Pipeline pressure test packet division method and system based on three-dimensional model

Abstract

The invention relates to a pipeline pressure test packet division method and system based on a three-dimensional model, and the method comprises the steps: constructing a pipeline construction three-dimensional model which comprises the attribute information of a pipeline; in a visual operation interface, receiving a labeling operation of a user on a pressure test packet range on the three-dimensional model, and calculating and displaying key parameters of the pressure test packet in real time based on attribute information of the three-dimensional model; the method comprises the following steps: matching pressure test packet parameters marked by a user with a pre-stored pressure test specification rule base, checking whether a pressure test packet conforms to a test specification or not, and marking parts which do not conform to the test specification; according to construction parameters set by a user, on the basis of a pipeline connection relation graph, equipment grouping is carried out, a pressure test packet division scheme is generated, integrity verification is carried out on the division scheme, and by introducing three-dimensional modeling and providing visual operation and intelligent verification, the technical problems that manual checking is large in workload, tedious in process and prone to making mistakes are solved.

Description

Technical Field

[0001] This invention belongs to the field of pipeline construction management technology, specifically relating to a method and system for dividing pipeline pressure testing packages based on a three-dimensional model. Background Technology

[0002] In the field of pipeline construction, pressure test package division is a core step before pressure testing, aiming to ensure that the sealing and strength of the pipeline system meet design standards. Currently, traditional pressure test package division mainly relies on construction technicians manually completing this task based on two-dimensional drawings and their personal experience. Two-dimensional drawings only present planar information and lack three-dimensional spatial data. Technicians need to repeatedly check the pipeline layout, connection relationships, and test range, a process that is extremely tedious and prone to errors. In addition, existing technology lacks automatic verification functions for pressure testing specifications, leading to frequent problems such as incomplete pressure test package coverage and mismatched test parameters during construction. This results in rework, potential safety hazards, and serious impacts on construction progress and increased costs. Based on at least one of the above shortcomings, this application is hereby proposed.

[0003] Furthermore, on the one hand, there are differences in understanding among those skilled in the art; on the other hand, the applicant studied a large number of documents and patents when making this invention, but due to space limitations, not all details and contents were listed in detail. However, this does not mean that the present invention does not possess the features of these prior art. On the contrary, the present invention already possesses all the features of the prior art, and the applicant reserves the right to add relevant prior art to the background art. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method and system for dividing pipeline pressure testing packages based on a three-dimensional model. By introducing three-dimensional modeling, it provides visual operation and intelligent verification, solving the technical problems of large workload, cumbersome process and easy error in manual verification.

[0005] This invention discloses a method for dividing pipeline pressure testing packages based on a three-dimensional model, comprising the following steps: Construct a three-dimensional model of the pipeline construction, wherein the three-dimensional model contains the pipeline's attribute information; In the visual operation interface, the system receives the user's annotation operation on the three-dimensional model of the test pressure pack range, and calculates and displays the key parameters of the test pressure pack in real time based on the attribute information of the three-dimensional model. By matching the user-annotated test pack parameters with the pre-stored pressure test specification rule library, the system verifies whether the test pack conforms to the test specification and marks the parts that do not conform to the test specification. Based on the construction parameters set by the user, the equipment is grouped according to the pipeline connection diagram, a pressure test package division scheme is generated, and the integrity of the division scheme is verified.

[0006] In some specific embodiments, the construction of the three-dimensional model for pipeline construction includes: It receives 3D data from one or multiple data sources, registers and stitches point clouds, meshes, or model data from different data sources, identifies object categories and component structures, and generates a unified 3D model of pipeline construction.

[0007] In some specific embodiments, at least one of the following features is included: (i) The data acquisition methods for the attribute information include: using a lidar to generate point cloud data by emitting laser pulses and measuring the reflection time; and / or using a structured light scanner to acquire the surface morphology of an object by using infrared grating projection; and / or using multi-view stereo vision to capture images from different angles; and / or using an RGB-D camera to simultaneously capture color images and depth information. (ii) Preprocessing the collected attribute information data, including noise filtering, missing value handling, and coordinate system alignment; (iii) Based on the integrated attribute information, a three-dimensional model is generated based on a model reconstruction algorithm, wherein the model reconstruction algorithm includes one or more of the following: voxel partitioning algorithm, generative adversarial network or variational autoencoder.

