A BIM-based construction progress dynamic management and control method

Abstract

The application relates to the technical field of building engineering management, in particular to a building construction progress dynamic management and control method based on BIM, which comprises the following steps: screening point cloud data according to constraint boundaries and generating a space volume through envelope operation, obtaining an overlapping collision region volume by utilizing volume deviation and boundary intersection, obtaining actual output conversion correction consumption period by calculating crowding degree proportion and efficiency attenuation, calculating the center of gravity base polar angle arrangement connection edge by aggregating unconnected nodes, and adjusting the network atlas by converting mechanical displacement time consumption according to the polar angle difference, in the application, a material characteristic envelope body is constructed to replace manual measurement and counting, a crowded efficiency reduction ratio is accounted by using deviation, real completion span is deduced, concurrent process execution is sequentially arranged according to equipment polar angles, hoisting machinery power time consumption is written as a scheduling core weight, three-dimensional space collision and power time consumption are combined, overall lag is eliminated, and a dynamic scheduling network is reconstructed.

Description

Technical Field

[0001] This invention relates to the field of building engineering management technology, and in particular to a BIM-based method for dynamic control of building construction progress. Background Technology

[0002] The field of construction project management technology encompasses core aspects such as project initiation, planning, cost control, quality supervision, safety management, and schedule coordination. This field combines engineering principles with organizational theory, spanning the entire lifecycle of a building from design and on-site construction to final completion and delivery. It primarily relies on allocating physical resources such as labor, building materials, and construction machinery, organizing the seamless transition between various construction processes, and coordinating construction drawings and technical standards to guide physical construction activities. Traditional dynamic management methods for construction progress involve verifying and adjusting the planned and actual timeframes for various construction tasks at the site. This typically involves construction workers carrying printed Gantt charts or network diagrams to the construction site. The project team used measuring tapes and manual visual inspection to obtain detailed quantitative data on the concrete volume (cubic meters), the number of steel bars (tons), and the area of ​​formwork (square meters) that had been laid. This quantitative data was then handwritten in the daily construction log. After the engineers returned to the project office, a dedicated staff member entered the numbers from the log into a spreadsheet on a desktop computer. The team then manually calculated the difference between the actual calendar days consumed for each completed task and the planned number of days on the initial chart. Finally, based on the calculated calendar day changes, the start and end dates for subsequent tasks were reset, and a new logical sequence diagram of each construction task was drawn.

[0003] Traditional construction progress control relies on construction workers going to the site to measure with tape measures and manually count the items to obtain detailed quantitative data on the physical structure of the project. The collected information is then handwritten in the construction log, and upon returning to the office, dedicated personnel input the data item by item into an electronic spreadsheet. The difference between the actual time consumed and the initial planned time is manually calculated, and the start and end dates of subsequent tasks that have not yet started are reset based on this value to redraw the schedule chart. The complicated manual comparison and multi-level handover process can easily lead to delays in information transmission and statistical errors, resulting in project scheduling decisions that are seriously out of touch with the objective reality of the site construction. Summary of the Invention

[0004] To achieve the above objectives, the present invention adopts the following technical solution: a BIM-based method for dynamic management and control of building construction progress, comprising the following steps: S1: Based on the compactness determination of the straight-line distance and the upper and lower limits of reflectivity, the point cloud data array is filtered and combined to generate the target material point cloud cluster array. The volume of the solid space is generated through edge coordinate envelope calculation. S2: Based on the planned volume and the volume of the physical space, calculate the deviation volume, combine the daily standard construction output with the original planned calendar time to estimate the expected completion calendar time, calculate the intersection of the three-dimensional boundary coordinate array of the working surface of the concurrent building information model component nodes within the target period, and generate the collision overlap volume. S3: Divide the collision overlap volume by the total volume of the construction floor space to generate the work surface congestion ratio, extract the corresponding labor efficiency attenuation coefficient and convert it to the actual engineering output, and divide the volume to be completed by the actual engineering output to calculate the corrected completion period. S4: Aggregate the unconnected concurrent building information model component nodes into a set of unconstrained component nodes in the same period, calculate the horizontal projection angle between the coordinates of the crane equipment base and the coordinate set of the three-dimensional centroid to generate the horizontal polar angle, generate an optimized operation sequence array by incremental sorting and connect them in sequence to generate serial directed connection edges. S5: Generate rotation angle deviation based on the difference in horizontal polar angles at both ends of the series directed connection edge, generate mechanical displacement period by combining it with the rated rotation angular velocity, and add it to the corrected completion period to generate comprehensive reconstruction time. Adjust the scheduling task network diagram and output the construction progress management results.

[0005] As a further embodiment of the present invention, the volume of the physical space includes the volume of the core tube concrete, the volume of the load-bearing column pouring, and the measured volume of the shear wall; the collision and overlap volume includes the volume occupied by scaffolding interference, the volume of temporary material storage conflict, and the volume of cross-interference of robotic arm operations; the corrected completion period includes the number of days for process extension, the buffer period for cross-efficiency reduction, and the ample time for machinery scheduling; the series directed connection edge includes the main line of single-trade flow construction, the cross-floor lifting sequence chain, and the trajectory of parallel component assembly sequence; and the construction progress management results include a dynamic list of machinery and equipment scheduling, a resource efficiency reduction early warning report, and a three-dimensional scheduling reconstruction Gantt chart.

[0006] As a further aspect of the present invention, the step of obtaining the volume of the physical space specifically includes: S101: Obtain the building material attribute configuration file, extract the compact judgment straight line distance and upper and lower limit boundaries of reflectivity, obtain the point cloud data array collected by the on-site laser scanning equipment, extract the laser reflectivity associated with the coordinate points in the point cloud data array, calculate the straight line distance between adjacent coordinate points, map and associate the laser reflectivity with the straight line distance, and establish a point cloud spatial feature matrix. S102: Call the point cloud spatial feature matrix, compare the straight line distance with the compact judgment straight line distance, compare the laser reflectivity with the upper and lower limit boundaries of reflectivity, filter coordinate points whose straight line distance is not greater than the compact judgment straight line distance, filter coordinate points whose laser reflectivity is within the upper and lower limit boundaries of reflectivity, aggregate the coordinate points after each filtering, and generate a target material point cloud cluster array. S103: For the target material point cloud cluster array, determine and extract the corresponding outer edge coordinate points, connect the outer edge coordinate points along the spatial distribution to construct a three-dimensional closed polygonal surface, and perform spatial volume integration and summation on the three-dimensional geometric shape inside the envelope of the three-dimensional closed polygonal surface to generate the solid spatial volume.

[0007] As a further aspect of the present invention, the process of extracting the compact determination straight-line distance and the upper and lower limits of reflectivity includes: The building material attribute configuration file is parsed, and the internal material feature mapping table is extracted. The target material number is extracted from the material feature mapping table, and the optical reflection range, scanning resolution level, and surface roughness parameters associated with the target material number are retrieved. The maximum and minimum values ​​of the optical reflection range are extracted and combined to generate the upper and lower limits of the reflectivity. The reference point spacing corresponding to the scanning resolution level is extracted as the basic distance. The basic distance is multiplied by the dimensionless roughness influence coefficient to generate a distance compensation value. The distance compensation value is added to the basic distance to generate the compact judgment straight line distance.

