An engineering structure measurement method, system, storage medium and program product

Abstract

The application provides an engineering structure measurement method and system, a storage medium and a program product, and relates to the technical field of engineering measurement. The method comprises the following steps: performing initialization processing on obtained three-dimensional model data of an engineering structure, engineering construction coordinate system parameters and user role information, to obtain a digital measurement basis environment; performing automatic multi-source data collection and processing on a construction site according to the digital measurement basis environment, to obtain original measurement data; performing data quality optimization processing on the original measurement data, to obtain target measurement data; performing spatial correlation analysis processing on the target measurement data and the digital measurement basis environment, to obtain a spatial correlation analysis result; performing multi-dimensional visual display processing on the spatial correlation analysis result, to obtain a multi-dimensional visual result; and when a user operation instruction is received, performing interactive operation and maintenance processing on the multi-dimensional visual result and the user operation instruction, to obtain an interactive operation and maintenance result.

Description

Technical Field

[0001] This application relates to the field of engineering measurement technology, and in particular to a method, system, storage medium and program product for actual measurement of engineering structures. Background Technology

[0002] With the continuous expansion of engineering construction scale and the increasing demands for refined management, actual measurement of engineering structures has become a core aspect of engineering quality control. Currently, the engineering surveying field widely adopts high-precision equipment such as total stations and 3D laser scanners for on-site data collection, and converts the massive amounts of collected measurement data into electronic spreadsheets or basic databases for storage. The biggest feature of this digital acquisition model is the realization of electronic archiving of measurement data, providing basic data support for the traceability of engineering quality and meeting the basic needs of conventional engineering projects for data recording and post-event review.

[0003] Currently, there are two main methods for achieving actual measurement of engineering structures in related technologies: one is manual measurement, where operators use equipment such as total stations and levels to measure the construction site of the project point by point and record the measurement data in Excel spreadsheets or paper documents (e.g., 2D CAD drawings). The other is basic GIS visualization, which uses the measurement data collected by the aforementioned manual measurement method as the data source. Operators first manually enter the raw measurement data into a basic general relational database for centralized storage. Then, the map loading module imports the 2D image or planar vector base map of the construction site corresponding to the project. Based on the planar physical location (such as coordinate information) of each measurement point recorded during the on-site measurement, operators use the annotation module to manually create annotation points on the 2D map corresponding to the actual physical measurement locations. Finally, the measured values ​​stored in the basic general relational database are associated and bound with the corresponding annotation points, thereby achieving basic electronic spatial display.

[0004] However, when using the above two methods, if the spatial shape of the engineering structure is complex (such as multiple overlapping layers, curved irregular shapes, etc.), it is difficult for operators to accurately determine the structural surface to which each measurement point belongs in three-dimensional space and the direction of deviation based solely on discrete numerical records or two-dimensional plane projection. This results in low reliability of actual measurement of complex spatial structures in related technologies. Summary of the Invention

[0005] This application provides a method, system, storage medium, and program product for actual measurement of engineering structures, which can improve the reliability of actual measurement of complex spatial structures.

[0006] Firstly, this application provides a method for actual measurement of engineering structures, applied to an engineering structure measurement GIS system. The method includes: acquiring 3D model data of the engineering structure, engineering construction coordinate system parameters, and user role information; performing basic environment initialization processing on the 3D model data, engineering construction coordinate system parameters, and user role information to obtain a digital measurement foundation environment; automatically collecting and processing multi-source data from the construction site of the engineering structure based on the digital measurement foundation environment to obtain raw measurement data; performing data quality optimization processing on the raw measurement data to obtain target measurement data; performing spatial correlation analysis processing on the target measurement data and the digital measurement foundation environment to obtain spatial correlation analysis results; performing multi-dimensional visualization processing on the spatial correlation analysis results to obtain multi-dimensional visualization results; and upon receiving user operation instructions, performing interactive operation and maintenance processing on the multi-dimensional visualization results and user operation instructions to obtain interactive operation and maintenance results.

[0007] By adopting the above technical solution, a digital measurement and verification environment is constructed by initializing the 3D model data of the engineering structure. This enables subsequent measurement data to establish a precise spatial correspondence with the specific components of the engineering structure in 3D space. Raw measurement and verification data containing spatial coordinate information is obtained through automated multi-source data acquisition and processing. Data quality optimization eliminates environmental interference and the influence of abnormal data. Spatial correlation analysis then precisely matches and associates the optimized measurement and verification data with the components in the 3D model, ensuring that each measurement point is accurately assigned to its corresponding structural surface in 3D space. Furthermore, multi-dimensional visualization and interactive operation and maintenance processes allow for intuitive determination of the spatial assignment and deviation direction of measurement points in 3D space. This solves the technical problem of low reliability in measurement and verification of complex spatial structures in related technologies, achieving a significant improvement in the reliability of measurement and verification of complex spatial structures.

[0008] Optionally, the system acquires the 3D model data of the engineering structure, engineering construction coordinate system parameters, and user role information, and performs basic environment initialization processing on the 3D model data, engineering construction coordinate system parameters, and user role information to obtain a digital measurement and verification basic environment. This includes: deploying a spatial database and a business database on the first server, and creating core data tables in the spatial database and business database; configuring daily automatic incremental backup program tasks and weekly full backup program tasks for the spatial database and business database, and setting off-site backup storage paths for the daily automatic incremental backup program tasks and weekly full backup program tasks; calling the format parsing interface in the GIS server to parse the 3D model data to obtain 3D scene data. When oblique photogrammetry data exists in the 3D model data, the 3D model data is converted into 3D tile data according to a preset tile resolution; and performing basic environment integration processing based on the core data table, 3D scene data and / or 3D tile data, engineering construction coordinate system parameters, and user role information to obtain a digital measurement and verification basic environment.

[0009] By adopting the above technical solution, a spatial database and a business database are deployed on the first server and a core data table is created to provide a structured storage foundation for the measured data. By configuring automatic incremental backup and full backup program tasks and setting off-site backup storage paths, the security and recoverability of the measured data are ensured. The format parsing interface is called in the GIS server to parse the format of the 3D model data, and when oblique photogrammetry data exists, it is converted into 3D tile data according to the preset slice resolution, thereby realizing the unified processing of multi-source 3D model data.

[0010] Optionally, a basic environment integration process is performed based on the core data table, 3D scene data and / or 3D tile data, engineering construction coordinate system parameters, and user role information to obtain a digital measurement and verification basic environment. This includes: acquiring the engineering construction coordinate system parameters input by surveyors through the first client device, performing coordinate system transformation mapping on the engineering construction coordinate system parameters, generating a coordinate transformation parameter file, and storing the coordinate transformation parameter file in a spatial database; calculating model weights based on the spatial attribute parameters and graphic attribute parameters of the 3D model data to obtain 3D model weights; using a preset weighted quadtree algorithm to perform spatial indexing and partitioning of the 3D model data according to the 3D model weights to generate a spatial index file; acquiring user role information, and using a role access control model to create accounts for user role information to obtain multi-level user accounts, and configuring module operation permissions for multi-level user accounts; and integrating and encapsulating the core data table, 3D scene data and / or 3D tile data, coordinate transformation parameter file, spatial index file, and module operation permissions to obtain a digital measurement and verification basic environment.

[0011] By adopting the above technical solution, coordinate system transformation and mapping processing is performed on the engineering construction coordinate system parameters to generate coordinate transformation parameter files, establishing a precise transformation relationship between the geodetic coordinate system and the construction coordinate system. The weight of the 3D model is calculated based on the spatial attribute parameters and graphic attribute parameters of the 3D model data, and spatial indexing is performed using a preset weighted quadtree algorithm, so that the area where the high-weight components are located obtains a finer index granularity, thereby improving the retrieval efficiency and matching accuracy of subsequent measurement points and components. Furthermore, by using a role-based access control model to create multi-level user accounts and configure module operation permissions, the security and operational standardization of measured data in multi-role collaborative scenarios are ensured.

[0012] Optionally, data quality optimization processing is performed on the original measured data to obtain target measured data, including: when a data addition event is detected in the business database, extracting the original measured data, the first environmental parameter, and related information from the business database; storing the original measured data, the first environmental parameter, and related information in a memory buffer; reading the original measured data from the memory buffer and performing outlier detection processing on the original measured data to obtain valid measured data; performing data missing completion processing on the valid measured data to obtain a complete measured data sequence; and performing environmental factor correction processing on the complete measured data sequence and the first environmental parameter to obtain the target measured data.

[0013] By adopting the above technical solution, the system monitors data addition events in the business database and stores the original measured data in a memory buffer, enabling real-time triggering and efficient reading of data processing. It performs outlier detection on the original measured data to remove outliers with excessive deviations, preventing them from interfering with subsequent spatial correlation analysis results. For valid measured data, it performs data completion processing to ensure the integrity of the measured data sequence. Subsequently, it performs environmental factor correction processing on the complete measured data sequence and the first environmental parameter, eliminating the influence of environmental factors such as temperature, humidity, and wind speed on measurement accuracy, thereby improving the accuracy of the target measured data.

