Method and system for spatially intelligent pressure vessel full life cycle digital leave and inspection data spatial association based on spatial intelligence technology

Abstract

A pressure vessel full life cycle digital leave bottom and inspection data space association method and system based on spatial intelligence technology relates to special equipment inspection and detection technology, and is used for solving the problems of poor space relationship of existing pressure vessel inspection data, low data review efficiency, difficult comparison of multi-period inspection and insufficient accident tracing efficiency. The pressure vessel full life cycle digital leave bottom and inspection data space association method based on spatial intelligence technology acquires pressure vessel full life cycle data, constructs a pressure vessel three-dimensional real scene digital leave bottom model and establishes a unified space coordinate system corresponding to the pressure vessel entity, constructs a pressure vessel space semantic association model, carries out three-dimensional space calibration of manufacturing supervision inspection data and corresponding entity parts in the pressure vessel three-dimensional real scene digital leave bottom model, carries out space superposition, change identification and evolution analysis of multi-period inspection data, constructs an accident investigation space evidence chain, and outputs inspection data spatial management and accident tracing results.

Description

Technical Field

[0001] This invention relates to the field of special equipment inspection and testing technology, and in particular to a method and system for spatial correlation of digital record-keeping and inspection data throughout the entire life cycle of pressure vessels based on spatial intelligence technology. Background Technology

[0002] Currently, pressure vessels are widely used in petrochemical, energy storage and transportation, equipment manufacturing, gas storage, and industrial production, and are an important target for special equipment safety supervision. During the manufacturing, installation, use, periodic inspection, maintenance, and accident investigation of pressure vessels, a large amount of inspection and supervision data is generated, including product quality certificates, material quality certificates, weld layout diagrams, welding process documents, welder qualification information, heat treatment records, non-destructive testing data, manufacturing supervision and inspection reports, periodic inspection reports, wall thickness measurement data, defect review records, repair and rework records, operation and maintenance data, accident scene photos, and accident investigation records.

[0003] In existing technologies, pressure vessel inspection data is mainly stored in the form of paper reports, electronic documents, tables, drawings, films, test charts, photographs, or scattered databases. Although these data can record the inspection results of pressure vessels at different stages, most of the data lack a direct correspondence between the data and the physical location of the equipment. For example, the same weld seam needs to be matched with the weld seam layout drawing, RT film, TOFD chart, phased array test record, manufacturing supervision and inspection report, and the actual site, which requires manual experience and drawing numbers. Wall thickness measurement points, corrosion pits, cracks, dents, repair areas, and re-inspection records in periodic inspections are also often difficult to locate and verify consistently in the physical space of the equipment over a long period of time.

[0004] However, the inventors of this application have discovered that with the development of 3D real-scene reconstruction, low-altitude vision, photogrammetry, 3D Gaussian sputtering, spatial intelligence, and artificial intelligence-assisted analysis technologies, the use of 3D spatial carriers to organize the physical structure and inspection data of pressure vessels has an engineering foundation. By creating a 3D real-scene digital record of the pressure vessel and uniformly labeling welds, nozzles, manholes, heads, supports, reinforcing rings, defect areas, inspection records, and historical data in 3D space, more intuitive and traceable data support can be provided for supervision and inspection, periodic inspection, and accident investigation. For example, some 3D modeling, digital twins, equipment management systems, non-destructive testing data management systems, or accident investigation information systems have been applied in the field of industrial equipment management. However, these technologies are mostly concentrated on equipment display, data archiving, condition monitoring, single defect identification, or general platform management, and have not yet formed an integrated technical solution of "digital record - spatial labeling - multi-stage evolution analysis - accident evidence tracing" for the entire life cycle supervision of pressure vessels.

[0005] Therefore, how to provide a method and system for spatial association of digital record and inspection data of pressure vessels throughout their entire life cycle based on spatial intelligence technology, which can unify the organization of pressure vessel entities, welds, inspection data, historical defects and accident evidence into a three-dimensional space, and realize the spatial expression, bidirectional traceability, multi-stage evolution analysis and construction of accident evidence chain of inspection data, has become a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for spatial association of digital record-keeping and inspection data throughout the entire life cycle of pressure vessels based on spatial intelligence technology, in order to solve the technical problems in the prior art such as unclear spatial relationships of pressure vessel inspection data, low data verification efficiency, difficulty in comparing multiple inspections, and insufficient efficiency in accident tracing.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: A method for spatially correlating digital records and inspection data throughout the entire lifecycle of pressure vessels based on spatial intelligence technology includes the following steps: Step S1: Obtain pressure vessel lifecycle data, which includes: field-collected data, manufacturing supervision and inspection data, in-service periodic inspection data, and accident investigation data. The pressure vessel lifecycle data is formatted, fields are extracted, numbered uniformly, and manually reviewed to form a data set that can be used for spatial calibration and correlation analysis. Step S2: Construct a three-dimensional real-scene digital model of the pressure vessel based on the full life cycle data of the pressure vessel, and establish a unified spatial coordinate system corresponding to the pressure vessel entity. The three-dimensional real-scene digital model of the pressure vessel is used to provide a spatial base for subsequent spatial calibration. Step S3: Construct a spatial semantic association model of the pressure vessel based on the three-dimensional real-scene digital retention model of the pressure vessel. The spatial semantic association model is used to establish unified organization rules for different types of data and to characterize the relationship between equipment entities, key components, weld positions, inspection records, original data and accident evidence. Step S4: Based on the unified spatial coordinate system and the spatial semantic association model, perform three-dimensional spatial calibration of the manufacturing supervision and inspection data and the corresponding entity parts in the three-dimensional real scene digital record model of the pressure vessel, so as to provide a basic reference for the overlay of periodic inspection data and accident tracing, and form a spatial association relationship between the manufacturing supervision and inspection data and the equipment entity. Step S5: Map the in-use periodic inspection data generated from different inspection cycles to the unified spatial coordinate system, and carry out spatial overlay, change identification and evolution analysis of multi-period inspection data to provide historical change basis for risk identification and accident investigation; Step S6: During the accident investigation phase, the on-site data after the accident is spatially registered with the three-dimensional real-scene digital record model of the pressure vessel before the accident, and the manufacturing supervision and inspection data, the in-use periodic inspection data, historical defect records and on-site evidence after the accident are associated to construct a spatial evidence chain for the accident investigation. The spatial evidence chain for the accident investigation is used to uniformly trace the digital record before the accident, the historical inspection data and the on-site information after the accident. Step S7: Based on the spatial correlation of manufacturing supervision and inspection data, the evolution analysis results of multi-period inspection data, and the spatial evidence chain of the accident investigation, output the spatial management of inspection data and the accident tracing results.

[0008] In practical applications, the method for digital record keeping and spatial association of inspection data throughout the entire life cycle of pressure vessels based on spatial intelligence technology further includes, after step S5: step S5', carrying out post-accident on-site data supplementation and spatial registration before and after the accident.