[0008] In some specific implementations, the 3D model is associated with the project's construction schedule. The colors of the pipes and equipment in the model change dynamically according to the "to be tested" and "tested" status. A heat map of test coverage is displayed through a visual partitioning operation interface to provide visual support for users to adjust the partitioning scheme. And / or, associate the 3D model with the project's work breakdown structure, dynamically change the color of the pipes in the model according to their "welded" and "unwelded" status, and display a coverage heatmap of the pipe connectivity status through a visual interface.

[0009] In some specific implementations, verifying whether the test pressure package meets the test specifications includes: Based on knowledge graph technology, pressure testing specifications are integrated to build a rule base covering sealing requirements, media matching, and pressure level rules; Using logical reasoning algorithms, the system automatically detects the compatibility between pipe materials and test media, verifies whether the isolation valve settings meet safety isolation requirements, and verifies the matching relationship between pressure holding time and pressure rating. Violations detected during the verification process will be marked to alert the user.

[0010] In some specific embodiments, the method for generating test pressure package partitioning includes: The connection relationships between equipment, valves, and pipelines are described using graph theory methods, generating directed graph models. Depth-first search (DFS) is used to identify connected subgraphs and automatically classify equipment that meets the pressure rating and media compatibility requirements into the same pressure test package. By filtering out combinations that do not meet the conditions using a predefined rule base, a structured solution containing test package numbers, equipment lists, and test parameters is generated.

[0011] In some specific implementations, the test pressure package scheme optimization step is also included, including: Establish a multi-objective function with resource cost, total testing time, and redundant test items as optimization objectives; An optimization algorithm is used to iteratively optimize the device grouping scheme in order to find the optimal solution of the multi-objective function.

[0012] In some specific implementations, after generating the partitioning scheme, a risk assessment is performed, including: Construct a risk knowledge graph that integrates multiple factors such as pipeline medium type, pressure level, and regional risk, and assign weights to each factor; A comprehensive risk value is calculated for each pressure test package, and high-risk packages will be specially marked by the system.

[0013] In some specific implementations, the method also includes updating the 3D model and re-executing the compliance verification and test package generation steps in response to design changes during construction, so as to update the test package division scheme.

[0014] This invention also discloses a pipeline pressure test package division system based on a three-dimensional model, comprising: The 3D model building module is used to build 3D models that integrate pipeline attribute information; The visualization and segmentation module provides an interface for users to mark the range of the pressure test package and dynamically displays key parameters. The intelligent verification module performs compliance verification on the parameters of the pressure test package based on the stress test specification rule base; The scheme generation module automatically generates a test pressure package division scheme based on construction parameters and performs integrity verification.

[0015] In some specific embodiments, the system further includes: The data storage module uses a distributed architecture to store 3D model data and test package solution data; The rule base management module maintains and updates the stress testing specification rule base; The collaborative management module supports collaborative operations and version management by multiple departments through a cloud platform.

[0016] Beneficial effects: This solution introduces 3D modeling, provides visualization operation and intelligent verification, and generates pressure packages, solving the technical problems of large workload, cumbersome process and easy error in manual verification. It also includes the following technical advantages: (1) Using an automated data processing and verification mechanism, it eliminates the errors caused by manual input and calculation, and greatly improves the accuracy and reliability of the data. (2) With the intuitive display function of the 3D model, it accurately presents the spatial layout of the pipeline, significantly improves the visualization effect, and avoids the problem of area omission due to visual blind spots. (3) It builds an intelligent verification system, automatically matches the pressure package parameters with industry standards, fills the gaps in the verification of test specifications, and ensures that the division scheme fully meets the requirements of the specifications. (4) It realizes the automated generation and optimization of the pressure package scheme, reduces manual intervention, improves work efficiency, and effectively shortens the project cycle. (5) It enhances the dynamic adjustment capability of the system, can respond to design changes in the construction process in real time, automatically optimize the pressure package division scheme, reduce resource waste, and ensure the continuity and efficiency of construction. Attached Figure Description

[0017] Figure 1 This is a block diagram illustrating the overall principle of the method of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] In pipeline construction, pressure testing package division is a crucial step before pressure testing, aiming to ensure that the pipeline system's sealing and strength meet design standards. Currently, traditional pressure testing package division relies primarily on construction technicians manually completing this process based on two-dimensional drawings and their personal experience. Two-dimensional drawings only present planar information, lacking three-dimensional spatial data. Technicians must repeatedly verify the pipeline layout, connection relationships, and test range—a process that is extremely tedious and prone to errors. Furthermore, existing technology lacks automatic verification functions for pressure testing specifications, frequently leading to problems such as incomplete pressure testing package coverage and mismatched test parameters during construction. This results in rework, potential safety hazards, and significant delays in construction progress and increased costs.

