Auxiliary data processing and real-time verification system for campus building construction paying-off

Abstract

The invention relates to the technical field of digital building construction, and discloses an auxiliary data processing and real-time verification system for campus building construction paying-off, and the system comprises an intelligent data center which is used for importing and analyzing an original design file of a campus building, and generating a digital paying-off semantic model with geometric information and paying-off semantic information; the dynamic verification engine is used for receiving field actual measurement data, comparing the field actual measurement data with design information in the digital paying-off semantic model in real time, and generating a verification conclusion data set with deviation judgment and type identification; and the man-machine cooperation guiding device is used for synthesizing a guiding instruction set for guiding field operation based on the digital paying-off semantic model and the verification conclusion data set and outputting the guiding instruction set to the terminal equipment. Through cooperative work of the modules, the problems of construction pay-off data splitting, verification lag and non-visual guidance in a complex campus building environment are solved, and the precision, efficiency and process controllability of pay-off operation are improved.

Description

Technical Field

[0001] This invention relates to the field of digital building construction technology, specifically an auxiliary data processing and real-time verification system for campus building construction layout. Background Technology

[0002] In campus building construction, layout work is a crucial step in ensuring the accurate alignment of the building structure with the design drawings. Current layout practices typically face the following complex challenges: First, design data (such as CAD drawings) and on-site measurement and layout operations are separate. Data transfer relies on manual interpretation and conversion, which is not only inefficient but also prone to introducing errors when dealing with irregular curved surfaces and dense axis grids common in campus buildings. Second, on-site verification is delayed. Traditional methods often involve spot checks only after layout is completed, making process control difficult and preventing the timely detection and correction of deviations. In campus construction sites with tight schedules and multiple disciplines, such deviations can easily trigger a chain reaction of problems in subsequent processes. Furthermore, guidance information is disconnected from the on-site environment, making it difficult for operators to intuitively and quickly translate abstract drawing coordinates or deviation data into correct operational actions. While some existing digital measurement tools or software can improve efficiency in certain areas, they often focus on single functions (such as only data viewing or only coordinate measurement), failing to form a complete technical chain from intelligent analysis of design data and real-time closed-loop on-site verification to intuitive collaborative guidance. Therefore, the overall intelligence and collaborative efficiency of the campus building construction layout process still have significant room for improvement.

[0003] Therefore, there is an urgent need for an integrated system that can achieve deep collaboration between design, verification and guidance to solve the core technical problems of data fragmentation, control lag and unintuitive guidance in campus building layout operations. Summary of the Invention

[0004] The purpose of this invention is to provide an auxiliary data processing and real-time verification system for campus building construction layout, so as to solve the technical problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention discloses the following technical solution: an auxiliary data processing and real-time verification system for campus building construction layout, comprising: The intelligent data hub is configured to: import and parse the original design files of campus buildings, and generate a digital layout semantic model with geometric information and layout semantic information; The dynamic verification engine, which is connected to the intelligent data hub, is configured to: receive measured data collected on-site, compare the measured data with the corresponding design information in the digital laying-out semantic model, and generate a verification conclusion dataset with deviation judgment and type identification. The human-machine collaborative guide is communicatively connected to the intelligent data hub and the dynamic verification engine, and is configured to: synthesize a set of guidance instructions to guide on-site operations based on the digital cable laying semantic model and the verification conclusion dataset, and output the set of guidance instructions and cable laying status information to the terminal device.

[0006] In one embodiment, the intelligent data hub includes: The intelligent drawing parsing unit is configured to: perform layer separation and element recognition on the original design file, and extract annotation information; The model reconstruction unit is configured to: construct a three-dimensional model based on the coordinates and topological relationships of the graphic elements output by the drawing parsing unit, associate the annotation information with the corresponding geometric elements, and add semantic attributes including design coordinates, component classification and allowable deviation values ​​to the three-dimensional model elements to form the digital layout semantic model.

[0007] In one embodiment, the drawing intelligent parsing unit is further configured to: identify axis elements and axis numbering labels in the drawing; infer and complete missing logical axes based on the spatial positional relationship and numbering rules of the axis elements; generate axis network data with complete and continuous numbering; and store the axis network data into the digital layout semantic model.

[0008] In one embodiment, the model reconstruction unit is further configured to: for irregular curved surface components, use the NURBS algorithm to reconstruct three-dimensional parametric surfaces using the extracted irregular structural feature lines; for the reconstructed surfaces, perform adaptive triangular meshing according to construction accuracy requirements, define the mesh vertices as the layout control points of the component, and add the classification attributes of irregular curved surface points and the corresponding accuracy thresholds to all layout control points in the digital layout semantic model.

[0009] In one embodiment, the dynamic verification engine includes: The data interface unit is configured to receive and parse the measured data from the measuring device; The real-time judgment unit is configured to: retrieve the corresponding design coordinates and the allowable deviation value from the digital layout semantic model based on the point identifiers in the measured data; calculate the Euclidean distance between the measured coordinates and the design coordinates as the deviation value; compare the deviation value with the allowable deviation value, and generate the verification conclusion dataset with status identifiers and deviation types.

[0010] In one embodiment, the real-time judgment unit is further configured to: after obtaining the design coordinates of the current measured point, call a preset spatial relationship rule library to check whether there are other laid-out points that violate the rules within a preset radius centered on the measured point; if so, add a type identifier representing the spatial conflict warning and conflict object information to the verification conclusion dataset.

[0011] In one embodiment, the dynamic verification engine further includes a dynamic threshold adjustment unit, which is configured to: dynamically query a threshold mapping table based on the component classification attributes of the current verification point in the digital laying semantic model and the field vibration level data from environmental sensors, and provide an updated allowable deviation value for the real-time judgment unit.