[0009] The on-site data collected includes any one or more of the following: multi-view images of the outer / inner / accessible parts of the pressure vessel, low-altitude visual images, close-up images, panoramic images, video data, control point coordinates, scale information, collection pose information, collection time, collection personnel, equipment status, and collection point number. The manufacturing supervision and inspection data includes any one or more of the following: pressure vessel product quality certificate, material quality certificate, equipment general drawing, weld layout drawing, welding process documents, welder qualification information, heat treatment record, RT film, TOFD spectrum, phased array test spectrum, ultrasonic test record, rework record, inspection report, and relevant witness documents for supervision and inspection. The in-use periodic inspection data includes any one or more of the following: macroscopic inspection records, wall thickness measurement data, magnetic particle testing data, penetrant testing data, ultrasonic testing data, radiographic testing data, corrosion pit measurement data, dent or deformation measurement data, defect review records, maintenance records, operation and maintenance records, and periodic inspection reports. The accident investigation data includes any one or more of the following: post-accident scene images, descriptions of the accident location, images of the damaged area, images of the fracture or leakage location, on-site investigation records, interview records, historical data retrieval records, evidence lists, and accident cause analysis related materials.

[0010] Specifically, the construction of a three-dimensional digital model of the pressure vessel based on the pressure vessel's entire lifecycle data includes the following steps: Based on the structural characteristics of pressure vessels and the needs of inspection operations, key collection targets are identified, including: any one or more of the following: shell, head, nozzle, manhole, reinforcing ring, support, saddle, skirt, weld, flange connection area, corrosion-sensitive area, and historical defect area. Control points, coded markers, or scales are set up on or around the pressure vessel body, and their spatial locations are recorded. For large spherical tanks, horizontal containers, vertical containers, or complex connection structures, spatial references are established by combining one or more of the following: equipment axis, circumferential angle, axial distance, elevation information, or on-site measurement points. Multiple-view images are acquired using low-altitude visual, close-range photogrammetry, or mobile acquisition methods. During acquisition, sufficient overlap between adjacent images is ensured, and local re-images are taken of welds, pipe roots, manhole edges, reinforcing rings, corrosion-sensitive areas, and areas with historical defects. The acquired images are screened to remove blurry, overexposed, underexposed, insufficiently overlapping, or severely occluded data; camera pose estimation and sparse point cloud reconstruction are performed based on multi-view geometric methods, and multi-view geometric consistency is optimized through bundle adjustment. Based on the three-dimensional Gaussian sputtering method, the overall structure, surface texture, weld area and key parts of the pressure vessel are expressed in high-fidelity three-dimensional form to form a three-dimensional real-scene digital model of the pressure vessel. The model retains the real appearance, spatial structure and texture information of key parts of the pressure vessel, which can serve as a unified spatial base for subsequent spatial calibration of inspection data, overlay of periodic inspection data and traceability of accident investigation.

[0011] Furthermore, the spatial semantic association model includes at least the following levels: The equipment entity layer is used to record any one or more of the following: pressure vessel equipment number, equipment name, equipment type, design parameters, manufacturing unit, user unit, and equipment status information. The critical component layer is used to record information on any one or more of the following components: cylinder, head, nozzle, manhole, reinforcing ring, support, flange, skirt, saddle, and safety accessories. The weld and critical component layer is used to record any one or more of the following spatial objects: circumferential welds, longitudinal welds, nozzle welds, fillet welds, reinforcing ring welds, repair areas, historical defect areas, and key monitoring areas. The detection recording layer is used to record any one or more of the following: RT film, TOFD spectrum, phased array detection spectrum, ultrasonic detection record, magnetic particle detection record, penetrant detection record, wall thickness measurement record, and macroscopic inspection record; The original data layer is used to record any one or more of the following: material quality certificates, welding process documents, heat treatment records, inspection reports, rework records, periodic inspection reports, and operation and maintenance records. The accident evidence layer is used to record one or more of the following: accident location, post-accident scene images, damaged area, historical defects, accident investigation records, and evidence list.

[0012] Furthermore, the process of performing three-dimensional spatial calibration between the manufacturing supervision and inspection data and the corresponding physical parts in the three-dimensional digital model of the pressure vessel specifically includes the following steps: For the circumferential and longitudinal welds of the cylinder, corresponding spatial curves are generated in the three-dimensional model based on the weld layout diagram, equipment axis, axial distance, circumferential angle and elevation information; for the head splice welds, nozzle welds, manhole welds, reinforcing ring welds and support welds, local coordinate systems are established based on local structural features, and local geometric constraints are used to achieve fine calibration. For non-destructive testing data such as RT films, TOFD maps, phased array test maps, and ultrasonic test records, the system associates the test data with the corresponding weld or weld section based on the weld number, test section number, film number, test location, and test ratio. For material quality certificates, heat treatment records, repair records, and inspection reports, the system associates them with the corresponding components or spatial areas based on the component number, material batch number, weld number, or report number. After calibration, users can click on a weld, a pipe, or a key part in the 3D model, and the system will automatically retrieve the RT film, TOFD map, phased array test map, material data, repair records, and inspection reports related to that location. Users can also reverse locate the corresponding entity in the 3D model by entering the weld number, film number, report number, or test record number.

[0013] Furthermore, the process of mapping in-use periodic inspection data generated from different inspection cycles to the unified spatial coordinate system, and conducting spatial overlay, change identification, and evolution analysis of multi-period inspection data, specifically includes the following steps: For wall thickness measurement data, each measurement point is recorded as a spatial point object, including one or more of the following: spatial coordinates, measurement time, thickness value, measurement method, measurement personnel, and measurement report number; for linear defects, they are recorded as spatial line objects, including one or more of the following: start point, end point, length, direction, defect type, and risk level; for planar corrosion, depressions, or deformed areas, they are recorded as spatial planar objects, including one or more of the following: boundary range, maximum depth, average value, area, risk level, and measurement cycle. The system establishes multiple data layers according to the inspection cycle and supports overlay display by time, inspection method, defect type, weld number and risk level. The system can calculate one or more of the following: wall thickness change rate, defect size change, defect location change, recurrence after repair and risk level change at the same measuring point or the same location, and classify the defect status as any one of the following: new, expanded, stable, reduced or eliminated.

[0014] Furthermore, the construction of the spatial evidence chain for accident investigation specifically includes the following steps: Collect one or more of the following: post-accident scene images, photos of damaged areas, images of fractured or leaking parts, images of deformed areas, photos of accident-related components, and on-site investigation records; construct post-accident scene spatial information or a post-accident local 3D model based on the post-accident scene data. Spatial registration is performed between the post-accident site model or post-accident site image and the pre-accident 3D digital real-scene model of the pressure vessel. The registration method adopts feature point matching, control point constraint, manual marker point correspondence, structural boundary matching or iterative nearest point method to ensure that the data before and after the accident are under the same spatial reference. Based on the spatial location of the accident site, the system automatically associates the corresponding manufacturing supervision and inspection data, periodic inspection data, historical defect records, repair and rework records, operation and maintenance records, and on-site evidence after the accident, forming a spatial evidence chain of "accident site - historical inspection records - original data - defect evolution process - accident site evidence". The system outputs one or more of the following: 3D spatial screenshots of the accident-related parts, a list of historical inspection data, a description of defect evolution, before-and-after comparison diagrams of the accident, an index of on-site evidence, and a traceability report, providing technical support for accident cause analysis, regulatory verification, and rectification decisions.