[0020] Traditional pressure testing package division techniques rely excessively on manual experience and two-dimensional drawings, revealing many significant shortcomings and directly impacting construction efficiency, testing quality, and cost control. (1) Low data accuracy: When manually extracting the parameters required for pressure test package division (such as pipe diameter, pressure rating, and medium type) from 2D drawings, errors are easily caused by unclear drawing annotations or misinterpretation by technicians. 2D drawings, as information carriers, only provide planar information and cannot be dynamically linked to real-time data on pipeline attributes (such as pressure rating, material, and medium type). When technicians manually extract and calculate, they are easily affected by drawing annotation errors or version inconsistencies. For complex pipeline systems, parameters such as pressure test package volume and pressure values ​​rely on manual calculation (such as using Excel spreadsheets), which easily leads to unit conversion errors or formula input mistakes. Moreover, if the 2D drawings are not updated in a timely manner after design changes, technicians will divide the pressure test packages based on the old version, causing the parameters to be inconsistent with the actual construction.

[0021] (2) Insufficient visualization: Two-dimensional drawings cannot show the three-dimensional direction of pipelines (such as buried pipelines and perpendicular intersections), nor can they show the three-dimensional layout of pipelines. Technicians can only rely on imagination to infer spatial relationships, which easily leads to the omission of key areas and makes it difficult for technicians to discover hidden areas. Due to the lack of three-dimensional model support, the scope of the pressure test package can only be marked with lines and text, and it is impossible to accurately determine key pipe sections through rotation, scaling, or layered views. Traditional tools also cannot generate heat maps or perform spatial coverage analysis, and whether the pressure test package covers the entire area to be tested depends entirely on the technician's experience and judgment.

[0022] (3) Lack of verification of test specifications: Pressure test specifications (such as ASME B31.3 and GB 50235) are complex, and manual verification is not only time-consuming but also prone to overlooking details (such as the requirements for pressure holding time based on the type of medium). Whether the test package division scheme meets the requirements for sealing and pressure matching needs to be checked manually by technicians, as conflicts cannot be automatically identified by algorithms. The test requirements in the design phase are not integrated into the division process, resulting in a disconnect between the on-site division scheme and the specifications.

[0023] (4) Inefficiency: The division of pressure test packages requires multiple measurements of drawings, calculation of parameters, and adjustment of the plan. Complex projects may take several weeks, and each adjustment requires starting from scratch. After design changes, technicians have to recheck the data and manually update the division plan, resulting in a high proportion of repetitive work. Information is exchanged between design, construction, and testing departments through paper documents or scattered spreadsheets, which is costly and prone to errors.

[0024] (5) Insufficient dynamic adjustment capability: Traditional technology relies on static drawings and tables, which cannot be linked with the construction progress in real time, and design changes are difficult to be reflected in the pressure test package division plan in a timely manner. When pipeline design changes during construction (such as valve position adjustment), the pressure test package division plan cannot be updated in real time. Manual re-division requires repeated calculation and verification, resulting in a slow response speed.

[0025] Based on the above shortcomings, as shown in the appendix Figure 1As shown, this solution provides a method for dividing pipeline pressure testing packages based on a three-dimensional model, including the following steps: S1: Construct a 3D model of the pipeline construction. This 3D model includes the pipeline's attribute information, such as specifications, material, pressure rating, media type, and connection method. It can also incorporate construction environment data to further refine the 3D model.

[0026] Traditional two-dimensional drawings, as information carriers, only provide planar information and cannot be dynamically linked to real-time data on pipeline attributes (such as pressure rating, material, and media type). When technicians manually extract and calculate information, they are easily affected by drawing annotation errors or version inconsistencies. For complex piping systems, they are too abstract, hindering technicians' understanding of the pipeline layout and thus impeding pressure package division. In contrast, three-dimensional models, by establishing a three-dimensional model, allow for a clear observation of the pipeline layout, significantly reducing the time technicians spend interpreting the drawings.

[0027] A 3D model of the pipeline construction is built, comprehensively integrating pipeline specifications, materials, pressure ratings, media types, connection methods, and other attribute information, as well as construction environment data. This 3D modeling technology constructs a digital model containing all pipeline elements, providing an accurate data foundation for pressure testing package division. Through standardized interfaces such as IFC and PCF, seamless integration with commonly used external design software, such as CAD and BIM, ensures the integrity and consistency of model data. The model has the ability to synchronize design change data in real time. Whether it's pipe diameter adjustments, valve position changes, or other design modifications, the changes can be promptly and accurately updated in the model, avoiding errors caused by manual data entry.