[0014] Optionally, spatial correlation analysis is performed on the target measured data and the digital measured environment to obtain spatial correlation analysis results. This includes: reading a coordinate transformation parameter file from a spatial database and converting the target measured data in the geodetic coordinate system to the first measured data in the construction coordinate system based on the coordinate transformation parameter file; obtaining the coordinates of the first measurement point in the first measured data and matching the coordinates of the first measurement point with the components in the 3D model data using a spatial index file to obtain the first component corresponding to the coordinates of the first measurement point; establishing a spatial correlation relationship among the coordinates of the first measurement point, the component identifier of the first component, and the first measured data, and writing the spatial correlation relationship into the spatial database; determining the spatial analysis requirement type of the first measured data based on the spatial correlation relationship to obtain the target spatial analysis requirement type; determining the spatial quality analysis result based on the target spatial analysis requirement type; and using the spatial correlation relationship and the spatial quality analysis result as the spatial correlation analysis result.

[0015] By adopting the above technical solution, the target measured data in the geodetic coordinate system is converted and mapped to the first measured data in the construction coordinate system according to the coordinate transformation parameter file. This achieves spatial alignment between the measurement data and the three-dimensional model in the same coordinate system. The spatial index file is used to match the coordinates of the first measurement point with the components in the three-dimensional model data, realizing the automated and accurate attribution of the measurement point to the specific component. The spatial relationship between the coordinates of the first measurement point, the component identification of the first component, and the first measured data is established, so that each measurement data has clear three-dimensional spatial attribution information. Then, the spatial quality analysis results are determined according to the target spatial analysis requirement type, realizing the quantitative spatial analysis of the quality status of the engineering structure.

[0016] Optionally, the spatial quality analysis results are determined based on the target spatial analysis requirement type, including: if the target spatial analysis requirement type is an anomaly area identification requirement type, the buffer analysis module is called to generate a buffer with a preset radius centered on the coordinates of the first measurement point, and the buffer is overlaid with the component design parameter layer for analysis, so as to filter out the abnormal measured data exceeding the preset design threshold from the first measured data; if the target spatial analysis requirement type is an engineering quantity calculation requirement type, the earthwork calculation module is called to perform cut and fill calculation processing based on the elevation data corresponding to the coordinates of the first measurement point to obtain the engineering quantity analysis results; the spatial correlation and abnormal measured data and / or engineering quantity analysis results are synchronously stored in the business database, and the latest measurement value field and trend prediction value field of the component attribute table in the business database are updated; in the case of abnormal measured data, the anomaly identifier field of the component attribute table is updated to an abnormal status.

[0017] By adopting the above technical solution, under the abnormal area identification requirement type, a buffer with a preset radius is generated with the coordinates of the first measurement point as the center, and the buffer is overlaid and analyzed with the component design parameter layer. This can automatically filter out abnormal measured data that exceed the preset design threshold, realizing the spatial automatic identification of abnormal areas. Under the engineering quantity calculation requirement type, the excavation and filling calculation is performed based on the elevation data, realizing accurate calculation of engineering quantities based on measured data. Then, by synchronously storing the analysis results in the business database and updating the relevant fields of the component attribute table, the real-time updating and persistent storage of component quality status information is realized.

[0018] Optionally, based on the digitally measured and verified environment, multi-source data is automatically collected and processed at the construction site of the engineering structure to obtain raw measured and verified data. This includes: responding to a device access request initiated by construction personnel through a second client device, scanning and connecting to the measurement devices to be connected at the construction site through a preset wireless communication interface to obtain the on-site measurement devices that are communicatively connected to the second client device; in the case of an outdoor construction site, calling a GNSS positioning module to receive satellite signals to obtain the coordinates of the second measurement point, or in the case of an indoor construction site, calling a UWB positioning module to obtain the coordinates of the second measurement point, and based on... The output protocol of the GNSS or UWB positioning module determines the coordinate system type to which the second measurement point coordinates belong; the target measurement parameters are determined based on the second component in the digitally measured environment, and acquisition commands are sent to the field measurement equipment according to the target measurement parameters, and the environmental sensors are simultaneously activated to acquire the second environmental parameters in real time; the measurement data returned by the field measurement equipment through a preset wireless communication interface is received, and the image acquisition module is called to perform image acquisition processing on the measurement part of the second component to obtain field image data; the coordinates of the second measurement point, the component identification of the second component, the second environmental parameters, the measurement data, and the field image data are correlated to generate the original measured data.

[0019] By adopting the above technical solution, a pre-set wireless communication interface is used to scan and connect to the measurement equipment to be connected at the construction site, realizing the automated access of multiple types of measurement equipment. The GNSS positioning module or UWB positioning module is automatically switched to obtain the coordinates of the second measurement point according to the indoor and outdoor scene of the construction site, ensuring the accuracy of the acquisition of the spatial coordinates of the measurement point under different construction environments. According to the target measurement parameters, the acquisition command is sent to the on-site measurement equipment and the environmental sensor is simultaneously activated to collect the second environmental parameters in real time, realizing the synchronous acquisition of measurement data and environmental parameters. Then, the coordinates of the second measurement point, the component identification of the second component, the second environmental parameters, the measurement data, and the on-site image data are associated to generate the original measured data, forming a multi-dimensional data package including spatial location, component ownership, environmental conditions, and on-site images, providing a complete data foundation for subsequent data quality optimization and spatial correlation analysis.

[0020] Secondly, embodiments of this application provide a GIS system for actual measurement of engineering structures. The GIS system for actual measurement of engineering structures includes: one or more processors and a memory; the memory is coupled to one or more processors, and the memory is used to store computer program code, the computer program code including computer instructions, and one or more processors call the computer instructions to cause the GIS system for actual measurement of engineering structures to perform the method described in the first aspect and any possible implementation thereof.

[0021] Thirdly, embodiments of this application provide a computer-readable storage medium including program instructions that, when executed on an engineering structure measurement GIS system, cause the engineering structure measurement GIS system to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, embodiments of this application provide a computer program product containing instructions that, when the computer program product is run on an engineering structure measurement GIS system, causes the engineering structure measurement GIS system to execute the method described in the first aspect and any possible implementation thereof.

[0023] By adopting the above technical solution, a digital measurement and verification environment is constructed by initializing the 3D model data of the engineering structure. This enables subsequent measurement data to establish a precise spatial correspondence with the specific components of the engineering structure in 3D space. Raw measurement and verification data containing spatial coordinate information is obtained through automated multi-source data acquisition and processing. Data quality optimization eliminates environmental interference and the influence of abnormal data. Spatial correlation analysis then precisely matches and associates the optimized measurement and verification data with the components in the 3D model, ensuring that each measurement point is accurately assigned to its corresponding structural surface in 3D space. Furthermore, multi-dimensional visualization and interactive operation and maintenance processes allow for intuitive determination of the spatial assignment and deviation direction of measurement points in 3D space. This solves the technical problem of low reliability in measurement and verification of complex spatial structures in related technologies, achieving a significant improvement in the reliability of measurement and verification of complex spatial structures. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating a method for actual measurement of engineering structures in an embodiment of this application;

[0025] Figure 2 This is a schematic diagram of a physical device structure of the GIS system for actual measurement of engineering structures in the embodiments of this application. Detailed Implementation

[0026] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0027] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0028] This application provides a method for the actual measurement of engineering structures, see reference. Figure 1 , Figure 1 This is a flowchart illustrating a method for actual measurement of engineering structures in an embodiment of this application, including the following steps:

[0029] Step S101: Obtain the three-dimensional model data of the engineering structure, the engineering construction coordinate system parameters, and the user role information, and perform basic environment initialization processing on the three-dimensional model data, engineering construction coordinate system parameters, and user role information to obtain the digital measured basic environment.

[0030] Step S102: Based on the digital measured and actual environment, perform multi-source automated data acquisition and processing on the construction site of the engineering structure to obtain the original measured and actual data.

[0031] Step S103: Perform data quality optimization processing on the original measured data to obtain the target measured data;

[0032] Step S104: Perform spatial correlation analysis on the target measured data and the digital measured environment to obtain the spatial correlation analysis results.

[0033] Step S105: Perform multi-dimensional visualization processing on the spatial correlation analysis results to obtain multi-dimensional visualization results;

[0034] Step S106: Upon receiving a user operation instruction, perform interactive operation and maintenance processing on the multidimensional visualization results and the user operation instruction to obtain the interactive operation and maintenance results.

[0035] Among them, the 3D model data of the engineering structure refers to the corresponding 3D digital model file of the engineering structure, including but not limited to BIM model file, CAD drawing file, oblique photogrammetry point cloud data file, etc.; the engineering construction coordinate system parameters refer to the definition parameters of the construction coordinate system adopted by the engineering project, including the longitude of the central meridian and the elevation of the projection plane; user role information refers to the identity identifiers and corresponding responsibilities of various personnel participating in the actual measurement work; the digital actual measurement basic environment refers to the digital working environment formed by the integration and encapsulation of core data tables, 3D scene data and / or 3D tile data, coordinate transformation parameter files, spatial index files, and module operation permissions; the original actual measurement data refers to the data obtained by the measurement... The data package generated by associating point coordinates, component identifiers, environmental parameters, measurement data, and on-site image data without quality optimization processing; the target measured data refers to the measured data after outlier removal, missing data completion, and environmental factor correction processing; the spatial correlation analysis results refer to the collection of spatial correlation relationships between measurement point coordinates and 3D model components, as well as the spatial quality analysis results obtained based on these spatial correlation relationships; the multidimensional visualization results refer to the visualization output formed by overlaying and rendering the spatial correlation analysis results on the 3D model and displaying them in thematic maps; the interactive operation and maintenance results refer to the data query results, early warning information, report files, or data management operation execution results generated after responding to user operation commands.