[0015] A spatial correlation system for digital record keeping and inspection data throughout the entire life cycle of pressure vessels based on spatial intelligence technology, comprising: The data acquisition module is used to acquire one or more of the following: pressure vessel field-collected data, manufacturing supervision and inspection data, in-service periodic inspection data, and accident investigation data. A three-dimensional digital retention module is used to construct a three-dimensional real-scene digital retention model of the pressure vessel based on field-collected data, control point information, and pressure vessel structural parameters. A spatial coordinate management module is used to establish the overall coordinate system, local coordinate system, and multi-period data registration relationship of the equipment. A spatial semantic modeling module is used to establish a spatial semantic association model between equipment entities, key components, weld locations, inspection records, original data, and accident evidence. The inspection data spatial calibration module is used to calibrate manufacturing supervision inspection data and in-use periodic inspection data to the corresponding entity parts in the three-dimensional real-scene digital retention model; The multi-period evolution analysis module is used to perform spatial overlay, change identification, trend analysis, and risk focus identification on data from different testing periods. The accident evidence chain module is used to spatially correlate the digital record model before the accident, historical inspection data and on-site evidence after the accident to form a spatial evidence chain for accident investigation. The visualization traceability module is used to display three-dimensional models, spatial annotations, inspection data, defect evolution process and accident evidence chain, and supports bidirectional retrieval and result export; The data archiving and version management module is used to record data source, upload time, operator, version information, file verification information, and data status.

[0016] Compared with existing technologies, the spatial correlation method and system for digital record keeping and inspection data of pressure vessels throughout their entire life cycle based on spatial intelligence technology described in this invention has the following advantages: The present invention provides a method and system for spatial association of pressure vessel full life cycle digital record and inspection data based on spatial intelligence technology. This is achieved by constructing a three-dimensional real-scene digital record model of the pressure vessel, a spatial semantic association model of the inspection data, a spatial comparative analysis method for multi-period inspection data, and a spatial evidence chain for accident investigation. This transforms pressure vessel inspection data from traditional two-dimensional data management to three-dimensional spatial expression, visual traceability, and intelligent assisted analysis. Specifically, this application uses the three-dimensional real-scene digital record model of the pressure vessel as a unified spatial carrier, the spatial semantic association model as the data organization basis, the three-dimensional spatial calibration of inspection data as the core connection mechanism, spatial comparison of multi-period inspection data and artificial intelligence-assisted evolutionary analysis as analytical means, and the spatial evidence chain for accident investigation as the traceability target, thereby realizing the full life cycle spatial management of pressure vessel supervision inspection, periodic inspection, and accident investigation data. Specific analysis reveals that constructing a 3D real-scene digital record model of pressure vessels can provide a unified spatial carrier for inspection data throughout the entire life cycle of pressure vessels; establishing a spatial semantic association model for pressure vessels can achieve hierarchical associations between equipment entities, key components, weld locations, inspection records, original data, and accident evidence; forming a 3D spatial calibration method for manufacturing supervision and inspection data can achieve precise correspondence between physical parts such as welds, nozzles, manholes, heads, cylinders, and supports and RT films, TOFD maps, phased array detection maps, material data, rework records, and inspection reports; and forming a spatial comparison and evolution of in-use periodic inspection data. The analytical method enables the overlay, comparison, and trend identification of multi-period wall thickness, defect, corrosion, maintenance, and review data in a unified three-dimensional space; the method for constructing and tracing spatial evidence chains for accident investigations enables rapid correlation and visual tracing between pre-accident digital record models, historical inspection data, historical defect records, and post-accident on-site evidence; in summary, the spatial correlation method and system for pressure vessel full life cycle digital record and inspection data based on spatial intelligence technology provided by this invention can effectively improve the efficiency, completeness, and repeatability of pressure vessel inspection data management, historical review, risk analysis, and accident investigation. Attached Figure Description

[0017] Figure 1 A flowchart illustrating the method for spatial association of digital record-keeping and inspection data throughout the entire lifecycle of pressure vessels based on spatial intelligence technology, provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of the process of three-dimensional real-scene digital record of pressure vessels in the method of spatial association of pressure vessel full life cycle digital record and inspection data based on spatial intelligence technology provided in the embodiments of the present invention; Figure 3This is a schematic diagram of the process for three-dimensional spatial calibration of manufacturing supervision and inspection data in the method for spatial association of pressure vessel full life cycle digital record and inspection data based on spatial intelligence technology provided in an embodiment of the present invention. Figure 4 This is a schematic diagram illustrating the process of spatial comparison and evolution analysis of periodic inspection data in the pressure vessel full life cycle digital record and inspection data spatial correlation method based on spatial intelligence technology provided in this embodiment of the invention. Figure 5 A schematic diagram illustrating the process of constructing and tracing the spatial evidence chain for accident investigation in the spatial association method for digital record-keeping and inspection data throughout the entire life cycle of pressure vessels based on spatial intelligence technology, provided in an embodiment of the present invention. Figure 6 A schematic diagram of the framework structure of a spatial correlation system for digital record-keeping and inspection data of pressure vessels throughout their entire lifecycle, based on spatial intelligence technology, provided in an embodiment of the present invention.

[0018] Figure label: 1-Data Acquisition Module; 2-Three-Digit Recording Module; 3-Spatial Coordinate Management Module; 4-Spatial Semantic Modeling Module; 5-Spatial Calibration of Inspection Data Module; 6-Multi-Period Evolution Analysis Module; 7-Accident Evidence Chain Module; 8-Visual Traceability Module; 9-Data Archiving and Version Management Module. Detailed Implementation

[0019] In other words, existing pressure vessel inspection data management and accident tracing technologies have the following main shortcomings: (1) Inspection data are mainly stored in two-dimensional documents and scattered files, with a lack of spatial semantics; that is, existing inspection reports, drawings, negatives, test charts and photos cannot directly reflect their real location on the pressure vessel entity. When reviewing historical data, inspectors need to repeatedly search for drawings, reports and numbers, resulting in low work efficiency. (2) There is a lack of a stable three-dimensional spatial calibration mechanism between manufacturing supervision and inspection data and equipment entities; that is, the spatial correspondence between data such as weld numbers, RT films, TOFD charts, phased array test charts, material quality certificates, heat treatment records and repair records and physical parts such as cylinders, heads, nozzles, manholes, and reinforcing rings is not intuitive enough, thus affecting the review of supervision and inspection data and regulatory verification. (3) The periodic inspection data lacks a unified spatial benchmark, making it difficult to compare data from multiple periods; that is, the wall thickness measurement, non-destructive testing, macroscopic inspection, corrosion pit measurement, defect review and maintenance records formed in different inspection cycles are usually stored in different reports and forms, making it difficult to conduct trend analysis under the same spatial coordinates. (4) The historical data retrieval period during the accident investigation stage is relatively long, and it is difficult to correlate data before and after the accident. That is, when a pressure vessel leaks, cracks, explodes, deforms or other abnormal events occur, investigators need to quickly retrieve the manufacturing data, historical inspection data, defect records and maintenance records of the accident site. However, the existing methods mainly rely on manual retrieval and experience judgment, which can easily lead to problems such as slow data retrieval, incomplete evidence chain and unintuitive spatial relationship.