[0028] In some specific embodiments, the construction of the three-dimensional model for pipeline construction includes: It receives 3D data from one or multiple data sources, registers and stitches point cloud, mesh, or model data from different data sources, identifies object categories and component structures to generate a unified 3D model of pipeline construction, registers and stitches 3D data from multiple data sources, binds pipelines with attribute information through data association technology, and establishes a complete digital twin model of the pipeline.

[0029] Feature extraction is a key step in connecting the attribute information of the original data with the 3D model. Based on curvature calculation, normal vector analysis and other methods, key points, edges and planar regions on the surface of an object are identified. Deep learning models (such as PointNet++, KPConv) are used to identify the object category and component structure from point cloud or mesh.

[0030] Large-scale data fusion requires massive computing resources. A collaborative architecture of edge computing and cloud computing can offload preprocessing tasks to terminal devices, while the cloud is responsible for model training and inference.

[0031] For example, in one specific implementation, when fusing multiple data sources, point clouds and image pixels can be registered and fused, resulting in more comprehensive data. The two sets of data can complement each other to construct a 3D model. Specifically, typical methods include the Iterative Closest Point (ICP) algorithm and SIFT-based feature matching. Multimodal information is integrated into the feature space, for example, embedding the geometric features of LiDAR point clouds and the texture features of cameras into the same feature vector. Inference results from multiple data sources are fused at the decision layer, for example, by fusing the output probabilities of semantic segmentation and object detection through a Bayesian network.

[0032] An iterative optimization method is used to gradually eliminate coordinate system deviations between data points, and spatial interpolation techniques are used to fill in missing data areas. A data fusion algorithm is then used to integrate the advantageous features of each data source to generate an optimal 3D model.

[0033] In some specific embodiments, at least one of the following features is included: (i) The data acquisition methods for the attribute information include: using a lidar to generate point cloud data by emitting laser pulses and measuring the reflection time; and / or using a structured light scanner to acquire the surface morphology of an object by projecting an infrared grating; and / or using multi-view stereo vision to capture images from different angles; and / or using an RGB-D camera to simultaneously capture color images and depth information; and collecting data through a multimodal sensor, wherein the multimodal sensor includes any one or more of lidar, structured light scanner, multi-view stereo vision acquisition device and RGB-D camera.

[0034] The first step in 3D model construction is acquiring raw data using multimodal sensors. Supported data acquisition methods include active acquisition: LiDAR generates point cloud data by emitting laser pulses and measuring reflection time, with an accuracy down to the millimeter level; structured light scanners use infrared grating projection to acquire the surface topography of objects. Passive acquisition includes: multi-view stereo vision (MVS) captures images from different angles using multiple cameras and reconstructs the 3D structure based on triangulation principles; RGB-D cameras (such as Kinect) simultaneously capture color images and depth information. The preprocessing stage addresses issues such as data noise, missing values, and coordinate system alignment. For example, point cloud data needs to be noise-removed using statistical outlier removal, and image data requires lens distortion correction and color balancing.

[0035] (ii) The collected attribute information data is preprocessed, including noise filtering, missing value processing and coordinate system alignment, in order to eliminate coordinate system differences and integrate attribute information to obtain a high-quality three-dimensional model.

[0036] (iii) Based on the integrated attribute information, a three-dimensional model is generated using a model reconstruction algorithm, wherein the model reconstruction algorithm includes one or more of the following: a voxel partitioning algorithm (Marching Cubes algorithm), a generative adversarial network, or a variational autoencoder.

[0037] Model reconstruction employs voxel-based methods, dividing the space into regular grids and generating isosurfaces using the Marching Cubes algorithm (e.g., the 3D-UNet network). Deep learning-based methods are also used, employing Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs) to directly predict 3D structures from 2D images (e.g., the Pix2Vox model). Hybrid approaches combine traditional geometric algorithms with neural networks, such as using Graph Convolutional Networks (GCNs) to optimize point cloud registration accuracy.

[0038] Understandably, 3D models can also be directly imported. For example, it supports importing models in various formats (such as PCF and IFC), performs lightweight processing, integrates pipeline attributes and test specification databases, and enables dynamic data association.

[0039] S2: In the visual operation interface, the system receives user annotations on the 3D model regarding the test chamber's range. Based on the attribute information of the 3D model, it calculates and displays the key parameters of the test chamber in real time. These key parameters include the pipeline's volume, pressure value, and number of nodes.