[0008] As a further aspect of the present invention, the step of obtaining the collision overlap volume specifically includes: S201: Obtain the planned volume of the building information model, extract the three-dimensional spatial geometric values ​​corresponding to the planned volume, compare the planned volume with the physical space volume, perform scalar subtraction operation on the two spatial dimension parameters, extract the reduction margin feature, obtain the absolute value of the numerical difference, and generate deviation volume information. S202: Call the deviation volume information, obtain the daily standard construction output predetermined in the engineering drawings, divide the deviation volume by the daily standard construction output to calculate, generate the schedule delay period, obtain the original planned calendar time, add the schedule delay period to the original planned calendar time, shift the time axis sequence, and generate the estimated completion calendar time. S203: Based on the estimated completion calendar time, retrieve the time mapping records of the construction scheduling network, compare the calendar timestamps of the component nodes, filter multiple concurrent building information model component nodes whose time intervals fall within the estimated completion calendar time, extract the associated work surface three-dimensional boundary coordinate array, analyze the spatial intersecting entities between multiple work surface three-dimensional boundary coordinate arrays, perform spatial Boolean intersection operation, and obtain the collision overlap volume.

[0009] As a further aspect of the present invention, the process of obtaining the predetermined daily standard construction output of the engineering drawings includes: The data tags of the building object model associated with the engineering drawings are parsed to extract the engineering material attributes and construction process types. A construction quota standard data table is obtained. The engineering material attributes and construction process types are used as joint retrieval keys to perform a matching query in the construction quota standard data table. The corresponding basic man-day quota volume parameters are extracted. The construction organization scheduling configuration records associated with the engineering drawings are read to extract the number of construction teams and the conversion factor of machinery and equipment shifts. The number of construction teams and the conversion factor of machinery and equipment shifts are multiplied to generate a comprehensive resource input weight parameter. The basic man-day quota volume parameter and the comprehensive resource input weight parameter are multiplied to generate the daily standard construction output.

[0010] As a further aspect of the present invention, the step of obtaining the revised completion date specifically includes: S301: Obtain the total volume of the construction floor space built into the building model, call the collision overlap volume, divide the collision overlap volume by the total volume of the construction floor space, extract the ratio feature of the local intersection overlap volume and the overall reference space, and generate the work surface crowding ratio. S302: Obtain a preset historical test sample mapping and matching data table, compare the congestion ratio of the work surface with the numerical boundary of the mapping and matching data table, extract the labor efficiency decay coefficient associated with the corresponding numerical interval, read the original planned engineering output from the scheduling record, multiply the original planned engineering output with the labor efficiency decay coefficient to generate the actual engineering output. S303: Collect the volume to be completed from the on-site scanning feedback, extract the geometric parameters of the three-dimensional unformed building associated with the volume to be completed, divide the volume to be completed by the actual engineering output for calculation, analyze the remaining workload to match the natural cycle span scalar of calendar progression, calculate the time series evolution day characteristics, and generate the corrected completion period.

[0011] As a further aspect of the present invention, the process of obtaining the preset historical test sample mapping and matching data table includes: The historical construction record database is analyzed to extract multiple historical construction test sample data. From the historical construction test sample data, the historical work surface congestion ratio, historical planned output, and historical actual output are extracted. The historical actual output is divided by the historical planned output to generate an associated historical labor efficiency decay scalar. The multiple historical work surface congestion ratios are sorted in ascending order of value, and the total number of sample distributions is extracted. Based on the total number of sample distributions, the ascending sorted sequence is divided into multiple continuous congestion boundary intervals to construct multiple congestion boundary intervals. The congestion ratios of multiple historical work surfaces falling within the same congestion boundary interval are extracted. The multiple historical labor efficiency decay scalars are retrieved and bound together. The retrieved multiple historical labor efficiency decay scalars are added together and divided by the number of samples contained in the interval to calculate a baseline decay coefficient. The multiple congestion boundary intervals are bound to each of the baseline decay coefficients as key-value pairs to generate the preset historical test sample mapping matching data table.

[0012] As a further aspect of the present invention, the step of obtaining the series directed connection edges specifically includes: S401: Obtain the network graph of construction scheduling tasks, parse the records of topological connection relationships between nodes within the graph, retrieve concurrent building information model component nodes that lack dependent order connections, extract node attributes and time-space temperature features for concurrent building information model component nodes that lack dependent order connections, aggregate the distribution status of nodes that lack dependent order connections, merge multiple isolated nodes, and establish a set of concurrent unconstrained component nodes. S402: Based on the set of unconstrained component nodes of the same period, analyze the boundary parameters of the three-dimensional geometric features associated with each node in the set, extract the spatial physical centroid of the node and construct a three-dimensional centroid coordinate set, collect the two-dimensional plane base coordinates of the hoisting machinery and equipment on site, calculate the relative angle between the base coordinates of the hoisting machinery and equipment and the two-dimensional projection ray of the three-dimensional centroid coordinate set on the horizontal plane, extract the angle numerical variable, and obtain the horizontal polar angle data. S403: Call the horizontal polar angle data, perform floating-point comparison and sorting operations on the polar angle values ​​of multiple nodes and construct an increasing sequence, establish a target node sequential processing sequence, generate an optimized job order array, read the unidirectional sequential index position of the elements inside the optimized job order array, perform unidirectional node topology connection calculation according to the sequential hierarchical position of the index sequence, and generate a series directed connection edge.

[0013] As a further aspect of the present invention, the steps for obtaining the construction progress management results are specifically as follows: S501: Call the series directed connection edge, extract the horizontal polar angle associated with the two end nodes, calculate the absolute difference of the subtraction for the angle parameters, establish the rotation angle deviation, obtain the preset rated rotation angular velocity of the lifting machinery and equipment, divide the rotation angle deviation by the rated rotation angular velocity and perform quotient algebra calculation to generate the mechanical displacement period. S502: Obtain the corrected completion period and add it to the mechanical displacement period. Add the node time dimension scalar parameters to extrapolate the overall task execution extension span and generate the comprehensive reconstruction time. S503: Write the comprehensive reconstruction time into the associated attribute domain of the series directed connection edge, adjust the internal topology sequence configuration of the scheduling task network graph according to the series directed connection edge carrying the weight value parameter, update the global node order, and obtain the construction progress management result.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, a target material feature envelope is constructed by relying on the dual constraints of laser reflectivity and spatial distance. This replaces the manual counting and precise extraction of the actual volume of components on site. The volume deviation and time extension axis are used to infer the spatial intersection and overlap area to calculate the efficiency reduction ratio of the work surface space congestion and to deduce the actual completion span. At the same time, for concurrent nodes without topological connection constraints, the spatial sequence is sorted in ascending order according to the center of gravity of the equipment and the projection polar angle of the mechanical base. The reciprocating swing angle of the rotating robotic arm is written into the task node network to calculate the physical displacement weight. This deeply integrates the collision conflict of three-dimensional entities with the time consumption of the lifting machinery dynamics and completely eliminates the errors and delays of manual scheduling, reconstructing a scheduling map with engineering adaptability. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the steps of the present invention; Figure 2 This is a detailed schematic diagram of S1 of the present invention; Figure 3 This is a detailed schematic diagram of S2 of the present invention; Figure 4 This is a detailed schematic diagram of S3 of the present invention; Figure 5 This is a detailed schematic diagram of S4 of the present invention; Figure 6 This is a detailed schematic diagram of S5 of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0018] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0019] Please see Figure 1 This invention provides a BIM-based method for dynamic management and control of building construction progress, comprising the following steps: S1: Based on the compactness determination of the straight-line distance and the upper and lower limits of reflectivity, the point cloud data array is filtered and combined to generate the target material point cloud cluster array. The volume of the solid space is generated through edge coordinate envelope calculation. S2: Based on the planned volume and the physical space volume, calculate the deviation volume, combine the daily standard construction output and the original planned calendar time to estimate the expected completion calendar time, calculate the intersection of the three-dimensional boundary coordinate array of the working surface of the concurrent building information model component nodes within the target period, and generate the collision overlap volume. S3: Divide the collision overlap volume by the total volume of the construction floor space to generate the work surface congestion ratio, extract the corresponding labor efficiency attenuation coefficient and convert it to the actual engineering output, and divide the volume to be completed by the actual engineering output to calculate the corrected completion period. S4: Aggregate the unconnected concurrent building information model component nodes into a set of unconstrained component nodes in the same period, calculate the horizontal projection angle between the coordinates of the crane equipment base and the coordinate set of the three-dimensional centroid to generate the horizontal polar angle, generate an optimized operation sequence array by incremental sorting and connect them in sequence to generate serial directed connection edges. S5: Generate rotation angle deviation based on the difference in horizontal polar angles at both ends of the series directed connection edge, generate mechanical displacement period by combining it with the rated rotation angular velocity, and add it to the corrected completion period to generate comprehensive reconstruction time. Adjust the scheduling task network diagram and output the construction progress management results.

