Method and system for identifying quantity data in bim pipeline generation

Abstract

The application provides a BIM pipeline generation engineering quantity data identification method and system, relates to the technical field of data processing, and comprises the following steps: step 1, an initial pipeline layout scheme and corresponding initial engineering quantity data are obtained; step 2, an avoidance optimization operation is performed on the pipeline in the building information model, and pipeline geometric property change information and connection relationship change information caused by the operation are synchronously captured and stored; and step 3, according to the captured change information, a spatial point sequence of the optimized pipeline is extracted and converted into a pipeline contour three-dimensional coordinate set, a space construction reference surface of an envelope coordinate set is constructed according to the distribution range of the three-dimensional coordinate set, and a basic outer boundary is defined on the space construction reference surface. The application realizes automatic, accurate and efficient identification and accounting of engineering quantity data after pipeline avoidance optimization.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for identifying engineering quantity data in BIM pipeline generation. Background Technology

[0002] In the process of digital transformation of building electromechanical engineering, BIM technology has become the core support for pipeline comprehensive optimization due to its advantages of visualization and collaboration. Accurate identification of engineering quantities after pipeline optimization is a key link in cost control, material procurement and project settlement. At present, the industry generally relies on BIM models for pipeline layout and collision avoidance optimization. However, the identification of engineering quantity data mostly relies on manual verification or simple model export, which is difficult to adapt to the dynamic changes in pipeline geometry and connection relationship during the optimization process.

[0003] Taking the underground parking garage of a residential project as an example, its electromechanical pipelines cover multiple systems such as water supply and drainage, electrical, and ventilation. Before construction, BIM technology was used to optimize pipeline avoidance to solve the problems of insufficient clearance and collision in the original plan. However, after optimization, pipelines underwent changes such as route adjustment, addition of bends, and length extension. Cost estimators had to manually check the pipeline length and count the number of accessories by comparing the models before and after optimization. This not only consumed a lot of time to complete the engineering quantity calculation for this area, but also caused deviations between the initial calculation results and actual needs due to manual omissions of added seismic supports and pipeline bend increments. This resulted in additional material purchase costs and construction delays. The corresponding technical defect is that the existing method cannot automatically capture the changes in geometric attributes and connection relationships caused by pipeline avoidance optimization, and it also lacks an intelligent calculation mechanism to correlate the changed data with the engineering quantity. This results in low efficiency and large errors in engineering quantity identification, making it difficult to meet the needs of refined management of complex projects. Summary of the Invention

[0004] The technical problem to be solved by this invention is to provide a method and system for identifying engineering quantity data in BIM pipeline generation, so as to realize the automatic, accurate and efficient identification and calculation of engineering quantity data after pipeline avoidance optimization.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0006] Firstly, a method for identifying engineering quantity data in BIM pipeline generation, the method comprising:

[0007] Step 1: Obtain the building information model containing the initial pipeline layout plan and corresponding initial engineering quantity data;

[0008] Step 2: By performing avoidance optimization operations on pipelines in the building information model, the changes in pipeline geometric attributes and connection relationships caused by the operations are captured and stored simultaneously.

[0009] Step 3: Based on the captured change information, extract the spatial point sequence of the optimized pipeline and convert it into a three-dimensional coordinate set of the pipeline outline; based on the distribution range of the three-dimensional coordinate set, construct a spatial construction reference surface of the envelope coordinate set, and define a basic outer boundary on the spatial construction reference surface;

[0010] Step 4: Based on the aggregation characteristics of the three-dimensional coordinate set, construct the outer boundary of the base and divide it into multiple related regions. At the same time, assign each spatial point in the coordinate set to the corresponding region.

[0011] Step 5: For the set of spatial points belonging to each divided region, identify the changes in the inflection point positions in the pipeline geometry before and after optimization, and quantify the differences in the number of inflection points, inflection point angles, and inflection point spacing; based on the correlation between the inflection point change characteristics and the pipeline bending stress distribution, calculate and generate the geometry correction coefficient corresponding to each divided region.

[0012] Step 6: Integrate the geometric shape correction coefficients of all divided areas, automatically identify pipeline changes caused by avoidance optimization operations; based on the pipeline changes, perform linked calculations and corrections on the initial engineering quantity data to obtain the final engineering quantity data matching the optimized pipeline layout scheme.

[0013] Furthermore, a building information model containing the initial pipeline layout plan and corresponding initial engineering quantity data is obtained, including:

[0014] Access the cloud-based BIM model library through a distributed storage interface, retrieve and load the original building information model file of the target building project, and use it as the basic source file for pipeline data extraction.

[0015] Based on the original building information model file, integrity verification and format standardization are performed to eliminate data anomalies and format differences, resulting in a standardized model data stream.

[0016] Based on the standardized model data flow, structured pipeline layout data is obtained by extracting the initial pipeline layout scheme from the model, including pipeline type, direction, connection nodes and spatial layout information;

[0017] By using structured pipeline layout data, the engineering quantity data associated with the initial pipeline layout plan is parsed synchronously, and a mapping index table between pipeline components and corresponding initial engineering quantity data is established to realize the accurate correspondence between pipeline components and engineering quantity data.

[0018] Based on the standardized model data stream, structured pipeline layout data, and mapping index table, data fusion and model reconstruction are performed to obtain a building information model with a complete initial pipeline layout scheme and corresponding initial engineering quantity data.

[0019] By performing avoidance optimization operations on pipelines in the Building Information Model (BIM), information on changes in pipeline geometric attributes and connectivity caused by the operations is simultaneously captured and stored, including:

[0020] Based on the building information model, avoidance optimization operations are performed on the pipelines in the model to obtain a preliminary optimized pipeline layout scheme.

[0021] Based on the preliminary optimized pipeline layout plan, the information on changes in the geometric attributes of the pipeline during the optimization process is captured simultaneously, including dynamic changes in pipeline length, diameter, bending radius, and spatial orientation.

[0022] By using information on changes in geometric properties, we can monitor and capture changes in pipeline connection relationships in real time, including topological changes such as offset of connection node positions, addition of new connection branches, deletion of redundant connections, and changes in connection methods.

[0023] By associating and integrating information on changes in geometric attributes and changes in connectivity, complete pipeline change information with complete change characteristics can be obtained.

[0024] Furthermore, based on the captured change information, the spatial point sequence of the optimized pipeline is extracted and converted into a three-dimensional coordinate set of the pipeline profile; according to the distribution range of the three-dimensional coordinate set, a spatial construction reference surface of the envelope coordinate set is constructed, and a basic outer boundary is defined on the spatial construction reference surface, including:

[0025] By using pipeline change information, a continuous spatial point sequence is extracted from the optimized pipeline geometry data to form pipeline spatial trajectory data.

[0026] Based on pipeline spatial trajectory data, a point cloud data set containing X, Y, and Z coordinate values ​​is generated by converting the spatial point sequence into a three-dimensional coordinate set of the pipeline outline under a unified coordinate system.

[0027] By analyzing the distribution range of coordinate points using the three-dimensional coordinate set of the pipeline profile, calculating the minimum bounding box of the coordinates, and constructing a spatial construction reference surface that completely encloses the three-dimensional coordinate set;

[0028] Based on the spatial construction reference surface, a closed basic outer boundary is automatically generated on the reference surface through a boundary definition algorithm.

[0029] Furthermore, based on the aggregation characteristics of the three-dimensional coordinate set, the basic outer boundary is divided into regions, forming multiple related regions. Simultaneously, each spatial point in the coordinate set is assigned to its corresponding region, including:

[0030] Based on the three-dimensional coordinate set of the pipeline outline, the distance density distribution characteristics between each spatial point are calculated, the natural clustering center location and clustering intensity parameters of the coordinate points are identified, and point clustering characteristic data are formed.

[0031] Based on the point clustering feature data and the basic outer boundary, the basic outer boundary is adaptively divided by the region segmentation algorithm. Multiple initial division regions are generated according to the location of the clustering center, and spatial topological relationships between adjacent regions are established.

