to the operating range and positioning accuracy of the roller; and associating each grid with the corresponding area of ​​the BIM engineering model, the material partition of the three-dimensional finite element model, and the finite element node according to the material zoning and the dam body graded construction procedure.

3. The method according to claim 2, characterized in that, S200 includes: a dump truck unloading positioning device that acquires dump truck positioning information and unloading signals, and generates dump truck unloading information; matching dump truck unloading information whose unloading location is within the boundary of the construction site and whose dam material name corresponds to the material zone to the grid; and calculating the layer thickness and flatness based on the bulldozer's real-time spatial positioning information and the initial information of the site.

4. The method according to claim 3, characterized in that, In S200, when the real-time spatial positioning information of the bulldozer covers the grid corresponding to the unloading information of the dump truck, and the paving thickness and flatness meet the quality control criteria, the paving monitoring result marks the corresponding grid to receive the compaction parameters. Otherwise, the paving monitoring results will mark the corresponding grid and continue paving.

5. The method according to claim 4, characterized in that, S300 includes: matching the target motion trajectory corresponding to the roller positioning data with the grid; when the target motion trajectory is located in the grid marked by the paving monitoring result, the running time is later than the paving completion time corresponding to the paving process information, the running speed conforms to the rolling scheme, and the excitation force output state corresponds to the vibration state, the target motion trajectory is included in the number of rolling passes.

6. The method according to claim 5, characterized in that, Analyze the positioning quality index and accuracy attenuation factor in the GNSS positioning data; when the positioning quality index indicates that the GNSS positioning result is not a fixed RTK solution, or when the accuracy attenuation factor is not less than 4.0, control the intelligent total station to track the 360° omnidirectional prism on the compactor and match the intelligent total station positioning data to the corresponding grid.

7. The method according to claim 5, characterized in that, The material source parameters include P5 content, non-uniformity coefficient, curvature coefficient, and maximum particle size; the continuous compaction index includes rolling wave velocity and compaction work per unit volume; the test pit test results include compaction degree, dry density, or porosity; the compaction quality evaluation model receives test pit test results when the test pit time is later than the target motion trajectory time of the last time the rolling passes were counted for the corresponding grid.

8. The method according to claim 1, characterized in that, In S400, when new dump truck unloading information appears, the paving monitoring results, rolling parameters, and compaction quality monitoring results are regenerated; when new paving process information appears, the paving monitoring results, rolling parameters, and compaction quality monitoring results are regenerated. When new compaction parameters appear, the compaction quality monitoring results are regenerated; the monitoring results before regeneration are saved as historical data according to the construction time.

9. The method according to claim 8, characterized in that, The knowledge graph records the correspondence between construction site and grid, grid and material zoning, grid and dam body graded construction procedure, dump truck unloading information and grid, paving process information and grid, compaction parameters and grid, test pit test results and grid, and grid and finite element nodes. Based on the new dump truck unloading information, paving process information, or compaction parameters, the corresponding grid and finite element nodes are matched, and the digital twin model of the matched area is updated. When the consistency between the three-dimensional finite element model calculation results and the sensor monitoring data does not meet the preset monitoring requirements, the dump truck unloading information, paving process information, compaction parameters, and test pit test results of the corresponding grid are reread, and the compaction quality monitoring results are regenerated.

10. A digital twin construction system for the construction conditions of a high-face rockfill dam oriented towards intelligent construction, applied to the method of any one of claims 1 to 9, characterized in that, include: The initial information generation module for the paving surface is used to obtain material zoning information, dam body graded construction procedures and paving coordinate data, grid the construction paving surface and associate it with the building information model engineering model and three-dimensional finite element model, generate the paving surface grid correspondence, and send the paving surface grid correspondence to the paving monitoring module; The paving monitoring module is used to receive the grid correspondence of the storage surface, match the dump truck unloading information, bulldozer real-time spatial positioning information and paving process information to the corresponding grid, generate paving monitoring results according to the layer thickness, flatness and quality control criteria, and send the paving monitoring results to the compaction quality evaluation module. The compaction quality evaluation module is used to receive the paving monitoring results, match the roller positioning data and excitation force data to the corresponding grid, filter the target motion trajectory and calculate the number of compaction passes, and generate compaction parameters. The rolling parameters, material source parameters, continuous compaction index and test pit test results are input into the compaction quality evaluation model to generate compaction quality monitoring results, and the compaction quality monitoring results are sent to the digital twin model update module. The digital twin model update module is used to receive the compaction quality monitoring results. When new dump truck unloading information, paving process information, or compaction parameters appear in the corresponding grid, the original monitoring results are no longer used as the basis for the current quality evaluation according to the construction sequence. The compaction quality monitoring results are regenerated, and the monitoring results before regeneration are saved as historical data according to the construction time. The module also updates the digital twin model of the matching area by matching the corresponding grid and finite element nodes according to the knowledge graph, and compares the calculation results of the three-dimensional finite element model with the sensor monitoring data.

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