[0020] The volume of the physical space includes the volume of the core tube concrete, the volume of the load-bearing column pouring, and the measured volume of the shear wall. The collision and overlap volume includes the volume occupied by scaffolding interference, the volume of conflict caused by temporary material stacking, and the volume of cross-interference of robotic arm operations. The revised completion period includes the number of days for process extension, the buffer period for cross-efficiency reduction, and the time for machinery scheduling. The series of directed connection edges includes the main line of single-trade flow construction, the lifting sequence chain across floors, and the trajectory of parallel component assembly sequence. The construction progress management results include the dynamic list of machinery and equipment scheduling, the resource efficiency reduction early warning report, and the three-dimensional scheduling reconstruction Gantt chart.

[0021] Please see Figure 2 The specific steps for obtaining the volume of a physical space are as follows: S101: Obtain the building material attribute configuration file, extract the compact judgment straight line distance and upper and lower limit boundaries of reflectivity, obtain the point cloud data array collected by the on-site laser scanning equipment, extract the laser reflectivity associated with the coordinate points in the point cloud data array, calculate the straight line distance between adjacent coordinate points, map and associate the laser reflectivity with the straight line distance, and establish a point cloud spatial feature matrix. The data nodes within the building material attribute configuration file are analyzed, and the material feature mapping table data structure distributed in the node hierarchy is extracted. The label text records corresponding to the current construction object are traversed and matched within the material feature mapping table to extract the target material label value. Based on this target material label value as the index key, associated optical reflection interval data, scanning resolution level parameters, and surface roughness parameters are retrieved. The maximum and minimum numerical boundaries of the optical reflection interval distribution range are read, and these boundaries are combined through data concatenation to generate upper and lower reflectivity limits. The reference point spacing data corresponding to the scanning resolution level is retrieved and used as... The subsequent calculation process assigns a basic distance value to the base distance measurement. The base distance is multiplied by the surface roughness parameter to generate a distance compensation value. This compensation value is then added to the base distance, and the summation is used to derive the compact judgment straight-line distance. The advantage of this calculation logic is that by introducing a roughness parameter to dynamically expand and compensate the spacing between reference points, it eliminates the misjudgment defect of point cloud cluster aggregation due to fixed distance limitations on complex and rough building material surfaces. It acquires the original spatial point cloud data array continuously collected by on-site 3D laser scanning equipment on the building entity's working surface, and traverses each 3D coordinate point within this point cloud data array, carrying relevant parameters. The laser reflectivity signal intensity values ​​are used to perform straight-line distance conversion operations on adjacent coordinate points in a three-dimensional coordinate system, accurately obtaining the straight-line distance values ​​between adjacent coordinate points. A multi-dimensional key-value pair mapping is established between the laser reflectivity values ​​corresponding to each three-dimensional coordinate point and the calculated straight-line distance values. The entire dataset is written into memory storage and a point cloud spatial feature matrix is ​​constructed. In the actual data assignment and calculation process, concrete material data with a label value of 104 is read, and the maximum optical reflectivity value of this material is found to be 85 and the minimum value is 20. The reference point spacing corresponding to its scanning resolution level is obtained as 5 mm, and the measured surface roughness is also obtained. With parameter 2, directly substituting it into the aforementioned logical operation, the reference point spacing of 5 mm is multiplied by the surface roughness parameter 2 to obtain a distance compensation value of 10 mm. Then, this distance compensation value of 10 mm is added to the basic distance of 5 mm to obtain a compact judgment straight-line distance of 15 mm. At the same time, the actual straight-line distance of the first test adjacent point cloud coordinates in the space is collected by the scanning device and is 12 mm. The laser reflectivity of the test point is read as 60. The above numerical result of 15 mm serves as a hard constraint threshold for subsequent distance screening, defining the spatial boundary of whether the material point cloud can be aggregated, and providing a reliable benchmark for noise reduction and simplification of the point cloud spatial feature matrix.

[0022] S102: Call the point cloud spatial feature matrix, compare the straight line distance with the compact judgment straight line distance, compare the laser reflectivity with the upper and lower limit boundaries of reflectivity, filter the coordinate points whose straight line distance is not greater than the compact judgment straight line distance, filter the coordinate points whose laser reflectivity is within the upper and lower limit boundaries of reflectivity, aggregate the coordinate points after each filtering, and generate the target material point cloud cluster array. The process involves calling the point cloud spatial feature matrix built in memory, extracting the parameters corresponding to each coordinate point stored in the matrix row by row, extracting the straight-line distance and laser reflectivity values ​​of the currently traversed coordinate point, performing a floating-point comparison operation between the extracted straight-line distance and the previously determined compact judgment straight-line distance to determine if the current straight-line distance is greater than the compact judgment straight-line distance, and performing a numerical interval envelope comparison operation between the extracted laser reflectivity value and the previously determined upper and lower limits of reflectivity to determine if the current laser reflectivity value is within the closed interval formed by the minimum and maximum numerical limits. If both conditions are met—the straight-line distance is not greater than the compact judgment straight-line distance and the laser reflectivity value is within the upper and lower limits of reflectivity—the currently traversed coordinate point is marked as a valid material point. This process continues until all coordinate points in the point cloud spatial feature matrix have been screened, extracting the coordinate point data of all marked valid material points, and aggregating and writing these valid material points in a three-dimensional spatial coordinate system to generate a target material point cloud cluster array. The advantage of this operation logic is that it synchronizes distance and reflectivity as dual factors. Cross-validation eliminates noise from airborne dust and free-floating debris, ensuring that the generated point cloud cluster array closely matches the physical distribution of the actual building entity. The actual calculation values ​​from the previous steps are then used for verification. The previously calculated compactness standard of 15 mm and the upper and lower limits of reflectivity (minimum value 20, maximum value 85) are used. The first test point cloud coordinates are found to have a straight-line distance of 12 mm and a laser reflectivity of 60. A comparison is then performed, as the straight-line distance of 12 mm is no greater than the standard of 15 mm, and the laser reflectivity of 60 falls completely within the range of 20. Within the defined boundary of 85, the first test point cloud is determined to be a valid material point and is entered into the target material point cloud cluster array. If the straight-line distance of the second test point cloud is 18 mm, which exceeds the standard of 15 mm, it is directly rejected. After multiple rounds of cyclic screening, the point cloud spatial feature matrix originally containing 500,000 discrete coordinate points is simplified, aggregated, and output as a target material point cloud cluster array containing 350,000 coordinate points. This numerical result of 350,000 point clouds constitutes the basic framework for subsequent three-dimensional spatial volume integration calculations, reducing the invalid spatial data occupancy rate.