[0032] Based on the initial division of regions and spatial topological relationships, region optimization processing is performed to adjust the shape and size of the region boundaries so that the boundaries of each region match the cluster density gradient of the three-dimensional coordinate set, resulting in a set of divided regions.

[0033] Based on the set of divided regions, each spatial point in the three-dimensional coordinate set of the pipeline outline is accurately assigned to the corresponding divided region according to the relationship between the spatial coordinates and the region boundary.

[0034] Furthermore, for each set of spatial points within a defined region, the changes in inflection point positions in the pipeline geometry before and after optimization are identified, and the differences in the number of inflection points, inflection point angles, and inflection point spacing are quantified. Based on the correlation between the inflection point change characteristics and the pipeline bending stress distribution, geometric correction coefficients corresponding to each defined region are calculated and generated, including:

[0035] For each region in the set of regions, obtain the three-dimensional coordinate set of the pipeline outline before and after optimization within the region, which serves as the set of spatial points to be analyzed for the corresponding region.

[0036] Based on the set of spatial points to be analyzed, the curve inflection point detection algorithm is applied to the pipeline spatial trajectories before and after optimization to identify and locate the key inflection points in their respective geometric shapes, and obtain the inflection point set before optimization and the inflection point set after optimization of the inflection point spatial coordinates.

[0037] Based on the inflection point set before optimization and the inflection point set after optimization, inflection point position matching and comparative analysis are performed to extract the inflection point position change characteristics caused by the avoidance optimization operation within the divided area.

[0038] Based on the characteristics of inflection point position changes, the difference in the number of inflection points before and after optimization, the change in the angle of the corresponding inflection points, and the change in the distance between adjacent inflection points are quantitatively calculated to form a set of quantitative indicators describing the degree of difference in pipeline geometry within the divided area.

[0039] Based on the set of quantitative indicators and the distribution pattern of pipeline bending stress, the influence weights of the differences in the number of inflection points, the change in angle, and the change in spacing on the local morphological changes of the pipeline are obtained. Through weighted fusion calculation, the geometric morphology correction coefficient is obtained.

[0040] Furthermore, it integrates the geometric correction coefficients of all divided areas, automatically identifies pipeline changes caused by avoidance optimization operations, and performs linked calculations and corrections on the initial engineering quantity data based on the pipeline changes to obtain the final engineering quantity data matching the optimized pipeline layout scheme, including:

[0041] Obtain all geometric shape correction coefficients generated by the division of regions, perform normalization and weight allocation processing to form a set of summary correction coefficients describing the degree of change in the overall pipeline geometry;

[0042] Based on the aggregated set of correction coefficients and combined with the predefined rule library for change determination, the system automatically analyzes and identifies specific pipeline changes caused by avoidance optimization operations.

[0043] Based on the pipeline change item, call the mapping index table to locate the original engineering quantity record associated with the changed pipeline component in the initial engineering quantity data;

[0044] For each original engineering quantity record located, based on the specific content and quantity of the pipeline change item, a linkage calculation is performed according to the preset engineering quantity calculation rules to generate the corresponding engineering quantity correction factor;

[0045] The original quantity records are numerically corrected and updated using the applied quantity correction factor. All updated quantity records are then integrated and verified to output the final quantity data that precisely matches the optimized pipeline layout scheme.

[0046] Secondly, the BIM pipeline generation quantity data identification system includes:

[0047] The acquisition module is used to acquire a building information model containing the initial pipeline layout plan and corresponding initial engineering quantity data.

[0048] The optimization module is used to perform avoidance optimization operations on pipelines in the building information model, and simultaneously capture and store information on changes in pipeline geometric properties and connection relationships caused by the operations.

[0049] The extraction module is used to extract the spatial point sequence of the optimized pipeline based on the captured change information and convert it into a three-dimensional coordinate set of the pipeline outline; based on the distribution range of the three-dimensional coordinate set, a spatial construction reference surface of the envelope coordinate set is constructed, and a basic outer boundary is defined on the spatial construction reference surface;

[0050] The partitioning module is used to construct regions based on the aggregation characteristics of the three-dimensional coordinate set, forming multiple related partitioned regions, and assigning each spatial point in the coordinate set to the corresponding partitioned region.

[0051] The calculation module is used to identify the changes in the inflection point positions in the pipeline geometry before and after optimization for the set of spatial points belonging to each divided region, and to quantify the differences in the number of inflection points, inflection point angles and inflection point spacing; based on the correlation between the inflection point change characteristics and the pipeline bending stress distribution, it calculates and generates the geometric correction coefficient corresponding to each divided region.

[0052] The correction module integrates the geometric correction coefficients of all divided areas, automatically identifies pipeline changes caused by avoidance optimization operations, and performs linked calculations and corrections on the initial engineering quantity data based on the pipeline changes to obtain the final engineering quantity data matching the optimized pipeline layout scheme.

[0053] Thirdly, a computing device includes:

[0054] One or more processors;

[0055] A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method.

[0056] Fourthly, a computer-readable storage medium storing a program that, when executed by a processor, implements the method.

[0057] The above-described solution of the present invention has at least the following beneficial effects:

[0058] This invention employs a technical approach that combines synchronous capture of pipeline optimization and change information with three-dimensional coordinate processing, regional division, inflection point change analysis, correction coefficient calculation, change discrimination, and linked quantity calculation. This overcomes the technical problems of existing BIM pipeline quantity identification methods, such as the inability to automatically adapt to dynamic changes in geometric attributes and connection relationships caused by pipeline avoidance optimization, low efficiency due to reliance on manual verification, large errors, and easy omission of change items. As a result, it achieves automatic and accurate identification and calculation of quantity data after pipeline optimization, significantly improving the efficiency and accuracy of quantity identification, effectively avoiding increased material replenishment costs and construction delays caused by manual omissions, and adapting to the refined management and control needs of complex building electromechanical projects. Attached Figure Description

[0059] Figure 1 This is a flowchart illustrating the method for identifying engineering quantity data in BIM pipeline generation provided by an embodiment of the present invention.

[0060] Figure 2 This is a schematic diagram of the engineering quantity data identification system in BIM pipeline generation provided by an embodiment of the present invention. Detailed Implementation

[0061] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0062] like Figure 1 As shown, an embodiment of the present invention proposes a method for identifying engineering quantity data in BIM pipeline generation, the method comprising the following steps:

[0063] Step 1: Obtain the building information model containing the initial pipeline layout plan and corresponding initial engineering quantity data;

[0064] Step 2: By performing avoidance optimization operations on pipelines in the building information model, the changes in pipeline geometric attributes and connection relationships caused by the operations are captured and stored simultaneously.

[0065] Step 3: Based on the captured change information, extract the spatial point sequence of the optimized pipeline and convert it into a three-dimensional coordinate set of the pipeline outline; based on the distribution range of the three-dimensional coordinate set, construct a spatial construction reference surface of the envelope coordinate set, and define a basic outer boundary on the spatial construction reference surface;

[0066] Step 4: Based on the aggregation characteristics of the three-dimensional coordinate set, construct the outer boundary of the base and divide it into multiple related regions. At the same time, assign each spatial point in the coordinate set to the corresponding region.

[0067] Step 5: For the set of spatial points belonging to each divided region, identify the changes in the inflection point positions in the pipeline geometry before and after optimization, and quantify the differences in the number of inflection points, inflection point angles, and inflection point spacing; based on the correlation between the inflection point change characteristics and the pipeline bending stress distribution, calculate and generate the geometry correction coefficient corresponding to each divided region.

[0068] Step 6: Integrate the geometric shape correction coefficients of all divided areas, automatically identify pipeline changes caused by avoidance optimization operations; based on the pipeline changes, perform linked calculations and corrections on the initial engineering quantity data to obtain the final engineering quantity data matching the optimized pipeline layout scheme.