[0012] In one embodiment, the human-machine collaboration guide includes: The instruction synthesis unit is configured to: select response rules from a preset strategy mapping table based on the deviation type and status in the verification conclusion dataset, and generate the guidance instruction set with spatial offset, text prompts or highlighted graphics; The rendering output unit is configured to encode the boot instruction set into target format data that is compatible with various terminal device interfaces and output it.

[0013] In one embodiment, the policy mapping table includes at least: When the verification conclusion dataset contains a status indicator representing a location exceeding the limit, a guiding instruction is triggered to generate a three-dimensional vector pointing from the measured point to the design point. When the verification conclusion dataset contains a type identifier representing a spatial conflict warning, a guidance instruction is triggered to generate a highlighting instruction for the conflict area and associated component identifiers.

[0014] In one embodiment, a timing conflict early warning module is also included, which is communicatively connected to both the intelligent data hub and the dynamic verification engine, and is configured as follows: The component construction process logic and planned operation time period contained in the digital layout semantic model are obtained from the intelligent data center; The dynamic verification engine continuously acquires the verification conclusion dataset and updates the actual completion status and timestamp of each layout point in real time. Based on the planned process logic, work periods, and actual progress, the system dynamically simulates the spatial and temporal overlap of multiple parallel laying tasks within a specified future time period. When the simulation predicts that the work areas and time windows of different shifts or processes will conflict, a timing conflict warning signal is generated and sent to the human-machine collaboration guide.

[0015] Beneficial Effects: This invention's auxiliary data processing and real-time verification system for campus building construction layout transforms original design documents into a semantically rich 3D digital layout model through an intelligent data hub. This solves the semantic gap between design data and construction applications, providing a precise and calculable data foundation for subsequent processes. The dynamic verification engine, by comparing measured data with model data in real time, enables immediate detection and diagnosis of construction deviations, effectively preventing error accumulation. The human-machine collaborative guide, based on the model and verification conclusions, synthesizes intuitive operation instructions, reducing the technical dependence and operational difficulty for on-site personnel. These three core modules form a closed loop of "data perception - real-time decision-making - precise execution," improving the automation, operational accuracy, and collaborative efficiency of campus building construction layout. This achieves a transformation from a traditional, discrete, experience-dependent operation mode to a digital, intelligent, and closed-loop controllable operation mode. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 The structural block diagram of the auxiliary data processing and real-time verification system for campus building construction layout provided in the embodiments of the present invention. Detailed Implementation

[0018] To facilitate understanding of the technical solutions provided in the embodiments of this application, the background technology involved in the embodiments of this application will be described below.

[0019] The construction environment of campus buildings is complex, with diverse building forms and compact layouts of functional areas, which places higher demands on the accuracy and efficiency of construction layout. In current practice, layout work relies heavily on manual interpretation and conversion of two-dimensional design drawings (such as CAD files). This is not only labor-intensive, but also prone to errors when dealing with irregular curved structures such as gymnasiums and lecture halls or complex main axis grids of teaching buildings. This leads to deviations in design intent during the initial data conversion stage, creating an initial semantic gap between design and construction.

[0020] Existing technologies attempt to improve upon these methods by incorporating digital tools, such as using precision measuring equipment like total stations to enhance single-point measurement accuracy or leveraging BIM models for 3D visualization. However, these technologies are often applied in isolation. They may focus on post-construction verification, failing to provide real-time deviation alerts during the layout process, leading to delayed exposure of quality issues; or they may only provide static 3D views, unable to dynamically generate intuitive and actionable correction guidelines based on actual on-site measurement data. This disconnect between data viewing, measurement, and guidance processes forces data flow between these stages to be interrupted, preventing the formation of a complete intelligent closed loop that enables real-time perception, immediate analysis, and dynamic guidance.

[0021] Therefore, the core pain point in the field of campus building construction layout has long been not the insufficient accuracy of individual equipment, but rather the lack of an integrated system that can understand design semantics, verify construction status in real time, and provide intelligent guidance throughout the entire chain from design data to on-site operation. This embodiment proposes an integrated solution to address this systemic technical problem.

[0022] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application. Secondly, in this document, the term "comprising" is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements, but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0023] This embodiment provides an auxiliary data processing and real-time verification system for campus building construction layout, such as... Figure 1 As shown, the method includes the following steps in sequence: The intelligent data hub is configured to: import and parse the original design files of campus buildings, and generate a digital layout semantic model with geometric information and layout semantic information; The dynamic verification engine, which communicates with the intelligent data hub, is configured to receive measured data collected on-site, compare the measured data with the corresponding design information in the digital layout semantic model, and generate a verification conclusion dataset with deviation judgment and type identification. The human-machine collaborative guide is connected to the intelligent data hub and the dynamic verification engine respectively. It is configured to synthesize a set of guidance instructions to guide on-site operations based on the digital cable laying semantic model and the verification conclusion dataset, and output the set of guidance instructions and cable laying status information to the terminal device.

[0024] In practice, the intelligent data hub, dynamic verification engine, and human-machine collaboration guide constitute the three core software modules of the system, which are deployed in one or more servers and field terminal devices interconnected by a network.

[0025] The intelligent data hub is responsible for processing the input original design files, which can be DWG format CAD drawings, RVT format BIM models, or IFC standard format files, imported into the system through a file parsing interface. The parsing process includes reading the basic structure of the file and identifying geometric elements such as points, lines, and surfaces, as well as their layer affiliation, line type, and color attributes. Generating a digital layout semantic model means that, based on the parsed geometric information, the system automatically or manually adds attribute data to specific elements in the model (such as axis intersections, wall lines, and column outlines). This attribute data is "semantic information." For example, assigning a point a component identifier such as "KZ-1 (center point of frame column No. 1)," design coordinates (X, Y, Z), its construction zone "Area A," and an "allowable deviation value" (such as ±5mm) determined according to construction specifications.

