Road and bridge engineering quality supervision method and system based on mobile internet multi-terminal cooperation

Abstract

The invention relates to a road and bridge engineering quality supervision method and system based on mobile internet multi-terminal cooperation. The method comprises the following steps: generating a space-time composite label by fusing engineering data collected by a mobile terminal, a Beidou time service signal, multi-source positioning information and BIM component characteristics; dynamic drift compensation is carried out on the digital twin model by utilizing the attenuation characteristic of the material, and position deviation caused by long-period construction is eliminated; intelligent arbitration of multi-terminal operation conflicts is realized based on space-time overlap analysis; generating a lightweight incremental data packet according to the change priority; and finally, synchronously generating a visual supervision interface fusing the BIM model, the quality anomaly annotation and the drift thermodynamic diagram at a mobile terminal, a PC terminal and a large screen terminal through a multi-resolution rendering engine. According to the invention, three core problems in the prior art that the positions of the model and the entity are mismatched, multi-terminal collaborative conflicts frequently occur and the visualization efficiency of heterogeneous equipment is low are solved, and the accuracy, the collaboration and the decision-making efficiency of road and bridge engineering quality supervision are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of engineering quality monitoring, in particular to a road and bridge engineering quality supervision method and system based on mobile internet multi-terminal collaboration. BACKGROUND

[0002] With the rapid development of China's transportation infrastructure construction, road and bridge engineering presents the characteristics of large scale, complex structure and diversified participation. The multi-terminal collaborative quality supervision method based on mobile internet realizes real-time collection of engineering site data, fusion analysis of multi-source information and collaborative control of quality risks by integrating heterogeneous devices such as mobile terminals, cloud platforms and command center large screens. The method uses Beidou positioning, BIM model and mobile communication technology to build a digital twin supervision system, enabling supervision, construction, detection and other personnel to work collaboratively under a unified space-time reference, significantly improving the timeliness and accuracy of quality supervision.

[0003] However, the existing technology in the multi-terminal collaboration scenario faces the following core problems that need to be solved urgently: first, due to the long-period construction characteristics of road and bridge engineering, cumulative position drift occurs between the digital twin model and the actual engineering entity, resulting in mismatch between quality detection data and spatial position; second, there is a lack of effective space-time conflict arbitration mechanism when multiple terminals operate concurrently, causing data overlap, command conflict and other problems; third, it is difficult to achieve efficient visualization of massive engineering data on devices with significant performance differences such as mobile terminals and large screens, which seriously affects the efficiency of supervision and decision-making. These defects make it difficult for existing supervision systems to meet the stringent requirements of large-scale road and bridge engineering for real-time, accuracy and collaboration. SUMMARY

[0004] Therefore, the purpose of the present application is to provide a road and bridge engineering quality supervision method and system based on mobile internet multi-terminal collaboration that can effectively eliminate model drift, intelligently resolve multi-terminal conflicts and achieve cross-device dynamic visualization.

[0005] The purpose of the present application is achieved by the following scheme:

[0006] In a first aspect, the present application provides a road and bridge engineering quality supervision method based on mobile internet multi-terminal collaboration, comprising the following steps:

[0007] S1: Aligning and fusing the time and space of the road and bridge engineering quality detection data collected by the on-site mobile terminal, the timing signal collected by the Beidou satellite timing system, the positioning data collected by the multi-source positioning device and the component information stored by the BIM system, and generating a time-space composite label;

[0008] S2: SLAM-Kalman joint calibration is performed on the space-time composite label, the material attenuation characteristic parameters pre-stored in the engineering material database are combined, the position deviation dynamic compensation of the preset digital twin BIM model is performed, and the theoretical position data and the transformation matrix after drift compensation are generated;

[0009] S3: The timestamp in the theoretical position data and the space-time composite label is verified for multi-terminal operation conflict, the arbitration result and the conflict position set are generated based on the spatial distance threshold and the time window overlap analysis;

[0010] S4: The difference amplitude of the transformation matrix is calculated, the priority of the changed component is determined according to the arbitration result, and the component-level incremental package is generated;

[0011] S5: The component-level incremental package is processed by multi-resolution rendering, the space is labeled combined with the conflict position set, and the dynamic supervision interface that can be synchronously displayed on the mobile terminal AR interface, the PC terminal management platform and the large screen command center is generated. The dynamic supervision interface is used to display the rendered BIM model three-dimensional structure, the labeled quality abnormal area and the drift deviation heat map of the theoretical position and the measured position.

[0012] In one of the embodiments, the S1 of the road and bridge engineering quality supervision method based on mobile internet multi-terminal collaboration provided by the application specifically includes the following steps:

[0013] S11: The time reference unification processing is performed on the time signal collected by the Beidou satellite time service system, the phase alignment and clock drift compensation are performed on the multi-source time signal through the clock synchronization protocol, and the timestamp is generated;

[0014] S12: The GPS positioning data and the UWB positioning data collected by the multi-source positioning device are processed by the credibility weighted fusion, the dynamic weight coefficient is calculated according to the signal strength variance, and the fusion positioning coordinate is generated;

[0015] S13: The attenuation modeling processing is performed on the transmission power and the receiving power of the obtained UWB positioning device, the power difference is converted through the logarithmic transformation formula, and the signal credibility is generated;

[0016] S14: The feature coding processing is performed on the component geometric topology data and the material attribute parameters stored in the BIM system, and the irreversible component identifier is generated by using the hash algorithm;

[0017] S15: The space-time correlation processing is performed on the quality detection data, the timestamp, the fusion positioning coordinate, the signal credibility and the component identifier collected by the on-site mobile terminal, the five-dimensional feature mapping relationship is established, and the space-time composite label is generated.

[0018] In one embodiment, step S2 of the road and bridge engineering quality supervision method based on multi-terminal collaboration of mobile Internet provided by the present invention specifically includes the following steps:

[0019] S21: Perform SLAM real-time point cloud matching on the fused positioning coordinates of the spatiotemporal composite label, and generate a pose transformation matrix by extracting the spatial correspondence between the on-site scanned point cloud and the feature points of the BIM model and optimizing the rigid body pose transformation parameters.

[0020] S22: Perform time integration processing on the material attenuation characteristic parameters pre-stored in the engineering material database, calculate the cumulative effect of material performance attenuation in combination with the time span of the current construction stage, and generate a dynamic drift weight factor.

[0021] S23: Based on the preset digital twin BIM model, the pose transformation matrix and dynamic drift weight factor are fused by coordinate transformation matrix. The vertex spatial coordinates of the digital twin model are adjusted by weighted interpolation to generate theoretical position data and transformation matrix after drift compensation.

[0022] In one embodiment, S3 of the road and bridge engineering quality supervision method based on multi-terminal collaboration of mobile Internet provided by the present invention specifically includes the following steps:

[0023] S31: Perform spatial clustering on the theoretical location data, identify the location clustering areas of high-density outliers using the density peak detection algorithm, and generate potential conflict areas;

[0024] S32: Perform sliding window analysis on the timestamps of the spatiotemporal composite tags, detect concurrent operation records of multiple terminals within a preset time threshold, and generate time conflict markers;

[0025] S33: Perform joint verification processing on potential conflict areas and time conflict markers, calculate arbitration weights based on terminal permission levels and data freshness, generate conflict event priority scores through weighted fusion of spatial overlap and temporal overlap, determine valid conflict events and their spatial coordinate sets based on the score ranking results, and generate arbitration results and conflict location sets.