[0040] An intuitive and user-friendly visual 3D operating interface was designed within the system. This interface provides various practical tools, such as drag-and-drop, selection boxes, and polygon delineation, allowing technicians to directly annotate the pressure test package area on the 3D model. Simultaneously, the interface offers multiple view modes, including orthographic view, isometric view, layered view, and walkthrough mode, allowing technicians to observe the pipeline layout from different angles and accurately locate hidden areas, such as buried pipelines and intersections. During the delineation process, the interface dynamically displays the pressure test package volume, pressure value, number of nodes, and a heatmap of test coverage in real time, providing strong support for technicians to quickly adjust the plan.

[0041] The 3D model is linked to the project's construction schedule. The colors of the pipes and equipment in the model change dynamically according to the "to be tested" and "tested" status. A heat map of test coverage is displayed through a visual partitioning interface to provide visual support for users to adjust the partitioning scheme.

[0042] Preferably, by outputting a heat map of test coverage, it is possible to intuitively and clearly know which equipment and pipelines have been divided into pressure test packages and which equipment and pipelines have not been divided into pressure test packages, which can prevent technicians from missing any in the division and ensure the completeness of the division.

[0043] The 3D model is linked to the project's work breakdown structure. The color of the pipes in the model dynamically changes according to their "welded" and "unwelded" status. A heatmap showing the coverage of pipe connectivity is displayed through a visual interface. This allows for quick and easy identification of the pipe's welding status, preventing unwelded pipes from being assigned to pressure testing packages.

[0044] The visual partitioning interface displays a heatmap of test coverage, providing visual support for users to adjust partitioning schemes.

[0045] Two-dimensional drawings cannot show the three-dimensional routing of pipelines (such as buried pipelines and perpendicular intersections). They also cannot display the three-dimensional layout of pipelines, forcing technicians to rely on imagination to infer spatial relationships, easily leading to the omission of critical areas and difficulty in identifying hidden areas. Due to the lack of 3D model support, the delineation of pressure test packages can only be done through lines and text annotations, making it impossible to accurately determine key pipe sections through rotation, scaling, or layered views. However, 3D models and visualization operations can solve these problems. The design features an intuitive visual delineation interface, providing multiple view modes to facilitate technicians' observation of the pipeline layout from different angles and accurately determine the delineation locations. Through graphic rendering and interactive design, efficient interaction between users and the 3D model is achieved.

[0046] S3: By matching the user-annotated test package parameters with a pre-stored pressure test specification rule library, the system verifies whether the test package conforms to the test specifications and marks any parts that do not conform to the test specifications. The pressure test specification rule library includes industry standard rules and project-specific requirement rules, etc.

[0047] The stress testing specifications are transformed into executable production rules. A rule-based reasoning engine performs multi-dimensional compliance checks on the stress testing package. Industry standards and project-specific requirements are integrated into a comprehensive verification system to ensure all checks are covered.

[0048] Verifying whether the test pressure package meets the test specifications includes: Based on knowledge graph technology, pressure testing specifications are integrated to build a rule base covering sealing requirements, media matching, and pressure level rules; Using logical reasoning algorithms, the system automatically detects the compatibility between pipe materials and test media, verifies whether the isolation valve settings meet safety isolation requirements, and verifies the matching relationship between pressure holding time and pressure rating. Violations detected during the verification process will be marked to alert the user.

[0049] Based on knowledge graph technology, pressure testing standards such as ASME B31.3 and GB 50235 are deeply integrated to construct a rule base covering various aspects such as sealing requirements, media matching, and pressure ratings. Logical reasoning algorithms are used to automatically verify the compliance of test package parameters with the standards.

[0050] For example, the system can automatically detect the compatibility of pipe materials with the test medium, verify whether the isolation valve settings meet safety isolation requirements, and verify the matching relationship between pressure holding time and pressure level. For violations found during the verification process, the system will highlight them and provide targeted correction suggestions, such as adjusting the test medium or adding isolation valves, to facilitate technicians in timely adjustments to the partitioning scheme.

[0051] S4: Based on the construction parameters set by the user, the equipment is grouped according to the pipeline connection diagram, a pressure test package division scheme is generated, and the integrity of the division scheme is verified.

[0052] The generation of the test package division scheme includes: The connection relationships between equipment, valves, and pipelines are described using graph theory methods, generating directed graph models. Depth-first search (DFS) is used to identify connected subgraphs and automatically classify equipment that meets the pressure rating and media compatibility requirements into the same pressure test package. By filtering out combinations that do not meet the conditions using a predefined rule base, a structured solution containing test package numbers, equipment lists, and test parameters is generated.