[0023] S103: For the target material point cloud cluster array, determine and extract the corresponding outer edge coordinate points, connect the spatial distribution of the outer edge coordinate points to construct a three-dimensional closed polygon surface, perform spatial volume integral summation on the three-dimensional geometric shape enclosed by the three-dimensional closed polygon surface to generate the solid spatial volume. For the target material point cloud cluster array generated in the previous process, all valid spatial coordinate points within the array are traversed. The triaxial numerical attributes of each 3D coordinate point are read, and the curvature change gradient of each coordinate point in its local spatial normal direction is calculated. The corresponding outer edge coordinate points distributed on the outermost boundary of the array are identified and extracted by using a set gradient abrupt change judgment threshold. After obtaining all outer edge coordinate points, the triangular mesh facet connection and division operation is performed sequentially along the topological distribution path of adjacent outer edge coordinate points in 3D space. The discrete point-line relationship is closed to construct a fully enclosed 3D closed polygon surface. Then, for the internal 3D geometric region enclosed by this 3D closed polygon surface, the micro-space hexahedral units are divided along the coordinate axes with a set step size. The number of micro-space hexahedral units is counted and multiplied by the reference volume of a single unit. The spatial volume integral summation operation is performed, and the total voxel values ​​are accumulated to finally derive the solid spatial volume. The advantage of this operation logic is that it can use the edge... The tracking and voxel integration method transforms disordered discrete point clouds into quantifiable engineering volume data, avoiding volume errors caused by traditional bounding box algorithms on irregular components. Using the data from the aforementioned steps for practical simulation, for a target material point cloud cluster array containing 350,000 coordinate points, 15,000 outer edge coordinate points are extracted through curvature gradient screening. Triangular facets are connected along these 15,000 outer edge coordinate points to generate a three-dimensional closed polygonal surface. The baseline volume of a single micro-space hexahedral unit is set to 1 cubic centimeter. After internal space voxel division and integration, a total of 120,000,000 micro-space hexahedral units are accumulated. Multiplying this by the baseline volume of 1 cubic centimeter yields a final solid space volume of 120 cubic meters. This result of 120 cubic meters objectively reveals the actual physical engineering volume that has been completed on-site, providing a reliable real-world verification data benchmark for subsequent comparison of planned engineering volumes.

[0024] Please see Figure 3 The specific steps for obtaining the collision overlap volume are as follows: S201: Obtain the planned volume of the building information model, extract the corresponding three-dimensional spatial geometric values ​​of the planned volume, compare the planned volume with the physical volume, perform scalar subtraction on the two spatial dimension parameters, extract the reduction margin features, obtain the absolute value of the numerical difference, and generate deviation volume information. The process involves acquiring the planned volume data from the initial building information model (BIM) configuration, parsing the attribute parameter list within the BIM file, extracting the 3D spatial geometric values ​​corresponding to the current construction node under review, aligning the extracted planned volume with the physical volume generated from previous measurements, and performing a scalar subtraction operation on the planned and physical volumes in the numerical domain. The resulting reduction margin characteristic value is then extracted, and a sign removal operation is performed on this reduction margin characteristic value to obtain the absolute value of the numerical difference. This absolute value is then entered into the status register area to generate deviation volume information. The advantage of this operation logic is that by removing the positive and negative signs and extracting only the absolute difference, it can intuitively reflect whether the construction progress is over-progressing or under-progressing. This is still a deviation in the scale of pure engineering quantities caused by the delay in progress. To prevent positive and negative data from canceling each other out and obscuring the actual problem, we will use the aforementioned actual example to illustrate this. We will analyze the building information model file associated with the current node, extract the three-dimensional spatial geometric value corresponding to the planned volume set in it, which is 240 cubic meters, retrieve the physical spatial volume of 120 cubic meters generated in the previous step, and perform a scalar subtraction operation, that is, subtract 120 cubic meters from 240 cubic meters. The calculated reduction margin characteristic value is positive 120 cubic meters. Taking the absolute value of the difference between the two values ​​confirms that it is 120 cubic meters, thus generating the exact deviation volume information of 120 cubic meters. This value result of 120 cubic meters clearly defines the specific material volume gap between the current physical engineering progress and the expected target, which constitutes the core data input source for subsequent delay cycle inference and resource reallocation.

[0025] S202: Call the deviation volume information, obtain the daily standard construction output predetermined by the engineering drawings, divide the deviation volume by the daily standard construction output to calculate the schedule delay period, obtain the original planned calendar time, add the schedule delay period to the original planned calendar time, shift the time axis sequence, and generate the estimated completion calendar time. The system retrieves the previously generated deviation volume information, parses the building object model data tags embedded in the construction site-related engineering drawings, extracts the engineering material attribute codes and corresponding construction process type codes from the text tags, and queries the pre-built construction quota standard data table. Using the engineering material attribute codes and construction process type codes as joint search keys, it performs a row-level matching query in the index fields of the construction quota standard data table to extract the basic man-day quota volume parameters corresponding to the row containing the matching records. Simultaneously, it reads the construction organization scheduling configuration record document associated with the engineering drawings and extracts the actual number of construction teams registered on-site and the set mechanical equipment shift conversion from the document parameter items. The coefficients are calculated by multiplying the extracted number of construction teams with the conversion factor for machine shifts, generating a resource input weight parameter. Then, the retrieved basic man-day quota volume parameter is multiplied with the generated resource input weight parameter to derive the predetermined daily standard construction output from the engineering drawings. Next, the aforementioned deviation volume is divided by this daily standard construction output to calculate the quotient, inferring the project delay period. Finally, the original planned calendar time specified in the initial schedule is obtained, and the delay period is added to the original planned calendar time. The timeline sequence is then shifted on the calendar coordinate system to generate a delayed schedule. The advantage of this calculation logic lies in its ability to fully integrate the combined efficiency of manpower and machinery shifts, making static quotas dynamic. This ensures that the calculated delay period aligns with the real-time resource allocation on-site. For example, if the material attribute is extracted as concrete and the construction process is pumped pouring, a query in the standard data table will yield the corresponding basic man-day quota volume parameter of 10 cubic meters per day. The number of actually registered construction teams is 2, and the configured machinery shift conversion factor is 1.5. Multiplying the number of construction teams (2) by the machinery shift conversion factor (1.5) yields a resource comprehensive input weight parameter of 3. The basic man-day quota volume parameter 1... Multiplying 0 cubic meters per day by the resource comprehensive input weight parameter 3 generates a standard daily construction output of 30 cubic meters per day. Substituting the previously calculated deviation volume information of 120 cubic meters, the deviation volume of 120 cubic meters is divided by the standard daily construction output of 30 cubic meters per day, resulting in a schedule delay period of 4 days. The original planned calendar time is set to day 150 of the project. The schedule delay period of 4 days is added to the original planned calendar time of day 150, and the time axis is extrapolated backward to generate the estimated completion calendar time of day 154. This result of day 154 directly re-anchors the end date of the current task node, providing a precise time section for investigating subsequent concurrent interferences.

[0026] Table 1 Daily Output Configuration Parameters Engineering material attribute coding Construction process type coding Basic quota volume parameters Number of work groups Conversion factor for daily shifts concrete materials Pumping casting process 10 cubic meters per day 2 1.5 Reinforcing steel auxiliary materials Binding and welding process 8 cubic meters per day 3 1.0 Timber formwork splicing support technology 12 cubic meters per day 1 1.2 Table 1 shows the resource configuration details for different material processes.