[0020] (5) Existing 3D models are mostly used for display and have not yet formed a deep connection with inspection data, historical defects and accident evidence; that is, some industrial 3D modeling or digital display systems can present the appearance of equipment, but have not formed a spatial calibration and two-way traceability mechanism at the weld level, defect level and data level, which makes it difficult to directly support the business of pressure vessel supervision and inspection, periodic inspection and accident investigation.

[0021] For ease of understanding, the following description, in conjunction with the accompanying drawings, details the method and system for spatial association of digital record-keeping and inspection data throughout the entire lifecycle of pressure vessels based on spatial intelligence technology, as provided in the embodiments of the present invention.

[0022] This invention provides a method for spatially associating digital records and inspection data throughout the entire lifecycle of pressure vessels based on spatial intelligence technology, such as... Figure 1 As shown, it includes the following steps: Step S1: Obtain pressure vessel lifecycle data, which includes: field-collected data, manufacturing supervision and inspection data, in-service periodic inspection data, and accident investigation data. The pressure vessel lifecycle data is formatted, fields are extracted, numbered uniformly, and manually reviewed to form a data set that can be used for spatial calibration and correlation analysis. Step S2: Construct a three-dimensional real-scene digital model of the pressure vessel based on the full life-cycle data of the pressure vessel, and establish a unified spatial coordinate system corresponding to the pressure vessel entity. The three-dimensional real-scene digital model of the pressure vessel is used to provide a spatial base for subsequent spatial calibration. Step S3: Construct a spatial semantic association model of the pressure vessel based on the three-dimensional real-scene digital record model of the pressure vessel. The spatial semantic association model is used to establish unified organization rules for different types of data and to characterize the relationship between equipment entities, key components, weld locations, inspection records, original data and accident evidence. Step S4: Based on a unified spatial coordinate system and spatial semantic association model, perform three-dimensional spatial calibration of the corresponding entity parts in the three-dimensional real-scene digital retention model of the pressure vessel, so as to provide a basic reference for the overlay of periodic inspection data and accident tracing, and form a spatial association relationship between the manufacturing supervision and inspection data and the equipment entity. Step S5: Map the in-use periodic inspection data generated from different inspection cycles to a unified spatial coordinate system, and carry out spatial overlay, change identification and evolution analysis of multi-period inspection data to provide historical change basis for risk identification and accident investigation. Step S5': Conduct post-accident on-site data re-collection and spatial registration before and after the accident; Step S6: During the accident investigation phase, the on-site data after the accident is spatially registered with the three-dimensional real-scene digital record model of the pressure vessel before the accident, and the manufacturing supervision and inspection data, in-use periodic inspection data, historical defect records and on-site evidence after the accident are associated to construct a spatial evidence chain for the accident investigation. The spatial evidence chain for the accident investigation is used to uniformly trace the digital record before the accident, historical inspection data and on-site information after the accident. Step S7: Based on the spatial correlation of manufacturing supervision and inspection data, the evolution analysis results of multi-period inspection data, and the spatial evidence chain of accident investigation, output the spatial management of inspection data and the results of accident tracing.

[0023] This invention further provides a spatial correlation system for digital record keeping and inspection data throughout the entire life cycle of pressure vessels based on spatial intelligence technology, such as... Figure 6 As shown, it includes: Data acquisition module 1 is used to acquire one or more of the following: on-site collected data of pressure vessels, manufacturing supervision and inspection data, in-service periodic inspection data, and accident investigation data. Three-dimensional digital retention module 2 is used to construct a three-dimensional real-scene digital retention model of the pressure vessel based on field-collected data, control point information and pressure vessel structural parameters. Spatial coordinate management module 3 is used to establish the overall coordinate system, local coordinate system and multi-period data registration relationship of the equipment; Spatial semantic modeling module 4 is used to establish a spatial semantic association model between equipment entities, key components, weld locations, inspection records, original data and accident evidence. Inspection data spatial calibration module 5 is used to calibrate manufacturing supervision inspection data and in-use periodic inspection data to the corresponding entity parts in the three-dimensional real scene digital retention model; Multi-period evolution analysis module 6 is used to perform spatial overlay, change identification, trend analysis, and risk focus identification on data from different testing periods. Accident Evidence Chain Module 7 is used to spatially correlate the digital record model before the accident, historical inspection data and on-site evidence after the accident to form a spatial evidence chain for accident investigation. The visualization traceability module 8 is used to display 3D models, spatial annotations, inspection data, defect evolution process and accident evidence chain, and supports bidirectional retrieval and result export. Data archiving and version management module 9 is used to record data source, upload time, operator, version information, file verification information and data status.

[0024] Compared with existing technologies, the spatial correlation method and system for digital record keeping and inspection data of pressure vessels throughout their entire life cycle based on spatial intelligence technology, as described in this invention, has the following advantages: The method and system for spatial association of pressure vessel full life cycle digital record and inspection data based on spatial intelligence technology provided in this invention realizes the transformation of pressure vessel inspection data from traditional two-dimensional data management to three-dimensional spatial expression, visual traceability, and intelligent assisted analysis by constructing a three-dimensional real-scene digital record model of the pressure vessel, a spatial semantic association model of inspection data, a spatial comparative analysis method for multi-period inspection data, and a spatial evidence chain for accident investigation. That is, this application uses the three-dimensional real-scene digital record model of the pressure vessel as a unified spatial carrier, the spatial semantic association model as the data organization basis, the three-dimensional spatial calibration of inspection data as the core connection mechanism, the spatial comparison of multi-period inspection data and artificial intelligence-assisted evolutionary analysis as analysis means, and the spatial evidence chain for accident investigation as the traceability target, thereby realizing the spatial management of pressure vessel supervision inspection, periodic inspection, and accident investigation data throughout their entire life cycle. Specific analysis reveals that constructing a 3D real-scene digital record model of pressure vessels can provide a unified spatial carrier for inspection data throughout the entire life cycle of pressure vessels; establishing a spatial semantic association model for pressure vessels can achieve hierarchical associations between equipment entities, key components, weld locations, inspection records, original data, and accident evidence; forming a 3D spatial calibration method for manufacturing supervision and inspection data can achieve precise correspondence between physical parts such as welds, nozzles, manholes, heads, cylinders, and supports and RT films, TOFD maps, phased array detection maps, material data, rework records, and inspection reports; and forming a spatial comparison and evolution of in-use periodic inspection data. The analytical method enables the overlay, comparison, and trend identification of multi-period wall thickness, defect, corrosion, maintenance, and review data in a unified three-dimensional space; the method for constructing and tracing spatial evidence chains for accident investigations enables rapid correlation and visual tracing between pre-accident digital record models, historical inspection data, historical defect records, and post-accident on-site evidence; in summary, the spatial correlation method and system for pressure vessel full life cycle digital record and inspection data based on spatial intelligence technology provided by this invention can effectively improve the efficiency, completeness, and repeatability of pressure vessel inspection data management, historical review, risk analysis, and accident investigation.