[0069] In this embodiment of the invention, by employing a series of technical means, including simultaneous capture of pipeline avoidance optimization and change information, extraction of optimized pipeline spatial point sequence and transformation of three-dimensional coordinate set, regional division based on coordinate aggregation characteristics, analysis of inflection point changes in zones and calculation of geometric shape correction coefficients, integrated judgment of changes based on correction coefficients, and linkage calculation of engineering quantities, the invention overcomes the technical problems of existing BIM pipeline engineering quantity identification methods, such as the inability to automatically respond to dynamic changes caused by pipeline avoidance optimization, reliance on manual verification leading to low efficiency, large calculation errors, and easy omission of change items. This achieves automated and accurate identification and calculation of engineering quantity data after pipeline optimization, significantly improving the efficiency and accuracy of engineering quantity calculation, avoiding the risk of increased material replenishment costs and construction delays caused by omissions in manual operation, and adapting to the actual needs of refined management of pipeline engineering quantities in complex building electromechanical projects.

[0070] In a preferred embodiment of the present invention, step 1 above may include:

[0071] Step 1.1: Access the cloud-based BIM model library through the distributed storage interface, retrieve and load the original building information model file of the target building project as the basic source file for pipeline data extraction. Specifically, this includes: accessing the BIM model library through the distributed storage interface with an access timeout threshold of 12 seconds, entering the project name, building address and model creation time of the target building project as search keywords, limiting the search scope to approved models uploaded within the last 12 months in the BIM model library, and automatically loading the original building information model file with the highest matching degree as the basic source file for pipeline data extraction after the search is completed.

[0072] Step 1.2: Based on the original Building Information Model (BIM) file, perform integrity verification and format standardization processing to eliminate data anomalies and format differences, and obtain a standardized model data stream. Specifically, this includes: performing integrity verification on the loaded original BIM file, requiring that the field completeness of pipeline-related data in the model reach 99% and the data missing rate not exceed 1%. If the requirements are not met, abnormal data will be automatically marked and a prompt will be issued. Then, format standardization processing will be performed to convert the model to IFC4.0 format, automatically delete duplicate invalid data, correct content with inconsistent data formats, eliminate data anomalies and format differences, and obtain a standardized model data stream.

[0073] Step 1.3: Based on the standardized model data flow, the initial pipeline layout scheme in the model is extracted, including pipeline type, direction, connection nodes, and spatial layout information, to obtain structured pipeline layout data. Specifically, based on the standardized model data flow, the initial pipeline layout scheme information in the model is extracted in the order of water supply and drainage, electrical, and ventilation. Specifically, the pipeline type, pipeline direction path, specific location of connection nodes, and spatial layout height range of the pipeline are extracted. After extracting information for each type of pipeline, data verification is performed to ensure that no information is omitted. All extracted information is classified and stored according to pipeline type to obtain structured pipeline layout data.

[0074] Step 1.4: Using structured pipeline layout data, synchronously parse the engineering quantity metadata associated with the initial pipeline layout plan, and establish a mapping index table between pipeline components and corresponding initial engineering quantity data to achieve a precise correspondence between pipeline components and engineering quantity data. Specifically, this includes: based on structured pipeline layout data, synchronously parsing the engineering quantity metadata associated with the initial pipeline layout plan, specifically parsing the pipeline length, pipe material type, and fitting quantity information; then establishing a mapping index table between pipeline components and corresponding initial engineering quantity data; assigning a unique identification code to each pipeline component; and assigning each code to a unique initial engineering quantity data record. The accuracy of the correspondence between pipeline components and engineering quantity data reaches 100%. The mapping index table is then classified and managed according to pipeline type to achieve a precise correspondence between pipeline components and engineering quantity data.

[0075] Step 1.5 involves data fusion and model reconstruction based on the standardized model data stream, structured pipeline layout data, and mapping index table to obtain a building information model containing a complete initial pipeline layout scheme and corresponding initial engineering quantity data. Specifically, this includes: importing the standardized model data stream, structured pipeline layout data, and mapping index table into a data fusion tool; performing data matching and fusion according to the spatial location information of the pipelines; then reconstructing the model by embedding the association information of the mapping index table into the pipeline component attributes of the model; and finally performing a comprehensive data verification after reconstruction. Each pipeline component in the model can be associated with the corresponding initial engineering quantity data, ultimately resulting in a building information model containing a complete initial pipeline layout scheme and corresponding initial engineering quantity data.

[0076] In this embodiment of the invention, a series of technical means are employed, including using a distributed storage interface to access the cloud-based BIM library to load the original model, verifying model integrity and standardizing format processing, extracting structured pipeline layout data, parsing engineering quantity data and establishing a mapping index table, and fusing and reconstructing the model from multiple sources. These methods overcome the technical problems of inconsistent formats, abnormal data, and inaccurate correspondence between pipeline components and engineering quantity data in the existing initial BIM model acquisition process, which lead to unreliable basic data for engineering quantity identification. As a result, a standardized and complete initial pipeline layout scheme and corresponding engineering quantity data building information model are obtained, and an accurate mapping relationship between pipeline components and engineering quantity data is established, ensuring the accuracy and reliability of the entire engineering quantity identification method.

[0077] In a preferred embodiment of the present invention, step 2 above may include:

[0078] Step 2.1: Based on the Building Information Model (BIM), perform avoidance optimization operations on the pipelines in the model to obtain a preliminary optimized pipeline layout scheme. Specifically, this includes: building a distributed avoidance optimization computing environment based on the BIM, setting the number of parallel computing nodes to 8, the time threshold for a single optimization calculation to 30 seconds, and pre-setting the judgment criteria for pipeline avoidance optimization as a minimum clearance of 150 mm between pipelines of different systems and a minimum clearance of 100 mm between pipelines and building structural components. Import the pipeline data in the BIM into the computing environment in the order of water supply and drainage, electrical, and ventilation, and detect the collision positions and insufficient clearance positions of the pipelines segment by segment. Automatically adjust the direction and layout of the pipelines. After each segment of pipeline optimization is completed, a collision re-check is performed until all collision points are eliminated and the clearance requirements are met, thus obtaining a preliminary optimized pipeline layout scheme.

[0079] Step 2.2: Based on the preliminary optimized pipeline layout scheme, synchronously capture the geometric attribute change information generated by the pipeline during the optimization process, including dynamic change data of pipeline length, diameter, bending radius, and spatial orientation. Specifically, based on the preliminary optimized pipeline layout scheme, during the distributed avoidance optimization calculation, synchronously start the geometric attribute capture program, set the monitoring accuracy of pipeline length to 1 mm, the monitoring accuracy of pipeline diameter to 0.1 mm, the monitoring accuracy of pipeline bending radius to 5 mm, and the sampling interval of spatial orientation to 100 mm. In each adjustment step of the optimization operation, synchronously record the increase or decrease value of pipeline length, the change value of diameter, the adjustment value of bending radius, and the coordinate change data of spatial orientation. Classify and store these dynamic change data according to the unique identification code of the corresponding pipeline to form a set of pipeline geometric attribute change information.

[0080] Step 2.3: Real-time monitoring and capture of pipeline connection relationship changes using geometric attribute change information, including topology changes such as connection node position offsets, newly added connection branches, deletion of redundant connections, and changes in connection methods. Specifically, based on the captured pipeline geometric attribute change information, a connection relationship monitoring program is initiated. The monitoring threshold for connection node position offset is set to 50 mm, the length threshold for newly added connection branches is set to 100 mm, and the length threshold for deleting redundant connections is set to 80 mm. When a pipeline geometric attribute change triggers the corresponding threshold, the position change of the pipeline connection node is tracked in real time, and the offset distance of the connection node is recorded. At the same time, it is monitored whether new connection branches are generated, whether redundant connection branches are deleted, and changes in connection methods are detected. The topology change data is classified and stored according to the unique identification code of the corresponding pipeline to form a set of pipeline connection relationship change information.

[0081] Step 2.4: Based on the geometric attribute change information and the connection relationship change information, the pipeline change information with complete change characteristics is obtained by association and integration. Specifically, this includes: importing the pipeline geometric attribute change information and the pipeline connection relationship change information into the data integration program, associating them according to the unique identification code of the corresponding pipeline and the time sequence of the change, matching each geometric attribute change information with the corresponding connection relationship change information, performing data integrity verification after the association is completed, and ensuring that all pipeline changes simultaneously include the change data of geometric attributes and connection relationships, and finally compiling the pipeline change information with complete change characteristics.