[0036] In the above embodiments, a cross-sea bridge project is used as an example for illustration. The three-dimensional model data of the cross-sea bridge project (including the Revit format BIM model file of the main bridge structure and the oblique photogrammetric point cloud data of the pier foundations), the engineering construction coordinate system parameters (central meridian longitude of 120°E, projection surface elevation of 0m), and user role information (including three types of roles: project administrator, technical supervisor, and on-site construction personnel) are acquired. Basic environment initialization processing is performed on the above-mentioned three-dimensional model data, engineering construction coordinate system parameters, and user role information to obtain the digital measured and quantitative basic environment corresponding to the cross-sea bridge project. Based on this digital measured and quantitative basic environment, multi-source automated data acquisition and processing is performed on the cross-sea bridge construction site. Cross-sectional dimension data of the bridge beams are acquired using a total station, spatial coordinates of each measurement point are acquired using a GNSS receiver, and environmental parameters such as on-site temperature, humidity, and wind speed, as well as on-site images of the measurement locations, are simultaneously acquired to obtain the original measured and quantitative data. The original measured data undergoes data quality optimization processing, sequentially performing outlier removal, missing data completion, and environmental factor correction to obtain the target measured data. Spatial correlation analysis is then performed on the target measured data and the digital measured environment. The measured data from each measurement point are automatically correlated to the corresponding bridge components in the 3D model (e.g., beam 3, pier 5) through coordinate transformation and spatial index matching. Buffer analysis or quantity calculations are then performed based on the spatial analysis requirements to obtain the spatial correlation analysis results. Next, the spatial correlation analysis results are visualized in a multi-dimensional manner. The quality status of each measurement point is marked with different colors on the 3D model (e.g., red indicates exceeding the design threshold, green indicates compliance), generating a thematic map of deformation distribution, resulting in a multi-dimensional visualization. When the technical manager issues a query command, the multi-dimensional visualization results and the query command are interactively processed, returning historical measurement data and trend analysis reports for the specified component as the interactive maintenance result.

[0037] Through the above steps, a basic environment initialization process is performed on the 3D model data of the engineering structure to construct a digital measurement foundation environment. This enables subsequent collected measurement data to establish a precise spatial correspondence with the specific components of the engineering structure in 3D space. Raw measurement data containing spatial coordinate information is obtained through automated multi-source data acquisition and processing. Data quality optimization eliminates environmental interference and the influence of abnormal data. Spatial correlation analysis then precisely matches and associates the optimized measurement data with the components in the 3D model, ensuring that each measurement point is accurately assigned to its corresponding structural surface in 3D space. Furthermore, multi-dimensional visualization and interactive operation and maintenance processes allow for intuitive determination of the spatial assignment and deviation direction of measurement points in 3D space. This solves the technical problem of low reliability in measurement of complex spatial structures in related technologies, achieving a significant improvement in the reliability of measurement of complex spatial structures.

[0038] The entity performing the above steps can be a system, such as a GIS system for measuring engineering structures, or equipment, or a controller or processor in a system or platform, or a standalone controller or processor, or other processing equipment or processing units with similar processing functions, but is not limited to these.

[0039] In an optional embodiment, the process involves acquiring 3D model data of the engineering structure, engineering construction coordinate system parameters, and user role information, and performing basic environment initialization processing on the 3D model data, engineering construction coordinate system parameters, and user role information to obtain a digital measurement and verification basic environment. This includes: deploying a spatial database and a business database on a first server, and creating core data tables in the spatial database and business database; configuring daily automatic incremental backup tasks and weekly full backup tasks for the spatial database and business database, and setting off-site backup storage paths for the daily automatic incremental backup tasks and weekly full backup tasks; calling a format parsing interface in the GIS server to parse the 3D model data to obtain 3D scene data, wherein when oblique photogrammetry data exists in the 3D model data, the 3D model data is converted into 3D tile data according to a preset tile resolution; and performing basic environment integration processing based on the core data tables, 3D scene data and / or 3D tile data, engineering construction coordinate system parameters, and user role information to obtain a digital measurement and verification basic environment.

[0040] In this context, the first server refers to the physical server or cloud server used to deploy the database service; the spatial database refers to the database that supports spatial data storage and spatial querying, and in this embodiment, it is implemented using a PostgreSQL database combined with a PostGIS spatial extension plugin; the business database refers to the relational database used to store business logic data, and in this embodiment, it is implemented using a MySQL database; the core data tables refer to the data tables created in the spatial database and the business database for storing measured and actual quantity related data, including the structure information table, the measured data table, the environmental parameter table, and the image data table; the daily automatic incremental backup program task refers to the scheduled program task that is automatically executed daily to back up only the newly added and changed data of the current day; and the weekly full backup program... The task refers to a scheduled program task that automatically executes weekly to back up all data in the database; the off-site backup storage path refers to the backup data storage address located in a different physical location from the primary server; the GIS server refers to the server with the core program of the GIS engine deployed; the format parsing interface refers to the program interface used to parse 3D model data files in different formats; 3D scene data refers to 3D scene files that can be loaded and rendered in the GIS engine after format parsing; oblique photogrammetry data refers to 3D point cloud or mesh model data obtained through oblique photogrammetry technology; the preset tile resolution refers to the spatial resolution parameters used when converting oblique photogrammetry data into 3D tile data; 3D tile data refers to tiled 3D model data organized according to the 3DTiles specification.

[0041] In the above embodiments, the cross-sea bridge project will continue to be used as an example for explanation. A PostgreSQL database is deployed on the first server with a PostGIS spatial extension plugin installed as the spatial database. Simultaneously, a MySQL database is deployed as the business database. Spatial coordinate data tables and spatial index data tables are created in the spatial database, while structure information tables, measured data tables, environmental parameter tables, and image data tables are created in the business database as core data tables. An incremental backup task is configured to be automatically executed daily at 2:00 AM and a full backup task is configured to be automatically executed every Sunday at 3:00 AM for both the spatial database and the business database. The off-site backup storage path is set to the storage server address located in the off-site disaster recovery center. The GIS engine core program is launched on the GIS server, and the format parsing interface is called to parse the 3D model data of the cross-sea bridge. For the Revit format bridge main structure BIM model file, the IFC format parsing interface is used to convert the bridge main structure BIM model file into 3D scene data that can be loaded by the GIS engine. For the oblique photography data of the bridge pier foundation (OBJ format 3D mesh model), since there is oblique photography data in the 3D model data, a tile conversion process is triggered to convert the oblique photography data into 3D tile data conforming to the 3DTiles specification according to the preset tile resolution. The preset tile resolution is determined according to the weight value of the corresponding region of the 3D model data in the weighted quadtree. Regions with higher weight values ​​use higher tile resolutions, and regions with lower weight values ​​use lower tile resolutions, and the tile resolution is not less than the measurement accuracy of the corresponding measuring equipment. In this embodiment, the tile resolution is set to 0.01m for high-weight regions (such as the connection between the bridge main beam and the bridge pier), and 0.05m for low-weight regions (such as flat areas of the bridge deck pavement). Based on the core data table, 3D scene data and 3D tile data, engineering construction coordinate system parameters and user role information, the basic environment is integrated and processed to obtain the digital measured basic environment corresponding to the cross-sea bridge project.

[0042] In an optional embodiment, a basic environment integration process is performed based on the core data table, 3D scene data and / or 3D tile data, engineering construction coordinate system parameters, and user role information to obtain a digital measurement and verification basic environment. This includes: acquiring the engineering construction coordinate system parameters input by the surveyor through a first client device, performing coordinate system transformation mapping on the engineering construction coordinate system parameters to generate a coordinate transformation parameter file, and storing the coordinate transformation parameter file in a spatial database; calculating model weights based on the spatial attribute parameters and graphic attribute parameters of the 3D model data to obtain 3D model weights; using a preset weighted quadtree algorithm to perform spatial indexing and partitioning of the 3D model data according to the 3D model weights to generate a spatial index file; acquiring user role information, and using a role access control model to create accounts for the user role information to obtain multi-level user accounts, and configuring module operation permissions for the multi-level user accounts; and integrating and encapsulating the core data table, 3D scene data and / or 3D tile data, coordinate transformation parameter file, spatial index file, and module operation permissions to obtain the digital measurement and verification basic environment.

[0043] The first client device refers to the terminal device used by surveyors to input engineering construction coordinate system parameters, including PC computers or mobile tablets; coordinate system transformation mapping processing refers to the process of establishing mathematical transformation relationships between the WGS84 geodetic coordinate system and the engineering construction coordinate system based on the engineering construction coordinate system parameters; coordinate transformation parameter file refers to the data file storing the seven parameters (three translation parameters, three rotation parameters, and one scale parameter) required for coordinate system transformation; spatial attribute parameters refer to the geographical location coordinates and bounding box dimensions of each component in the 3D model data; graphic attribute parameters refer to the number of vertices and texture map size of each component in the 3D model data; 3D model weight refers to the numerical values ​​representing the importance and complexity of each component, calculated comprehensively based on spatial attribute parameters and graphic attribute parameters; preset weight quadtree algorithm refers to a non-uniform quadtree partitioning algorithm that determines the spatial segmentation position based on the cumulative distribution of 3D model weights; spatial index file refers to the index data file generated after spatial indexing of the 3D model data using the preset weight quadtree algorithm; role-based access control model refers to role-based access control. AccessControl model; multi-level user accounts refer to system accounts with different permission levels created according to user role information; module operation permissions refer to the operation permissions of various functional modules of the system assigned to user accounts at all levels.