[0026] In one embodiment, the calculation formula for the conflict event priority score of the road and bridge engineering quality supervision method based on multi-terminal collaboration of mobile Internet provided by the present invention is as follows:

[0027]

[0028] in, Prioritize conflict events and score them. Terminal type weights, This is the freshness decay coefficient. , For spatiotemporal factor weights, , The area of ​​spatial overlap. For the duration of time overlap, The total area is This refers to the duration of the time window.

[0029] In one embodiment, S4 of the road and bridge engineering quality supervision method based on multi-terminal collaboration of mobile Internet provided by the present invention specifically includes the following steps:

[0030] S41: Perform Frobenius norm difference processing on the transformation matrix, calculate the Euclidean space distance between the current transformation matrix and the historical transformation matrix, and generate the matrix difference quantity;

[0031] S42: Perform threshold triggering processing on the matrix difference. When the value of the matrix difference exceeds the preset tolerance threshold, extract the geometric topology data of the corresponding component and generate a set of changed components.

[0032] S43: Perform priority sorting on the changed component set, calculate the transmission priority score based on the arbitration result and the component security level, generate the component priority sequence by multiplying the security level mapping score by the arbitration weight, and generate the component-level incremental package by lightweight encapsulating the component data in sequence order.

[0033] In one embodiment, step S5 of the road and bridge engineering quality supervision method based on multi-terminal collaboration via mobile internet provided by the present invention specifically includes the following steps:

[0034] S51: Perform multi-terminal adaptive rendering on component-level incremental packages, generate AR loadable model primitives through lightweight parsing on mobile devices, and generate BIM models for global engineering status monitoring in the command center through full-precision rendering on large screen devices, generating multi-resolution visualization primitives.

[0035] S52: Spatial labeling of conflict location set, overlaying semi-transparent warning area markers on AR interface generated by mobile 3D rendering engine, overlaying heat diffusion effect on GIS map on large screen, generating quality anomaly spatial labeling layer;

[0036] S53: Perform drift deviation analysis on theoretical position data and measured position data collected in real time by on-site mobile terminals. Calculate Euclidean distance to generate a scalar field to quantify the position deviation between the model and the entity. Generate a multi-gradient heatmap through Gaussian smoothing and chromatographic mapping to generate a drift deviation visualization layer.

[0037] S54: Perform multi-terminal synchronous fusion processing on multi-resolution visualization primitives, quality anomaly spatial annotation layer and drift deviation visualization layer, generate AR supervision interface with gesture interaction on mobile terminal, generate management console interface with data panel on PC terminal, generate command and decision interface with multi-viewport linkage on large screen terminal, and generate dynamic supervision interface.

[0038] Secondly, the present invention provides a road and bridge engineering quality supervision system based on multi-terminal collaboration via mobile Internet, which is configured with the following modules:

[0039] The multi-source data spatiotemporal fusion module is used to perform spatiotemporal alignment and feature fusion on road and bridge engineering quality inspection data collected by on-site mobile terminals, timing signals collected by the Beidou satellite timing system, positioning data collected by multi-source positioning devices, and component information stored in the BIM system to generate spatiotemporal composite tags.

[0040] The joint calibration and offset compensation module is used to perform SLAM-Kalman joint calibration on the spatiotemporal composite label. Combined with the material attenuation characteristic parameters pre-stored in the engineering material database, it performs dynamic position offset compensation on the preset digital twin BIM model and generates theoretical position data and transformation matrix after drift compensation.

[0041] The multi-terminal conflict verification module is used to perform multi-terminal operation conflict verification on theoretical location data and timestamps in spatiotemporal composite tags. Based on spatial distance threshold and time window overlap analysis, it generates arbitration results and conflict location sets.

[0042] An incremental module is constructed to calculate the difference magnitude of the transformation matrix, filter the changed components according to the priority determined by the arbitration result, and generate a component-level incremental package;

[0043] The multi-terminal dynamic supervision interface generation module is used to perform multi-resolution rendering processing on component-level incremental packages, and to perform spatial annotation by combining conflict location sets to generate a dynamic supervision interface that can be displayed simultaneously on mobile AR interfaces, PC management platforms, and large-screen command centers. The dynamic supervision interface is used to display the rendered BIM model 3D structure, annotated quality anomaly areas, and a heat map of the drift deviation between theoretical and measured positions.

[0044] Thirdly, this application provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement any of the above-mentioned methods for supervising the quality of road and bridge engineering based on multi-terminal collaboration of mobile Internet.

[0045] Fourthly, this application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements any of the above-mentioned methods for supervising the quality of road and bridge engineering based on multi-terminal collaboration of the mobile Internet.

[0046] In summary, this application provides a multi-source data spatiotemporal fusion and dynamic drift compensation mechanism for road and bridge engineering quality supervision based on multi-terminal collaboration via mobile internet. This mechanism achieves centimeter-level spatial consistency between the digital twin model and the engineering entity, effectively eliminating long-term positional drift caused by the time-varying characteristics of materials. Utilizing a spatiotemporally coupled conflict arbitration algorithm, it enables intelligent collaborative decision-making for concurrent multi-terminal operations, resolving command conflicts and data overlay issues during collaborative work among supervision, construction, and design parties. Based on lightweight incremental transmission and multi-resolution rendering technology, it achieves adaptive visualization of BIM models on mobile, PC, and large-screen devices, overcoming the limitations imposed by performance differences in heterogeneous devices on supervision efficiency. Through the spatial overlay of drift heatmaps and quality anomaly annotations, a closed-loop supervision system of "data-model-decision" can be constructed to achieve precise location of quality defects and risk warning. Ultimately, this forms a comprehensive solution covering data acquisition, model calibration, conflict resolution, incremental synchronization, and multi-terminal visualization, significantly improving the quality supervision accuracy, cross-terminal collaborative efficiency, and decision response speed of complex road and bridge engineering projects, providing technical support for large-scale infrastructure construction.

[0047] To better understand and implement this invention, the following detailed description is provided in conjunction with the accompanying drawings. Attached Figure Description

[0048] Figure 1 A flowchart illustrating a method for supervising the quality of road and bridge engineering based on multi-terminal collaboration via mobile internet, provided in an embodiment of this application;

[0049] Figure 2 A schematic diagram illustrating the process of generating arbitration results and a set of conflict locations provided in an embodiment of this application;

[0050] Figure 3 This is a schematic diagram of the structure of a road and bridge engineering quality supervision system based on multi-terminal collaboration via mobile Internet, provided as another embodiment of this application. Detailed Implementation

[0051] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Preferred embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a thorough and complete understanding of the disclosure of the invention.

[0052] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0053] In one embodiment, such as Figure 1 As shown, a method for quality supervision of road and bridge engineering based on multi-terminal collaboration via mobile internet is provided. This embodiment illustrates the method by applying it to a terminal. It is understood that this method can also be applied to a server, or to a system including both a terminal and a server, and is implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0054] S1: Spatiotemporal alignment and feature fusion are performed on the road and bridge engineering quality inspection data collected by the on-site mobile terminal, the timing signal collected by the Beidou satellite timing system, the positioning data collected by the multi-source positioning device, and the component information stored in the BIM system to generate spatiotemporal composite tags.