[0025] The aforementioned field-collected data may include any one or more of the following: multi-view images of the outer / inner / accessible parts of the pressure vessel, low-altitude visual images, close-up images, panoramic images, video data, control point coordinates, scale information, collection pose information, collection time, collection personnel, equipment status, and collection point number. The aforementioned manufacturing supervision and inspection data may include any one or more of the following: pressure vessel product quality certificate, material quality certificate, equipment general drawing, weld layout drawing, welding process documents, welder qualification information, heat treatment records, RT film, TOFD spectrum, phased array test spectrum, ultrasonic test records, rework records, inspection reports, and relevant witness documents for supervision and inspection. The aforementioned periodic inspection data may include any one or more of the following: macroscopic inspection records, wall thickness measurement data, magnetic particle testing data, penetrant testing data, ultrasonic testing data, radiographic testing data, corrosion pit measurement data, dent or deformation measurement data, defect review records, maintenance records, operation and maintenance records, and periodic inspection reports. The aforementioned accident investigation data may include any one or more of the following: post-accident scene images, descriptions of the accident location, images of the damaged area, images of the fracture or leakage location, on-site investigation records, interview records, historical data retrieval records, evidence lists, and accident cause analysis related materials.

[0026] Specifically, the above-mentioned three-dimensional real-scene digital model of the pressure vessel is constructed based on the pressure vessel's entire life cycle data, such as... Figure 2 As shown, the specific steps may include: Step S21: Based on the structural characteristics of the pressure vessel and the needs of the inspection business, determine the key collection objects, which may include any one or more of the following: shell, head, nozzle, manhole, reinforcing ring, support, saddle, skirt, weld, flange connection area, corrosion-sensitive area and historical defect area; Step S22: Set up control points, coded markers, or scales on or around the pressure vessel body and record their spatial locations; for large spherical tanks, horizontal containers, vertical containers, or complex connection structures, establish spatial references by combining one or more of the following: equipment axis, circumferential angle, axial distance, elevation information, or on-site measurement points. Step S23: Acquire multi-view images using low-altitude visual, close-range photogrammetry, or mobile acquisition methods; ensure sufficient overlap between adjacent images during acquisition, and take local supplementary photos of welds, pipe roots, manhole edges, reinforcing rings, corrosion-sensitive areas, and historical defect areas; Step S24: Filter the acquired images and remove blurry, overexposed, underexposed, insufficiently overlapping or severely occluded data; Step S25: Perform camera pose estimation and sparse point cloud reconstruction based on multi-view geometric methods, and optimize multi-view geometric consistency through bundle adjustment. Steps S26-S29: Based on the three-dimensional Gaussian sputtering method, the overall structure, surface texture, weld area and key parts of the pressure vessel are expressed in high-fidelity three-dimensional form to form the three-dimensional real scene digital model of the pressure vessel. The real appearance, spatial structure and key part texture information of the pressure vessel are retained as a unified spatial base for subsequent inspection data spatial calibration, periodic inspection data superposition and accident investigation tracing. Meanwhile, when establishing a unified spatial coordinate system corresponding to the pressure vessel entity, for cylindrical pressure vessels, the equipment axis can be used as the main reference axis, and the positions of welds and key parts can be described by axial distance, circumferential angle, and radial position; for spherical pressure vessels, spatial description relationships can be established using the center of the sphere, azimuth angle, height, or latitude and longitude spatial parameters; for locally complex structures such as nozzles, manholes, reinforcing rings, supports, and flanges, local coordinate systems can be established, and the local coordinate systems can be transformed with the global coordinate system. A unified spatial coordinate system can be determined by comprehensively utilizing control point coordinates, equipment design dimensions, weld layout diagrams, actual measurement data, and model reconstruction results. For three-dimensional data re-acquired in different inspection cycles, registration with the initial digital baseline model can be performed through control point constraints, feature point matching, or iterative nearest point methods to ensure that data from multiple periods are superimposed and compared under the same spatial reference.

[0027] Furthermore, the aforementioned spatial semantic association model may include at least the following levels: The equipment entity layer is used to record one or more of the following: pressure vessel equipment number, equipment name, equipment type, design parameters, manufacturing unit, user unit, and equipment status information. Critical component layer, which records information on one or more of the following components: shell, head, nozzle, manhole, reinforcing ring, support, flange, skirt, saddle and safety accessories; The weld and critical component layer is used to record any one or more of the following spatial objects: circumferential welds, longitudinal welds, nozzle welds, fillet welds, reinforcing ring welds, repair areas, historical defect areas, and key monitoring areas. The detection recording layer is used to record any one or more of the following: RT film, TOFD spectrum, phased array detection spectrum, ultrasonic detection record, magnetic particle detection record, penetrant detection record, wall thickness measurement record, and macroscopic inspection record; The original data layer is used to record any one or more of the following: material quality certificates, welding process documents, heat treatment records, inspection reports, rework records, periodic inspection reports, and operation and maintenance records; The accident evidence layer is used to record one or more of the following: accident location, post-accident scene images, damaged areas, historical defects, accident investigation records, and evidence list. In addition, this application generates unique spatial identifiers for welds, inspection points, defect areas, material zones, repair areas, and accident evidence; these unique spatial identifiers are mapped to any one or more of the following: weld number, inspection record number, film number, report number, material batch number, defect number, and accident evidence number, thereby effectively enabling bidirectional traceability between the three-dimensional model and the data. This two-way traceability can include: retrieving corresponding inspection data from welds, inspection points, defect areas, or key parts in the three-dimensional real-scene digital retention model of the pressure vessel, and reversing the location to the corresponding physical part in the three-dimensional real-scene digital retention model by weld number, film number, inspection record number, report number, or accident evidence number.

[0028] Furthermore, the aforementioned manufacturing supervision and inspection data are compared with the corresponding physical parts in the three-dimensional digital model of the pressure vessel using three-dimensional spatial calibration, such as... Figure 3 As shown, the specific steps may include: Steps S41-S42: For the circumferential and longitudinal welds of the cylinder, generate corresponding spatial curves in the three-dimensional model based on the weld layout diagram, equipment axis, axial distance, circumferential angle, and elevation information; for the head splice welds, nozzle welds, manhole welds, reinforcing ring welds, and support welds, establish a local coordinate system based on local structural features, and use local geometric constraints to achieve refined calibration; Steps S43-S45: For non-destructive testing data such as RT films, TOFD maps, phased array test maps, and ultrasonic test records, the system associates the test data with the corresponding weld or weld section based on the weld number, test section number, film number, test location, and test ratio; for material quality certificates, heat treatment records, repair records, and inspection reports, the system associates them with the corresponding components or spatial areas based on the component number, material batch number, weld number, or report number. Steps S46-S49: After calibration, the user can click on a weld, a pipe, or a key part in the 3D model, and the system will automatically retrieve the RT film, TOFD map, phased array test map, material data, repair records, and inspection reports related to that location. The user can also reverse locate the corresponding entity in the 3D model by entering the weld number, film number, report number, or test record number.