[0044] In the above embodiment, the cross-sea bridge project will continue to be used as an example for explanation. Surveyors input the engineering construction coordinate system parameters of the cross-sea bridge project via a PC (first client device), including the central meridian longitude of 120°E and the projection elevation of 0m. Coordinate system transformation mapping is performed on these parameters. The transformation parameters between the WGS84 geodetic coordinate system and the engineering construction coordinate system are calculated using the Bursa seven-parameter model (including three translation parameters ΔX, ΔY, ΔZ, three rotation parameters εx, εy, εz, and one scale parameter m). A coordinate transformation parameter file is generated and stored in the spatial database. The specific usage of the above seven parameters in the coordinate transformation process is as follows: Let the three-dimensional rectangular coordinates of a measurement point in the WGS84 geodetic coordinate system be (X...). WGS ,Y WGS Z WGS The corresponding three-dimensional rectangular coordinates in the construction coordinate system are (X... local ,Y local Z local If X ), then the Bursa seven-parameter conversion formula is: X local ==ΔX+(1+m)×(X WGS +εz×Y WGS -εy×Z WGS ), Y local =ΔY+(1+m)×(-εz×X WGS +Y WGS +εx×Z WGS Z local =ΔZ+(1+m)×(εy×X WGS -εx×Y WGS +Z WGS), where ΔX, ΔY, and ΔZ are the translations of the origin of the WGS84 geodetic coordinate system relative to the origin of the construction coordinate system in the X, Y, and Z axes, respectively, in meters; εx, εy, and εz are the rotation angles of the coordinate axes of the WGS84 geodetic coordinate system around the X, Y, and Z axes of the construction coordinate system, respectively, in radians; and m is the scale factor between the two coordinate systems, a dimensionless parameter. The solution process for the above seven parameters is as follows: Select at least three known control points within the construction site that simultaneously possess both WGS84 and construction coordinate system coordinates (in this embodiment, four known control points are selected to increase redundant observations), substitute the two sets of coordinate values ​​of each known control point into the above transformation formula, establish an overdetermined system of equations, and obtain the optimal estimates of ΔX, ΔY, ΔZ, εx, εy, εz, and m by the least squares method. In the subsequent spatial correlation analysis and processing stage, the seven parameter values ​​stored in the coordinate transformation parameter file are read from the spatial database. The three-dimensional rectangular coordinates of each measurement point in the target measured data under the geodetic coordinate system are calculated point by point according to the above transformation formula to obtain the first measured data under the construction coordinate system. The model weight is calculated based on the spatial attribute parameters and graphic attribute parameters of the three-dimensional model data. Specifically, for each component in the three-dimensional model data, the geographical location coordinates of the component are extracted to calculate the geographical location weight component (weight percentage 30%), the number of vertices of the component is counted to calculate the vertex number weight component (weight percentage 30%), the texture map size of the component is calculated to obtain the texture size weight component (weight percentage 20%), and the bounding box volume of the component is calculated to obtain the bounding box weight component (weight percentage 20%). The weighted sum of the above four weight components is used to obtain the three-dimensional model weight of the component. For example, components at the connection between the main beam and the pier of a bridge have a higher weight in their 3D model due to their complex geometry (many vertices) and location in a critical position in the structure; while components in the flat area of ​​the bridge deck have a lower weight in their 3D model due to their simple geometry and non-critical position.

[0045] In the above embodiments, a pre-weighted quadtree algorithm is used to spatially index and partition the 3D model data according to the weights of the 3D model, generating a spatial index file. The specific partitioning process of the pre-weighted quadtree algorithm is as follows: For the current spatial region to be partitioned, the cumulative weight distribution of all components in the region is calculated along the X-axis. The X-axis coordinate position where the sum of the weights of the components to the left of the dividing line and the sum of the weights of the components to the right of the dividing line each account for 50% of the total weight of the region is found as the dividing line position in the X direction. Similarly, the Y-axis coordinate position where the sum of the weights of the components above the dividing line and the sum of the weights of the components below the dividing line each account for 50% of the total weight of the region is found as the dividing line position in the Y direction. In this way, the position of the dividing line is dynamically determined by the weight distribution of the components in the sub-region. The region where the high-weight components are located is divided into sub-nodes with smaller physical space but finer index granularity, and the region where the low-weight components are located is divided into sub-nodes with larger physical space but coarser index granularity, thus forming a non-uniform weighted quadtree spatial index structure. For example, if the connection between the main beam and the pier of the bridge (component A, with extremely high weight) is located at the left 20% position of the spatial area, and the flat area of ​​the bridge deck pavement (component B, with lower weight) is located at the right 80% position, then the median coordinate of the weight in the X direction is biased towards the side of component A (for example, at the 20% position of the X-axis). The high-weight area where component A is located is compressed into a sub-node that occupies only 20% of the physical space to obtain a finer index granularity, while the low-weight area where component B is located obtains 80% of the physical space.

[0046] In the above embodiments, the above partitioning process is recursively executed for each child node until the number of components within the child node is less than a preset threshold or the preset maximum recursion depth is reached, at which point the partitioning stops. It should be noted that for multi-layered structures with obvious elevation stratification characteristics (such as multi-layered overpasses, high-rise buildings, etc.), before performing the above quadtree partitioning in the XY plane, a layered preprocessing in the Z-axis direction is first performed based on the elevation range of each component in the 3D model data: the minimum and maximum elevation values ​​of the bounding boxes of all components in the Z-axis direction are extracted, and the entire 3D space is divided into multiple horizontal levels in the Z-axis direction according to the elevation distribution characteristics of the components (such as floor boundary elevation, bridge deck elevation, etc.), with each horizontal level corresponding to an elevation interval; within each horizontal level, the above non-uniform quadtree partitioning process based on the 3D model weights is executed independently, thereby forming a layered non-uniform spatial index structure with Z-axis layering first and then XY plane quadtree partitioning. When matching measurement points with components using spatial index files, the horizontal level to which the measurement point belongs is first determined based on its elevation value. Then, a layer-by-layer search is performed in the quadtree corresponding to that horizontal level, thereby effectively distinguishing components with similar spatial positions at different elevation levels and avoiding confusion in the component attribution of measurement points in multi-layered overlapping structures.

[0047] In the above embodiments, user role information is obtained, and a role-based access control model is used to create user roles and accounts. Three levels of user accounts are created: administrator, technical lead, and construction worker accounts. Module operation permissions are configured for each level of user account: the administrator account has operation permissions for all functional modules; the technical lead account has operation permissions for data viewing, analysis, export, and early warning reception; and the construction worker account only has operation permissions for data collection and viewing. The core data table, 3D scene data, 3D tile data, coordinate transformation parameter files, spatial index files, and module operation permissions are integrated and encapsulated to obtain the digital measured and quantitative foundation environment corresponding to the cross-sea bridge project.

[0048] In an optional embodiment, data quality optimization processing is performed on the original measured data to obtain target measured data, including: when a data addition event is detected in the business database, extracting the original measured data, a first environmental parameter, and related information from the business database; storing the original measured data, the first environmental parameter, and related information in a memory buffer; reading the original measured data from the memory buffer and performing outlier detection processing on the original measured data to obtain valid measured data; performing data missing completion processing on the valid measured data to obtain a complete measured data sequence; and performing environmental factor correction processing on the complete measured data sequence and the first environmental parameter to obtain the target measured data.

[0049] Among them, "data addition event" refers to the database event triggered when a new data record is written to the measured data table in the business database; "first environmental parameter" refers to the environmental parameters of the construction site collected synchronously with the original measured data, including temperature, humidity, and wind speed; "association information" refers to the associated identification information such as the coordinates of the measurement points, component identification, and collection time recorded in the original measured data; "memory buffer" refers to the cache space allocated in the server memory for temporarily storing data to be processed; "outlier detection processing" refers to the process of detecting and removing outliers in the original measured data based on statistical methods; "valid measured data" refers to the measured data after outlier detection processing to remove outliers; "data missing completion processing" refers to the process of interpolating and supplementing missing data points in the valid measured data due to equipment failure or communication interruption; "complete measured data sequence" refers to the continuous measured data sequence without missing data points after data missing completion processing; and "environmental factor correction processing" refers to the process of compensating for the environmental impact of the measured values ​​in the complete measured data sequence based on the first environmental parameter.

[0050] In the above embodiment, the measurement data of the cross-section dimensions of beam No. 3 of the aforementioned cross-sea bridge project is used as an example for explanation. The server-side data processing program continuously monitors the measured data table in the business database. When a new data addition event is detected (i.e., after the on-site construction personnel complete a data collection and upload), the program extracts the original measured data corresponding to that collection (including the cross-sectional width and height values ​​of multiple measurement points of beam No. 3), the first environmental parameters (temperature value of 28.5℃, humidity value of 72%RH, and wind speed value of 3.2m / s at the time of collection), and related information (coordinates of each measurement point, component identification "beam No. 3", and collection timestamp) from the business database. The original measured data, the first environmental parameters, and the related information are stored in a memory buffer to improve the reading efficiency of subsequent data processing. The original measured data is read from the memory buffer, and outlier detection processing is performed on the original measured data. Specifically, the Grubbs criterion was used to test the measured values ​​of each measurement point at a significance level of α=0.05: the mean and standard deviation of all measured values ​​were calculated, and if the deviation of a measured value from the mean exceeded 3 times the standard deviation, the measured value was marked as an outlier and removed. At the same time, the removal log (including the coordinates of the measurement point of the removed data, the original measured value, and the reason for removal) was recorded in the database to obtain valid measured data.