[0053] The system automatically generates a list of pressure testing packages and a reasonable testing sequence based on preset construction parameters, such as maximum test pressure, medium flow rate, and resource limitations. It trains a model using historical project data to optimize the combination of pressure testing packages, improving equipment reuse rates. The system automatically verifies the completeness and compliance of the partitioning scheme, reducing manual review workload.

[0054] Based on BIM (Building Information Modeling) and industrial digital twin technology, a parametric model of the pressure test package is constructed, including the following elements: Equipment topology: The connection relationships of equipment, valves, and pipelines are described using graph theory methods to generate a directed graph model; Test parameter matrix: The constraints of key parameters such as pressure rating, medium type, and test temperature are defined to form an n×m dimensional parameter matrix; Boundary condition rule base: Industry standards (such as ASME B31.3) and project-specific requirements are integrated to form a configurable set of rules.

[0055] Develop an expert system based on production rules (IF-THEN) to automate the generation of pressure test package plans. The equipment grouping algorithm uses depth-first search (DFS) to identify connected subgraphs based on the connectivity graph, automatically grouping equipment that meets pressure rating and media compatibility requirements into the same pressure test package. The rule matching module filters out combinations that do not meet the predefined rule base (e.g., "high-pressure equipment should be packaged separately" and "corrosive media require isolated testing"). The plan output module generates a structured plan document containing pressure test package numbers, equipment lists, test parameters, and required resources.

[0056] Case Study: In a petrochemical project, the system automatically identified 15 connected subgraphs among 127 pieces of equipment. After rule filtering, it generated 12 pressure test package schemes, improving efficiency by 70% compared to manual design.

[0057] In some specific implementations, the test pressure package scheme optimization step is also included, including: Establish a multi-objective function with resource cost, total testing time, and redundant test items as optimization objectives; An optimization algorithm is used to iteratively optimize the device grouping scheme in order to find the optimal solution of the multi-objective function.

[0058] The optimization problem of the pressure testing package scheme is transformed into a multi-objective decision model: ; Where: minZ: the comprehensive optimization objective value, which needs to be minimized to achieve multi-objective balance; C 资源 Resource cost target: This typically represents the total resource cost (such as funds, testing equipment, manpower, materials, etc.) consumed during the decision-making process; T 总 : Represents the total time required to complete the decision-making process (such as project duration, production time, etc.); R 冗余 : Percentage of redundant test items; α1, α2, α3: Weight coefficients, all ranging from 0.1 to 1, set by the user according to project requirements. The larger the weight, the higher the priority of the corresponding target in optimization; G(x) constraint function vector: contains specific conditions such as resource limits, time constraints, and technical feasibility (e.g., C 资源 ≤C max x: a vector of decision variables, which includes controllable decision elements such as resource allocation and time allocation.

[0059] Establish a multi-objective optimization model that comprehensively considers resource costs, testing time, and testing quality. Employ modern optimization algorithms to find the optimal solution within a vast solution space, and use intelligent search strategies to balance conflicts between different objectives. Utilize an adaptive adjustment mechanism to automatically adjust the optimization direction and intensity based on project characteristics.

[0060] The optimization algorithm includes: a hybrid strategy of genetic algorithm (GA) and simulated annealing (SA) to balance global search and local optimization capabilities; encoding method: the grouping scheme of the test package is used as chromosomes, and integer encoding is used to represent the device affiliation relationship; fitness function: the Z value is calculated based on the multi-objective model and normalized as the fitness evaluation standard; genetic operation: new schemes are generated through selection, crossover, and mutation, and the Metropolis criterion of SA is used to accept inferior solutions to avoid local optima; termination condition: a maximum number of iterations or a fitness convergence threshold is set.

[0061] In some specific implementations, after generating the partitioning scheme, a risk assessment is performed, including: constructing a risk knowledge graph, integrating multiple factors such as pipeline medium type, pressure level, and regional risk, and assigning weights to each factor; calculating a comprehensive risk value for each pressure test package, with high-risk packages being specially marked by the system.

[0062] Pipeline media factors include toxicity level (e.g., none, low, medium, high), flammability level (e.g., non-flammable, combustible, flammable), and explosiveness level (e.g., none, explosive). Pressure level factors include pressure value and pressure level classification (e.g., low pressure, medium pressure, high pressure, ultra-high pressure). Regional environmental factors include region type (e.g., ordinary area, densely populated area, equipment critical area, environmentally sensitive area). Through the attribute information of the pipeline model, each test package is associated with the entities it contains, such as the medium, pressure, region, and pipe material, forming a graphical network describing "Test Package-A-Contains-Medium-X, Located in-Region-Y, Bearing-Pressure-P".