[0026] The dynamic verification engine establishes a Socket communication connection with on-site surveying equipment such as total stations and GNSS receivers via a wireless network (e.g., 4G / 5G or Wi-Fi) to receive measured data transmitted in real time in a specific data packet format (e.g., a JSON string containing timestamps, point numbers, and X / Y / Z coordinates). The comparison operation is performed by a computation thread within the dynamic verification engine. This thread indexes design points with the same identifier in the digital surveying semantic model based on the "point number" in the measured data and calculates the spatial Euclidean distance between the two coordinates. The verification conclusion dataset is a structured data object containing at least the following fields: {point identifier, measured coordinates, design coordinates, deviation value, whether it exceeds the limit (Boolean value), deviation type (e.g., "positional deviation", "elevation deviation", or "spatial conflict")}.

[0027] The human-machine collaborative guide subscribes to the verification conclusion dataset from the dynamic verification engine and generates a set of guidance instructions based on preset mapping logic. For example, when the deviation type is "position deviation" and "whether it exceeds the limit" is true, the guidance instruction set may contain a text instruction "move 0.05 meters east" and a 3D arrow graphic data pointing from the measured point to the design point. The rendering output component encodes this instruction set into a format adapted to the target terminal (such as the HDMI display interface of a tablet or the SLAM SDK data interface of augmented reality glasses) before pushing it.

[0028] Based on the above, the intelligent data hub transforms static design data into a computable 3D model rich in engineering semantics, laying a foundation for accurate data. The dynamic verification engine continuously compares on-site measured data with the model benchmark in real time, enabling immediate diagnosis of construction deviations. The human-machine collaborative guide transforms diagnostic conclusions into intuitive and operable instructions. These three elements are tightly coupled through data flow, forming a complete closed loop of "data perception - real-time decision-making - precise execution." This changes the traditional discrete mode of data flow relying on manual labor, lagging quality control, and experience-based operation in surveying operations. Through the synergistic effect of the integrated system, it improves the overall accuracy, operational efficiency, and process controllability of construction surveying in the complex environment of campus buildings.

[0029] To address the issues of complex design drawings and the inefficiency and error-prone nature of manually extracting layout information, in one embodiment, the intelligent data hub includes: The intelligent drawing parsing unit is configured to: separate layers and identify graphic elements in the original design file, and extract annotation information; The model reconstruction unit is configured to: construct a 3D model based on the coordinates and topological relationships of the graphic elements output by the drawing parsing unit, associate the annotation information with the corresponding geometric elements, and add semantic attributes including design coordinates, component classification and allowable deviation values ​​to the elements of the 3D model to form a digital layout semantic model.

[0030] In practice, the drawing intelligent parsing unit and the model reconstruction unit are two core processing sub-modules within the intelligent data hub.

[0031] The intelligent drawing parsing unit can be implemented using secondary development technologies based on AutoCAD ObjectARX or Open Design Alliance Teigha libraries to perform low-level parsing of DWG format files. Layer separation refers to reading all layers in the file and filtering and classifying them according to layer names (e.g., "AXIS" for axes, "COLUMN" for columns, and "DIM" for dimensions). Element recognition refers to identifying the type of each graphic object within a specific layer. For example, in the "AXIS" layer, all "AcDbLine" (straight line) objects are identified as axis elements, and in the "COLUMN" layer, all "AcDbCircle" (circle) or "AcDbPolyline" (polyline) objects are identified as column outline elements. Extracting annotation information refers to reading annotation objects such as "AcDbAlignedDimension" (aligned dimension) and "AcDbRotatedDimension" (rotated dimension) in the "DIM" layer and parsing their associated dimension values, annotation text, and the handles of the annotated geometric elements.

[0032] The model reconstruction unit receives the data stream output by the parsing unit. This data stream contains the geometric coordinates of primitives (such as the start and end coordinates of a line, the center and radius of a circle) and their topological relationships (such as two axes intersecting at a point). The process of constructing a 3D model involves first creating an empty 3D scene graph data structure in memory, then assigning Z-coordinate values ​​to the extracted 2D geometric primitives based on their elevation (read via layer name or specific attributes), converting them into objects in 3D space (e.g., converting a 2D axis into a horizontal or vertical line segment in 3D space). Associating annotation information refers to using the handles of the annotated primitives stored in the annotation object to find the corresponding geometric object in the 3D scene graph, and binding the annotation text (e.g., "5000") as a "size" attribute of that geometric object. Adding semantic attributes is an interactive and automated process: the system provides a graphical interface where users can click on an object in the 3D scene (such as a column) and manually select or enter the "component category" in the attribute panel (selected from a preset list such as "frame column," "structural column," and "shear wall"); the "design coordinates" are automatically calculated by the system from the geometric center of the object; and the "allowable deviation value" is automatically retrieved from a pre-set deviation value database within the system that conforms to standards such as the "Code for Engineering Surveying" (GB50026-2020), based on the selected "component category," allowing users to fine-tune it. Finally, this 3D scene map data, containing geometric, annotation, and semantic attributes, is serialized and stored in the system's proprietary format file, namely the digital layout semantic model.

[0033] Based on the above, through the automated processing of the intelligent drawing parsing unit, the system efficiently and accurately extracts key information from the original drawings; the model reconstruction unit then structurally integrates and semantically enhances the discrete geometric and textual information, generating a machine-understandable and computationally comprehensible 3D model. This automatically completes the conversion from unstructured design drawings to a structured, semantically meaningful construction guidance model, providing a precise and engineering-meaningful data source for subsequent real-time verification and intelligent guidance, and solving the efficiency bottlenecks and error introduction problems inherent in manual conversion processes.