[0051] In the above embodiments, data missingness is filled in for the valid measured data. Specifically, the Kriging interpolation algorithm is used to perform spatial interpolation calculations on missing data points caused by equipment communication interruptions, etc. Based on the spatial positional relationship and measured values ​​of existing measurement points around the missing data points, the measured values ​​of the missing data points are estimated, so that the data integrity reaches more than 99.9%, resulting in a complete measured data sequence. Environmental factor correction processing is then performed on the complete measured data sequence and the first environmental parameter. Specifically, a pre-established multiple linear regression environmental correction model is loaded. This model uses temperature, humidity, and wind speed as independent variables and the environmental deviation of the measured values ​​as the dependent variable. The correction formula is: Corrected measured value = Original measured value - (β1 × Temperature deviation + β2 × Humidity deviation + β3 × Wind speed deviation + β4 × Temperature deviation × Humidity deviation), where β1 is the temperature linear correction coefficient, representing the degree of linear influence of temperature changes on the measured value; β2 is the humidity linear correction coefficient, representing the degree of linear influence of humidity changes on the measured value; β3 is the wind speed linear correction coefficient, representing the degree of influence of wind speed changes on the stability of the measuring equipment; and β4 is the temperature and humidity interaction correction coefficient, representing the degree of coupling influence of simultaneous temperature and humidity changes on the measured value. The four coefficients were obtained as follows: Benchmark measurements were performed on the same measurement point of the same component under standard environmental conditions (temperature 20℃, humidity 50%RH, wind speed 0m / s) to obtain the benchmark measurement value. Then, repeated measurements were performed on the same measurement point of the same component under multiple sets of different environmental conditions (including at least 5 sets of different temperature values, 5 sets of different humidity values, 5 sets of different wind speed values, and 5 sets of combinations of simultaneous temperature and humidity changes) to obtain the measurement values ​​under each set of environmental conditions. The temperature deviation, humidity deviation, wind speed deviation, and the product of the temperature deviation and humidity deviation for each set of environmental conditions were used as independent variables, and the difference between the benchmark measurement value and the measurement values ​​under each set of environmental conditions was used as the dependent variable. Multiple linear regression fitting was performed using the least squares method to obtain the values ​​of β1, β2, β3, and β4. The temperature deviation, humidity deviation, and wind speed deviation are the differences between the actual environmental parameter values ​​and the standard environmental parameter values ​​(temperature 20℃, humidity 50%RH, wind speed 0m / s), respectively.

[0052] In the above embodiments, the environmental factor correction process eliminates the effects of thermal expansion and contraction, humidity on material dimensions, and wind speed on measurement stability, thus obtaining the target measured data. After obtaining the target measured data, the accuracy analysis program is started, and the error is calculated according to the standard error formula M=±√[ΣA]. 2The mean error of the measurement data is calculated as [ / (n-1)], where A is the deviation of each measurement value from the mean, and n is the number of measurements. The relative error is also calculated (relative error = mean error M / average value of the measurements). If the calculated relative error is greater than 0.1%, the environmental correction model coefficient parameters are iteratively optimized and adjusted before the environmental factor correction process is re-executed. Specifically, the iterative optimization and adjustment process of the coefficient parameters is as follows: with minimizing the mean error of all corrected measurements as the optimization objective, the partial derivatives of the current mean error with respect to each coefficient parameter (β1, β2, β3, β4) are calculated. The values ​​of each coefficient parameter are updated along the negative gradient direction using the gradient descent method with a preset learning rate (initial learning rate is 0.01). After the update, the corrected measurement values ​​and their relative errors are recalculated. If the relative error is still greater than 0.1%, the next iteration update is performed until the relative error converges to no greater than 0.1% or the number of iterations reaches the preset maximum number of iterations (maximum number of iterations is 1000). If the relative error still exceeds 0.1% after reaching the maximum number of iterations, an alarm message indicating coefficient optimization failure is generated and pushed to the administrator account. The administrator then determines whether to re-collect benchmark calibration data to update the environment correction model. Once the relative error meets the accuracy requirement of no more than 0.1%, the mean square error value, relative error value, number of measurements involved in the calculation, and coefficient parameters of the environment correction model are summarized to generate an accuracy analysis report. This accuracy analysis report is then associated with the corresponding measurement task (i.e., the component identifier and collection timestamp corresponding to this collection) and stored in the business database, providing accuracy analysis data for report generation in subsequent interactive operation and maintenance processes.

[0053] In the above embodiment, after obtaining the target measured data and ensuring the relative error meets the accuracy requirements, a trend analysis program is triggered. This program calls a time-series ARIMA (Autoregressive Integrated Moving Average) model, imports historical measurement data (up to 10 periods) of the component from the business database, performs time-series fitting on the historical measurement data, generates the component's deformation trend curve or settlement trend curve, and predicts short-term (1 month) and long-term (1 year) trend values ​​based on the fitting results, with a prediction error of no more than 5%. The trend analysis program outputs the predicted trend values ​​and stores them in the corresponding trend prediction value field of the business database.

[0054] In an optional embodiment, spatial correlation analysis is performed on the target measured data and the digital measured environment to obtain spatial correlation analysis results. This includes: reading a coordinate transformation parameter file from a spatial database and converting the target measured data in the geodetic coordinate system to the first measured data in the construction coordinate system based on the coordinate transformation parameter file; obtaining the coordinates of the first measurement point in the first measured data and matching the coordinates of the first measurement point with the components in the 3D model data using a spatial index file to obtain the first component corresponding to the coordinates of the first measurement point; establishing a spatial correlation relationship among the coordinates of the first measurement point, the component identifier of the first component, and the first measured data, and writing the spatial correlation relationship into the spatial database; determining the spatial analysis requirement type of the first measured data based on the spatial correlation relationship to obtain the target spatial analysis requirement type; determining the spatial quality analysis result based on the target spatial analysis requirement type; and using the spatial correlation relationship and the spatial quality analysis result as the spatial correlation analysis result.

[0055] Among them, the coordinate transformation parameter file refers to the coordinate transformation data file containing seven parameters generated and stored in the spatial database in step S101; the geodetic coordinate system refers to the WGS84 world geodetic coordinate system; the construction coordinate system refers to the local coordinate system of the project defined according to the parameters of the engineering construction coordinate system; the first measured data refers to the measured data obtained after transforming the target measured data from the geodetic coordinate system to the construction coordinate system; the first measurement point coordinates refer to the spatial coordinates of each measurement point in the first measured data in the construction coordinate system; the first component refers to the three-dimensional model component corresponding to the first measurement point coordinates in space obtained by matching the spatial index file; and the component identifier. This refers to the unique identifier number pre-assigned to each component in the 3D model data; spatial association refers to the mapping correspondence established between the coordinates of the first measurement point, the component identifier of the first component, and the first measured data; spatial analysis requirement type determination processing refers to the process of determining the type of spatial analysis to be performed based on the data characteristics and preset rules in the spatial association; target spatial analysis requirement type refers to the type of spatial analysis to be performed after the determination processing, including abnormal area identification requirement type and engineering quantity calculation requirement type; spatial quality analysis result refers to the analysis result obtained after performing the corresponding spatial analysis according to the target spatial analysis requirement type.

[0056] In the above embodiment, the measurement data of beam No. 3 of the aforementioned cross-sea bridge project is used as an example for explanation. The coordinate transformation parameter file is read from the spatial database. Based on the seven parameters stored in the coordinate transformation parameter file, the latitude and longitude coordinates and geodetic height of each measurement point in the target measured data under the geodetic coordinate system (WGS84) are transformed and mapped to the plane coordinates and normal height under the construction coordinate system, obtaining the first measured data under the construction coordinate system. The first measurement point coordinates (three-dimensional coordinates under the construction coordinate system) of each measurement point in the first measured data are obtained. The spatial index file is used to match the first measurement point coordinates with the components in the three-dimensional model data. The specific matching process is as follows: Based on the coordinates of the first measurement point, a layer-by-layer search is performed in the weighted quadtree spatial index to locate the leaf node containing the coordinates of the first measurement point, and the set of candidate components within the leaf node is obtained; for each candidate component in the set of candidate components, the matching cost is calculated. The formula for calculating the matching cost is: Matching cost = Spatial distance between the coordinates of the first measurement point and the surface of the candidate component / 3D model weight of the candidate component; When calculating the matching cost, if the 3D model weight of a candidate component is zero or less than the preset minimum weight threshold (the preset minimum weight threshold is 0.001), the matching cost of the candidate component is set to the preset maximum matching cost value (the preset maximum matching cost value is positive infinity), so that it is placed at the last position in the matching sorting, avoiding calculation anomalies caused by division by zero or close to zero. The spatial distance between the coordinates of the first measurement point and the surface of the candidate component is calculated as follows: Calculate the shortest Euclidean distance from the coordinates of the first measurement point to the surface of the 3D mesh model of the candidate component, that is, traverse all triangular facets on the surface of the candidate component, calculate the point-to-face distance from the coordinates of the first measurement point to each triangular facet, and take the minimum value as the spatial distance of the candidate component. The candidate component with the lowest matching cost is selected as the first component corresponding to the coordinates of the first measurement point. Through the above matching cost calculation method, components with higher weights in the 3D model have lower distance sensitivity (i.e., higher fault tolerance). When the measurement point is located at the boundary of a component, the high-weight component has a larger matching attraction range.