[0027] S203: Based on the expected completion calendar time, retrieve the time mapping records of the construction scheduling network, compare the calendar timestamps of the component nodes, filter multiple concurrent building information model component nodes whose time intervals fall within the expected completion calendar time, extract the associated work surface three-dimensional boundary coordinate array, analyze the spatial intersecting entities between multiple work surface three-dimensional boundary coordinate arrays, perform spatial Boolean intersection operation, and obtain the collision overlap volume. Based on the projected completion calendar time determined by the simulation, the global construction scheduling network is accessed and the internally stored time mapping record form is retrieved. Each subsequent component node is compared against the start and end intervals of its bound calendar timestamp. Multiple concurrent building information model (BIM) component nodes whose execution time intervals fall entirely or partially within the projected completion calendar time are selected. The coordinate array sets of the work surface boundaries associated with these concurrent component nodes are extracted. Matrix alignment is performed on the extracted work surface boundary coordinate array sets in the spatial coordinate system. Spatial intersection entities between the various work surface boundary coordinate arrays are analyzed and determined. For multiple overlapping boundaries, a spatial Boolean intersection operation is performed, stripping away non-interference parts and retaining overlapping meshes. The spatial volume of the overlapping meshes is calculated to obtain the collision overlap volume. The advantage of this operation logic is that, based on the parallel processing caused by time extension, it accurately extracts implicit spatial interference regions through spatial Boolean intersection, thus reducing invisible resource encroachment conflicts. Transforming it into a concrete three-dimensional geometric volume, and using the previous example for demonstration, based on the determined estimated completion date of day 154, it was found that the calendar timestamp intervals of component node 4 and component node 5 both cover day 154. The three-dimensional boundary coordinate array of the working surface associated with component node 4 and component node 5 was extracted. The boundary span of component node 4 is the coordinate interval X from 10 to 20 and Y from 10 to 20, and the boundary span of component node 5 is the coordinate interval X from 15 to 25 and Y from 15 to 25. The Z-axis height... All are consistent and fall within the range of Z from 0 to 0.6. After performing a spatial Boolean intersection operation, it is determined that the intersecting entity region is within a cube with X from 15 to 20, Y from 15 to 20, and Z from 0 to 0.6. After integration, the collision overlap volume of the Boolean intersection is calculated to be 15 cubic meters. This value of 15 cubic meters accurately represents the scale of spatial interference caused by concurrent tasks in a narrow working area due to delays, and directly constitutes the key geometric judgment benchmark for subsequent evaluation of the efficiency reduction rate of construction personnel congestion.

[0028] Please see Figure 4 The steps for obtaining the revised completion date are as follows: S301: Obtain the total volume of the construction floor space built into the building model, call the collision overlap volume, divide the collision overlap volume by the total volume of the construction floor space, extract the ratio feature of local intersection overlap volume to the overall reference space, and generate the work surface crowding ratio. The system retrieves the total volume data of the construction floor space from the structural attributes of the current building model. It then calls the collision overlap volume value obtained from the aforementioned Boolean operation, divides this value by the total volume of the construction floor space, and performs a scalar division calculation. This extracts the ratio of the locally overlapping volume to the overall baseline space, retaining a fixed decimal place to generate the work surface congestion percentage. The advantage of this calculation logic is that by using a relative ratio instead of absolute volume values, it eliminates the interference of differences in the scale of different construction floors on congestion assessment, establishing a globally universal spatial interference measurement. Using the aforementioned calculation data, the scale is used to perform calculations. The total volume of the current third construction floor is obtained by accessing the floor structure attributes, which is 300 cubic meters. The concurrent collision overlap volume determined in the previous step is 15 cubic meters. The collision overlap volume of 15 cubic meters is divided by the total volume of the construction floor space of 300 cubic meters to perform a division calculation, resulting in a ratio feature of 0.05. That is, the proportion of congestion of the generated work surface is 0.05. This value result of 0.05 quantitatively illustrates the degree of spatial interweaving interference in the current work surface, providing a standardized comparison index key value for subsequent retrieval of the labor efficiency attenuation coefficient.

[0029] S302: Obtain the preset historical test sample mapping and matching data table, compare the congestion ratio of the work surface with the numerical boundary of the mapping and matching data table, extract the corresponding numerical interval associated labor efficiency decay coefficient, read the original planned engineering output from the scheduling record, multiply the original planned engineering output with the labor efficiency decay coefficient to generate the actual engineering output. This process involves analyzing a historical construction record database, extracting multiple completed historical construction test samples, and extracting the corresponding historical work surface congestion ratio, planned output, and actual output from each sample. The extracted actual output is divided by the planned output to generate a historical labor efficiency decay scalar reflecting efficiency loss. All samples are then aggregated and sorted in ascending order of historical work surface congestion ratio. The total number of samples in the current sequence is counted, and the sorted sequence is divided using equal-frequency segmentation based on the calculated total number of samples. Multiple contiguous congestion boundary intervals are constructed based on the segmentation nodes. Multiple historical work surface congestion ratio records within the same congestion boundary interval are extracted. The historical labor efficiency decay scalars mapped to these records in the database are retrieved, summed, and then divided by the number of samples within that congestion boundary interval. The mean is used to calculate the baseline decay coefficient representing that interval. Finally, the multiple congestion boundary intervals are mapped to... Each of the calculated baseline attenuation coefficients is bound using a two-way key-value pair and written to a storage unit to generate a pre-set historical test sample mapping matching data table. The advantage of this operational logic lies in using equal-frequency segmentation and averaging mechanisms to perform interval-based noise reduction and smoothing of discrete historical records, extracting an objective and realistic efficiency attenuation curve matrix. For example, using actual historical data, 100 historical construction test sample data points are extracted. The historical actual output of a certain historical sample is 24 cubic meters, and the historical planned output is 30 cubic meters. Dividing this by the historical labor efficiency attenuation scalar value, we find it to be 0.8. The 100 historical work surfaces were sorted in ascending order of congestion percentage and divided into 5 congestion boundary intervals with equal frequency. The second congestion boundary interval contains 20 samples with values ​​ranging from 0.04 to 0.06. The historical labor efficiency attenuation scalar associated with these 20 samples were summed to 16. The sum of 16 was divided by the number of samples 20 to obtain a baseline attenuation coefficient of 0.8. The interval 0.04 to 0.06 was then bound to the coefficient 0.8 as a key-value pair and entered into a pre-set historical test sample mapping and matching data table. This table provides a reliable historical data reference dictionary for subsequent current efficiency extrapolation.

[0030] Table 2 Historical Mapping Matching Data Boundary Table Interval sequence number Lower limit of congestion Crowding limit Includes sample size Reference attenuation coefficient Interval 1 0.01 0.03 20 0.95 Second section 0.04 0.06 20 0.80 Section 3 0.07 0.09 20 0.65 Table 2 lists the specific boundary division criteria for data segmentation and coefficient binding.

[0031] S303: Collect on-site scanning feedback of the volume to be completed, extract the numerical parameters of the geometric quantities of the three-dimensional unformed building associated with the volume to be completed, divide the volume to be completed by the actual engineering output for calculation, analyze the remaining workload to match the natural cycle span scalar of calendar progression, calculate the time series evolution day characteristics, and generate the corrected completion period. The system reads the pre-built historical test sample mapping and matching data table constructed in the previous program flow, calls the latest calculated work surface congestion ratio value of the current engineering surface, compares and retrieves this work surface congestion ratio value with the boundary values ​​of each interval recorded in the mapping and matching data table, determines the specific interval position where the current value falls, and directly extracts the labor efficiency decay coefficient associated with the corresponding value interval. It accesses the global scheduling record storage area, reads the originally planned engineering output value of the current task batch, multiplies the read originally planned engineering output value with the just extracted labor efficiency decay coefficient, and derives the actual engineering output through discount dimensionality reduction. It collects the latest feedback data of the unfinished volume from the 3D scanning device at the construction site, extracts the associated 3D unformed building geometric parameters within the unfinished volume, divides the extracted unfinished volume value by the previously calculated actual engineering output value, performs quotient division, analyzes the remaining workload corresponding to the quotient, matches the calendar progression natural cycle span scalar, rounds to calculate the evolution of the time series in the calendar, and generates the corrected output. The advantage of this calculation logic for the project duration is that it directly lowers the expected output based on the space congestion ratio and reverses the calculation to determine the actual project duration, preventing project delays caused by scheduling based on ideal output rates. Combining all the aforementioned retained data, the calculation uses the currently calculated work surface congestion ratio of 0.05. Comparing this with the mapping and matching data table, it is found that 0.05 falls within the second interval of 0.04 to 0.06. The associated labor efficiency attenuation coefficient for this interval is directly extracted as 0.8, and the original planned output for this task node is read as 30 cubic meters per day. Multiplying the original planned daily output of 30 cubic meters by a labor efficiency reduction coefficient of 0.8 yields an actual daily output of 24 cubic meters after the efficiency reduction. Simultaneously, the remaining unfinished volume of 120 cubic meters is extracted from the on-site scan. Dividing the unfinished volume of 120 cubic meters by the actual daily output of 24 cubic meters yields a natural cycle evolution of 5 days, generating a revised completion period of 5 days. This result of 5 days reflects the exact expected number of days to complete the remaining work due to the efficiency reduction caused by congestion, becoming the core driving value for schedule extension.