[0029] Furthermore, the above-mentioned process maps in-use periodic inspection data from different inspection cycles to a unified spatial coordinate system, and conducts spatial overlay, change identification, and evolution analysis of multi-period inspection data, such as... Figure 4As shown, the specific steps may include: Steps S51-S53: For wall thickness measurement data, each measurement point is recorded as a spatial point object, which may include any one or more of the following: spatial coordinates, detection time, thickness value, detection method, detection personnel, and detection report number; for linear defects, they are recorded as spatial line objects, which may include any one or more of the following: start point, end point, length, direction, defect type, and risk level; for planar corrosion, depressions, or deformation areas, they are recorded as spatial planar objects, which may include any one or more of the following: boundary range, maximum depth, average value, area, risk level, and detection cycle. Steps S54-S55: The system establishes multi-period data layers according to the inspection cycle and supports overlay display by time, inspection method, defect type, weld number, and risk level; the system can calculate one or more of the following: wall thickness change rate, defect size change, defect location change, recurrence after repair, and risk level change at the same measuring point or the same location, and classify the defect status into any one of the following: new, expanded, stable, reduced, or eliminated; Preferably, such as Figure 4 As shown, this application may further include the following steps: Steps S56-S58, using an artificial intelligence-assisted spatiotemporal feature analysis method to identify trends in multi-period inspection data; the method takes spatial location features, inspection time features, defect attribute features, maintenance record features, and inspection method features as inputs, constructs a unified feature expression through spatial location encoding, time encoding, and defect feature embedding, and uses a spatiotemporal feature analysis model based on the Transformer architecture to learn the correlation between multi-period inspection data, and outputs defect evolution trends, abnormal change locations, and key risk concern areas; To improve the transparency and verifiability of the analysis results, interpretable artificial intelligence methods can be used to visualize key features or key areas that affect risk assessment, assisting inspectors in understanding the analysis results. Of course, while the artificial intelligence analysis results serve as an auxiliary basis for judgment, the final inspection conclusions are still confirmed by inspectors based on regulations, standards, on-site conditions, and test results.

[0030] Furthermore, the aforementioned construction of a spatial evidence chain for accident investigation, such as... Figure 5 As shown, the specific steps may include: Steps S61-S63: Collect one or more of the following: post-accident scene images, photos of damaged areas, images of fractured or leaking parts, images of deformed areas, photos of accident-related components, and on-site investigation records; construct post-accident scene spatial information or a post-accident local 3D model based on the post-accident scene data; Step S64: Spatial registration is performed between the post-accident site model or post-accident site image and the pre-accident three-dimensional real-scene digital retention model of the pressure vessel; the registration method adopts feature point matching, control point constraint, manual marker point correspondence, structural boundary matching or iterative nearest point method to ensure that the data before and after the accident are under the same spatial reference. Steps S65-S68: Based on the spatial location of the accident site, the system automatically associates the manufacturing supervision and inspection data, periodic inspection data, historical defect records, repair and rework records, operation and maintenance records, and on-site evidence after the accident with the corresponding location, forming a spatial evidence chain of "accident site - historical inspection records - original data - defect evolution process - accident on-site evidence". Step S69: The system outputs one or more of the following: a 3D spatial screenshot of the accident-related part, a list of historical inspection data, a description of defect evolution, a comparison diagram before and after the accident, an index of on-site evidence, and a traceability report, providing technical support for accident cause analysis, regulatory verification, and rectification decisions.

[0031] The following is a detailed description of the workflow of the spatial correlation system for digital record-keeping and inspection data throughout the entire lifecycle of pressure vessels based on spatial intelligence technology, provided in this embodiment of the invention: Select pressure vessels to be documented and compile their design data, manufacturing data, weld layout diagrams, supervision and inspection data, and existing periodic inspection data. Develop a 3D real-scene data acquisition plan, set up control points and scale references, and acquire multi-view images of the overall structure, welds and key parts of the pressure vessel. Based on the acquired images and control point information, a three-dimensional digital model of the pressure vessel was constructed, and a unified spatial coordinate system was established. A spatial semantic association model is established based on the equipment structure and inspection business logic to set unique spatial identifiers for welds, inspection points, defect areas and accident evidence. Manufacturing supervision and inspection data are calibrated into a three-dimensional model to achieve two-way traceability between welds, inspection data, material data, and inspection reports; In subsequent periodic inspections, the newly generated wall thickness, defect, corrosion, repair and re-inspection data will be mapped to a unified spatial coordinate system to carry out multi-period data overlay and evolution analysis; When an accident or abnormal event occurs, collect on-site data after the accident and spatially register it with the digital record model before the accident. By linking historical inspection data, defect evolution records, and on-site evidence corresponding to the accident site, a spatial evidence chain for accident investigation is formed; Outputs 3D visualization traceability results, historical data list, accident-related evidence package, and auxiliary analysis results.

[0032] The spatial intelligence mentioned in the method and system for spatial association of digital record and inspection data of pressure vessels throughout their entire life cycle based on spatial intelligence technology provided in this invention refers to the technical method of organizing information such as the physical structure of the pressure vessel, weld location, key parts, inspection records, original data, historical defects and accident evidence into a three-dimensional space, and performing association, retrieval, analysis and traceability based on spatial positional relationships, temporal relationships and business semantic relationships. Furthermore, this application is not simply about 3D modeling or 3D display, but rather uses a 3D real-world model as a unified carrier for spatial association of pressure vessel inspection data throughout its entire lifecycle; this application is not simply about managing non-destructive testing data, but rather about establishing a two-way association between non-destructive testing data, weld numbers, material data, repair records, and inspection reports and the spatial location of the pressure vessel entity; this application is not simply about AI defect identification, but rather about using artificial intelligence-assisted analysis for spatiotemporal evolution analysis of multi-period inspection data and identification of areas of risk concern; this application is not about general accident data archiving, but rather about constructing a spatial evidence chain based on pre-accident digital retention models, historical inspection data, and post-accident on-site evidence; this application is not a general digital twin platform, but rather a spatial intelligent inspection data organization and traceability method for pressure vessel supervision and inspection, periodic inspection, and accident investigation.