[0034] To address the issues of dense and complex numbering of campus building axis networks, which can easily lead to omissions or logical errors when manually sorted, in one embodiment, the drawing intelligent parsing unit is further configured to: identify axis elements and axis numbering labels in the drawings; deduce and complete missing logical axes based on the spatial positional relationships and numbering rules of the axis elements; generate axis network data with complete and continuous numbering; and store the axis network data in the digital layout semantic model.

[0035] In practice, identifying axis elements and numbering labels means that the system identifies all straight line elements in the "AXIS" layer as candidate axes, searches for text elements (AcDbText) near their endpoints or extensions, and identifies text content that conforms to the axis numbering naming pattern (such as "1", "A", "1 / A", etc.) as axis numbers.

[0036] The algorithm steps for inferring and completing axes based on spatial relationships and numbering patterns are as follows: First, all identified axes are clustered, grouping nearly parallel axes with uniform spacing into the same group (e.g., all longitudinal axes). Then, the text sequence pattern of the numbering at both ends of the axes in this group is analyzed. For example, axes numbered "1", "2", "4", and "5" are found. By analyzing their positions and numerical values ​​on the drawing, it is inferred that axis number "3" should logically exist, but may not be drawn on the drawing due to simplification or may be drawn on another layer. The system will virtually generate an axis at the logical position based on the average spacing between adjacent axes (e.g., "2" and "4") and assign it the number "3". Generating axis network data refers to constructing a two-dimensional grid data structure from the actually identified axes and the virtually completed axes according to their spatial position, direction (horizontal / vertical), and number. This structure records the starting coordinates, ending coordinates, number, and intersection coordinates of each axis with other axes. Finally, this axis network data structure, as an independent set of attributes, is associated with and stored in the digital layout semantic model, so that any component in the model (such as columns and walls) can quickly find the axis number on which its positioning is based.

[0037] The reasoning and completion of logical axes can be done as follows: First, sort the identified axes in the same direction according to their coordinate values; second, analyze the arithmetic difference between the numbers of adjacent axes (e.g., the difference between 1, 2, and 4 is 1 and 2), and compare it with their actual spacing ratio; if the numbering sequence is found to be interrupted but there is a uniform physical space, insert a virtual axis at the interruption point according to the average spacing between the preceding and following axes, and assign it the interruption number.

[0038] Based on the above, the algorithm automatically completes the logical verification and integrity repair of the axis network, thereby automatically handling situations where there may be incomplete or simplified expressions in the drawings. This ensures that the axis positioning benchmarks in the generated digital layout semantic model are logically complete and continuous, avoiding the loss or error of on-site layout benchmarks due to problems in the drawing, and further improving the robustness and reliability of the system in processing complex design drawings.

[0039] To address the problem that traditional two-dimensional layout methods struggle to accurately locate irregularly shaped curved surface components commonly found in campus buildings, such as gymnasiums and lecture halls, in one embodiment, the model reconstruction unit is further configured to: for irregularly shaped curved surface components, use the NURBS algorithm to perform three-dimensional parametric surface reconstruction on the extracted irregular structural feature lines; for the reconstructed surface, perform adaptive triangular meshing according to construction accuracy requirements, define the mesh vertices as layout control points for the component, and add classification attributes of irregularly shaped curved surface points and corresponding accuracy thresholds to all layout control points in the digital layout semantic model.

[0040] In practical implementation, the feature lines of irregular structures refer to the two-dimensional or three-dimensional curves extracted from the original design documents to define the boundaries or shapes of the surface. Examples include roof outlines and grandstand section lines extracted from elevation drawings or details. Using the NURBS (Non-Uniform Rational B-Spline) algorithm for three-dimensional parametric surface reconstruction means that the system calls APIs from geometric kernel libraries such as OpenCASCADE, using the extracted feature lines as the boundary lines or section lines for constructing the surface. Through surface modeling methods such as lofting, sweeping, or filling, a smooth NURBS surface defined by control points, node vectors, and weight factors is generated. This mathematical expression can accurately describe complex free-form surfaces. Adaptive triangular meshing based on construction accuracy requirements means that after surface reconstruction, the system dynamically calculates the required degree of triangular mesh refinement based on user-defined construction tolerances (e.g., the deviation between the actual construction position and the theoretical design position must be controlled within 10mm). Algorithms (such as Delaunay triangulation or the advancing front method) preferentially generate denser, smaller triangular patches in areas of high surface curvature (such as edges and corners), and sparser, larger triangular patches in areas of gentle curvature. The mesh vertices are the corner points of these triangular patches. Defining mesh vertices as layout control points means that the system extracts the three-dimensional coordinates of these vertices (usually hundreds to thousands) as a set of discretized reference points to guide the on-site support or positioning of curved templates. In the digital layout semantic model, the system creates a special component classification called "irregular surface points" for this set of control points and uniformly assigns them a higher accuracy requirement than ordinary structural components (such as ±10mm), namely a "precision threshold" (e.g., ±5mm). This threshold originates from industry consensus or specific project requirements for higher construction accuracy for irregular structures.

[0041] Adaptive triangulation refers to discretizing NURBS surfaces by invoking existing mesh generation algorithms, such as the Delaunay refinement algorithm or the forward propagation method, under the constraint of meeting construction accuracy requirements (e.g., the maximum side length of the triangular mesh does not exceed L millimeters). The system iteratively subdivides the mesh to ensure that denser meshes are generated in regions of high surface curvature and sparser meshes are generated in flat regions.