[0032] Please see Figure 5 The specific steps for obtaining the concatenated directed connection edges are as follows: S401: Obtain the network graph of construction scheduling tasks, parse the records of topological connection relationships between nodes within the graph, retrieve concurrent building information model component nodes that lack dependent order connections, extract node attributes and time-space temperature features for concurrent building information model component nodes that lack dependent order connections, aggregate the distribution status of nodes that lack dependent order connections, merge multiple isolated nodes, and establish a set of concurrent unconstrained component nodes. The system acquires the network graph data structure of the construction schedule task for global maintenance and updates. It then analyzes the pointers to the existing topological connections between each active node within the graph layer by layer. Using a graph traversal algorithm, it retrieves concurrent building information model component nodes without in-degree or out-degree dependent connections. For these isolated nodes lacking connections, it performs internal attribute decomposition, extracting their node attribute labels and three-dimensional parameters of temporal and spatial degree characteristics. The system then performs a peer-to-peer aggregation operation on the distribution of multiple nodes lacking dependent connections in memory, merging all identified isolated nodes and dynamically creating an array of concurrent unconstrained component nodes without logical before-after constraints. The advantage of this operational logic is that it automatically identifies nodes capable of parallel construction through topological relationship review. A group of scattered components with potential but lacking orderly management provides a preliminary processing queue for subsequent spatial polar angle dynamic sorting. Taking a specific scheduling record execution example, after analyzing the construction scheduling task network graph, it was found that component nodes 7, 8, and 9 all lack dependent order connections in the current graph topology, presenting an isolated concurrent state. The node attributes and feature parameters of component node 7 were extracted, and the attributes of component nodes 8 and 9 were extracted simultaneously. The above three component nodes were aggregated and merged, and a set of unconstrained component nodes containing three elements was successfully established in memory. The generation of this set clarified the scope of objects that urgently need spatial order reorganization at the current stage, avoiding logical damage to existing fixed link nodes in subsequent sorting.

[0033] S402: Based on the set of unconstrained component nodes in the same period, analyze the boundary parameters of the three-dimensional geometric features associated with each node in the set, extract the spatial physical centroid of the node and construct a three-dimensional centroid coordinate set, collect the two-dimensional plane base coordinates of the hoisting machinery and equipment on site, calculate the relative angle between the base coordinates of the hoisting machinery and equipment and the two-dimensional projection ray on the horizontal plane of the three-dimensional centroid coordinate set, extract the angle numerical variable, and obtain the horizontal polar angle data. Based on the previously established array of unconstrained component node sets, the list of 3D geometric feature boundary parameters associated with each node within the set is analyzed one by one. Integration is performed along the three-axis coordinates of each component model boundary to extract and calculate the 3D spatial physical centroid coordinates of the corresponding entity model for each node. These spatial physical centroid coordinates are then aggregated to form the three-dimensional centroid coordinate set of the target task group. The current 2D plane base coordinates of the crane equipment at the construction site are collected via the sensor interface. Using the crane equipment base coordinates as the origin and the positive direction of the set coordinate axes as the polar axes, the coordinates of the crane equipment base coordinates and the coordinates of each point in the three-dimensional centroid coordinate set are calculated. The relative horizontal offset angle between the two-dimensional projection rays of the punctuation points on a uniform horizontal plane is calculated. For each calculated node angle, the angle value variable is extracted to obtain the horizontal polar angle data of all nodes within the set. The advantage of this operational logic is that it uses the center of the construction machinery's operation as a reference to perform polar coordinate transformation on spatial components, conforming to the physical laws of the boom swing of tower cranes. Combining the example and substituting the data, based on component nodes 7, 8, and 9 within the set, the spatial physical centroid coordinates of component node 7 are calculated to be X-coordinate 10 and Y-coordinate 10. The coordinate points of the two-dimensional plane base of the hoisting machinery on site are collected at the origin (X=0, By calculating the relative angle between the origin and the projected ray from component node 7 (Y=0), the horizontal polar angle of component node 7 is found to be 45 degrees. Similarly, after coordinate integration and ray angle conversion, the horizontal polar angle of component node 8 is found to be 90 degrees and the horizontal polar angle of component node 9 is found to be 135 degrees. These angle values ​​directly reduce the complex three-dimensional absolute coordinates to a one-dimensional scale of the robot arm's rotation angle, providing a unified parameter benchmark for the reshaping equipment workflow.

[0034] S403: Call the horizontal polar angle data, perform floating-point comparison and sorting operations on the polar angle values ​​of multiple nodes and construct an increasing sequence, establish the target node sequential processing sequence, generate the optimized job order array, read the unidirectional sequential index position of the elements inside the optimized job order array, perform unidirectional node topology connection calculation according to the sequential hierarchical position of the index sequence, and generate serial directed connection edges. The system calls upon a set of horizontal polar angle data for multiple component nodes obtained through prior conversion. It performs floating-point comparison operations on the polar angle values ​​associated with each node, and constructs an increasing sequence in memory storage based on the ascending rule of angle values. A target node processing sequence is established according to the natural ascending arrangement mechanism of polar angles, and this sequence is transferred and output to generate an optimized job order array. Subsequently, it reads the unidirectional sequential index sequence of elements within the optimized job order array, following the hierarchical position relationship from the first index position to the second, and performs a rewrite calculation of the unidirectional node topology connection data between adjacent elements. A series of directed edges are generated by connecting pointers. The advantage of this operational logic is that, based on the sequential arrangement of tasks swept unidirectionally by the mechanical boom, it eliminates the need for lifting... The energy consumption and idle time of frequent left and right swaying of the equipment are reduced. To optimize the spatial flow of mechanical scheduling, for example, the calculation is continued by calling the horizontal polar angle of component node 7 (45 degrees), component node 8 (90 degrees), and component node 9 (135 degrees). A floating-point comparison operation is performed to determine their ascending sequence from smallest to largest. This generates an optimized job order array with the internal elements arranged in the following order: component node 7 at the beginning, component node 8 in the middle, and component node 9 at the end. After reading their sequential index positions, a topological connection calculation pointer pointing unidirectionally from component node 7 to component node 8 is automatically written into the graph. Similarly, a pointer pointing unidirectionally from component node 8 to component node 9 is written. Two key series directed connection edges are generated continuously. The result of this edge generation completes the logical gap of the original free nodes and integrates subsequent tasks into the standard pipeline of the crane's clockwise rotation operation.