[0033] In summary, the method and system for spatial correlation of digital record-keeping and inspection data throughout the entire lifecycle of pressure vessels based on spatial intelligence technology provided in this embodiment of the invention have the following main advantages: I. Achieving three-dimensional spatial management of pressure vessel inspection data: This application organizes the physical structure of the pressure vessel, weld locations, inspection records, and original data into a unified three-dimensional space, overcoming the technical problem that the spatial relationship between traditional two-dimensional reports, films, drawings, and photographs is not intuitive. II. Improve the efficiency of manufacturing supervision and inspection data verification: This application establishes a spatial association between RT film, TOFD charts, phased array test charts, material data, rework records and inspection reports and welds and key parts in the three-dimensional model, enabling inspection personnel to quickly complete data retrieval, spatial positioning and result verification. III. Support for periodic verification and comparison of data from multiple periods: This application can overlay wall thickness, defects, corrosion, repair and re-inspection data from different inspection cycles into a unified spatial coordinate system, which facilitates the identification of new defects, defect expansion, wall thickness reduction and changes in risk level; IV. Improve the efficiency of evidence collection and tracing in accident investigations: This application can quickly link the manufacturing supervision and inspection data, periodic inspection data, historical defect records and on-site evidence corresponding to the accident site during the accident investigation stage, forming a spatial evidence chain, thereby effectively improving the completeness and verifiability of accident investigation data; V. Enhance the ability to retain and reproduce test data over a long period of time: This application enables long-term preservation, continuous updating, and repeated verification of data from different life stages of pressure vessels through three-dimensional real-scene digital record keeping, spatial identification, version management, and data archiving. VI. Enhance risk-oriented testing and regulatory decision support capabilities: This application provides auxiliary basis for determining key inspection sites, detection methods, and risk concerns in the next cycle by spatial comparison of multi-period inspection data and artificial intelligence-assisted evolutionary analysis; VII. It has good adaptability for promotion: This application is not only applicable to pressure vessels such as spherical tanks, horizontal containers, and vertical containers, but can also be extended to other special equipment inspection and supervision scenarios such as boilers, pressure pipelines, and large storage tanks, depending on business needs.

[0034] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for spatially correlating digital records and inspection data throughout the entire lifecycle of pressure vessels based on spatial intelligence technology, characterized in that, Includes the following steps: Step S1: Obtain pressure vessel lifecycle data, which includes: field-collected data, manufacturing supervision and inspection data, in-service periodic inspection data, and accident investigation data. The pressure vessel lifecycle data is formatted, fields are extracted, numbered uniformly, and manually reviewed to form a data set that can be used for spatial calibration and correlation analysis. Step S2: Construct a three-dimensional real-scene digital model of the pressure vessel based on the full life cycle data of the pressure vessel, and establish a unified spatial coordinate system corresponding to the pressure vessel entity. The three-dimensional real-scene digital model of the pressure vessel is used to provide a spatial base for subsequent spatial calibration. Step S3: Construct a spatial semantic association model of the pressure vessel based on the three-dimensional real-scene digital retention model of the pressure vessel. The spatial semantic association model is used to establish unified organization rules for different types of data and to characterize the relationship between equipment entities, key components, weld positions, inspection records, original data and accident evidence. Step S4: Based on the unified spatial coordinate system and the spatial semantic association model, perform three-dimensional spatial calibration of the manufacturing supervision and inspection data and the corresponding entity parts in the three-dimensional real scene digital record model of the pressure vessel, so as to provide a basic reference for the overlay of periodic inspection data and accident tracing, and form a spatial association relationship between the manufacturing supervision and inspection data and the equipment entity. Step S5: Map the in-use periodic inspection data generated from different inspection cycles to the unified spatial coordinate system, and carry out spatial overlay, change identification and evolution analysis of multi-period inspection data to provide historical change basis for risk identification and accident investigation; Step S6: During the accident investigation phase, the on-site data after the accident is spatially registered with the three-dimensional real-scene digital record model of the pressure vessel before the accident, and the manufacturing supervision and inspection data, the in-use periodic inspection data, historical defect records and on-site evidence after the accident are associated to construct a spatial evidence chain for the accident investigation. The spatial evidence chain for the accident investigation is used to uniformly trace the digital record before the accident, the historical inspection data and the on-site information after the accident. Step S7: Based on the spatial correlation of manufacturing supervision and inspection data, the evolution analysis results of multi-period inspection data, and the spatial evidence chain of the accident investigation, output the spatial management of inspection data and the accident tracing results.

2. The method for spatial correlation of digital record-keeping and inspection data throughout the entire lifecycle of pressure vessels based on spatial intelligence technology as described in claim 1, characterized in that, The step S5 is followed by step S5', which involves collecting post-accident field data and spatial registration before and after the accident.

3. The method for spatial correlation of digital record-keeping and inspection data throughout the entire lifecycle of pressure vessels based on spatial intelligence technology, as described in claim 1 or 2, is characterized in that... The on-site data collected includes any one or more of the following: multi-view images of the outer / inner / accessible parts of the pressure vessel, low-altitude visual images, close-up images, panoramic images, video data, control point coordinates, scale information, collection pose information, collection time, collection personnel, equipment status, and collection point number. The manufacturing supervision and inspection data includes any one or more of the following: pressure vessel product quality certificate, material quality certificate, equipment general drawing, weld layout drawing, welding process documents, welder qualification information, heat treatment record, RT film, TOFD spectrum, phased array test spectrum, ultrasonic test record, rework record, inspection report, and relevant witness documents for supervision and inspection. The in-use periodic inspection data includes any one or more of the following: macroscopic inspection records, wall thickness measurement data, magnetic particle testing data, penetrant testing data, ultrasonic testing data, radiographic testing data, corrosion pit measurement data, dent or deformation measurement data, defect review records, maintenance records, operation and maintenance records, and periodic inspection reports. The accident investigation data includes any one or more of the following: post-accident scene images, descriptions of the accident location, images of the damaged area, images of the fracture or leakage location, on-site investigation records, interview records, historical data retrieval records, evidence lists, and accident cause analysis related materials.

4. The method for spatial correlation of digital record-keeping and inspection data throughout the entire lifecycle of pressure vessels based on spatial intelligence technology according to claim 3, characterized in that, The construction of a three-dimensional digital model of the pressure vessel based on the pressure vessel's entire lifecycle data specifically includes the following steps: Based on the structural characteristics of pressure vessels and the needs of inspection operations, key collection targets are identified, including: any one or more of the following: shell, head, nozzle, manhole, reinforcing ring, support, saddle, skirt, weld, flange connection area, corrosion-sensitive area, and historical defect area. Control points, coded markers, or scales are set up on or around the pressure vessel body, and their spatial locations are recorded. For large spherical tanks, horizontal containers, vertical containers, or complex connection structures, spatial references are established by combining one or more of the following: equipment axis, circumferential angle, axial distance, elevation information, or on-site measurement points. Multiple-view images are acquired using low-altitude visual, close-range photogrammetry, or mobile acquisition methods. During acquisition, sufficient overlap between adjacent images is ensured, and local re-images are taken of welds, pipe roots, manhole edges, reinforcing rings, corrosion-sensitive areas, and areas with historical defects. The acquired images are screened to remove blurry, overexposed, underexposed, insufficiently overlapping, or severely occluded data; camera pose estimation and sparse point cloud reconstruction are performed based on multi-view geometric methods, and multi-view geometric consistency is optimized through bundle adjustment. Based on the three-dimensional Gaussian sputtering method, the overall structure, surface texture, weld area and key parts of the pressure vessel are expressed in high-fidelity three-dimensional form to form a three-dimensional real-scene digital model of the pressure vessel. The model retains the real appearance, spatial structure and texture information of key parts of the pressure vessel, which can serve as a unified spatial base for subsequent spatial calibration of inspection data, overlay of periodic inspection data and traceability of accident investigation.