[0042] Based on the above, continuous and complex curved surfaces that are difficult to construct directly are discretized into a series of discrete, measurable, and layout-capable spatial points, and given higher quality control standards. This provides a practical digital solution for the construction layout of irregular curved surfaces in campus buildings, transforming abstract curved surface designs into a series of specific and executable spatial coordinate points. By applying stricter accuracy thresholds to these points, the final construction quality of irregular curved surfaces is ensured, solving the technical problem of lacking a basis for layout and difficulty in guaranteeing accuracy for such components.

[0043] To detect construction deviations in real time, in one embodiment, the dynamic verification engine includes: The data interface unit is configured to receive and parse measured data from the measuring equipment. The real-time judgment unit is configured to: retrieve the corresponding design coordinates and allowable deviation values ​​from the digital layout semantic model based on the point identifiers in the measured data; calculate the Euclidean distance between the measured coordinates and the design coordinates as the deviation value; compare the deviation value with the allowable deviation value, and generate a verification conclusion dataset with status identifiers and deviation types.

[0044] In practical implementation, the data interface unit and the real-time judgment unit are the two core components of the dynamic verification engine. The data interface unit is a network service that runs resident in the background (such as a TCP / UDP server implemented based on the Netty framework). It listens on a specific port and receives network data forwarded by a total station (such as a Leica TS16) through a serial port server, or RTK data sent by a GNSS receiver (such as a Trimble R12) via the NTRIP protocol. It has pre-built or can load standard communication protocol parsing libraries for various measurement devices (such as Leica's GeoCOM, Trimble's GDK, etc.). For a specific device, by selecting the corresponding protocol parsing library, the raw data stream sent by the device is parsed into a coordinate data format that is unified within the system. Moreover, the construction and use of this protocol parsing library are well-known technologies in the field of measurement technology.

[0045] The process of parsing the measured data includes: unpacking the data stream according to the pre-agreed communication protocol with the equipment (such as Leica GeoCOM or custom JSON format), extracting the valid fields, including point ID, easting, northing, elevation, timestamp, measurement status (fixed solution / floating solution), etc., and converting them into a coordinate format that is unified within the system.

[0046] The real-time judgment unit is a high-priority computation thread that obtains the parsed measured data object from the data interface unit. Based on the point identifier (i.e., point number), it first performs a fast search in the index of the digital layout semantic model (e.g., through a hash table) to retrieve the design coordinates bound to that point number. , , ) and allowable deviation (Tolerance), based on measured coordinates ( , , The formula for calculating the Euclidean distance (deviation value) is: The comparison process determines whether the condition "deviation value ≤ allowable deviation value" is true. If true, the status is marked as "normal"; otherwise, it is marked as "out of limit". The initial default deviation type is "position deviation". Finally, the calculation thread outputs a verification conclusion dataset instance, whose data structure can be defined as: {pointId: "P001", measuredCoord: [1000.005, 2000.012, 50.101], designCoord: [1000.000, 2000.000, 50.000], deviation: 0.018, tolerance: 0.020, status: "normal", type: "position deviation"}.

[0047] Based on the above, the interface unit is responsible for efficiently and stably acquiring field data; the judgment unit is responsible for performing rapid and automated mathematical calculations and logical judgments based on accurate model benchmarks. This enables online and automated inspection of construction layout quality, transforming traditional post-construction sampling into full-process inspection, providing real-time feedback on the accuracy status of each layout point, and effectively preventing the transmission and accumulation of errors from a single point to subsequent processes.

[0048] To address the frequent spatial conflicts between embedded parts and the structure caused by the intersection of multiple disciplines (such as structure, architecture, and mechanical and electrical) during campus construction, the real-time judgment unit is also configured to: after obtaining the design coordinates of the current measured point, call the preset spatial relationship rule library to check whether there are other marked points that violate the rules within the space centered on the measured point and within a preset radius; if so, add a type identifier representing the spatial conflict warning and conflict object information to the verification conclusion dataset.

[0049] In practice, the pre-set spatial relationship rule base is a set of rules stored in the system database. Each rule defines the spatial constraint relationship between different professional components. For example, rule 1: Within a radius of 300mm of the "center point of the structural column", there shall be no "center point of the electromechanical reserved sleeve". Rule 2: The net distance between the "finished surface line of the shear wall" and the "outer contour line of the main ventilation duct" shall not be less than 200mm.

[0050] The inspection process is as follows: After the real-time judgment unit processes a measured point (let's assume it's a "center point of an electromechanical reserved sleeve" P1), the system uses its design coordinates as the center and a preset radius (e.g., 500mm, which can be configured in the system according to project needs) to draw a three-dimensional spatial buffer. Then, the system queries all other points in the digital layout semantic model that are also marked as "layouted" and located within this buffer. For each queried point (e.g., a "center point of a structural column" P2), the system matches applicable rules in the spatial relationship rule base based on the "component classification" attribute of P1 and P2. If a rule is matched (e.g., rule 1 above), the actual spatial distance between P1 and P2 is calculated and compared with the limit specified by the rule (300mm). If the actual distance is less than the limit, it is determined to be a violation of the rule, i.e., a spatial conflict exists. Subsequently, the system will not only modify or add the deviation type in the verification conclusion of the current point P1 to "spatial conflict warning", but also add a "conflict object" field to the conclusion dataset to record information such as the identifier, type and actual distance of the conflict point P2.

[0051] Based on the above, construction specifications and multi-disciplinary collaboration requirements are encoded into computer-executable logical rules, and the surrounding environment is automatically scanned during each layout verification. This expands the verification dimension from the absolute accuracy of a single point to the relative relationship between points, enabling proactive detection of potential spatial interference problems between components of different disciplines in the early stages of construction. This provides early warning for on-site adjustments and avoids rework and delays caused by conflicts that are only exposed during the installation stage in the traditional method. This improves the efficiency of multi-disciplinary collaborative construction and the first-time success rate.