[0055] Specifically, the system receives road and bridge engineering quality inspection data collected by mobile terminals on-site. This data includes information such as subgrade compaction, concrete cube compressive strength, steel reinforcement protective layer thickness, bridge deck smoothness, and steel structure weld flaw detection data. The system also receives timing signals from the BeiDou satellite timing system. These signals are used to calibrate the timestamps of the inspection and positioning data to eliminate time synchronization errors between multiple devices. The timing signals are transmitted via the NTPv4 protocol, and the system corrects transmission delays using a network latency compensation algorithm to ensure time synchronization across all terminals. Simultaneously, the system receives positioning data collected by multi-source positioning devices. These devices integrate BeiDou BDS, GPS, GLONASS, and inertial measurement units. The positioning data can use the CGCS2000 national geodetic coordinate system, and the output includes latitude, longitude, elevation, and positioning accuracy factors. The system fuses multi-system positioning data using a multi-source data fusion algorithm based on extended Kalman filtering to improve positioning accuracy in obstructed environments. In addition, the system calls the component information stored in the BIM system. The BIM system can store component data in the IFC4.0 standard format. Each component is associated with a unique identifier and includes attributes such as design parameters, construction schedule, and quality acceptance standards. The BIM system is deployed on a cloud server, and the system calls the component information in real time through a RESTful API interface.

[0056] Furthermore, the system performs time alignment, using the BeiDou satellite timing signal as the reference time axis to linearly interpolate and correct the timestamps of the quality inspection data and positioning data. For data with missing timestamps, the system uses the mean-filling method based on the time interval of adjacent valid data to ensure that the timestamp accuracy of all data is consistent. Subsequently, the system performs spatial alignment, converting the CGCS2000 coordinates collected by multi-source positioning devices to the design coordinate system of the BIM model through the Bursa seven-parameter transformation model. During the conversion process, the system introduces elevation anomaly correction parameters to ensure spatial coordinate matching. For inspection points without direct positioning data, the system infers their spatial position through the association relationship of components in the BIM model, that is, using the design coordinates of the component to which the inspection point belongs and the relative offset to obtain the spatial position of the inspection point.

[0057] Preferably, the system can employ a feature-level fusion algorithm to weight and fuse the physical parameters of the quality inspection data, the spatial features of the positioning data, and the attribute features of the BIM components. The weight coefficients are determined using the analytic hierarchy process (AHP) based on the degree of influence of each feature on quality supervision. After fusion, the system generates a spatiotemporal composite label. The spatiotemporal composite label adopts a structured data format and includes a unique label ID, a data source terminal ID, a calibrated timestamp, spatial coordinates in the design coordinate system, a quality inspection feature vector, an associated component ID, and a data credibility score. The data credibility score is calculated based on the positioning accuracy and the accuracy of the inspection equipment. The system stores the spatiotemporal composite label in a cloud database after JSON serialization to support rapid retrieval and related queries.

[0058] S2: Perform SLAM-Kalman joint calibration on the spatiotemporal composite label, combine the material attenuation characteristic parameters pre-stored in the engineering material database, perform dynamic position offset compensation on the preset digital twin BIM model, and generate theoretical position data and transformation matrix after drift compensation.

[0059] Specifically, the system acquires images of the surrounding environment through the mobile terminal's built-in camera, and simultaneously collects angular velocity and acceleration data through the mobile terminal's built-in inertial measurement unit. Based on the acquired images and inertial data, the system constructs a local environmental point cloud map, employing a visual-inertial SLAM scheme during the construction process. After completing the point cloud map construction, the system performs feature matching between the point cloud map and the BIM model. The matching objects include component corners, marker points, etc. Environmental constraint information is obtained through feature matching to correct positioning drift. The system uses the SIFT algorithm during the feature matching process.

[0060] Furthermore, the system uses the positioning results output by SLAM and the raw positioning data from multi-source positioning devices as observations. The system constructs state equations and observation equations. The state equations include state vectors such as position, velocity, and drift error, while the observation equations are established based on a Gaussian noise model. The system optimizes the positioning results through a prediction-update iterative process. During the iteration, the system adaptively adjusts the filter gain using the covariance matrix, ultimately outputting calibrated positioning data. Simultaneously, the system accesses an engineering materials database, a cloud-based distributed database that stores attenuation characteristic parameters of common materials used in road and bridge engineering. These parameters include material strength attenuation coefficients, elastic modulus attenuation rates, and geometric deformation coefficients. The material strength attenuation coefficient is affected by time and environmental humidity. The parameter values ​​are obtained through indoor experiments combined with actual engineering monitoring data. The engineering materials database supports dynamic updates, and the system can supplement parameter samples by adding new engineering cases.

[0061] After calibration and database access are completed, the system performs dynamic compensation for the positional offset of the digital twin BIM model. Based on the calibrated positioning data and the design coordinates of the BIM model, the actual positional offset of each component is calculated. Subsequently, the system combines the attenuation characteristic parameters from the engineering material database and calculates the component deformation caused by material attenuation through a finite element analysis model. The system then superimposes the positioning offset and the deformation to obtain the total offset compensation. The system uses a homogeneous transformation matrix to describe the component's positional compensation. The transformation matrix includes translation, rotation, and scaling parameters. The translation parameters are determined based on the total offset compensation, the rotation parameters are determined based on the component's attitude change (calculated using inertial measurement unit data), and the scaling parameter is set to a fixed value by default. The system applies the transformation matrix to the component coordinates of the BIM model through matrix multiplication to achieve dynamic positional offset compensation. After compensation, the system stores the compensated component coordinates as theoretical position data, associated with the calibrated positioning data. The theoretical position data includes a timestamp synchronized with the compensation time, the component ID, the compensated 3D coordinates, and the compensation error, which is the difference between the total offset compensation and the measured deviation.

[0062] S3: Perform multi-terminal operation conflict verification on the theoretical location data and the timestamps in the spatiotemporal composite label, and generate arbitration results and conflict location set based on spatial distance threshold and time window overlap analysis.

[0063] Specifically, the system collects operation data from multiple terminals, including mobile terminals of supervisors, PC management terminals of construction units, terminals of testing equipment, and operation terminals of the large-screen command center. Operation data from each terminal is uploaded to the cloud platform in real time. The system then retrieves operation data from the cloud platform. This data includes the terminal ID, operation type, target component ID, operation time window, operation location coordinates, and operation content. Operation types cover quality parameter modification, component location annotation, and abnormal command issuance. Based on the acquired operation data, the system defines conflict types: the first type is data overlay conflict, where different terminals modify the same quality parameter of the same component within an overlapping time window; the second type is command execution conflict, where different terminals issue contradictory operation commands to the same component; and the third type is location annotation conflict, where different terminals annotate different types of quality abnormalities within an overlapping spatial range.

[0064] After defining the conflict type, the system performs conflict verification and determines the spatial distance threshold. This threshold is dynamically configured based on component size and monitoring accuracy requirements. The system categorizes components by size and configures corresponding spatial distance thresholds for different component types. These thresholds can be manually adjusted via a cloud platform to adapt to different engineering scenarios. The system then performs time window overlap analysis using an interval overlap judgment algorithm. For the operation time windows of two terminals, the system calculates the maximum start time and minimum end time of the two time windows. If the maximum start time is less than the minimum end time, the time windows are considered to overlap. The minimum unit of the time window is set to a fixed duration to avoid misjudgments due to minor time differences.