[0057] In the above embodiment, after the first component is matched, if the component identifier of the first component is inconsistent with the component identifier of the second component pre-selected by the construction personnel during the data collection phase, the system sends a confirmation prompt to the construction personnel, who then confirm the final component assignment. If the construction personnel confirm that the system matching result is the standard, the first component is used as the final matched component; if the construction personnel confirm that the pre-selected component is the standard, the second component is used as the final matched component. A spatial relationship is established between the coordinates of the first measurement point, the component identifier of the first component, and the first measured data, and this spatial relationship is written into the spatial database. For example, a spatial relationship is established between the coordinates of the measurement point (X=1234.567, Y=2345.678, Z=15.432), the component identifier "Beam No. 3 - Section A," and the measured width of the section (1200.3 mm). Based on the spatial relationship, the first measured data is processed for spatial analysis and requirement type determination. Specifically, if the first measured data contains data whose measured values ​​exceed the allowable deviation range of the corresponding component design parameters, the target spatial analysis requirement type is determined to be an anomaly area identification requirement type; if the first measured data includes elevation measurement data and the corresponding engineering task type is earthwork engineering, the target spatial analysis requirement type is determined to be an engineering quantity calculation requirement type. The spatial quality analysis results are determined based on the target spatial analysis requirement type, and the spatial correlation and spatial quality analysis results are used as the spatial correlation analysis results.

[0058] In an optional embodiment, determining the spatial quality analysis result based on the target spatial analysis requirement type includes: if the target spatial analysis requirement type is an anomaly area identification requirement type, then calling the buffer analysis module to generate a buffer with a preset radius centered on the coordinates of the first measurement point, and overlaying the buffer with the component design parameter layer for analysis, so as to filter out abnormal measured data exceeding the preset design threshold from the first measured data; if the target spatial analysis requirement type is an engineering quantity calculation type, then calling the earthwork calculation module to perform cut and fill calculation processing based on the elevation data corresponding to the coordinates of the first measurement point, and obtaining the engineering quantity analysis result; synchronously storing the spatial correlation relationship and the abnormal measured data and / or engineering quantity analysis result in the business database, and updating the latest measurement value field and trend prediction value field of the component attribute table in the business database; in the case of abnormal measured data, updating the anomaly identifier field of the component attribute table to an abnormal state.

[0059] Among them, the abnormal area identification requirement type refers to the analysis requirement type that needs to identify spatial areas with quality abnormalities in engineering structures; the buffer analysis module refers to the functional module used to generate a spatial buffer with a specified radius centered on a specified coordinate point and perform spatial overlay analysis; the preset radius refers to the fixed radius value used when generating the buffer, which is 0.5m in this embodiment; the buffer refers to a circular spatial area generated with the first measurement point coordinates as the center and the preset radius as the radius; the component design parameter layer refers to the spatial data layer containing design parameters such as the design dimensions and design strength of each component; the preset design threshold refers to the maximum allowable deviation value of the design parameters of each component; abnormal measured data refers to measured data that exceeds the preset design threshold; engineering The quantity calculation requirement type refers to the analysis requirement type that needs to calculate the earthwork excavation and filling volume based on measured elevation data; the earthwork calculation module refers to the functional module used to calculate the excavation and filling volume based on elevation data; elevation data refers to the elevation component values ​​in the coordinates of the first measurement point; the engineering quantity analysis result refers to the excavation and filling volume data calculated by the earthwork calculation module; the component attribute table refers to the data table in the business database that stores the attribute information of each component; the latest measurement value field refers to the data field in the component attribute table that records the most recent measurement value of each component; the trend prediction value field refers to the data field in the component attribute table that records the predicted value of the deformation or settlement trend of each component; the anomaly identification field refers to the data field in the component attribute table that marks whether each component has quality anomalies.

[0060] In the above embodiments, the aforementioned cross-sea bridge project is used as an example for explanation. If the target spatial analysis requirement type is the abnormal area identification requirement type (i.e., the measured cross-sectional dimensions of beam No. 3 exceed the design allowable deviation), the buffer analysis module is invoked to generate a buffer with a radius of 0.5m centered on the coordinates of the first measurement point. The purpose of this buffer is to determine whether the measurement point itself is located in an abnormal area (such as a cracked area or a deformed area). By overlaying the buffer with the component design parameter layer, the deviation between the measured values ​​and the corresponding design values ​​of all measurement points within the buffer range is extracted. Abnormal measured data exceeding the preset design threshold (such as allowable deviation of cross-sectional width ±5mm, allowable deviation of cross-sectional height ±8mm) are filtered from the first measured data. If the target spatial analysis requirement type is the engineering quantity calculation requirement type (such as earthwork engineering of bridge approach roadbed), the earthwork calculation module is invoked. Based on the elevation data corresponding to the coordinates of the first measurement point, the design elevation surface is used as the reference surface to calculate the difference between the measured elevation and the design elevation. The elevation difference is spatially integrated to obtain the excavation and filling volumes as the engineering quantity analysis results. Spatial correlation relationships (mapping relationship between measurement point coordinates, component identifiers, and measured data), as well as abnormal measured data (e.g., measurement data exceeding design thresholds and the spatial location of the corresponding measurement points) and / or engineering quantity analysis results (e.g., excavation and filling volumes) are synchronously stored in the business database. The latest measurement value field of the component attribute table in the business database is updated to the measured value of this measurement, and the trend prediction value field is updated to the next period's change value predicted based on historical data. If abnormal measured data exists, the abnormality identifier field of the component attribute table is updated to an abnormal state. If no abnormal measured data exists, the abnormality identifier field of the component attribute table is updated to a normal state.

[0061] In the above embodiments, the specific process of performing multi-dimensional visualization processing on the spatial correlation analysis results to obtain multi-dimensional visualization results is as follows: After the user logs into the measurement application on the PC or mobile terminal, the visualization loading program is triggered. If the user accesses through the PC terminal, the 3D rendering engine is called in conjunction with the WebGL rendering module to load 3D tile data from the spatial database to form a 3D model scene; if the user accesses through the mobile terminal, the model lightweighting program is called to perform volume compression processing on the 3D model data (compression ratio of 60%). During the model lightweighting compression process, a conventional model simplification algorithm combined with an engineering feature protection strategy is adopted: the flat surface areas in the 3D model data are meshed to reduce the number of triangular faces, while the key geometric features of the engineering structure (including edge lines, contour lines, component boundary lines, and other feature edges that affect the measurement accuracy) are identified. The mesh in the area where the key geometric features are located is kept in its original accuracy without simplification, ensuring that the geometric accuracy of the lightweight 3D model data in the key measurement parts is not affected. The compressed lightweight 3D model data is then loaded onto the mobile terminal interface. After the 3D model scene is loaded, the data overlay rendering program is launched to overlay and annotate the quality status information of each measurement point in the spatial correlation analysis results onto the corresponding spatial location of the 3D model, using different colors according to data type (red indicates abnormal measurement points exceeding the preset design threshold, and green indicates qualified measurement points). When the user clicks on a measurement point annotation on the 3D model, the correlation query program is triggered, and the first measured data, the first environmental parameter, and the on-site image data corresponding to that measurement point are displayed simultaneously within 200ms. The on-site image data supports 20x magnification for viewing.

[0062] In the above embodiments, a thematic map generation program is invoked to generate thematic maps such as deformation distribution maps, strength level distribution maps, and trend curves based on spatial correlation analysis results, supporting the overlay and comparison of up to 10 historical data periods. When performing multi-period data overlay and comparison, a registration method based on stable component characteristics is adopted: one period of data is set as the baseline period, and known stable components (such as deeply buried pile foundations, bedrock anchor points, and other components that are not prone to deformation) are selected from the 3D model data as registration reference components. The feature point coordinates of the registration reference components in each period of data are extracted, and the pure coordinate system deviation of each period of data relative to the baseline period is calculated (excluding the influence of the deformation of the structure itself). Based on the calculated coordinate system deviation, coordinate transformation and registration are performed on the data of non-baseline periods, so that the data of all periods can be overlaid and compared under the same spatial reference, avoiding misalignment of multi-period data due to control point movement or coordinate system parameter adjustment. If the user accesses the program through a mobile device, the program automatically adapts to the mobile screen resolution, hides complex texture layers, retains core data annotation information, and supports offline caching of multi-dimensional visualization results. The offline cache is valid for 7 days, and the cache validity period is determined based on the data version: when the mobile device reconnects to the network, it checks whether the server-side data version is higher than the local cache version. If the server-side data version is updated, the local cache is reloaded and updated. If the network is unavailable and the local cache has exceeded its 7-day validity period, the expired cached data continues to be used, but a warning message indicating that the data may have expired is displayed on the interface.

[0063] In an optional embodiment, multi-source data is automatically collected and processed at the construction site of the engineering structure based on the digital measured environment to obtain raw measured data. This includes: responding to a device access request initiated by construction personnel through a second client device, scanning and connecting to the measurement devices to be connected at the construction site through a preset wireless communication interface to obtain the on-site measurement devices that are communicatively connected to the second client device; if the construction site is in an outdoor construction scenario, calling a GNSS positioning module to receive satellite signals to obtain the coordinates of the second measurement point, or if the construction site is in an indoor construction scenario, calling a UWB positioning module to obtain the coordinates of the second measurement point. The coordinate system type of the second measurement point is determined according to the output protocol of the GNSS or UWB positioning module; the target measurement parameters are determined according to the second component in the digital measured environment, and acquisition instructions are sent to the field measurement equipment according to the target measurement parameters, and the environmental sensor is started to collect the second environmental parameters in real time; the measurement data returned by the field measurement equipment through the preset wireless communication interface is received, and the image acquisition module is called to perform image acquisition processing on the measurement part of the second component to obtain the field image data; the coordinates of the second measurement point, the component identification of the second component, the second environmental parameters, the measurement data, and the field image data are associated to generate the original measured data.