[0052] To address the issue that dynamic changes in the construction site environment (such as vibrations caused by large machinery operations nearby) can affect measurement accuracy and necessitate dynamic adjustments to acceptance standards, in one embodiment, the dynamic verification engine further includes a dynamic threshold adjustment unit. The dynamic threshold adjustment unit is configured to dynamically query a threshold mapping table based on the component classification attributes of the current verification point in the digital layout semantic model and the on-site vibration level data from environmental sensors, providing an updated allowable deviation value for the real-time judgment unit.

[0053] In practical implementation, the dynamic threshold adjustment unit is an independent background service module in the dynamic verification engine. It has two inputs: one is the "component classification" attribute (such as "precision equipment foundation anchor bolt") of the current point to be verified, which is read from the digital layout semantic model; the other is the real-time data stream from vibration sensors deployed on the construction site (for example, a wireless triaxial vibration sensor based on MEMS technology, model STEVAL-MKI197V1, which has the advantages of small size, wireless transmission, and real-time monitoring of acceleration data; in practical applications, other models can also be selected for this component, which is not limited in this application embodiment) accessed through the Internet of Things gateway.

[0054] The system calculates and extracts "vibration levels" reflecting ground vibration intensity from sensor data, such as classifying the effective value of acceleration (RMS) into levels 0-5. The threshold mapping table can be determined based on historical construction data statistics, the accuracy attenuation model of measuring instruments under specific vibration environments, and engineering specifications. For example, through experimental calibration, when the vibration level reaches level 3, the ranging error of a certain model of total station will increase by 50%. Based on this, the allowable deviation value under the corresponding working condition is relaxed by 50% from the benchmark value. An exemplary threshold mapping table is constructed using a three-dimensional lookup table, with the three dimensions being: "component classification," "vibration level," and "construction stage." This table is pre-set and entered by project technicians during system initialization based on construction experience, the nominal accuracy of measuring instruments under vibration environments, and engineering specifications. For example, one record in the mapping table is: {Component Classification: "Ordinary Floor Slab Line", Vibration Level: 0-2, Construction Stage: "Main Structure", Allowable Deviation: ±10mm}; another is: {Component Classification: "Ordinary Floor Slab Line", Vibration Level: 3-5, Construction Stage: "Main Structure", Allowable Deviation: ±15mm}.

[0055] The workflow of the dynamic threshold adjustment unit is as follows: When the real-time judgment unit prepares to verify a certain point, it sends a query request to the dynamic threshold adjustment unit, passing the "component classification" and current "construction stage" information for that point (the construction stage information is provided by the project schedule module or manually set). Simultaneously, the dynamic threshold adjustment unit reads the latest "vibration level" data, and then, based on these three key values, queries the threshold mapping table to obtain a new allowable deviation value suitable for the current working condition. This new value is then returned to the real-time judgment unit, replacing the static allowable deviation value stored in the digital layout semantic model, for use in this deviation comparison.

[0056] Based on the above, the system's calibration standards are no longer rigid but can be intelligently adjusted according to the actual on-site environmental conditions. When adverse environmental factors such as vibration affect measurement accuracy, appropriately relaxing the deviation tolerance of non-critical parts can avoid unnecessary false alarms caused by environmental interference, making the system's judgment more consistent with the actual construction situation. For critical components or in good environments, stricter standards are applied, thereby improving the rationality of the system's judgment and the user-friendliness of human-computer interaction while ensuring the core quality of the project.

[0057] To address the problem of abstract deviation information and the difficulty for on-site operators to quickly understand and respond correctly, in one embodiment, the human-machine collaborative guide includes: The instruction synthesis unit is configured to: select response rules from a preset strategy mapping table based on the deviation type and status in the verification conclusion dataset, and generate a set of guiding instructions with spatial offset, text prompts or highlighted graphics; The rendering output unit is configured to encode the boot instruction set into target format data that is compatible with various terminal device interfaces and output it.

[0058] In practice, the instruction synthesis unit receives the verification conclusion dataset from the dynamic verification engine. The pre-set policy mapping table is a dictionary structure stored in memory. Its "key" is a combination of the "deviation type" and "state" in the verification conclusion, and its "value" is the corresponding "response rule," defining the mapping from "deviation condition" to "guidance action." For example, one response rule can be defined as: {"deviation type": "position deviation", "state": "out of limit"} → {"action": "generate navigation arrow", "parameter": {"start point": measured coordinates, "end point": design coordinates}}; another rule is: {"deviation type": "spatial conflict warning", "state": "out of limit"} → {"action": "highlight area", "parameter": {"center point": measured coordinates, "radius": 500mm, "color": "red"}}. The instruction synthesis unit queries this table based on the input dataset, finds the matching response rule, and then executes the "action" defined by the rule. Generating spatial offsets involves calculating the three-dimensional vector difference (ΔX, ΔY, ΔZ) from the measured coordinates to the design coordinates. Generating text prompts involves formatting a piece of text, such as "Point P001 is offset 15mm east and 8mm north." Generating highlighted graphics involves generating drawing parameters for a geometry (such as a red transparent sphere) with a specified location, size, and color in three-dimensional space. All these outputs together constitute the guidance instruction set.

[0059] The rendering output unit is responsible for translating the instruction set into a language that the terminal can understand. For example, for a client installed on a tablet, this unit encodes the instruction set into JSON instructions based on the WebSocket protocol, sends them wirelessly, and has them parsed and drawn onto the screen by the client's 3D rendering engine (such as one based on Three.js). For augmented reality (AR) glasses that support a specific SDK, this unit may encapsulate spatial offsets and highlight graphic information into corresponding spatial anchor points and holographic model data packets via APIs provided by the glasses manufacturer (such as Microsoft HoloLens' Mixed Reality Toolkit) and send them.