[0065] Furthermore, the system determines conflicts based on spatial distance thresholds and time window overlap analysis results. For the same component ID, if there is time window overlap and the Euclidean distance between the operation position coordinates is less than the spatial distance threshold, it is determined that there is a conflict. For different conflict types, the system adopts different judgment conditions. Data coverage conflict judgment requires that the operation parameter types be consistent, instruction execution conflict judgment requires that the instruction logic be mutually exclusive, and position annotation conflict judgment requires that the annotation types be different.

[0066] After conflict determination, the system generates an arbitration result and a set of conflict locations. The system has preset arbitration priority rules, set according to terminal type, with emergency operations having higher priority than regular operations. Emergency operations include anomaly reports and stop-work orders marked "emergency." Priority rules can be configured and modified through the cloud platform. The system determines valid and invalid operations based on the priority rules. Valid operations are those with the highest priority, while invalid operations are the remaining conflicting operations. The arbitration result includes the conflict ID, a list of involved terminals, the valid operation ID, and the handling method for invalid operations. Invalid operation handling methods include revocation, overwriting, and delayed execution. If conflicting operations with the same priority exist, the system uses the earliest timestamp to determine the valid operation. The system also generates a set of conflict locations, which includes the conflict ID, conflict type, conflict location coordinate range, and a list of involved component IDs. The conflict location coordinate range is represented by minimum bounding box coordinates. This set of conflict locations is used for subsequent spatial annotation and visualization.

[0067] S4: Calculate the difference magnitude of the transformation matrix, select the changed components according to the priority determined by the arbitration result, and generate a component-level incremental package.

[0068] Specifically, the system calculates the difference magnitude of the transformation matrix. This difference magnitude quantifies the degree of change in the BIM model before and after compensation. The calculated difference magnitude indices include translational difference magnitude, rotational difference magnitude, and overall difference magnitude. The translational difference magnitude is the Euclidean distance of the translation parameters of the transformation matrix, the rotational difference magnitude is the absolute value of the rotation angle of the transformation matrix, and the overall difference magnitude is calculated using a weighted summation method. During the weighted summation, translational and rotational difference magnitudes are assigned corresponding weights, determined based on the degree of impact of the changes on model visualization. The system sets difference magnitude thresholds, including translational difference magnitude thresholds, rotational difference magnitude thresholds, and overall difference magnitude thresholds. The overall difference magnitude threshold is obtained through equivalent conversion. The system compares the difference magnitude of a component with the corresponding threshold. If the difference magnitude of a component exceeds any threshold, the component is determined to be a component that needs to be updated.

[0069] Furthermore, the system combines the arbitration results to filter and prioritize the components that need updating. The system first categorizes components into priority levels: the first priority includes components involved in the valid operations determined by the arbitration results, and critical components whose differences exceed a threshold (critical components are identified by their critical level attributes in the BIM model); the second priority includes non-critical components whose differences exceed the threshold; and the third priority includes components whose differences do not exceed the threshold but have quality anomaly markings. The system filters components according to priority levels, prioritizing first-priority components, then second-priority, and third-priority components. Within the same priority level, the system sorts components by the magnitude of difference from largest to smallest to ensure that important changes are synchronized first. Based on this, the system generates component-level incremental packages. These packages use incremental update technology, containing only the changed data to reduce data transmission volume. Each incremental package includes a package identifier, component information, changed data, and verification information. The package identifier includes the package ID, version number, and generation timestamp; the component information includes the component ID, critical level, and priority; the changed data includes transformation matrix parameters, quality inspection data update items, and an attribute change list; and the verification information is used for data integrity verification.

[0070] S5: Perform multi-resolution rendering on component-level incremental packages, combine conflict location sets for spatial annotation, and generate a dynamic supervision interface that can be simultaneously displayed on mobile AR interfaces, PC management platforms, and large-screen command centers. The dynamic supervision interface is used to display the rendered BIM model 3D structure, annotated quality anomaly areas, and a heat map of the drift deviation between theoretical and measured positions.

[0071] Specifically, the system performs multi-resolution rendering processing on component-level incremental packages. The system dynamically adjusts the rendering precision of the BIM model based on the performance differences of different terminals. For mobile AR interfaces, the system uses a low-resolution hierarchical detail model to simplify component surface textures and details, ensuring a high real-time AR rendering frame rate. For PC management platforms, the system uses a medium-resolution hierarchical detail model to retain key details and support interactive operations. For large-screen command centers, the system uses a high-resolution hierarchical detail model to fully preserve component textures and details, supporting global rendering and local magnification. The system uses a GPU-accelerated ray casting algorithm for rendering, combined with occlusion culling technology to reduce unnecessary rendering overhead. For large components, the system can use block rendering technology, loading rendering data in blocks according to spatial regions to improve loading speed.

[0072] After rendering, the system performs spatial annotation based on the set of conflict locations, employing different annotation methods for different conflict types. Data coverage conflicts are indicated by a red dashed bounding box combined with text annotations of the data conflict; command execution conflicts are indicated by a yellow solid bounding box combined with text annotations of the command conflict and valid command prompts; and location annotation conflicts are indicated by a blue gradient bounding box combined with text annotations of the annotation conflict and a display of the highest priority annotation content. During the annotation process, the system supports interactive querying of annotation information. When a user clicks on an annotated area, the system displays conflict details, including the involved terminal, operation time, and arbitration result. The system also sets annotation transparency to avoid obscuring key structures in the BIM model.

[0073] Furthermore, the system generates a dynamic monitoring interface that can be simultaneously displayed on mobile AR interfaces, PC management platforms, and large-screen command centers. The interface includes the rendered 3D structure of the BIM model, marked quality anomaly areas, and a heatmap showing the drift deviation between theoretical and measured locations. The rendered 3D structure of the BIM model supports free rotation, scaling, and translation, and also supports hiding or showing different types of components. The marked quality anomaly areas are generated based on quality inspection data and are highlighted in red. The marked areas are associated with the anomaly data, and users can click on the marked areas to view detailed inspection parameters. The drift deviation heatmap uses the BIM model surface as a carrier and employs color gradients to represent the magnitude of the drift deviation. The heatmap supports dynamic updates, with the update frequency matching the compensation frequency.

[0074] Preferably, the system achieves multi-terminal synchronization through a real-time communication protocol. The cloud platform pushes component-level incremental packages, annotation information, and heatmap data to each terminal. The mobile AR interface uses the corresponding SDK to integrate the BIM model with the actual site, supporting virtual-real alignment. The PC management platform provides data statistical analysis functions, including statistics on the number of abnormal areas and drift deviation distribution. The large-screen command center supports multi-screen split display to adapt to the high-resolution display requirements of large screens. The system sets an upper limit for interface synchronization delay to ensure the consistency of content displayed on each terminal. It also supports offline caching; when the network is interrupted, the terminal loads the latest locally cached data, and the system automatically synchronizes and updates the content after the network is restored.