[0063] A weighted scoring method is used as the core calculation model to quantify each factor. The weight of each factor ranges from 0 to 1, and is adaptively adjusted according to the actual degree of risk.

[0064] S5: It also includes updating the 3D model and re-executing the compliance verification and test package generation steps in response to design changes during construction, so as to update the test package division scheme.

[0065] When design changes occur during construction, the system automatically updates the pressure testing package allocation scheme and triggers a re-verification process to ensure that the scheme is synchronized with actual construction. It supports collaborative operations between design, construction, and testing departments via a cloud platform, with traceable version change records to avoid data conflicts.

[0066] Establish a version control-based design change management system, automatically assessing the scope of change impact through change propagation algorithms. Employ incremental update technology, recalculating and validating only the affected parts. Through collaborative workflows, ensure all relevant departments receive the latest solutions promptly, avoiding information inconsistencies.

[0067] This solution also provides a pipeline pressure test package division system based on a three-dimensional model, including: The 3D model building module is used to build 3D models that integrate pipeline attribute information; The visualization and segmentation module provides an interface for users to mark the range of the pressure test package and dynamically displays key parameters. The intelligent verification module performs compliance verification on the parameters of the pressure test package based on the stress test specification rule base; The scheme generation module automatically generates a test pressure package division scheme based on construction parameters and performs integrity verification.

[0068] By introducing 3D modeling, providing visual operation and intelligent verification, and generating pressure packages, the technical problems of large workload, tedious process, and susceptibility to errors in manual verification are solved. Utilizing automated data processing and verification mechanisms, errors generated in manual input and calculation are eliminated, significantly improving data accuracy and reliability. The intuitive display function of 3D models accurately presents the spatial layout of pipelines, significantly improving visualization effects and avoiding omissions due to blind spots. An intelligent verification system is built to automatically match pressure package parameters with industry standards, filling gaps in test specification verification and ensuring that the division scheme fully complies with specification requirements. This achieves automated generation and optimization of pressure package schemes, reducing manual intervention, improving work efficiency, and effectively shortening project cycles.

[0069] It also includes a dynamic adjustment module that responds to design changes and updates the test pressure package allocation scheme. This enhances the system's dynamic adjustment capabilities, enabling it to respond in real time to design changes during construction, automatically optimize the test pressure package allocation scheme, reduce resource waste, and ensure the continuity and efficiency of construction.

[0070] In some specific embodiments, the system further includes: The data storage module employs a distributed architecture to store 3D model data and test package scheme data. Relational databases (such as MySQL and PostgreSQL) or NoSQL databases (such as MongoDB) are used to store relevant data for prefabricated components, including but not limited to geometric information, physical properties, and connection methods. For large design files and models, a distributed file system (such as HDFS) is used to ensure efficient data read / write speeds and data redundancy.

[0071] The rule base management module maintains and updates the stress testing specification rule base.

[0072] The collaborative management module supports collaborative operations and version management by multiple departments through a cloud platform.

[0073] Technical Framework and Tools: The user interface is built using a modern JavaScript framework such as Vue.js, enabling UI componentization and improving code reusability and maintainability. To simplify the front-end and back-end data communication process, Axios is used as the HTTP request library, encapsulating a unified API.

[0074] It should be noted that the specific embodiments described above are exemplary. Those skilled in the art can devise various solutions inspired by the disclosure of this invention, and these solutions all fall within the scope of this invention and its protection. Those skilled in the art should understand that this specification and its accompanying drawings are illustrative and not intended to limit the scope of the claims. The scope of protection of this invention is defined by the claims and their equivalents. This specification contains multiple inventive concepts; terms such as "preferredly," "according to a preferred embodiment," or "optionally" indicate that the corresponding paragraph discloses an independent concept. The applicant reserves the right to file divisional applications based on each inventive concept.

Claims

1. A method for dividing pipeline pressure test packages based on a three-dimensional model, characterized in that, Includes the following steps: Construct a three-dimensional model of the pipeline construction, wherein the three-dimensional model contains the pipeline's attribute information; In the visual operation interface, the system receives the user's annotation operation on the three-dimensional model of the test pressure pack range, and calculates and displays the key parameters of the test pressure pack in real time based on the attribute information of the three-dimensional model. By matching the user-annotated test pack parameters with the pre-stored pressure test specification rule library, the system verifies whether the test pack conforms to the test specification and marks the parts that do not conform to the test specification. Based on the construction parameters set by the user, the equipment is grouped according to the pipeline connection diagram, a pressure test package division scheme is generated, and the integrity of the division scheme is verified.