[0082] In this embodiment of the invention, by employing a series of technical means—including using a distributed avoidance optimization calculation module to perform pipeline avoidance optimization, synchronously capturing dynamic changes in pipeline geometric attributes, real-time monitoring and capturing changes in the topology of pipeline connection relationships, and associating and integrating the two types of change information—the technical problem of existing pipeline avoidance optimization processes being unable to synchronously and comprehensively capture changes in pipeline geometric attributes and connection relationships, resulting in a lack of complete change data support for engineering quantity calculation and a tendency for omissions and errors in calculation, is overcome. This results in obtaining pipeline change information containing complete change characteristics, achieving full and accurate capture of pipeline optimization change information, and ensuring the completeness and accuracy of engineering quantity identification.

[0083] In a preferred embodiment of the present invention, step 3 above may include:

[0084] Step 3.1: Based on pipeline change information and optimized pipeline geometry data, extract a continuous sequence of spatial points to form pipeline spatial trajectory data. Specifically, this includes: reading the optimized pipeline geometry data from the pipeline change information, setting the spatial point extraction interval to 50 mm, and extracting continuous spatial points sequentially from the starting point of the optimized pipeline, ensuring that there are no breaks or repetitions in the extraction process. Each point is marked with a unique identification code corresponding to the pipeline. All extracted spatial points are arranged in chronological order according to the pipeline direction to form pipeline spatial trajectory data.

[0085] Step 3.2: Based on the pipeline spatial trajectory data, a point cloud data set containing X, Y, and Z coordinate values ​​is generated by converting the spatial point sequence into a three-dimensional coordinate set of the pipeline outline under a unified coordinate system. Specifically, this includes: obtaining the building construction coordinate system as a unified transformation coordinate system, setting the coordinate transformation accuracy to 0.1 mm, converting each spatial point in the pipeline spatial trajectory data into a three-dimensional coordinate under this unified coordinate system one by one, calibrating the X, Y, and Z coordinate values ​​of each point and retaining 2 decimal places, summarizing all the transformed three-dimensional coordinates, and generating a point cloud data set containing X, Y, and Z coordinate values.

[0086] Step 3.3 involves analyzing the distribution range of coordinate points using the pipeline profile's 3D coordinate set, calculating the minimum bounding box of the coordinates, and constructing a spatial construction reference surface that completely encloses the 3D coordinate set. Specifically, this includes: first, initiating the data reading process to fully retrieve the generated pipeline profile's 3D coordinate set, while simultaneously performing data integrity and validity checks. The checks include verifying whether there are points in the coordinate set with missing X-axis, Y-axis, or Z-axis coordinate values, and whether there are duplicate coordinate points. A point missing rate threshold of 1% is set. If the check finds that the missing rate exceeds 1% or the number of duplicate points is greater than 5, the process returns to step 3.2 to regenerate the pipeline profile's 3D coordinate set until the check passes. Subsequently, the coordinate point distribution range analysis stage begins, traversing each 3D coordinate point in the coordinate set, extracting and recording the X-axis, Y-axis, and Z-axis coordinate values ​​for each point. For the X-axis direction, the maximum and minimum X-coordinate values ​​of all points are selected, and the difference between them is calculated to obtain the distribution span in the X-axis direction. Using the same method, the maximum and minimum coordinate values ​​in the Y-axis direction are selected, and the distribution span is calculated. Similarly, the maximum and minimum coordinate values ​​in the Z-axis direction are selected, and the distribution span is calculated. This yields the complete distribution range and corresponding extreme values ​​of all coordinate points in the X, Y, and Z axes. Next, the minimum bounding box calculation precision is set to 1 mm. Based on the extreme values ​​obtained from the above analysis in the three coordinate axes, the spatial structure of the minimum bounding box is constructed: using the minimum values ​​of the X-axis, Y-axis, and Z-axis as the starting vertices, and the maximum values ​​of the X-axis, Y-axis, and Z-axis as the diagonal vertices, the coordinates of the eight vertices of the minimum bounding box are obtained sequentially. During the calculation process, the calculation precision of each vertex coordinate is strictly controlled, with the error of each coordinate value not exceeding 1 mm. This ensures that the constructed minimum bounding box can completely accommodate all coordinate points in the three-dimensional coordinate set of the pipeline contour, without any omissions. Finally, the spatial construction reference plane is constructed. Using the calculated minimum bounding box as a reference, the coordinates of the center point of the bounding box in the Z-axis direction are first calculated, specifically the average of the maximum and minimum Z-axis values. Then, the horizontal plane where the center point is located is used as the spatial construction reference plane, which is parallel to the plane formed by the X and Y axes. After the construction is completed, the validity of the reference plane is checked. All points in the three-dimensional coordinate set of the pipeline outline are projected onto the reference plane to check whether there are any projected points that exceed the range of the reference plane. If so, the Z-axis height of the reference plane is readjusted until the projection of all points can fall within the range of the reference plane, and finally the spatial construction reference plane that completely encloses the three-dimensional coordinate set is obtained.

[0087] Step 3.4: Based on the spatial construction datum plane, a closed outer boundary of the foundation is automatically generated on the datum plane using a boundary definition algorithm. Specifically, this includes: first, retrieving the completed spatial construction datum plane data, and simultaneously loading the generated 3D coordinate set of the pipeline profile. All points in the coordinate set are projected onto the spatial construction datum plane according to a preset projection rule, forming a set of pipeline profile projection points. The preset projection rule is an orthogonal projection rule, with the projection direction perpendicular to the spatial construction datum plane, i.e., perpendicular projection along the Z-axis of the spatial coordinate system. During projection, the X-axis and Y-axis coordinate values ​​of the pipeline profile 3D coordinate points are retained unchanged, while the Z-axis coordinate values ​​of all points are uniformly corrected to the Z-axis height value of the spatial construction datum plane, thereby achieving the projection of the 3D coordinate points onto the datum plane. The conversion of two-dimensional projection points is performed, with the projection accuracy controlled at 0.5 mm during the projection process to ensure the positional accuracy of the projection points. Subsequently, a boundary definition operation is performed on the spatial construction reference plane. First, the boundary sampling interval is set to 30 mm, and then the edge region of the pipeline contour projection point set on the reference plane is located. By filtering the extreme values ​​of the projection point set in the X-axis and Y-axis directions of the reference plane, the approximate edge range of the projection area is obtained, that is, the outer region from the minimum projection X value to the maximum projection X value in the X-axis direction and from the minimum projection Y value to the maximum projection Y value in the Y-axis direction. Along the obtained edge region, boundary points are collected sequentially in a clockwise direction, strictly adhering to the 30 mm sampling interval during the collection process. The two-dimensional coordinates on the reference plane are recorded for each point collected. After data acquisition, the positions of all boundary points are automatically adjusted. First, the straight-line distance between two adjacent points is calculated. If the distance deviates from the 30 mm sampling interval by more than 5 mm, the position of the next point is moved until the spacing meets the requirements. Then, all boundary points are connected sequentially to form a preliminary polygon shape. The straight-line distance between the first and last points of the shape is calculated. If the distance is greater than 3 mm, it is determined to be unclosed. By adjusting the positions of the first and last points and adjacent points, the shape is made to be completely connected, and the closure error does not exceed 3 mm. Finally, the validity of the generated closed foundation outer boundary is verified. It is checked whether all pipeline contour projection points on the reference plane fall within the boundary range. If there are projection points that exceed the boundary, boundary points are collected at the edge of the exceeding area and the boundary shape is readjusted until all projection points are completely covered by the boundary. After the verification is passed, the final closed foundation outer boundary is obtained.

[0088] In this embodiment of the invention, by employing a series of technical means—extracting spatial point sequences from pipeline change information to form trajectory data, converting the point sequences into a three-dimensional coordinate set of pipeline contours under a unified coordinate system, constructing a spatial construction reference surface for the envelope coordinate set using a minimum bounding box, and generating a closed foundation outer boundary using a boundary definition algorithm—the technical problems of inconsistent coordinate systems and ambiguous pipeline spatial range definition in existing pipeline spatial data processing, which lead to a lack of precise spatial basis for regional division, are overcome. This results in a standardized three-dimensional coordinate set of pipeline contours and the obtained spatial construction reference surface and closed outer boundary, achieving precise digital representation of pipeline spatial morphology and ensuring the orderliness and accuracy of subsequent analysis processes.