[0075] In summary, this application provides a multi-source data spatiotemporal fusion and dynamic drift compensation mechanism for road and bridge engineering quality supervision based on multi-terminal collaboration via mobile internet. This mechanism achieves centimeter-level spatial consistency between the digital twin model and the engineering entity, effectively eliminating long-term positional drift caused by the time-varying characteristics of materials. Utilizing a spatiotemporally coupled conflict arbitration algorithm, it enables intelligent collaborative decision-making for concurrent multi-terminal operations, resolving command conflicts and data overlay issues during collaborative work among supervision, construction, and design parties. Based on lightweight incremental transmission and multi-resolution rendering technology, it achieves adaptive visualization of BIM models on mobile, PC, and large-screen devices, overcoming the limitations imposed by performance differences in heterogeneous devices on supervision efficiency. Through the spatial overlay of drift heatmaps and quality anomaly annotations, a closed-loop supervision system of "data-model-decision" can be constructed to achieve precise location of quality defects and risk warning. Ultimately, this forms a comprehensive solution covering data acquisition, model calibration, conflict resolution, incremental synchronization, and multi-terminal visualization, significantly improving the quality supervision accuracy, cross-terminal collaborative efficiency, and decision response speed of complex road and bridge engineering projects, providing technical support for large-scale infrastructure construction.

[0076] In one embodiment, S1 of the road and bridge engineering quality supervision method based on multi-terminal collaboration of mobile Internet provided by the present invention specifically includes the following steps:

[0077] S11: Perform time base unification processing on the timing signals collected by the BeiDou satellite timing system, perform phase alignment and clock drift compensation on the multi-source timing signals through the clock synchronization protocol, and generate timestamps.

[0078] Specifically, the system receives timing signals collected by the BeiDou satellite timing system. These signals include second pulse signals, time information data frames, and satellite ephemeris data. The system first preprocesses the timing signal, filtering out high-frequency noise with a bandpass filter, then amplifying the weak signal to a recognizable range. Simultaneously, it demodulates the signal to restore the complete content of the time information data frames, ensuring that the time information carried by the signal is not lost. The system selects a clock synchronization protocol to construct a unified time reference framework. This protocol supports multi-terminal access, can receive time synchronization requests from different terminals, and provide feedback on the reference time. The system sets the BeiDou satellite timing signal as the master clock source, ensuring that the time of all access terminals is referenced to this master clock source, guaranteeing a consistent time scale.

[0079] If auxiliary time synchronization signals such as ground time stations are available, the system can collect phase information of each signal through the phase detection module, calculate the phase difference with the master clock source, and adjust the phase offset of the auxiliary signals. If auxiliary signals are missing, the system relies solely on the master clock source to maintain the time reference. To address clock drift, the system collects historical clock drift data at fixed intervals, establishes a drift prediction model, predicts the drift amount by analyzing clock frequency change trends, adjusts the clock frequency in advance to offset the impact, and periodically collects actual clock deviation data to correct the model. After completing the above processing, the system generates a timestamp containing complete time information, associates it with the time synchronization signal source identifier and processing time, stores it in a local cache, and synchronizes it to the cloud time server at set intervals for other terminals to access via an interface.

[0080] S12: Perform credibility-weighted fusion processing on GPS positioning data and UWB positioning data collected by multi-source positioning devices, calculate dynamic weight coefficients based on signal strength variance, and generate fused positioning coordinates.

[0081] Specifically, the system receives GPS and UWB positioning data. GPS data includes satellite identification numbers, pseudorange measurements, positioning coordinates, and positioning accuracy information. UWB data includes anchor node identifiers, signal flight time, positioning coordinates, and signal strength data. The system performs validity checks on both types of data. In addition to judging the format and coordinate range, it also checks the number of GPS satellite locks and the communication status of UWB anchor nodes. If the number of GPS satellite locks is insufficient or the UWB anchor node communication is abnormal, the corresponding data is directly marked as invalid and discarded.

[0082] The system selects signal strength samples within a continuous time period to calculate the variance. The sample time span is set according to the construction schedule, and the variance reflects the degree of signal interference. The system calls a preset weight calculation model to convert the signal strength variance into weight coefficients, ensuring that the sum of the weights for the two types of data is 1. During fusion, the system multiplies the coordinate components of the two types of data by their corresponding weights and then superimposes them to obtain the components of the fused positioning coordinates. The system monitors the signal status in real time. If a certain type of data has no valid input for a continuous period, it automatically assigns its weight to the other type of data to avoid coordinate interruption. After fusion, the system binds the fused positioning coordinates to the corresponding timestamps through data identifiers, providing spatial data for subsequent processing.

[0083] S13: Perform attenuation modeling on the transmit and receive power of the acquired UWB positioning device, and convert the power difference using a logarithmic transformation formula to generate signal reliability.

[0084] Specifically, the system acquires the transmit and receive power data of the UWB positioning device. The transmit power data includes set values, actual detected values, and power stability parameters, while the receive power data includes instantaneous values, average values, and fluctuation ranges. The system preprocesses the power data, removing outliers that exceed the normal range and supplementing missing data by extrapolating from valid data at adjacent times to ensure data continuity.

[0085] The system constructs a power attenuation model based on electromagnetic wave propagation theory. Input parameters include transmit power, receive power, propagation distance, and environmental parameters. The propagation distance is calculated from the initial distance between the UWB anchor node and the tag node, and the environmental parameters are pre-set according to the environmental type of the construction area. The system substitutes the difference between the transmit and receive power into a logarithmic transformation formula to convert it into a power attenuation coefficient that conforms to the attenuation characteristics. The system compares the power attenuation coefficient with a preset reasonable range, and combines this with power stability parameters to determine signal stability, quantifying and generating a signal reliability index. The reliability index is divided into different levels, corresponding to different weights in subsequent data processing.

[0086] S14: Perform feature encoding processing on the component geometric topology data and material property parameters stored in the BIM system, and use a hash algorithm to generate irreversible component identifiers.

[0087] Specifically, the system extracts component geometric topology data and material property parameters from the BIM system. The geometric topology data includes vertex coordinates, edge connectivity, surface composition, and component assembly relationships. The material property parameters include material type, density, and elastic modulus. The system converts the geometric topology data into a standardized geometric description language format to meet the reading needs of different data processing modules. The material property parameters are categorized by component type, and an index is created to associate components with corresponding material parameters for easy and rapid querying.

[0088] The system extracts volume, surface area, feature dimensions, and topological features from geometric topology data. Volume is calculated using vertex coordinates, and feature dimensions are determined by key dimensions based on component morphology. Key features such as strength grade and type encoding are extracted from material property parameters and combined to form a comprehensive feature dataset. The system serializes this comprehensive feature dataset into a binary data stream, compresses it to remove redundant information, and then inputs it into a hash algorithm to generate a fixed-length hash value as the component identifier.

[0089] S15: Perform spatiotemporal correlation processing on the quality inspection data, timestamps, fused positioning coordinates, signal reliability and component identification collected by the on-site mobile terminal, establish a five-dimensional feature mapping relationship, and generate spatiotemporal composite tags.

[0090] Specifically, the system collects quality inspection data gathered by mobile terminals on-site, covering inspection parameters for roadbed, pavement, bridge structures, and other parts. It establishes a categorized index based on the inspection location and project, allowing for quick retrieval of corresponding inspection data by location or project. The system retrieves timestamps, fused positioning coordinates, signal reliability, and component identification data from the pre-processing results, ensuring that all types of data carry terminal identification and processing time.