[0064] The term "second client device" refers to the mobile terminal device used by construction workers at the construction site, including smartphones or tablets with a measurement application installed. "Device access request" refers to the operation request initiated by construction workers through the second client device to connect to the on-site measurement equipment. "Preset wireless communication interface" refers to the interface configured on the second client device for wireless communication with the measurement equipment, including Bluetooth 5.0 and Wi-Fi 6 interfaces. "Measurement equipment to be connected" refers to measurement equipment that is discoverable within the construction site. "On-site measurement equipment" refers to measurement equipment that establishes a communication connection with the second client device after scanning and connection processing, including total stations, GNSS receivers, and rebound hammers. "Outdoor construction scenario" refers to a construction site located outside a building with satellite signal reception capabilities. "Indoor construction scenario" refers to a construction site located inside a building or where satellite signals are blocked. "GNSS positioning module" refers to a functional module that uses Global Navigation Satellite System signals for positioning. "UWB positioning module" refers to a functional module that uses ultra-wideband (UWB) wireless signals for precise indoor positioning. "Second measurement point coordinates" refers to the coordinates of the second measurement point obtained through the GNSS positioning module or UWB wireless signal. The spatial coordinates of the current measurement point obtained by the positioning module; the coordinate system type refers to the coordinate reference system type to which the coordinates of the second measurement point belong, including the WGS84 coordinate system (the global geodetic coordinate system output by the GNSS positioning module) and the UWB local coordinate system (the local three-dimensional rectangular coordinate system defined by the UWB positioning module based on the pre-deployed base station locations); the second component refers to the target component to be measured pre-selected by the construction personnel in the three-dimensional model of the digitally measured basic environment; the target measurement parameters refer to the measurement items and parameter settings to be collected according to the type of the second component and the measurement task; the acquisition command refers to the data acquisition control command generated according to the target measurement parameters and sent to the on-site measurement equipment; the environmental sensor refers to the sensor equipment used to collect environmental parameters of the construction site in real time; the second environmental parameters refer to the temperature, humidity and wind speed values ​​of the construction site collected in real time by the environmental sensor; the measurement data refers to the original measurement values ​​returned by the on-site measurement equipment after completing the measurement according to the acquisition command; the image acquisition module refers to the high-definition camera and the control program of the high-definition camera on the second client device; the on-site image data refers to the high-resolution digital images captured by the image acquisition module of the measurement part of the second component. The AES-256 algorithm is a symmetric encryption algorithm with a key length of 256 bits in the Advanced Encryption Standard, used to encrypt and protect raw measured data during network transmission.

[0065] In the above embodiment, the on-site data acquisition process of the aforementioned cross-sea bridge project is used as an example for explanation. Construction personnel log into the measurement application on a tablet computer (second client device) at the construction site and initiate a device connection request by clicking the device connection button. Responding to this request, the system scans the measurement devices to be connected at the construction site via Bluetooth 5.0 and Wi-Fi 6 interfaces (preset wireless communication interfaces), discovering three devices: a total station, a GNSS receiver, and a rebound hammer. The system automatically verifies the compatibility of the communication protocol for each device, checking whether the data transmission rate is not less than 10Mbps and whether the data acquisition delay is not greater than 50ms. After successful verification, the connection is established, and the on-site measurement devices connected to the second client device are obtained. Since the bridge deck construction of this cross-sea bridge is an outdoor construction scenario, the system calls the GNSS positioning module to receive satellite signals, obtain the coordinates (longitude, latitude, and geodetic height) of the second measurement point at the current measurement location, with a positioning accuracy of ±1cm. Based on the output protocol of the GNSS positioning module, the system determines that the coordinate system type of the second measurement point is the WGS84 coordinate system. Specifically, the coordinates output by different positioning modules may belong to different coordinate reference systems: the original output of the GNSS positioning module is usually in the WGS84 coordinate system; the output of the UWB positioning module is in a local three-dimensional rectangular coordinate system defined based on the pre-deployed base station locations. The system automatically marks the corresponding coordinate system type according to the positioning module type to select the correct transformation path during subsequent coordinate transformation processing. If construction personnel enter the interior of the bridge box girder for inspection (indoor construction scenario), the system automatically switches to the UWB positioning module to obtain the coordinates of the second measurement point through the pre-deployed UWB base stations, with a positioning accuracy of ±5cm. Construction personnel pre-select beam number 3 as the second component in the three-dimensional model of the digitally measured foundation environment. The system determines the target measurement parameters as cross-sectional width, cross-sectional height, and concrete rebound strength based on the component type of beam number 3 (prestressed concrete box girder). The system sends cross-sectional dimension acquisition commands to the total station and concrete strength acquisition commands to the rebound hammer based on the target measurement parameters, and simultaneously activates environmental sensors to collect the second environmental parameters in real time (temperature 28.5℃, humidity 72%RH, wind speed 3.2m / s).

[0066] In the above embodiment, after the total station and rebound hammer complete the measurement according to the acquisition instructions, they return the measurement data (section width 1200.3mm, section height 2400.5mm, rebound strength 42.3MPa) to the second client device via Bluetooth 5.0 interface. The system calls the image acquisition module (tablet rear camera, 13 megapixels) to perform image acquisition processing on the measurement part of beam No. 3, capturing on-site images with a resolution of not less than 4096×3072 pixels, obtaining on-site image data. The coordinates of the second measurement point (latitude and longitude and geodetic height in WGS84 coordinate system), the component identification of the second component (beam No. 3), the second environmental parameters (temperature 28.5℃, humidity 72%RH, wind speed 3.2m / s), the measurement data (section width 1200.3mm, section height 2400.5mm, rebound strength 42.3MPa), and the on-site image data are correlated to generate the original measured data. If the construction site network is connected, the system will encrypt the original measured data using the AES-256 algorithm and upload it to the server's business database in real time. If the construction site network is interrupted, the system will store the original measured data in the local cache space of the second client device (cache capacity not less than 10GB). After the network is restored, the system will automatically trigger a data synchronization program to upload the locally cached original measured data to the server. The local cache adopts a validity period management mechanism based on data version: when the second client device reconnects to the network, it checks whether the server-side data version has been updated. If the server-side data version is higher than the local cache version, the local cache is updated. If the network is unavailable and the local cache has expired (7 days), the expired cached data will continue to be used, but a warning message indicating that the data may have expired will be displayed on the interface. If the construction personnel collect new original measured data while offline, the newly collected data will be temporarily added to the local cache. After the network is restored, the data will be synchronized to the server and the local cache will be updated.

[0067] In the above embodiments, when a user operation instruction is received, the multi-dimensional visualization results and the user operation instruction are interactively processed to obtain the interactive operation results. The specific process is as follows: The user initiates a data operation request (including a data query request, a data export request, or a data audit request) through the PC or mobile measurement application. The program calls the permission verification module to verify the module operation permissions corresponding to the multi-level user account of the currently logged-in user. It determines whether the current user has the permission to execute the data operation request. If the permission verification passes, the corresponding operation is executed and the operation result is returned. If the permission verification fails, a permission insufficient prompt message is returned. Regarding the early warning function, the program monitors the trend prediction values ​​output by the trend analysis program and the anomaly identification field in the spatial correlation analysis results in real time. If the latest measurement value of a component exceeds the preset design threshold, or the trend prediction value shows that the deformation or settlement trend of the component is abnormal (such as the predicted value exceeding the safety warning line), the program immediately calls the early warning push module to send early warning information to the technical manager's account through PC pop-up window and mobile APP notification. The early warning information includes details of abnormal data (including the component identification of the abnormal component, the abnormal measurement value, and the magnitude of exceeding the threshold) and a spatial location link of the abnormal component in the 3D model. After the technical manager clicks the spatial location link, he can directly jump to the 3D spatial location of the abnormal component in the multi-dimensional visualization results.

[0068] In the above embodiments, regarding report generation, when a user initiates a report generation request, the report generation program is invoked, loading the user-selected custom report template (supporting Excel and PDF formats). It automatically extracts data statistics, accuracy analysis reports, trend charts, and 3D model screenshots of the corresponding components from the business database and spatial database, filling these into the corresponding positions in the report template to generate a complete measured report file as the interactive operation and maintenance result output. Regarding data management, the administrator account periodically triggers data cleanup tasks through the system configuration program, deleting expired historical data exceeding a 3-year retention period from the business database and expired cached data from the local cache to maintain database storage performance. The administrator account can also modify the module operation permission configurations of user accounts at all levels, update the wireless communication interface protocol parameters of field measurement equipment, and upgrade the algorithm versions of the environmental correction model and trend analysis model through the system configuration program.

[0069] It should be noted that the examples of all the specific values ​​mentioned above are merely exemplary embodiments, and the specific values ​​are not limited to the examples mentioned above.