[0060] Based on the above, the abstract deviation data calculated in the background is transformed into multimodal and intuitive interactive information through a set of rule-driven conversion mechanisms. This lowers the threshold for on-site operators to interpret complex technical data, directly converts deviation values ​​into operational guidelines, and improves the efficiency and accuracy of deviation correction operations.

[0061] To ensure the accuracy and relevance of the guidance instructions, in one embodiment, the policy mapping table includes at least: Rule 1: When the verification conclusion dataset contains a status indicator representing an out-of-limit location, trigger the generation of a guiding instruction with a three-dimensional vector pointing from the measured point to the design point; Rule 2: When the verification conclusion dataset contains a type identifier representing a spatial conflict warning, a guidance instruction is triggered to generate a highlighting instruction for the conflict area and associated component identifiers.

[0062] In specific implementation, the triggering condition for the first rule is: the value of the status field in the verification conclusion dataset is "out of limit," and the value of the deviation type field is "position deviation." When the instruction synthesis unit detects this combination of conditions, it triggers the generation of a three-dimensional vector guidance instruction. The specific content of this instruction includes two three-dimensional coordinate points: the starting point is the coordinates of the measured point (…). The endpoint is the coordinates of the design point ( In a graphical interface, this might be represented as a line segment with a directional arrow pointing from the measured location to the correct location.

[0063] The trigger condition for the second rule is: the value of the deviation type field in the verification conclusion dataset is "spatial conflict warning". After triggering, the instruction synthesis unit will extract information from the conflict object field of the dataset. The generated guidance instruction consists of two parts: one is "highlighting the conflict area", whose parameters usually include the center position of the conflict area (generally the current measured point or the midpoint of the two conflicting parties) and a display radius. In the 3D view, this area may be covered by a semi-transparent red sphere; the other is "displaying associated component identifiers". The system will retrieve the component names of the two conflicting parties (such as "structural column KZ-3" and "duct FD-01") from the digital layout semantic model and display these text labels near the highlighted area.

[0064] Based on the above, the two common key issues (inaccurate positioning and spatial discrepancies) are mapped to two intuitive forms of visual feedback (pointing arrows and highlighted warnings). This ensures that when facing the most significant types of construction deviations, the system can provide clear and unambiguous operational guidance or problem alerts, enabling on-site personnel to grasp the core of the problem and take countermeasures immediately.

[0065] To address the issue of limited construction space on campuses and the potential for time-series conflicts arising from overlapping multi-process operations, leading to downtime or safety risks, in one embodiment, as shown, the system further includes a time-series conflict early warning module. This module is communicatively connected to both the intelligent data hub and the dynamic verification engine, and is configured as follows: Obtain the component construction process logic and planned operation time period contained in the digital layout semantic model from the intelligent data hub; The dynamic verification engine continuously obtains the verification conclusion dataset and updates the actual completion status and timestamp of each layout point in real time. Based on the planned process logic, work periods, and actual progress, the system dynamically simulates the spatial and temporal overlap of multiple parallel laying tasks within a specified future time period. When the simulation predicts that the work areas and time windows of different shifts or processes will conflict, a timing conflict warning signal is generated and sent to the human-machine collaboration guide.

[0066] In practice, the timing conflict early warning module communicates with the intelligent data hub and dynamic verification engine through an internal message bus (such as RabbitMQ) or direct API calls.

[0067] The component construction process logic and planned operation time period are semantic information added by project planners when creating the digital layout semantic model. For example, the task of "second floor slab layout" is set with the prerequisite task of "second floor column positioning layout", and the planned operation time period is "2023-10-10 08:00 to 2023-10-10 18:00". This module subscribes to the verification conclusion dataset stream published by the dynamic verification engine. When the "status" of a point in the dataset changes from "out of limit" to "normal" (meaning that the point has been corrected and qualified), the module marks this point as "actually completed" and records the completion timestamp.

[0068] The core of the dynamic simulation is a scheduling algorithm based on discrete event simulation. Starting from the current system time, this algorithm, based on the process dependencies defined in the model, the remaining workload of each task (estimated based on the number of unfinished layout points), the team's work efficiency (historical average speed), and the planned time window, predicts the evolution of the work area (represented by the outer envelope box of the components involved in the task) and the expected time period occupied by each layout task in three-dimensional space within a future period (e.g., the next 24 hours). The algorithm continuously detects whether the expected spatiotemporal envelopes of any two tasks intersect; if so, it is determined to be a spatiotemporal conflict. For example, the simulation finds that the "Roof Steel Structure Layout of Area A" task (requiring space for large hoisting equipment) and the "Exterior Wall Decoration Keel Layout of Area A" task (requiring space for aerial work platforms) are both planned to occupy the same spatial area from 10:00 AM to 11:00 AM tomorrow. Once such a conflict is detected, the module immediately generates a temporal conflict warning signal. This signal is a structured message containing the conflicting task pair, the spatial range of the conflict, and the predicted conflict time window. The signal is sent to the human-machine collaboration guide through the internal system channel. The human-machine collaboration guide can then convert it into a prominent scheduling reminder message, which is displayed on the construction administrator's monitoring screen or the mobile terminal of the relevant work team.

[0069] Based on the above, by combining the construction schedule plan with the real-time collected work completion data, and using simulation technology to prospectively extrapolate future work scenarios and predict risks, the system's capabilities are expanded from real-time control of current work quality to predictive coordination of future work plans. This provides valuable decision support for construction managers, helps to proactively optimize the construction sequence, avoid resource conflicts and safety hazards, and further enhance the overall coordination and controllability of the construction plan in the complex and compact campus construction environment.