[0091] Furthermore, the system constructs a spatiotemporal association framework, using timestamps and component identifiers as the core association keys. First, it matches the collection time and timestamp of the quality inspection data, binding a unique time marker to each piece of inspection data. Then, based on the location information in the inspection data, it matches the corresponding BIM component identifier, achieving association between the inspection data and the component. The system combines the spatial information of the fused positioning coordinates to determine the spatial location corresponding to the inspection data and complete the binding. Signal reliability is used as the basis for data reliability, associating it with the corresponding fused positioning coordinates and inspection data to form a five-dimensional data set. The system constructs a mapping matrix, where rows represent data entries and columns represent five-dimensional features. The matrix elements identify the feature correspondences, and the matrix is ​​dynamically adjusted during data updates. After mapping, the system generates spatiotemporal composite labels, adds signal reliability and fused positioning coordinate fields, stores relevant information in a structured format, serializes it into JSON, stores it in a cloud database, and incrementally updates it to the cache of each terminal according to terminal needs, providing data support for subsequent model calibration and conflict verification.

[0092] It should be noted that the spatiotemporal composite label is a structured data carrier generated by spatiotemporal alignment and feature fusion of quality inspection data collected by mobile terminals on site, Beidou satellite timing signals, positioning data collected by multi-source positioning devices, and component information stored in the BIM system. The core includes information such as fused positioning coordinates, calibrated timestamps, signal reliability, unique component identifiers, and quality inspection feature vectors, providing a unified data benchmark for subsequent spatial matching and model calibration.

[0093] In one embodiment, step S2 of the road and bridge engineering quality supervision method based on multi-terminal collaboration of mobile Internet provided by the present invention specifically includes the following steps:

[0094] S21: Perform SLAM real-time point cloud matching on the fused positioning coordinates of the spatiotemporal composite labels. Generate a pose transformation matrix by extracting the spatial correspondence between the on-site scanned point cloud and the feature points of the BIM model and optimizing the rigid body pose transformation parameters.

[0095] Specifically, the system acquires the fused positioning coordinates from the spatiotemporal composite tag, and simultaneously collects real-time point cloud data of the engineering entity through on-site scanning equipment. The point cloud data contains spatial point information on the surface of the engineering components. The system preprocesses the collected real-time point cloud data, removing stray points caused by environmental interference through filtering algorithms, and then simplifies the point cloud data volume through downsampling processing to ensure subsequent matching efficiency. At the same time, it extracts feature point data of the components from the preset digital twin BIM model. Feature points include spatially identifiable points such as component corners and structural joints.

[0096] Furthermore, the system establishes a spatial correspondence between real-time point clouds and feature points in the BIM model. A feature point matching algorithm identifies spatially related point pairs between the two types of feature points, forming a matching point set. For this matching point set, the system constructs a rigid body pose transformation parameter optimization model. This model aims to minimize spatial point position errors by adjusting pose transformation parameters, including translation and rotation parameters. The system iteratively calculates and optimizes these parameters until the point position error meets preset conditions. After stopping the iteration, the final rigid body pose transformation parameters are generated, and a pose transformation matrix is ​​constructed based on these parameters.

[0097] S22: Perform time integration processing on the material attenuation characteristic parameters pre-stored in the engineering material database, calculate the cumulative effect of material performance attenuation in combination with the time span of the current construction stage, and generate a dynamic drift weight factor.

[0098] Specifically, the system retrieves the material degradation characteristic parameters of the corresponding component from the engineering material database. These parameters include the performance degradation law of the material over time, covering the time-related change curves of performance indicators such as material strength and elastic modulus. The system obtains the time span data of the current construction stage, which is the duration of the component from the completion of construction to the current monitoring time. This data is calculated by comparing the construction progress record with the current timestamp.

[0099] The system performs time integration on material attenuation characteristic parameters, using the time span as the integration interval. An integration algorithm calculates the cumulative amount of material performance attenuation within this time period, reflecting the degree of influence of material performance changes on the spatial position of the component. Existing technologies often use fixed material attenuation coefficients for drift compensation, failing to consider the cumulative impact of the construction phase's time span on the attenuation effect. This results in compensation results that cannot dynamically adapt to the characteristics of long-term construction projects. This system, through time integration, correlates the time span with attenuation characteristic parameters, enabling the quantitative calculation of the cumulative effect of material performance attenuation and providing a precise weighting basis for dynamic drift compensation. The dynamic drift weighting factor is a parameter generated based on the cumulative amount of material performance attenuation, used to quantify the degree of influence of material attenuation on the spatial position drift of the component. Its value is positively correlated with the cumulative amount of material performance attenuation and can be dynamically adjusted according to the material type and construction stage of different components to differentiate the varying contributions of material attenuation to drift under different scenarios. The system converts the cumulative amount of material performance attenuation into a dynamic drift weighting factor, which is positively correlated with the cumulative amount; that is, the larger the cumulative amount, the larger the weighting factor, indicating a more significant impact of material attenuation on component drift.

[0100] S23: Based on the preset digital twin BIM model, the pose transformation matrix and dynamic drift weight factor are fused by coordinate transformation matrix. The vertex spatial coordinates of the digital twin model are adjusted by weighted interpolation to generate theoretical position data and transformation matrix after drift compensation.

[0101] Specifically, the system uses a pre-set digital twin BIM model as its foundation, retrieves the pose transformation matrix and dynamic drift weight factor, and constructs a coordinate transformation matrix fusion framework. Existing technologies for digital twin model drift compensation rely solely on the pose transformation matrix for spatial coordinate adjustment, failing to incorporate dynamic drift caused by material attenuation into the compensation system, thus failing to eliminate the deviation between the model and the entity's position caused by changes in material properties. This system, through coordinate transformation matrix fusion processing, combines the pose transformation matrix reflecting real-time spatial pose with the dynamic drift weight factor reflecting the cumulative effect of material attenuation, achieving accurate compensation under both factors. The system performs weighted fusion of the pose transformation matrix and the dynamic drift weight factor, with weight allocation determined based on component type and construction stage, ensuring that the fusion result simultaneously reflects the combined impact of real-time spatial pose and material attenuation.

[0102] Preferably, the system can use a weighted interpolation algorithm to adjust the vertex spatial coordinates of the digital twin BIM model. Based on the original vertex coordinates of the model, and combined with the fused coordinate transformation matrix, the system calculates the compensated spatial coordinates of each vertex point by point. During the interpolation process, the system refers to the geometric topology of the components to ensure that the adjusted vertex coordinates maintain the original structural shape of the components and avoid geometric distortion. After all vertex coordinates are adjusted, the system generates theoretical position data after drift compensation, which includes the compensated coordinates of each vertex of the component; at the same time, based on the vertex coordinate adjustment parameters, the system constructs the final transformation matrix. The system associates the theoretical position data and the transformation matrix with the component identifier and processing timestamp, stores them in the cloud BIM model database, and pushes them to each terminal.

[0103] In one embodiment, S3 of the road and bridge engineering quality supervision method based on multi-terminal collaboration of mobile Internet provided by the present invention specifically includes the following steps:

[0104] S31: Perform spatial clustering on theoretical location data, identify high-density outlier clusters using density peak detection algorithms, and generate potential conflict areas.

[0105] Specifically, the system acquires theoretical location data, performs spatial clustering processing on this data, and divides it into several data subsets according to the spatial distribution characteristics of the theoretical location data. Each subset corresponds to the spatial range of a specific group of components in the project, ensuring coverage of the theoretical locations of all components. The system uses a density peak detection algorithm to process the data subsets, first calculating the local density of each data point using the following formula:

[0106]

[0107] in, For data points Local density, For data points With data points Euclidean distance, To preset the cutoff distance, For indicator functions, when hour ,when hour The system sets a density threshold based on the overall spatial distribution characteristics of the theoretical location data. When a certain data point... Exceeding this threshold, and the data point is compared with other data points. Between data points exceeding the threshold When the distance is less than a set threshold, the system determines that the data point is a high-density anomaly. The system tracks the spatial distribution of high-density anomalies, identifies their concentrated areas, and states that the spatial position deviation of data points in such areas exceeds the normal error range of engineering, indicating a possibility of multi-terminal operation conflicts. The system marks these areas as potential conflict areas and records the spatial range of the potential conflict areas, the number of theoretical location data points contained therein, and the associated component identifiers.