[0089] In a preferred embodiment of the present invention, step 4 above may include:

[0090] Step 4.1: Based on the 3D coordinate set of the pipeline outline, calculate the distance density distribution characteristics between spatial points, identify the natural clustering center location and clustering intensity parameters of the coordinate points, and form point clustering characteristic data. Specifically, this includes: reading all spatial point data from the 3D coordinate set of the pipeline outline; pre-setting a neighborhood radius of 200 mm as the standard range for distance density calculation; calculating the number of other spatial points within the neighborhood radius for each spatial point as the center, using this as the distance density value for that point; during the calculation process, setting the distance calculation accuracy to 1 mm, ensuring the accuracy of the density value. After completing the distance density calculation for all points, statistically analyze the density value distribution of each point, setting the clustering intensity threshold to 8 neighborhoods per 200 mm, and identifying point clusters with density values ​​greater than or equal to this threshold as natural clustering areas; for each natural clustering area, calculating the average XYZ coordinates of all points within the area, and obtaining the spatial location corresponding to the average value as the natural clustering center location; simultaneously, statistically analyzing the total number of points, maximum density value, and average density value within the clustering area, and integrating the parameters to form point clustering characteristic data.

[0091] Step 4.2: Based on the point clustering feature data and the basic outer boundary, the basic outer boundary is adaptively divided using a region segmentation algorithm. Multiple initial segmented regions are generated according to the location of the clustering centers, and spatial topological relationships between adjacent regions are established. Specifically, this includes: calling the generated point clustering feature data and the obtained basic outer boundary data, setting the region segmentation accuracy to 5 mm, and obtaining the initial number of segments based on the distribution of clustering center locations, with each clustering center corresponding to a core segmented region. An adaptive region segmentation method is adopted, with each clustering center as the origin, gradually expanding the segmentation range towards the basic outer boundary, avoiding overlap between different segmentation ranges during the expansion process, until all clustering regions are covered and the segmentation range does not exceed the basic outer boundary. After completing the initial segmentation, multiple independent initial segmented regions are generated, each initial segmented region being labeled with the corresponding clustering center number. Subsequently, all initial segmented regions are traversed, and the spatial positional relationships between adjacent regions are detected.

[0092] Step 4.3: Based on the initial division of regions and spatial topological relationships, perform region optimization processing, adjust the shape and size of the region boundaries, and make the boundaries of each division region match the clustering density gradient of the three-dimensional coordinate set to obtain the set of division regions. Specifically, this includes: reading the initial division region data and the corresponding spatial topological relationship table, setting the region boundary adjustment step size to 10 mm, and setting the clustering density gradient matching degree threshold to 90%. First, the density variation of spatial points at the boundaries of each initially divided region is analyzed to obtain the direction of density gradient change, i.e., the trend of density value changing from high to low. Based on the direction of density gradient change, the shape and size of the boundaries of the initially divided regions are gradually adjusted in units of adjustment step size: for the edges of regions with high density gradient, the boundary range is appropriately reduced to ensure relatively uniform point density within the region; for the edges of regions with low density gradient, the boundary range is appropriately expanded to avoid missing associated points. During the adjustment process, the topological association relationship between the adjusted region and adjacent regions is verified in real time to prevent region overlap or gaps. At the same time, the matching degree of each region boundary and the density gradient of the three-dimensional coordinate set is calculated. When the matching degree of all divided regions reaches or exceeds the threshold of 90%, the boundary adjustment is stopped, and finally a set of divided regions with spatial association relationships is formed.

[0093] Step 4.4: Based on the set of partitioned regions, accurately allocate each spatial point in the 3D coordinate set of the pipeline outline to the corresponding partitioned region according to the relationship between the spatial coordinates and the region boundary. Specifically, this includes: obtaining the generated final set of partitioned regions, extracting the boundary coordinate range data of each partitioned region, and setting the point allocation accuracy to 2 mm; traversing each spatial point in the 3D coordinate set of the pipeline outline, comparing the XYZ coordinates of the point with the boundary coordinate range of each partitioned region one by one. If the point coordinates fall within the X-axis, Y-axis, and Z-axis boundary range of a certain partitioned region, then the point is determined to belong to that partitioned region; during the comparison process, if the point coordinates are at the intersection of two region boundaries, according to the spatial topology association table, the point is assigned to the partitioned region closer to the corresponding cluster center; after completing the preliminary allocation of all points, statistical verification is performed on the assigned points in each partitioned region. Each point has a unique assigned region, with no omissions or duplicate allocations.

[0094] In this embodiment of the invention, by employing a series of technical means—including calculating the spatial point distance density distribution characteristics of the three-dimensional coordinate set of the pipeline contour to identify the clustering center and clustering intensity, combining the outer boundary of the foundation to adaptively generate an initial division region with spatial topological association through a region segmentation algorithm, optimizing the coordinate clustering density gradient of the region boundary, and accurately assigning spatial points to the corresponding division region—the invention overcomes the technical problems in existing pipeline spatial analysis, such as the failure to combine point clustering characteristics for region division, mismatch between region boundaries and pipeline spatial distribution, resulting in the inability to finely focus on local features in pipeline geometric morphology analysis and the lack of targeted analysis results. This achieves fine-grained region division that conforms to the spatial distribution characteristics of the pipeline, establishes spatial topological associations between regions, and improves the accuracy and targetedness of geometric morphology analysis.

[0095] In a preferred embodiment of the present invention, step 5 above may include:

[0096] Step 5.1: For each region in the region set, obtain the 3D coordinate sets of the pipeline contours before and after optimization belonging to the region, as the set of spatial points to be analyzed for the corresponding region. Specifically, this includes: reading the generated region point attribution lookup table, obtaining the unique number of each region in the region set and the corresponding spatial point number; for each region, extracting all spatial point numbers belonging to that region from the attribution lookup table based on the region number; then, respectively associating and calling the 3D coordinate sets of the pipeline contours before and after optimization, and filtering out the corresponding points within the region based on the point numbers. The spatial points of pipelines before and after optimization were assigned to each other. During the screening process, the data matching accuracy was set to 2 mm, and the selected points were strictly assigned to the current division area. The spatial points before and after optimization corresponding to each division area were organized into two independent datasets as the set of spatial points to be analyzed in the division area. Each set was labeled with the corresponding division area number and pipeline unique identification code. At the same time, data integrity was checked, requiring that the missing point rate of each set to be analyzed should not exceed 1%. If the standard was not met, the previous step was returned to re-extract until the requirement was met, and then the points were classified and stored according to the division area order.

[0097] Step 5.2: Based on the set of spatial points to be analyzed, apply the curve inflection point detection algorithm to the pipeline spatial trajectories before and after optimization to identify and locate key inflection points in their respective geometric shapes, obtaining the inflection point sets before and after optimization. Specifically, this includes: calling the set of spatial points to be analyzed for each divided region, preprocessing the spatial point data before and after optimization, removing duplicate points and abnormal discrete points. The criterion for judging abnormal discrete points is that the distance from most surrounding points is greater than 50 mm. The curvature threshold for curve inflection point detection is set to 0.5 radians per meter, and the sampling interval is 10 mm. Based on the pre-... After processing, the set of spatial points to be analyzed is used to perform curve inflection point detection on the pipeline spatial trajectories before and after optimization. During the detection process, the curvature change of the pipeline trajectory is calculated segment by segment. When the curvature value of a certain point reaches or exceeds the curvature threshold, the point is determined to be a key inflection point, and the three-dimensional coordinates are recorded. After completing the full trajectory detection, the identified key inflection points are re-checked, and redundant inflection points with an adjacent spacing of less than 20 mm are removed to verify the effectiveness of the inflection point identification. Finally, the set of spatial coordinates of the inflection points of the pipeline before optimization (i.e., the set of inflection points before optimization) and the set of spatial coordinates of the inflection points of the pipeline after optimization (i.e., the set of inflection points after optimization) are obtained in each divided region.