[0070] In the embodiments provided by this invention, it should be understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, code, or any suitable combination thereof. For hardware implementation, the processor can be implemented in one or more of the following: application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, other electronic units designed to implement the functions described herein, or combinations thereof. For software implementation, some or all of the processes of the embodiments can be performed by a computer program instructing the associated hardware. During implementation, the program can be stored in a computer-readable storage medium or transmitted as one or more instructions or code on a computer-readable storage medium. Computer-readable storage media include computer storage media and communication media, wherein communication media include any medium that facilitates the transmission of a computer program from one place to another. Storage media can be any available medium accessible to a computer. Computer-readable storage media can include, but are not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code having the form of instructions or data structures and accessible to a computer.

[0071] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A system for auxiliary data processing and real-time verification of construction layout for campus buildings, characterized in that, include: The intelligent data hub is configured to: import and parse the original design files of campus buildings, and generate a digital layout semantic model with geometric information and layout semantic information; The dynamic verification engine, which is connected to the intelligent data hub, is configured to: receive measured data collected on-site, compare the measured data with the corresponding design information in the digital laying-out semantic model, and generate a verification conclusion dataset with deviation judgment and type identification. The human-machine collaborative guide is communicatively connected to the intelligent data hub and the dynamic verification engine, and is configured to: synthesize a set of guidance instructions to guide on-site operations based on the digital cable laying semantic model and the verification conclusion dataset, and output the set of guidance instructions and cable laying status information to the terminal device.

2. The auxiliary data processing and real-time verification system for campus building construction layout according to claim 1, characterized in that, The intelligent data hub includes: The intelligent drawing parsing unit is configured to: perform layer separation and element recognition on the original design file, and extract annotation information; The model reconstruction unit is configured to: construct a three-dimensional model based on the coordinates and topological relationships of the graphic elements output by the drawing parsing unit, associate the annotation information with the corresponding geometric elements, and add semantic attributes including design coordinates, component classification and allowable deviation values ​​to the three-dimensional model elements to form the digital layout semantic model.

3. The auxiliary data processing and real-time verification system for campus building construction layout according to claim 2, characterized in that, The intelligent drawing parsing unit is also configured to: identify axis elements and axis numbering labels in the drawing; infer and complete missing logical axes based on the spatial positional relationship and numbering rules of the axis elements; generate axis network data with complete and continuous numbering; and store the axis network data into the digital layout semantic model.

4. The auxiliary data processing and real-time verification system for campus building construction layout according to claim 2, characterized in that, The model reconstruction unit is further configured to: for irregular curved surface components, use the NURBS algorithm to reconstruct three-dimensional parametric surfaces using the extracted irregular structural feature lines; for the reconstructed surfaces, perform adaptive triangular meshing according to construction accuracy requirements, define the mesh vertices as the layout control points of the component, and add the classification attributes of irregular curved surface points and the corresponding accuracy thresholds to all layout control points in the digital layout semantic model.

5. The auxiliary data processing and real-time verification system for campus building construction layout according to claim 1, characterized in that, The dynamic verification engine includes: The data interface unit is configured to receive and parse the measured data from the measuring device; The real-time judgment unit is configured to: retrieve the corresponding design coordinates and the allowable deviation value from the digital layout semantic model based on the point identifiers in the measured data; calculate the Euclidean distance between the measured coordinates and the design coordinates as the deviation value; compare the deviation value with the allowable deviation value, and generate the verification conclusion dataset with status identifiers and deviation types.

6. The auxiliary data processing and real-time verification system for campus building construction layout according to claim 5, characterized in that, The real-time judgment unit is also configured to: after obtaining the design coordinates of the current measured point, call the preset spatial relationship rule library to check whether there are other laid-out points that violate the rules within the space centered on the measured point and within a preset radius. If present, add a type identifier representing the spatial conflict warning and conflict object information to the verification conclusion dataset.

7. The auxiliary data processing and real-time verification system for campus building construction layout according to claim 5, characterized in that, The dynamic verification engine also includes a dynamic threshold adjustment unit, which is configured to: dynamically query the threshold mapping table based on the component classification attributes of the current verification point in the digital laying semantic model and the field vibration level data from the environmental sensor, and provide the real-time judgment unit with an updated allowable deviation value.

8. The auxiliary data processing and real-time verification system for campus building construction layout according to claim 1, characterized in that, The human-machine collaboration guide includes: The instruction synthesis unit is configured to: select response rules from a preset strategy mapping table based on the deviation type and status in the verification conclusion dataset, and generate the guidance instruction set with spatial offset, text prompts or highlighted graphics; The rendering output unit is configured to encode the boot instruction set into target format data that is compatible with various terminal device interfaces and output it.

9. The auxiliary data processing and real-time verification system for campus building construction layout according to claim 8, characterized in that, The policy mapping table includes at least: When the verification conclusion dataset contains a status indicator representing a location exceeding the limit, a guiding instruction is triggered to generate a three-dimensional vector pointing from the measured point to the design point. When the verification conclusion dataset contains a type identifier representing a spatial conflict warning, a guidance instruction is triggered to generate a highlighting instruction for the conflict area and associated component identifiers.

10. The auxiliary data processing and real-time verification system for campus building construction layout according to any one of claims 1 to 9, characterized in that, It also includes a timing conflict early warning module, which is communicatively connected to the intelligent data hub and the dynamic verification engine, and is configured as follows: The component construction process logic and planned operation time period contained in the digital layout semantic model are obtained from the intelligent data center; The dynamic verification engine continuously acquires the verification conclusion dataset and updates the actual completion status and timestamp of each layout point in real time. Based on the planned process logic, work periods, and actual progress, the system dynamically simulates the spatial and temporal overlap of multiple parallel laying tasks within a specified future time period. When the simulation predicts that the work areas and time windows of different shifts or processes will conflict, a timing conflict warning signal is generated and sent to the human-machine collaboration guide.

Images