[0108] S32: Perform sliding window analysis on the timestamps of the spatiotemporal composite tags, detect concurrent operation records of multiple terminals within a preset time threshold, and generate time conflict markers.

[0109] Specifically, the system extracts timestamps from the spatiotemporal composite tags and performs sliding window analysis on these timestamps. Based on the time characteristics of each construction process in the road and bridge project, the system sets the duration of the sliding time window and the step size of the sliding time window based on the timestamp collection frequency, ensuring that all timestamps are included in the window analysis without duplication or omission. The system sequentially includes the timestamps in the sliding time window according to their chronological order and counts the number of concurrent operations within each window. The calculation formula is as follows:

[0110]

[0111] in, For sliding windows Concurrent operands within, The time interval corresponding to the current sliding time window. This refers to the collection of all terminals involved in project quality supervision. For indicator functions, when the terminal In time When operation records exist ,otherwise When a certain sliding time window corresponds to When a preset threshold is reached, the system determines that these terminals are performing concurrent operations within that time window. Such concurrent operations may cause data overwriting or instruction execution conflicts. The system associates and marks the sliding time window with concurrent operations, the corresponding terminal identifier, and the operation record number to generate a time conflict marker. The time conflict marker must clearly record the start time, end time, and terminal type involved in the concurrent operation.

[0112] S33: Perform joint verification processing on potential conflict areas and time conflict markers, calculate arbitration weights based on terminal permission levels and data freshness, generate conflict event priority scores through weighted fusion of spatial overlap and temporal overlap, determine valid conflict events and their spatial coordinate sets based on the score ranking results, and generate arbitration results and conflict location sets.

[0113] Specifically, the system calls potential conflict areas and time conflict markers to obtain the permission level of each terminal, and determines the terminal type weight corresponding to each terminal according to the preset terminal permission rules. permission levels and There is a positive correlation. The system obtains the current time. and the corresponding operation time for each conflict event Combined with the preset freshness decay coefficient Calculate the freshness coefficient of the operational data. The system measures the spatial overlap area between the potential conflict area and the operational area corresponding to the time conflict marker. and the total area of ​​the operating area Calculate spatial overlap Simultaneously calculate the time overlap duration of concurrent operations. With time window duration The ratio of these values ​​yields the time overlap. The system sets the spatiotemporal factor weights. and And satisfy Substitute the above parameters into the conflict event priority scoring formula:

[0114]

[0115] in, Prioritize conflict events and score them. Terminal type weights, This is the freshness decay coefficient. , For spatiotemporal factor weights, , The area of ​​spatial overlap. For the duration of time overlap, The total area is This is the duration of the time window. The system operates according to... Conflict events are sorted from largest to smallest and then filtered. Events exceeding a preset scoring threshold are considered valid conflict events. The spatial coordinates corresponding to the valid conflict events are extracted to form a set of spatial coordinates. Finally, an arbitration result containing information on the valid conflict events, the ranking result, and the set of spatial coordinates is generated. At the same time, a set of conflict locations recording the spatial coordinates and conflict types of the valid conflict events is generated.

[0116] In one embodiment, S4 of the road and bridge engineering quality supervision method based on multi-terminal collaboration of mobile Internet provided by the present invention specifically includes the following steps:

[0117] S41: Perform Frobenius norm difference processing on the transformation matrix, calculate the Euclidean space distance between the current transformation matrix and the historical transformation matrix, and generate the matrix difference quantity.

[0118] Specifically, the system obtains the current transformation matrix and the historically stored transformation matrices, and performs Frobenius norm difference processing on the two types of matrices. The system first confirms the dimensionality consistency between the current transformation matrix and the historical transformation matrix, ensuring that both are homogeneous coordinate transformation matrices of the same order, satisfying the prerequisite for subsequent norm calculation. Preferably, the system can use the Frobenius norm to calculate the difference between the two types of matrices, with the following formula:

[0119]

[0120] in, The current transformation matrix With historical transformation matrix The Frobenius norm, Let be the order of the transformation matrix. for The element in the i-th row and j-th column, for The element in the i-th row and j-th column of the matrix. This norm value is equivalent to the distance between the two types of matrices in Euclidean space, and the system defines this value as the matrix difference. The system stores the matrix difference obtained for each calculation, and associates it with the corresponding calculation time, matrix source, and associated component identifier.

[0121] S42: Perform threshold-triggered processing on the matrix difference. When the value of the matrix difference exceeds the preset tolerance threshold, extract the geometric topology data of the corresponding component and generate a set of changed components.

[0122] Specifically, the system calls the matrix difference quantity and simultaneously obtains a preset tolerance threshold. The tolerance threshold is set according to the positional accuracy requirements of road and bridge engineering components and the allowable range of construction errors. The system compares each matrix difference quantity with the tolerance threshold one by one. When the value of a matrix difference quantity exceeds the tolerance threshold, the system determines that the component corresponding to that matrix has a positional offset or geometric shape change and must be included in the change processing scope. Based on the component identifier associated with the matrix difference quantity, the system extracts the geometric topology data of the corresponding component from the BIM system. The geometric topology data includes the component's vertex coordinates, edge connection relationships, face composition information, and spatial relationships with other components. The system performs integrity verification on the extracted geometric topology data to ensure that the data contains all the information required for component change analysis and avoids subsequent processing deviations due to missing data. After the verification is passed, the system integrates the identifiers of these components and the corresponding geometric topology data to generate a change component set. The change component set must clearly record the identifier of each component, the matrix difference quantity value, and the data extraction time.

[0123] S43: Perform priority sorting on the changed component set, calculate the transmission priority score based on the arbitration result and the component security level, generate the component priority sequence by multiplying the security level mapping score by the arbitration weight, and generate the component-level incremental package by lightweight encapsulating the component data in sequence order.

[0124] Specifically, the system obtains the set of changed components and simultaneously calls upon the arbitration results and the component safety levels stored in the BIM system to prioritize the changed component set. The system first determines the safety level mapping score based on the component safety levels. The higher the security level, The larger the value, the greater the arbitration weight of the corresponding component is extracted from the arbitration result. The arbitration weight reflects the importance of a component in conflict resolution. The system calculates the transmission priority score for each component using the following formula:

[0125]

[0126] in, This is a score assigning priority to the transmission of components. The system prioritizes transmission according to... The system sorts the components in the changed component set from largest to smallest, generating a component priority sequence. Based on this sequence, the system performs lightweight encapsulation of the data for each component, retaining only the changed data and removing redundant information, such as retaining only the changed coordinate values ​​of the component's vertices instead of the full coordinate data. The system then integrates the encapsulated component data according to the priority sequence, generating a component-level incremental packet. This incremental packet must include the component identifier, priority score, lightweight data, and data verification information to ensure the integrity and identifiability of the incremental packet during cross-device transmission.