[0098] Step 5.3: Based on the inflection point set before optimization and the inflection point set after optimization, perform inflection point position matching and comparative analysis to extract the inflection point position change features caused by the avoidance optimization operation within the divided area. Specifically, this includes: obtaining the inflection point set before optimization and the inflection point set after optimization for the same divided area; setting the distance threshold for inflection point position matching to 30 mm; adopting the nearest matching principle; taking each inflection point in the inflection point set before optimization as a reference, searching for the nearest inflection point in the inflection point set after optimization; if the straight-line distance between the two is less than or equal to the matching distance threshold, they are determined to be a pair of matched inflection points; if an inflection point before optimization has no match in the inflection point set after optimization... If a matching inflection point meets the criteria, it is determined to be a disappearing inflection point. If, after optimization, there is no matching inflection point in the set of inflection points before optimization, the inflection point is determined to be a newly added inflection point. After completing the matching of all inflection points, the matching results are compared and analyzed to extract the inflection point position change features: for matching inflection points, the difference between the two three-dimensional coordinates is calculated to obtain the offset direction and offset distance of the inflection point; for disappearing and newly added inflection points, their specific coordinates and positions in the pipeline trajectory are recorded, and the change features are classified and organized according to the types of matching, newly added, and disappearing inflection points to form the inflection point position change features corresponding to each divided region.

[0099] Step 5.4: Based on the inflection point position change characteristics, quantitatively calculate the difference in the number of inflection points before and after optimization, the change in the angle of the corresponding inflection points, and the change in the distance between adjacent inflection points, forming a set of quantitative indicators describing the degree of difference in pipeline geometry within the divided area. Specifically, this includes: calculating the quantitative indicators based on the extracted inflection point position change characteristic table; firstly, counting the total number of inflection points in the inflection point set before and after optimization, and calculating the difference between the two, i.e., the difference in the number of inflection points. If the number of inflection points after optimization is greater than that before optimization, the difference is positive; otherwise, it is negative. For each pair of matching inflection points, the angle between the tangent direction of the pipeline where the inflection point before optimization and the inflection point after optimization are located is calculated. This angle is the change in angle of the corresponding inflection point. The angle calculation accuracy is set to 0.1 degrees. For the inflection point sets before and after optimization, the straight-line distance between two adjacent inflection points is calculated in the order of pipeline direction, and the spacing sequence between adjacent inflection points before optimization and the spacing sequence between adjacent inflection points after optimization are obtained respectively. The difference in the spacing between adjacent inflection points at corresponding positions is calculated one by one to obtain the change in spacing between adjacent inflection points. The spacing calculation accuracy is set to 1 millimeter. The calculated difference in the number of inflection points, the change in angle of each matching inflection point, and the change in spacing between each adjacent inflection point are integrated to form a set of quantitative indicators describing the degree of difference in pipeline geometry within the divided area.

[0100] Step 5.5: Based on the quantitative index set and the distribution pattern of pipeline bending stress, obtain the influence weights of the differences in the number of inflection points, the change in angle, and the change in spacing on the local morphological changes of the pipeline. Through weighted fusion calculation, obtain the geometric morphological correction coefficient. Specifically, this includes: reading the quantitative index set for each divided region, and combining it with the bending stress distribution pattern of common pipeline materials in building electromechanical engineering, obtaining the influence weights of the differences in the number of inflection points, the change in angle, and the change in spacing on the local morphological changes of the pipeline. The influence weight of the difference in the number of inflection points is set to 0.3, the influence weight of the change in the angle of inflection points is set to 0.4, and the influence weight of the change in the spacing between adjacent inflection points is set to 0.3. For each of the quantitative index sets... The indicators are normalized to a range of 0 to 1, eliminating differences between different indicator dimensions. A weighted fusion calculation method is used, where each normalized quantitative indicator is multiplied by its corresponding influence weight, and all products are summed to obtain the initial correction coefficient for the degree of pipeline morphology change in the divided area. The geometric morphology correction coefficient is set to a range of 0.8 to 1.2. If the initial correction coefficient exceeds this range, it is corrected according to the boundary value, i.e., 1.2 is used when it is greater than 1.2, and 0.8 is used when it is less than 0.8. Finally, the corrected geometric morphology correction coefficient is verified to match the actual pipeline morphology change in the divided area. The final geometric morphology correction coefficient is obtained after the verification is passed.

[0101] In this embodiment of the invention, a series of technical means are employed to overcome the technical problems of existing pipeline morphology analysis, which fails to conduct refined inflection point change analysis for subdivided regions and fails to correlate the degree of morphological change with pipeline bending stress distribution. These methods include obtaining the three-dimensional coordinate sets of pipeline contours before and after optimization for each divided region, using a curve inflection point detection algorithm to identify and locate pipeline inflection points before and after optimization, extracting morphological change features by inflection point matching and comparison, quantitatively calculating inflection point-related difference indicators, and calculating geometric morphology correction coefficients by combining the distribution law of pipeline bending stress with a weighted fusion algorithm. This results in a lack of precise local basis for engineering quantity correction and a large deviation between the calculation results and the actual state of the pipeline. As a result, a refined and quantitative characterization of the geometric morphological change of the pipeline after optimization in each divided region is achieved. The obtained geometric morphology correction coefficient can accurately reflect the impact of pipeline morphology change on local bending stress, providing a scientific and accurate local correction basis for the linkage calculation of engineering quantities, and improving the rationality and accuracy of engineering quantity calculation.

[0102] In a preferred embodiment of the present invention, step 6 above may include:

[0103] Step 6.1: Obtain the geometric shape correction coefficients generated from all the divided regions, perform normalization and weight allocation processing to form a summary correction coefficient set describing the degree of change in the overall pipeline geometry. Specifically, this includes: reading the geometric shape correction coefficients corresponding to all the divided regions; firstly, verifying the completeness of the geometric shape correction coefficients, ensuring that the correction coefficients for each divided region have been obtained without any missing or abnormally blank areas; if any are missing, recalculating the correction coefficients for the corresponding region; then performing normalization processing, setting the normalized value range to 0 to 1, and converting each geometric shape correction coefficient to this range using a uniform linear scaling method. Specifically, this involves subtracting the average value of all coefficients from each coefficient. The minimum value is then divided by the difference between the maximum and minimum values ​​of all coefficients. If the maximum and minimum values ​​are equal, all coefficients are directly normalized to 0.5. After normalization, weights are assigned based on the proportion of the total pipeline length in each division area to the total pipeline length of the entire project. First, the total length of all pipelines in each division area is calculated, and then the percentage of the total length of each area to the total pipeline length of the project is calculated. This percentage is the weight of the normalization correction coefficient for the corresponding area. The normalization correction coefficient for each area is multiplied by the corresponding weight to obtain the weighted correction coefficient for each area. The weighted correction coefficients of all areas together constitute a set of summary correction coefficients describing the degree of change in the overall pipeline geometry.

[0104] Step 6.2: Based on the aggregated correction coefficient set and the predefined change judgment rule library, automatically analyze and determine the specific pipeline changes caused by the avoidance optimization operation. This includes: calling the generated aggregated correction coefficient set and loading the predefined change judgment rule library. The rule library pre-sets change type judgment criteria for different correction coefficient ranges: a aggregated correction coefficient greater than 1.05 is judged as a major change; a correction coefficient between 0.95 and 1.05 is judged as a minor change; and a correction coefficient less than 0.95 is judged as a reduction change. Weighted correction coefficients are extracted sequentially from the aggregated correction coefficient set according to pipeline type and compared one by one with the judgment criteria in the rule library to obtain... For each pipeline, the change type is determined, and during the comparison process, the inflection point change characteristics and pipeline change-related data of the corresponding division area are simultaneously associated to obtain the specific change content. For example, pipeline length extension caused by the addition of an inflection point, adjustment of fitting type caused by the change of inflection point angle, and addition of connection branches caused by pipeline route change. The determined change type, specific change content, unique identification code of the corresponding pipeline and the division area number are integrated to form a pipeline change item list. Each change item in the list must be marked with the change impact range and associated correction coefficient information. After the list is completed, logical verification is performed to ensure that the judgment logic of the change item and the correction coefficient are consistent and there are no contradictions or misjudgments.