[0035] Please see Figure 6 The specific steps for obtaining construction progress management results are as follows: S501: Call the series directed connection edge, extract the horizontal polar angle associated with the two end nodes, calculate the absolute difference of the subtraction for the angle parameters, establish the rotation angle deviation, obtain the preset rated rotation angular velocity of the lifting machinery and equipment, divide the rotation angle deviation by the rated rotation angular velocity and perform quotient algebra calculation to generate the mechanical displacement period. The newly written serial directed connection edge record is invoked, and the initial horizontal polar angle values ​​associated with the component nodes at both ends of each serial directed connection edge are extracted. An algebraic subtraction operation is performed on the polar angles of the starting and ending nodes of the directed edge connection, and the absolute value of the subtraction result is extracted to establish the rotational angle deviation value between the two nodes. The factory-preset rated rotational angular velocity parameter of the currently operating lifting machinery is obtained through the equipment ledger reading port. The previously calculated rotational angle deviation value is divided by the obtained rated rotational angular velocity parameter to perform a quotient algebraic calculation, converting it into the physical time required for the robotic arm to traverse two work points. Based on this, the generation machine... During the mechanical displacement period, the actual node angle data is substituted, and the series directed connection edge from component node 7 to component node 8 is called. The horizontal polar angle of component node 8 (90 degrees) and the horizontal polar angle of component node 7 (45 degrees) are extracted. The absolute difference is calculated by subtracting them to obtain a rotation angle deviation of 45 degrees. The rated rotational angular velocity of the tower crane is read as 15 degrees per minute. The rotation angle deviation of 45 degrees is divided by the rated rotational angular velocity of 15 degrees per minute and algebraic calculation is performed to obtain a mechanical displacement period of 3 minutes across these two nodes. This calculated value of 3 minutes accurately outlines the physical time of pure mechanical operation of the equipment between two processes. It is used as a transition time supplement for task switching and incorporated into the overall time consumption parameter system.

[0036] S502: Obtain the corrected completion date and add it numerically to the mechanical displacement period. Overlay the scalar parameters of the node time dimension, deduce the overall task execution extension span, and generate the comprehensive reconstruction time. Obtain the revised completion time value required for the task at this node, as calculated above, and call the mechanical displacement period value between nodes that was just converted. After unifying the dimensions of these two time-consuming features representing different dimensions, add the revised completion time and mechanical displacement period values ​​together for comprehensive calculation. Through superposition, integrate the two node time dimension scalar parameters of component entity processing time and peripheral equipment circulation time, comprehensively extrapolate the extended span of the overall task execution, accumulate and generate a comprehensive reconstruction time weight, and use the data calculated in the previous steps for full summarization. The revised completion time of the current task stage is obtained as 5 days. In order to match... The mechanical time is standardized and calculated based on a standard engineering day of 8 hours, which is converted into a total of 2400 minutes. The mechanical displacement period, calculated from the connection gap, is 3 minutes. The 2400 minutes converted from the corrected completion time are added to the 3 minutes of mechanical displacement period. The overall task extension span is calculated to be 2403 minutes. Thus, the complete comprehensive reconstruction time weight is officially generated as 2403 minutes. This weight value of 2403 minutes serves as a key variable for the subsequent empowerment graph edge weight vector, ensuring that the duration variable referenced in the rearranged graph has an irrefutable comprehensiveness and accuracy.

[0037] S503: Write the comprehensive reconstruction time into the associated attribute domain of the serial directed connection edge, adjust the internal topology sequence configuration of the scheduling task network graph according to the serial directed connection edge carrying the weight value parameter, update the global node order, and obtain the construction progress management result. The comprehensive reconstruction time weight values, including the time consumed by the main body and the time consumed by gaps, calculated in the previous step, are fully written into the related attribute domain dictionary attached to the corresponding serial directed connection edge, overwriting the original empty or default time weight field of the edge. Based on these newly added serial directed connection edges carrying specific time weight parameters, the graph parsing engine is driven to trigger the reordering logic, adjusting the topology sequence configuration parameters such as the earliest start time and latest end time of all subsequently affected nodes in the scheduling task network graph, recalculating the critical path chain and updating the succession order combination of global nodes. After graph refresh and network weight traversal, all time nodes and new links are summarized, and the final construction progress management result is presented and output to the terminal interface. The advantage of this operation logic is that... By utilizing closed-loop feedback, the local mechanical polar angle scheduling and efficiency reduction / delay assessment results are comprehensively mapped to the macroscopic network graph. This enables the engineering scheduling neural center to achieve adaptive iterative correction based on 3D collision and mechanical dynamic feedback. Finally, in the demonstration and verification, the weight of the comprehensive reconstruction time of 2403 minutes is written into the associated attribute domain of the unidirectional directed connection edge from component node 7 to component node 8. Based on this 2403-minute time weight, the earliest start time of component node 8 is postponed. At the same time, the scheduling task network graph is adjusted and the starting positions of hundreds of related topological sequences are updated globally. Finally, a new construction progress management result network diagram containing accurate polar angle order and compensating for collision efficiency reduction time is output to the screen, eliminating various delay risks of traditional manual coordination.

[0038] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A BIM-based method for dynamic control of building construction progress, characterized in that, Includes the following steps: S1: Based on the compactness determination of the straight-line distance and the upper and lower limits of reflectivity, the point cloud data array is filtered and combined to generate the target material point cloud cluster array. The volume of the solid space is generated through edge coordinate envelope calculation. S2: Based on the planned volume and the volume of the physical space, calculate the deviation volume, combine the daily standard construction output with the original planned calendar time to estimate the expected completion calendar time, calculate the intersection of the three-dimensional boundary coordinate array of the working surface of the concurrent building information model component nodes within the target period, and generate the collision overlap volume. S3: Divide the collision overlap volume by the total volume of the construction floor space to generate the work surface congestion ratio, extract the corresponding labor efficiency attenuation coefficient and convert it to the actual engineering output, and divide the volume to be completed by the actual engineering output to calculate the corrected completion period. S4: Aggregate the unconnected concurrent building information model component nodes into a set of unconstrained component nodes in the same period, calculate the horizontal projection angle between the coordinates of the crane equipment base and the coordinate set of the three-dimensional centroid to generate the horizontal polar angle, generate an optimized operation sequence array by incremental sorting and connect them in sequence to generate serial directed connection edges. S5: Generate rotation angle deviation based on the difference in horizontal polar angles at both ends of the series directed connection edge, generate mechanical displacement period by combining it with the rated rotation angular velocity, and add it to the corrected completion period to generate comprehensive reconstruction time. Adjust the scheduling task network diagram and output the construction progress management results.

2. The BIM-based dynamic management and control method for building construction progress according to claim 1, characterized in that, The physical space volume includes the core tube concrete volume, the load-bearing column pouring volume, and the measured volume of the shear wall. The collision and overlap volume includes the volume occupied by scaffolding interference, the volume of temporary material storage conflict, and the volume of cross-interference of robotic arm operations. The revised completion period includes the number of days for process extension, the buffer period for cross-efficiency reduction, and the ample time for machinery scheduling. The serial directed connection edge includes the main line of single-trade flow construction, the cross-floor lifting sequence chain, and the trajectory of parallel component assembly sequence. The construction progress management results include the dynamic list of machinery and equipment scheduling, the resource efficiency reduction early warning report, and the three-dimensional scheduling reconstruction Gantt chart.

3. The BIM-based dynamic management and control method for building construction progress according to claim 1, characterized in that, The specific steps for obtaining the volume of the physical space are as follows: S101: Obtain the building material attribute configuration file, extract the compact judgment straight line distance and upper and lower limit boundaries of reflectivity, obtain the point cloud data array collected by the on-site laser scanning equipment, extract the laser reflectivity associated with the coordinate points in the point cloud data array, calculate the straight line distance between adjacent coordinate points, map and associate the laser reflectivity with the straight line distance, and establish a point cloud spatial feature matrix. S102: Call the point cloud spatial feature matrix, compare the straight line distance with the compact judgment straight line distance, compare the laser reflectivity with the upper and lower limit boundaries of reflectivity, filter coordinate points whose straight line distance is not greater than the compact judgment straight line distance, filter coordinate points whose laser reflectivity is within the upper and lower limit boundaries of reflectivity, aggregate the coordinate points after each filtering, and generate a target material point cloud cluster array. S103: For the target material point cloud cluster array, determine and extract the corresponding outer edge coordinate points, connect the outer edge coordinate points along the spatial distribution to construct a three-dimensional closed polygonal surface, and perform spatial volume integration and summation on the three-dimensional geometric shape inside the envelope of the three-dimensional closed polygonal surface to generate the solid spatial volume.