5. The method for spatial correlation of digital record-keeping and inspection data throughout the entire lifecycle of pressure vessels based on spatial intelligence technology according to claim 4, characterized in that, The spatial semantic association model includes at least the following levels: The equipment entity layer is used to record any one or more of the following: pressure vessel equipment number, equipment name, equipment type, design parameters, manufacturing unit, user unit, and equipment status information. The critical component layer is used to record information on any one or more of the following components: cylinder, head, nozzle, manhole, reinforcing ring, support, flange, skirt, saddle, and safety accessories. The weld and critical component layer is used to record any one or more of the following spatial objects: circumferential welds, longitudinal welds, nozzle welds, fillet welds, reinforcing ring welds, repair areas, historical defect areas, and key monitoring areas. The detection recording layer is used to record any one or more of the following: RT film, TOFD spectrum, phased array detection spectrum, ultrasonic detection record, magnetic particle detection record, penetrant detection record, wall thickness measurement record, and macroscopic inspection record; The original data layer is used to record any one or more of the following: material quality certificates, welding process documents, heat treatment records, inspection reports, rework records, periodic inspection reports, and operation and maintenance records. The accident evidence layer is used to record one or more of the following: accident location, post-accident scene images, damaged area, historical defects, accident investigation records, and evidence list.

6. The method for spatial correlation of digital record-keeping and inspection data throughout the entire lifecycle of pressure vessels based on spatial intelligence technology according to claim 5, characterized in that, The process of performing three-dimensional spatial calibration between the manufacturing supervision and inspection data and the corresponding physical parts in the three-dimensional digital model of the pressure vessel includes the following steps: For the circumferential and longitudinal welds of the cylinder, corresponding spatial curves are generated in the three-dimensional model based on the weld layout diagram, equipment axis, axial distance, circumferential angle and elevation information; for the head splice welds, nozzle welds, manhole welds, reinforcing ring welds and support welds, local coordinate systems are established based on local structural features, and local geometric constraints are used to achieve fine calibration. For non-destructive testing data such as RT films, TOFD maps, phased array test maps, and ultrasonic test records, the system associates the test data with the corresponding weld or weld section based on the weld number, test section number, film number, test location, and test ratio. For material quality certificates, heat treatment records, repair records, and inspection reports, the system associates them with the corresponding components or spatial areas based on the component number, material batch number, weld number, or report number. After calibration, users can click on a weld, a pipe, or a key part in the 3D model, and the system will automatically retrieve the RT film, TOFD map, phased array test map, material data, repair records, and inspection reports related to that location. Users can also reverse locate the corresponding entity in the 3D model by entering the weld number, film number, report number, or test record number.

7. The method for spatial correlation of digital record-keeping and inspection data throughout the entire lifecycle of pressure vessels based on spatial intelligence technology according to claim 6, characterized in that, The process of mapping in-use periodic inspection data generated from different inspection cycles to the unified spatial coordinate system, and conducting spatial overlay, change identification, and evolution analysis of multi-period inspection data, specifically includes the following steps: For wall thickness measurement data, each measurement point is recorded as a spatial point object, including one or more of the following: spatial coordinates, measurement time, thickness value, measurement method, measurement personnel, and measurement report number; for linear defects, they are recorded as spatial line objects, including one or more of the following: start point, end point, length, direction, defect type, and risk level; for planar corrosion, depressions, or deformed areas, they are recorded as spatial planar objects, including one or more of the following: boundary range, maximum depth, average value, area, risk level, and measurement cycle. The system establishes multiple data layers according to the inspection cycle and supports overlay display by time, inspection method, defect type, weld number and risk level. The system can calculate one or more of the following: wall thickness change rate, defect size change, defect location change, recurrence after repair and risk level change at the same measuring point or the same location, and classify the defect status as any one of the following: new, expanded, stable, reduced or eliminated.

8. The method for spatial correlation of digital record-keeping and inspection data throughout the entire lifecycle of pressure vessels based on spatial intelligence technology according to claim 7, characterized in that, The construction of the spatial evidence chain for accident investigation specifically includes the following steps: Collect one or more of the following: post-accident scene images, photos of damaged areas, images of fractured or leaking parts, images of deformed areas, photos of accident-related components, and on-site investigation records; construct post-accident scene spatial information or a post-accident local 3D model based on the post-accident scene data. Spatial registration is performed between the post-accident site model or post-accident site image and the pre-accident 3D digital real-scene model of the pressure vessel. The registration method adopts feature point matching, control point constraint, manual marker point correspondence, structural boundary matching or iterative nearest point method to ensure that the data before and after the accident are under the same spatial reference. Based on the spatial location of the accident site, the system automatically associates the corresponding manufacturing supervision and inspection data, periodic inspection data, historical defect records, repair and rework records, operation and maintenance records, and on-site evidence after the accident, forming a spatial evidence chain of "accident site - historical inspection records - original data - defect evolution process - accident site evidence". The system outputs one or more of the following: 3D spatial screenshots of the accident-related parts, a list of historical inspection data, a description of defect evolution, before-and-after comparison diagrams of the accident, an index of on-site evidence, and a traceability report, providing technical support for accident cause analysis, regulatory verification, and rectification decisions.

9. A spatial correlation system for digital record keeping and inspection data throughout the entire life cycle of pressure vessels based on spatial intelligence technology, characterized in that, include: The data acquisition module is used to acquire one or more of the following: pressure vessel field-collected data, manufacturing supervision and inspection data, in-service periodic inspection data, and accident investigation data. A three-dimensional digital retention module is used to construct a three-dimensional real-scene digital retention model of the pressure vessel based on field-collected data, control point information, and pressure vessel structural parameters. A spatial coordinate management module is used to establish the overall coordinate system, local coordinate system, and multi-period data registration relationship of the equipment. The spatial semantic modeling module is used to establish a spatial semantic association model between equipment entities, key components, weld locations, inspection records, original data, and accident evidence. The inspection data spatial calibration module is used to calibrate manufacturing supervision inspection data and in-use periodic inspection data to the corresponding entity parts in the three-dimensional real-scene digital retention model; The multi-period evolution analysis module is used to perform spatial overlay, change identification, trend analysis, and risk focus identification on data from different testing periods. The accident evidence chain module is used to spatially correlate the digital record model before the accident, historical inspection data and on-site evidence after the accident to form a spatial evidence chain for accident investigation. The visualization traceability module is used to display three-dimensional models, spatial annotations, inspection data, defect evolution process and accident evidence chain, and supports bidirectional retrieval and result export; The data archiving and version management module is used to record data source, upload time, operator, version information, file verification information, and data status.

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