[0105] Step 6.3: Based on the pipeline change item, call the mapping index table to locate the original quantity records associated with the changed pipeline components in the initial quantity data. Specifically, this includes: extracting the unique identification code of each change item in the generated pipeline change item list; calling the established mapping index table between pipeline components and initial quantity data; performing a precise search in the mapping index table based on the unique identification code of the pipeline to locate the original quantity record associated with the pipeline component. The search process is set to a matching accuracy of 100%, and the retrieved original records completely correspond to the changed pipeline components. After the location is completed, extract the key information of each original quantity record, including pipeline material type, original length, original number of fittings, original cost, etc., and associate and bind this information with the corresponding pipeline change item. At the same time, verify the location results to check whether there is a situation where one change item corresponds to multiple original quantity records or one original record corresponds to multiple change items. If so, further confirm the association relationship. The mapping between change items and original records is accurate, and finally, change items and original quantity records are formed.

[0106] Step 6.4: For each located original quantity record, based on the specific content and quantity of the pipeline change item, perform linked calculations using pre-set quantity calculation rules to generate a corresponding quantity correction factor. This includes: obtaining a correlation table between the generated change item and the original quantity record; loading a pre-set quantity calculation rule library, which contains quantity calculation standards corresponding to different change types. For example, when the pipeline length is extended, the correction factor is calculated based on the ratio of the extended length to the original length; when new fittings are added, the correction factor is calculated based on the ratio of the new quantity to the original quantity; when the pipeline diameter changes, the correction factor is calculated based on the proportion of cross-sectional area change. For each change item in the correlation table... Each original engineering quantity record and its corresponding pipeline change item are matched one by one with the calculation standards in the rule base. The calculation is carried out in conjunction with the specific content and quantity of the change item. For example, if the change item is a pipeline length extension of 5 meters, and the original length is 20 meters, then the length correction factor is calculated as (20+5)÷20=1.25; if the change item is the addition of 3 elbow fittings, and the original number of fittings is 10, then the fitting correction factor is calculated as (10+3)÷10=1.3. The calculation precision is set to 0.01 during the calculation process. The accuracy of the correction factor is ensured. Each correction factor must be labeled with the corresponding change item type, original record information and calculation basis to generate a set of engineering quantity correction factors.

[0107] Step 6.5: Apply quantity correction factors to the original quantity records for numerical correction and update, and integrate and verify all updated quantity records. Output the final quantity data that precisely matches the optimized pipeline layout scheme. Specifically, this includes: calling the generated set of quantity correction factors and the corresponding original quantity records; multiplying the quantity values ​​in each original record by the corresponding correction factor to complete the numerical correction and update. For example, if the original pipeline length of 20 meters corresponds to a correction factor of 1.25, then the updated length is 20 × 1.25 = 25 meters; if the original number of fittings is 10, corresponding to a correction factor of 1.3, then the updated number is 10 × 1.3 = 13. After completing the correction and update of all original records, integrate the updated quantity records and... The pipeline systems, including water supply and drainage, electrical, and ventilation systems, are categorized and summarized to form a preliminary project quantity summary table. Subsequently, an integration verification is performed. This verification includes checking the logical rationality of the corrected project quantity values ​​and the consistency between the total quantity of each item within the same pipeline system and the total project quantity. A verification error threshold of 0.5% is set, meaning the difference between the total quantity of each item and the total project quantity must be controlled within 0.5% of the total project quantity. If the verification passes, the preliminary summary table becomes the final project quantity data. If the verification fails, the process is traced back to the correction factor generation stage for re-verification and calculation. Errors are corrected, and verification is repeated until the error threshold requirement is met. Finally, the output is final data categorized by pipeline, including detailed item quantities and the total project quantity, with a data format adapted to the standard requirements of project settlement and material procurement.

[0108] In this embodiment of the invention, by employing a series of technical means—including summarizing the geometric shape correction coefficients of each divided region and performing normalization and weight allocation processing, automatically identifying pipeline change items by combining a predefined change judgment rule library, locating the associated original engineering quantity records by calling the mapping index table, calculating the engineering quantity correction factor according to the change items through pre-set rules, correcting and updating the engineering quantity records, and integrating and verifying the output of the final data—this invention overcomes the technical problems of existing pipeline optimization engineering quantity calculations, which cannot integrate and judge the overall change type based on the pipeline shape change situation in each region, nor can they achieve accurate linkage correction between pipeline changes and initial engineering quantities. Furthermore, the reliance on manual verification leads to low calculation efficiency and results that are prone to mismatch with the actual layout of the optimized pipelines. This invention achieves automated and accurate calculation of pipeline optimization engineering quantities, accurately identifying all pipeline change items and specifically correcting engineering quantities, while ensuring accurate matching between the final engineering quantity data and the optimized pipeline layout scheme through integrated verification. This improves the efficiency and accuracy of engineering quantity calculation, avoids errors and omissions caused by manual calculation, and meets the needs of refined cost control in building electromechanical engineering.

[0109] like Figure 2 As shown, embodiments of the present invention also provide a system for identifying engineering quantity data in BIM pipeline generation, including:

[0110] The acquisition module is used to acquire a building information model containing the initial pipeline layout plan and corresponding initial engineering quantity data.

[0111] The optimization module is used to perform avoidance optimization operations on pipelines in the building information model, and simultaneously capture and store information on changes in pipeline geometric properties and connection relationships caused by the operations.

[0112] The extraction module is used to extract the spatial point sequence of the optimized pipeline based on the captured change information and convert it into a three-dimensional coordinate set of the pipeline outline; based on the distribution range of the three-dimensional coordinate set, a spatial construction reference surface of the envelope coordinate set is constructed, and a basic outer boundary is defined on the spatial construction reference surface;

[0113] The partitioning module is used to construct regions based on the aggregation characteristics of the three-dimensional coordinate set, forming multiple related partitioned regions, and assigning each spatial point in the coordinate set to the corresponding partitioned region.

[0114] The calculation module is used to identify the changes in the inflection point positions in the pipeline geometry before and after optimization for the set of spatial points belonging to each divided region, and to quantify the differences in the number of inflection points, inflection point angles and inflection point spacing; based on the correlation between the inflection point change characteristics and the pipeline bending stress distribution, it calculates and generates the geometric correction coefficient corresponding to each divided region.

[0115] The correction module integrates the geometric correction coefficients of all divided areas, automatically identifies pipeline changes caused by avoidance optimization operations, and performs linked calculations and corrections on the initial engineering quantity data based on the pipeline changes to obtain the final engineering quantity data matching the optimized pipeline layout scheme.