4. The BIM-based dynamic management and control method for building construction progress according to claim 3, characterized in that, The process of extracting the compact determination straight-line distance and the upper and lower limits of reflectivity includes: The building material attribute configuration file is parsed, and the internal material feature mapping table is extracted. The target material number is extracted from the material feature mapping table, and the optical reflection range, scanning resolution level, and surface roughness parameters associated with the target material number are retrieved. The maximum and minimum values ​​of the optical reflection range are extracted and combined to generate the upper and lower limits of the reflectivity. The reference point spacing corresponding to the scanning resolution level is extracted as the basic distance. The basic distance is multiplied by the dimensionless roughness influence coefficient to generate a distance compensation value. The distance compensation value is added to the basic distance to generate the compact judgment straight line distance.

5. The BIM-based dynamic management and control method for building construction progress according to claim 3, characterized in that, The specific steps for obtaining the collision overlap volume are as follows: S201: Obtain the planned volume of the building information model, extract the three-dimensional spatial geometric values ​​corresponding to the planned volume, compare the planned volume with the physical space volume, perform scalar subtraction operation on the two spatial dimension parameters, extract the reduction margin feature, obtain the absolute value of the numerical difference, and generate deviation volume information. S202: Call the deviation volume information, obtain the daily standard construction output predetermined in the engineering drawings, divide the deviation volume by the daily standard construction output to calculate, generate the schedule delay period, obtain the original planned calendar time, add the schedule delay period to the original planned calendar time, shift the time axis sequence, and generate the estimated completion calendar time. S203: Based on the estimated completion calendar time, retrieve the time mapping records of the construction scheduling network, compare the calendar timestamps of the component nodes, filter multiple concurrent building information model component nodes whose time intervals fall within the estimated completion calendar time, extract the associated work surface three-dimensional boundary coordinate array, analyze the spatial intersecting entities between multiple work surface three-dimensional boundary coordinate arrays, perform spatial Boolean intersection operation, and obtain the collision overlap volume.

6. The BIM-based dynamic management and control method for building construction progress according to claim 5, characterized in that, The process of obtaining the predetermined daily standard construction output from the engineering drawings includes: The data tags of the building object model associated with the engineering drawings are parsed to extract the engineering material attributes and construction process types. A construction quota standard data table is obtained. The engineering material attributes and construction process types are used as joint retrieval keys to perform a matching query in the construction quota standard data table. The corresponding basic man-day quota volume parameters are extracted. The construction organization scheduling configuration records associated with the engineering drawings are read to extract the number of construction teams and the conversion factor of machinery and equipment shifts. The number of construction teams and the conversion factor of machinery and equipment shifts are multiplied to generate a comprehensive resource input weight parameter. The basic man-day quota volume parameter and the comprehensive resource input weight parameter are multiplied to generate the daily standard construction output.

7. The BIM-based dynamic management and control method for building construction progress according to claim 5, characterized in that, The specific steps for obtaining the revised completion date are as follows: S301: Obtain the total volume of the construction floor space built into the building model, call the collision overlap volume, divide the collision overlap volume by the total volume of the construction floor space, extract the ratio feature of the local intersection overlap volume and the overall reference space, and generate the work surface crowding ratio. S302: Obtain a preset historical test sample mapping and matching data table, compare the congestion ratio of the work surface with the numerical boundary of the mapping and matching data table, extract the labor efficiency decay coefficient associated with the corresponding numerical interval, read the original planned engineering output from the scheduling record, multiply the original planned engineering output with the labor efficiency decay coefficient to generate the actual engineering output. S303: Collect the volume to be completed from the on-site scanning feedback, extract the geometric parameters of the three-dimensional unformed building associated with the volume to be completed, divide the volume to be completed by the actual engineering output for calculation, analyze the remaining workload to match the natural cycle span scalar of calendar progression, calculate the time series evolution day characteristics, and generate the corrected completion period.

8. The BIM-based dynamic management and control method for building construction progress according to claim 7, characterized in that, The process of obtaining the preset historical test sample mapping and matching data table includes: The historical construction record database is analyzed to extract multiple historical construction test sample data. From the historical construction test sample data, the historical work surface congestion ratio, historical planned output, and historical actual output are extracted. The historical actual output is divided by the historical planned output to generate an associated historical labor efficiency decay scalar. The multiple historical work surface congestion ratios are sorted in ascending order of value, and the total number of sample distributions is extracted. Based on the total number of sample distributions, the ascending sorted sequence is divided into multiple continuous congestion boundary intervals to construct multiple congestion boundary intervals. The congestion ratios of multiple historical work surfaces falling within the same congestion boundary interval are extracted. The multiple historical labor efficiency decay scalars are retrieved and bound together. The retrieved multiple historical labor efficiency decay scalars are added together and divided by the number of samples contained in the interval to calculate a baseline decay coefficient. The multiple congestion boundary intervals are bound to each of the baseline decay coefficients as key-value pairs to generate the preset historical test sample mapping matching data table.

9. The BIM-based dynamic management and control method for building construction progress according to claim 7, characterized in that, The specific steps for obtaining the series directed connection edges are as follows: S401: Obtain the network graph of construction scheduling tasks, parse the records of topological connection relationships between nodes within the graph, retrieve concurrent building information model component nodes that lack dependent order connections, extract node attributes and time-space temperature features for concurrent building information model component nodes that lack dependent order connections, aggregate the distribution status of nodes that lack dependent order connections, merge multiple isolated nodes, and establish a set of concurrent unconstrained component nodes. S402: Based on the set of unconstrained component nodes of the same period, analyze the boundary parameters of the three-dimensional geometric features associated with each node in the set, extract the spatial physical centroid of the node and construct a three-dimensional centroid coordinate set, collect the two-dimensional plane base coordinates of the hoisting machinery and equipment on site, calculate the relative angle between the base coordinates of the hoisting machinery and equipment and the two-dimensional projection ray of the three-dimensional centroid coordinate set on the horizontal plane, extract the angle numerical variable, and obtain the horizontal polar angle data. S403: Call the horizontal polar angle data, perform floating-point comparison and sorting operations on the polar angle values ​​of multiple nodes and construct an increasing sequence, establish a target node sequential processing sequence, generate an optimized job order array, read the unidirectional sequential index position of the elements inside the optimized job order array, perform unidirectional node topology connection calculation according to the sequential hierarchical position of the index sequence, and generate a series directed connection edge.

10. The BIM-based dynamic management and control method for building construction progress according to claim 9, characterized in that, The specific steps for obtaining the construction progress management results are as follows: S501: Call the series directed connection edge, extract the horizontal polar angle associated with the two end nodes, calculate the absolute difference of the subtraction for the angle parameters, establish the rotation angle deviation, obtain the preset rated rotation angular velocity of the lifting machinery and equipment, divide the rotation angle deviation by the rated rotation angular velocity and perform quotient algebra calculation to generate the mechanical displacement period. S502: Obtain the corrected completion period and add it to the mechanical displacement period. Add the node time dimension scalar parameters to extrapolate the overall task execution extension span and generate the comprehensive reconstruction time. S503: Write the comprehensive reconstruction time into the associated attribute domain of the series directed connection edge, adjust the internal topology sequence configuration of the scheduling task network graph according to the series directed connection edge carrying the weight value parameter, update the global node order, and obtain the construction progress management result.

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