[0070] Through the embodiments of this application, it is possible to accurately determine the structural surface to which each measurement point belongs and the direction of deviation in three-dimensional space, which solves the problem that it is difficult to accurately determine the spatial attribution of measurement points of complex spatial structures based solely on discrete numerical records or two-dimensional plane projection, and improves the reliability of actual measurement of complex spatial structures.

[0071] The following describes the GIS system for actual measurement of engineering structures in the embodiments of this invention from the perspective of hardware processing. (See attached document.) Figure 2 , Figure 2 This is a schematic diagram of a physical device structure of the GIS system for actual measurement of engineering structures in the embodiments of this application.

[0072] It should be noted that, Figure 2 The structure of the engineering structure measurement GIS system shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0073] like Figure 2 As shown, the engineering structure measurement GIS system includes a Central Processing Unit (CPU) 201, which can perform various appropriate actions and processes according to the program stored in the Read-Only Memory (ROM) 202 or the program loaded from the storage section 208 into the Random Access Memory (RAM) 203, such as executing the methods described in the above embodiments. The RAM 203 also stores various programs and data required for platform operation. The CPU 201, ROM 202, and RAM 203 are interconnected via a bus 204. An I / O interface 205 is also connected to the bus 204. The following components are connected to the I / O interface 205: an input section 206 including audio input devices, push-button switches, etc.; an output section 207 including a Liquid Crystal Display (LCD) and audio output devices, indicator lights, etc.; a storage section 208 including a hard disk, etc.; and a communication section 209 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 209 performs communication processing via a network such as the Internet. The drive 210 is also connected to the I / O interface 205 as needed. Removable media 211, such as disks, optical disks, magneto-optical disks, semiconductor memories, etc., are installed on the drive 210 as needed so that computer programs read from them can be installed into the storage section 208 as needed.

[0074] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 209, and / or installed from removable medium 211. When the computer program is executed by central processing unit (CPU) 201, it performs the various functions defined in the present invention.

[0075] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution platform, apparatus, or device.

[0076] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation that may be implemented in platforms, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than that shown in the drawings.

[0077] Specifically, the engineering structure measurement GIS system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, it implements the engineering structure measurement method provided in the above embodiment.

[0078] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the engineering structure measurement and verification GIS system described in the above embodiments; or it may exist independently and not assembled into the engineering structure measurement and verification GIS system. The storage medium carries one or more computer programs, which, when executed by a processor of the engineering structure measurement and verification GIS system, cause the engineering structure measurement and verification GIS system to implement the engineering structure measurement and verification method provided in the above embodiments.

[0079] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0080] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for actual measurement of engineering structures, characterized in that, include: The three-dimensional model data of the engineering structure, the engineering construction coordinate system parameters, and the user role information are acquired, and the basic environment initialization processing is performed on the three-dimensional model data, the engineering construction coordinate system parameters, and the user role information to obtain the digital measured basic environment. Based on the digital measured and verified basic environment, the construction site of the engineering structure is subjected to automated multi-source data acquisition and processing to obtain the original measured and verified data. The original measured data is subjected to data quality optimization processing to obtain the target measured data; Spatial correlation analysis is performed on the target measured data and the digital measured environment to obtain spatial correlation analysis results. The spatial correlation analysis results are processed for multi-dimensional visualization to obtain multi-dimensional visualization results; Upon receiving a user operation command, the multidimensional visualization result and the user operation command are interactively processed to obtain an interactive operation result.

2. The method according to claim 1, characterized in that, The process involves acquiring the 3D model data of the engineering structure, engineering construction coordinate system parameters, and user role information, and then performing basic environment initialization processing on the 3D model data, engineering construction coordinate system parameters, and user role information to obtain a digital, measured, and quantifiable basic environment, including: Deploy a spatial database and a business database on the first server, and create core data tables in the spatial database and the business database; Configure daily automatic incremental backup program tasks and weekly full backup program tasks for the spatial database and the business database, and set off-site backup storage paths for the daily automatic incremental backup program tasks and weekly full backup program tasks; The format parsing interface is called in the GIS server to parse the format of the 3D model data to obtain 3D scene data. When there is oblique photogrammetry data in the 3D model data, the 3D model data is converted into 3D tile data according to the preset slice resolution. The digital measured basic environment is obtained by integrating and processing the core data table, the 3D scene data and / or the 3D tile data, the engineering construction coordinate system parameters, and the user role information.

3. The method according to claim 2, characterized in that, The process of integrating and processing the basic environment based on the core data table, the 3D scene data and / or the 3D tile data, the engineering construction coordinate system parameters, and the user role information to obtain the digital measured basic environment includes: The engineering construction coordinate system parameters input by the surveyor through the first client device are obtained, and the engineering construction coordinate system parameters are subjected to coordinate system transformation mapping processing to generate a coordinate transformation parameter file, and the coordinate transformation parameter file is stored in the spatial database; The model weights are obtained by calculating the model weights based on the spatial and graphical attribute parameters of the 3D model data. The spatial indexing process is performed on the 3D model data according to the weights of the 3D model using a preset weighted quadtree algorithm to generate a spatial index file. Obtain user role information, and use the role access control model to process the user role information to create accounts, thereby obtaining multi-level user accounts, and configure module operation permissions for the multi-level user accounts; The core data table, the 3D scene data and / or the 3D tile data, the coordinate transformation parameter file, the spatial index file, and the module operation permissions are integrated and encapsulated to obtain the digital measurement and verification basic environment.

4. The method according to claim 1, characterized in that, The step of performing data quality optimization processing on the original measured data to obtain the target measured data includes: When a data addition event is detected in the business database, the original measured data, the first environmental parameter, and the associated information are extracted from the business database. The original measured data, the first environmental parameter, and the associated information are stored in a memory buffer. The original measured data is read from the memory buffer, and outlier detection processing is performed on the original measured data to obtain valid measured data. The valid measured data is filled with missing data to obtain a complete measured data sequence; The complete measured data sequence and the first environmental parameter are subjected to environmental factor correction processing to obtain the target measured data.

5. The method according to claim 3, characterized in that, The spatial correlation analysis processing of the target measured data and the digital measured environment to obtain spatial correlation analysis results includes: The coordinate transformation parameter file is read from the spatial database, and the target measured data in the geodetic coordinate system is transformed and mapped to the first measured data in the construction coordinate system according to the coordinate transformation parameter file. Obtain the coordinates of the first measurement point in the first measured data, and use the spatial index file to match the coordinates of the first measurement point with the components in the three-dimensional model data to obtain the first component corresponding to the coordinates of the first measurement point. Establish a spatial relationship between the coordinates of the first measurement point, the component identifier of the first component, and the first measured data, and write the spatial relationship into a spatial database; Based on the spatial correlation, the first measured data is processed to determine the spatial analysis requirement type, thereby obtaining the target spatial analysis requirement type. The spatial quality analysis results are determined based on the target spatial analysis requirement type. The spatial correlation relationship and the spatial quality analysis results are used as the spatial correlation analysis results.

6. The method according to claim 5, characterized in that, The step of determining the space quality analysis results based on the target space analysis requirement type includes: If the target space analysis requirement type is the abnormal area identification requirement type, then the buffer analysis module is called to generate a buffer with a preset radius centered on the coordinates of the first measurement point, and the buffer is overlaid with the component design parameter layer for analysis, so as to filter out abnormal measured data that exceed the preset design threshold from the first measured data. If the target space analysis requirement type is the engineering quantity calculation requirement type, then the earthwork calculation module is called to perform cut and fill calculation processing based on the elevation data corresponding to the coordinates of the first measurement point to obtain the engineering quantity analysis result. The spatial relationship, the abnormal measured data, and / or the engineering quantity analysis results are synchronously stored in the business database, and the latest measurement value field and trend prediction value field of the component attribute table in the business database are updated; if the abnormal measured data exists, the abnormality identifier field of the component attribute table is updated to an abnormal status.

7. The method according to claim 1, characterized in that, The process involves automated multi-source data acquisition and processing of the construction site of the engineering structure based on the digitally measured and verified foundation environment to obtain raw measured and verified data, including: In response to a device access request initiated by a construction worker through a second client device, the system scans and connects the measurement devices to be connected at the construction site through a preset wireless communication interface to obtain the on-site measurement devices that are connected to the second client device. When the construction site is in an outdoor construction scenario, the GNSS positioning module is invoked to receive satellite signals to obtain the coordinates of the second measurement point; or when the construction site is in an indoor construction scenario, the UWB positioning module is invoked to obtain the coordinates of the second measurement point, and the coordinate system type to which the coordinates of the second measurement point belong is determined according to the output protocol of the GNSS positioning module or the UWB positioning module. The target measurement parameters are determined based on the second component in the digitally measured basic environment, and an acquisition command is sent to the field measurement equipment based on the target measurement parameters to simultaneously start the environmental sensor to acquire the second environmental parameters in real time. The system receives measurement data returned by the field measurement device through the preset wireless communication interface, and calls the image acquisition module to perform image acquisition processing on the measurement part of the second component to obtain field image data. The original measured data is generated by associating the coordinates of the second measurement point, the component identifier of the second component, the second environmental parameter, the measurement data, and the on-site image data.

8. A GIS system for actual measurement of engineering structures, characterized in that, The engineering structure measurement and verification GIS system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the engineering structure measurement and verification GIS system to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising program instructions, characterized in that, When the program instructions are run on the engineering structure measurement GIS system, the engineering structure measurement GIS system performs the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on the engineering structure measurement GIS system, the engineering structure measurement GIS system performs the method as described in any one of claims 1-7.

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