[0116] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for identifying quantity data in BIM pipeline generation, characterized by, The method comprises: Step 1, obtaining a building information model containing an initial pipeline layout scheme and corresponding initial engineering quantity data; Step 2, synchronously capturing and storing the changes in the geometric properties and connection relationships of the pipelines caused by the operation by performing an avoidance optimization operation on the pipelines in the building information model; Step 3, extracting the spatial point sequence of the optimized pipelines according to the captured change information, and converting the spatial point sequence into a pipeline contour three-dimensional coordinate set; constructing a spatial construction reference surface of an envelope coordinate set according to the distribution range of the three-dimensional coordinate set, and defining a basic outer boundary on the spatial construction reference surface; Step 4, based on the aggregation characteristics of the three-dimensional coordinate set, performing region division on the basic outer boundary to form a plurality of division regions having a correlation relationship, and attributing each spatial point in the coordinate set to a corresponding division region, including: calculating the distance density distribution characteristics between each spatial point according to the pipeline contour three-dimensional coordinate set, identifying the natural aggregation center position and aggregation intensity parameter of the coordinate point, and forming point aggregation characteristic data; based on the point aggregation characteristic data and the basic outer boundary, adaptively dividing the basic outer boundary by a region segmentation algorithm, generating a plurality of initial division regions according to the aggregation center position, and establishing the spatial topological correlation relationship between adjacent regions; performing region optimization processing according to the initial division regions and the spatial topological correlation relationship, adjusting the shape and size of the region boundary, so that the boundary of each division region matches the aggregation density gradient of the three-dimensional coordinate set, to obtain a division region set; based on the division region set, accurately allocating each spatial point in the pipeline contour three-dimensional coordinate set to the corresponding division region according to the relationship between the spatial position coordinates and the region boundary; Step 5, for each set of spatial points attributed to each division region, identifying the inflection point position change characteristics in the geometric shape of the pipeline before and after optimization, quantifying the difference degree of the number of inflection points, the angle of inflection points and the distance between inflection points; based on the correlation between the inflection point change characteristics and the pipeline bending stress distribution, calculating and generating a geometric shape correction coefficient corresponding to each division region, including: for each division region in the division region set, obtaining the pipeline contour three-dimensional coordinate set attributed to the region before and after optimization, respectively, as the set of spatial points to be analyzed corresponding to the division region; based on the set of spatial points to be analyzed, applying a curve inflection point detection algorithm to the spatial trajectories of the pipeline before and after optimization, respectively, identifying and positioning the key inflection points in the respective geometric shapes, to obtain an optimized inflection point set and an optimized inflection point set of the spatial coordinates of the inflection points; based on the optimized inflection point set and the optimized inflection point set, performing inflection point position matching and comparative analysis, and extracting the inflection point position change characteristics caused by the avoidance optimization operation in the division region; based on the inflection point position change characteristics, quantitatively calculating the difference value of the number of inflection points before and after optimization, the angle change value of the corresponding inflection points, and the distance change value between adjacent inflection points, to form a quantitative index set describing the difference degree of the geometric shape of the pipeline in the division region; Based on the set of quantitative indicators and the distribution law of pipeline bending stress, the influence weights of the difference in the number of inflection points, the change in angle and the change in spacing on the local shape of the pipeline are obtained. Through weighted fusion calculation, the geometric shape correction coefficient is obtained. Step 6: Integrate the geometric shape correction coefficients of all divided areas, automatically identify pipeline changes caused by avoidance optimization operations; based on the pipeline changes, perform linked calculations and corrections on the initial engineering quantity data to obtain the final engineering quantity data matching the optimized pipeline layout scheme.

2. The method of claim 1, wherein, Obtain a building information model containing the initial pipeline layout plan and corresponding initial engineering quantity data, including: Access the cloud-based BIM model library through a distributed storage interface, retrieve and load the original building information model file of the target building project, and use it as the basic source file for pipeline data extraction. Based on the original building information model file, integrity verification and format standardization are performed to eliminate data anomalies and format differences, resulting in a standardized model data stream. Based on the standardized model data flow, structured pipeline layout data is obtained by extracting the initial pipeline layout scheme from the model, including pipeline type, direction, connection nodes and spatial layout information; By using structured pipeline layout data, the engineering quantity data associated with the initial pipeline layout plan is parsed synchronously, and a mapping index table between pipeline components and corresponding initial engineering quantity data is established to realize the accurate correspondence between pipeline components and engineering quantity data. Based on the standardized model data stream, structured pipeline layout data, and mapping index table, data fusion and model reconstruction are performed to obtain a building information model with a complete initial pipeline layout scheme and corresponding initial engineering quantity data.

3. The method of claim 2, wherein, By performing avoidance optimization operations on pipelines in the Building Information Model (BIM), information on changes in pipeline geometric attributes and connectivity caused by the operations is simultaneously captured and stored, including: Based on the building information model, avoidance optimization operations are performed on the pipelines in the model to obtain a preliminary optimized pipeline layout scheme. Based on the preliminary optimized pipeline layout plan, the information on changes in the geometric attributes of the pipeline during the optimization process is captured simultaneously, including dynamic changes in pipeline length, diameter, bending radius, and spatial orientation. By using information on changes in geometric properties, we can monitor and capture changes in pipeline connection relationships in real time, including topological changes such as offset of connection node positions, addition of new connection branches, deletion of redundant connections, and changes in connection methods. By associating and integrating information on changes in geometric attributes and changes in connectivity, complete pipeline change information with complete change characteristics can be obtained.

4. The method of claim 3, wherein, Based on the captured change information, the spatial point sequence of the optimized pipeline is extracted and converted into a three-dimensional coordinate set of the pipeline outline; Based on the distribution range of the three-dimensional coordinate set, a spatial construction datum surface for the envelope coordinate set is constructed, and a basic outer boundary is defined on the spatial construction datum surface, including: By using pipeline change information, a continuous spatial point sequence is extracted from the optimized pipeline geometry data to form pipeline spatial trajectory data. Based on pipeline spatial trajectory data, a point cloud data set containing X, Y, and Z coordinate values ​​is generated by converting the spatial point sequence into a three-dimensional coordinate set of the pipeline outline under a unified coordinate system. By analyzing the distribution range of coordinate points using the three-dimensional coordinate set of the pipeline profile, calculating the minimum bounding box of the coordinates, and constructing a spatial construction reference surface that completely encloses the three-dimensional coordinate set; Based on the spatial construction reference surface, a closed basic outer boundary is automatically generated on the reference surface through a boundary definition algorithm.

5. The method of claim 4, wherein, Integrate the geometric correction coefficients of all divided regions and automatically identify pipeline changes caused by avoidance optimization operations; Based on the pipeline change items, the initial engineering quantity data is recalculated and corrected to obtain the final engineering quantity data matching the optimized pipeline layout scheme, including: Obtain all geometric shape correction coefficients generated by the division of regions, perform normalization and weight allocation processing to form a set of summary correction coefficients describing the degree of change in the overall pipeline geometry; Based on the aggregated set of correction coefficients and combined with the predefined rule library for change determination, the system automatically analyzes and identifies specific pipeline changes caused by avoidance optimization operations. Based on the pipeline change item, call the mapping index table to locate the original engineering quantity record associated with the changed pipeline component in the initial engineering quantity data; For each original engineering quantity record located, based on the specific content and quantity of the pipeline change item, a linkage calculation is performed according to the preset engineering quantity calculation rules to generate the corresponding engineering quantity correction factor; The original quantity records are numerically corrected and updated using the applied quantity correction factor. All updated quantity records are then integrated and verified to output the final quantity data that precisely matches the optimized pipeline layout scheme.

6. A system for identifying quantities of data in the generation of BIM pipelines, the system implementing the method of any one of claims 1 to 5, characterized in that, include: The acquisition module is used to acquire a building information model containing the initial pipeline layout plan and corresponding initial engineering quantity data. The optimization module is used to perform avoidance optimization operations on pipelines in the building information model, and simultaneously capture and store information on changes in pipeline geometric properties and connection relationships caused by the operations. The extraction module is used to extract the spatial point sequence of the optimized pipeline based on the captured change information and convert it into a three-dimensional coordinate set of the pipeline outline; based on the distribution range of the three-dimensional coordinate set, a spatial construction reference surface of the envelope coordinate set is constructed, and a basic outer boundary is defined on the spatial construction reference surface; The partitioning module is used to construct regions based on the aggregation characteristics of the three-dimensional coordinate set, forming multiple related partitioned regions, and assigning each spatial point in the coordinate set to the corresponding partitioned region. The calculation module is used to identify the changes in the inflection point positions in the pipeline geometry before and after optimization for the set of spatial points belonging to each divided region, and to quantify the differences in the number of inflection points, inflection point angles and inflection point spacing. Based on the correlation between the inflection point change characteristics and the pipeline bending stress distribution, the geometric shape correction coefficient corresponding to each divided region is calculated and generated. The correction module is used to integrate the geometric correction coefficients of all divided regions and automatically identify pipeline changes caused by avoidance optimization operations. Based on the pipeline change items, the initial engineering quantity data is recalculated and corrected to obtain the final engineering quantity data matching the optimized pipeline layout scheme.

7. A computing device, comprising: include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program that, when executed by a processor, implements the method as described in any one of claims 1 to 5.

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