2. The method for dividing pipeline pressure test packages based on a three-dimensional model according to claim 1, characterized in that, The construction of the three-dimensional model for pipeline construction includes: It receives 3D data from one or multiple data sources, registers and stitches point clouds, meshes, or model data from different data sources, identifies object categories and component structures, and generates a unified 3D model of pipeline construction.

3. The method for dividing pipeline pressure test packages based on a three-dimensional model according to claim 1 or 2, characterized in that, It contains at least one of the following features: (i) The data acquisition methods for the attribute information include: using a lidar to generate point cloud data by emitting laser pulses and measuring the reflection time; and / or using a structured light scanner to acquire the surface morphology of an object by using infrared grating projection; and / or using multi-view stereo vision to capture images from different angles; and / or using an RGB-D camera to simultaneously capture color images and depth information. (ii) Preprocessing the collected attribute information data, including noise filtering, missing value handling, and coordinate system alignment; (iii) Based on the integrated attribute information, a three-dimensional model is generated based on a model reconstruction algorithm, wherein the model reconstruction algorithm includes one or more of the following: voxel partitioning algorithm, generative adversarial network or variational autoencoder.

4. The method for dividing pipeline pressure test packages based on a three-dimensional model according to any one of claims 1 to 3, characterized in that, The 3D model is linked to the project's construction schedule. The colors of the pipes and equipment in the model change dynamically according to the "to be tested" and "tested" status. A heat map of test coverage is displayed through a visual partitioning interface to provide visual support for users to adjust the partitioning scheme. And / or, associate the 3D model with the project's work breakdown structure, dynamically change the color of the pipes in the model according to their "welded" and "unwelded" status, and display a coverage heatmap of the pipe connectivity status through a visual interface.

5. The method for dividing pipeline pressure test packages based on a three-dimensional model according to any one of claims 1 to 4, characterized in that, Verifying whether the test pressure package meets the test specifications includes: Based on knowledge graph technology, pressure testing specifications are integrated to build a rule base covering sealing requirements, media matching, and pressure level rules; Using logical reasoning algorithms, the system automatically detects the compatibility between pipe materials and test media, verifies whether the isolation valve settings meet safety isolation requirements, and verifies the matching relationship between pressure holding time and pressure rating. Violations detected during the verification process will be marked to alert the user.

6. The method for dividing pipeline pressure test packages based on a three-dimensional model according to any one of claims 1 to 5, characterized in that, The generation of the test package division scheme includes: The connection relationships between equipment, valves, and pipelines are described using graph theory methods, generating directed graph models. Depth-first search (DFS) is used to identify connected subgraphs and automatically classify equipment that meets the pressure rating and media compatibility requirements into the same pressure test package. By filtering out combinations that do not meet the conditions using a predefined rule base, a structured solution containing test package numbers, equipment lists, and test parameters is generated.

7. The method for dividing pipeline pressure test packages based on a three-dimensional model according to any one of claims 1 to 6, characterized in that, It also includes steps for optimizing the test package solution, including: Establish a multi-objective function with resource cost, total testing time, and redundant test items as optimization objectives; An optimization algorithm is used to iteratively optimize the device grouping scheme in order to find the optimal solution of the multi-objective function.

8. The method for dividing pipeline pressure test packages based on a three-dimensional model according to any one of claims 1 to 7, characterized in that, After generating the partitioning scheme, a risk assessment is performed on it, including: Construct a risk knowledge graph that integrates multiple factors such as pipeline medium type, pressure level, and regional risk, and assign weights to each factor; A comprehensive risk value is calculated for each test package, and high-risk packages will be specially marked by the system. And / or, It also includes updating the 3D model and re-executing the compliance verification and test package generation steps in response to design changes during construction, so as to update the test package division scheme.

9. A pipeline pressure test package division system based on a three-dimensional model, characterized in that, include: The 3D model building module is used to build 3D models that integrate pipeline attribute information; The visualization and segmentation module provides an interface for users to mark the range of the pressure test package and dynamically displays key parameters. The intelligent verification module performs compliance verification on the parameters of the pressure test package based on the stress test specification rule base; The scheme generation module automatically generates a test pressure package division scheme based on construction parameters and performs integrity verification.

10. The system according to claim 9, characterized in that, The system also includes: The data storage module uses a distributed architecture to store 3D model data and test package solution data; The rule base management module maintains and updates the stress testing specification rule base; The collaborative management module supports collaborative operations and version management by multiple departments through a cloud platform.

Images