[0127] In one embodiment, step S5 of the road and bridge engineering quality supervision method based on multi-terminal collaboration via mobile internet provided by the present invention specifically includes the following steps:

[0128] S51: Perform multi-terminal adaptive rendering on component-level incremental packages, generate AR loadable model primitives through lightweight parsing on mobile devices, and generate BIM models for global engineering status monitoring in the command center through full-precision rendering on large screens, generating multi-resolution visualization primitives.

[0129] Specifically, the system acquires component-level incremental packages, parses the component data structure within these packages, and extracts geometric topology information and attribute parameters. For mobile devices, the system employs a lightweight parsing algorithm to simplify non-critical geometric details of components while retaining core structural features. The parsed data is then converted into AR-loadable model primitives, which are adapted to the mobile device's processor performance and memory capacity, supporting fast loading and real-time interaction. For large-screen devices, the system uses a full-precision rendering algorithm to fully preserve the geometric details, texture information, and material attributes of components. Combined with global engineering coordinate information, a BIM model is constructed, which is used for global engineering status monitoring in the command center. Through a resolution adaptation algorithm, the system adjusts the polygon count and texture precision of the model primitives based on the differences in display resolution and hardware performance between mobile and large-screen devices. This ultimately generates multi-resolution visualization primitives adapted to different terminals, ensuring smooth display of component information on all devices.

[0130] S52: Spatial labeling of conflict location sets, overlaying semi-transparent warning area markers on the AR interface generated by the mobile 3D rendering engine, and overlaying heat diffusion effects on the GIS map on the large screen to generate a spatial labeling layer for quality anomalies.

[0131] Specifically, the system calls upon a set of conflict locations, extracts spatial coordinates and conflict type information from the set, and establishes a data association table. For mobile devices, the system uses a 3D rendering engine to convert the conflict location coordinates into mobile AR spatial coordinates, overlaying semi-transparent warning area markers on the AR interface. The markers cover the surrounding area of ​​the conflict location, and different conflict types correspond to different warning area display modes. For large screens, the system converts the conflict location coordinates into GIS map coordinates and uses a heat dissipation algorithm to generate heat effects. The heat dissipation range is related to the degree of conflict impact; the greater the conflict impact, the wider the dissipation range. The system integrates the mobile AR warning marker data with the large screen GIS heat dissipation data to form a spatial annotation layer for quality anomalies. This annotation layer can be independently loaded onto the corresponding terminal's visualization interface to intuitively display the location and impact range of quality anomalies.

[0132] S53: Perform drift deviation analysis on theoretical position data and measured position data collected in real time by on-site mobile terminals. Calculate Euclidean distance to generate a scalar field to quantify the position deviation between the model and the entity. Generate a multi-gradient heatmap through Gaussian smoothing and chromatographic mapping to generate a drift deviation visualization layer.

[0133] Specifically, the system acquires theoretical position data and measured position data collected in real time by a mobile terminal on site, and performs drift deviation analysis. The system matches the coordinates of the same component in the two sets of data and calculates the Euclidean distance between the corresponding coordinates, using the following formula:

[0134]

[0135] in, This is the drift deviation value. , , These are the theoretical position coordinate components. , , These are the measured position coordinate components. The system is based on the coordinates of all components. The system generates a scalar field that reflects the global drift deviation distribution of the project. This scalar field is processed using a Gaussian smoothing algorithm to eliminate interference from local outliers. Then, a chromatographic mapping algorithm converts the smoothed scalar field into a multi-gradient heatmap, with different drift deviation values ​​corresponding to different chromatographic colors. The system encapsulates the multi-gradient heatmap into an independent data layer, generating a drift deviation visualization layer. This layer can be used to display the global distribution of the model's positional deviation from the entity's position.

[0136] S54: Perform multi-terminal synchronous fusion processing on multi-resolution visualization primitives, quality anomaly spatial annotation layer and drift deviation visualization layer, generate AR supervision interface with gesture interaction on mobile terminal, generate management console interface with data panel on PC terminal, generate command and decision interface with multi-viewport linkage on large screen terminal, and generate dynamic supervision interface.

[0137] Specifically, the system integrates multi-resolution visualization primitives, a spatial annotation layer for quality anomalies, and a drift deviation visualization layer. For mobile devices, the system merges AR-loadable model primitives, AR warning signs, and mobile-adapted thermal image segments, developing gesture interaction functions that support zooming, rotation, and clicking to view details, generating an AR supervision interface that supports gesture interaction. For PCs, the system combines medium-precision visualization primitives, annotation layer data, and a data statistics module, setting up a data panel in the interface to display component parameters, deviation values, conflict statistics, and other information, generating a management console interface with a data panel. For large-screen devices, the system combines a full-precision BIM model, a GIS heat map, and a multi-viewport control module, setting up a global viewport, a local detail viewport, and a deviation statistics viewport to achieve multi-viewport linkage and generate a command and decision-making interface with multi-viewport linkage. The system ensures real-time updates of data on each terminal interface through a data synchronization protocol, ultimately generating a dynamic supervision interface covering multiple terminals.

[0138] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0139] Based on the same inventive concept, this application also provides a road and bridge engineering quality supervision system based on mobile internet multi-terminal collaboration for implementing the aforementioned method for road and bridge engineering quality supervision based on mobile internet multi-terminal collaboration. The solution provided by this system is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the road and bridge engineering quality supervision system based on mobile internet multi-terminal collaboration provided below can be found in the limitations of the road and bridge engineering quality supervision method based on mobile internet multi-terminal collaboration described above, and will not be repeated here.

[0140] Preferably, such as Figure 3 As shown, this invention provides a road and bridge engineering quality supervision system 600 based on multi-terminal collaboration via mobile internet. This system is configured with the following modules:

[0141] The multi-source data spatiotemporal fusion module 610 is used to perform spatiotemporal alignment and feature fusion on road and bridge engineering quality inspection data collected by on-site mobile terminals, timing signals collected by the Beidou satellite timing system, positioning data collected by multi-source positioning devices, and component information stored in the BIM system to generate spatiotemporal composite tags.

[0142] The joint calibration and offset compensation module 620 is used to perform SLAM-Kalman joint calibration on the spatiotemporal composite label. Combined with the material attenuation characteristic parameters pre-stored in the engineering material database, it performs dynamic position offset compensation on the preset digital twin BIM model and generates theoretical position data and transformation matrix after drift compensation.

[0143] The multi-terminal conflict verification module 630 is used to perform multi-terminal operation conflict verification on theoretical location data and timestamps in spatiotemporal composite labels, and generates arbitration results and conflict location sets based on spatial distance threshold and time window overlap analysis.

[0144] An incremental module 640 is constructed to calculate the difference magnitude of the transformation matrix, filter the changed components according to the priority determined by the arbitration result, and generate a component-level incremental package.

[0145] The multi-terminal dynamic supervision interface generation module 650 is used to perform multi-resolution rendering processing on component-level incremental packages, and to perform spatial annotation by combining conflict location sets to generate a dynamic supervision interface that can be displayed simultaneously on mobile AR interfaces, PC management platforms, and large-screen command centers. The dynamic supervision interface is used to display the rendered BIM model 3D structure, annotated quality anomaly areas, and a heat map of the drift deviation between theoretical and measured positions.

[0146] Preferably, the multi-source data spatiotemporal fusion module 610 provided in this application is configured with the following units:

[0147] The time reference unification unit is used to unify the time reference of the time signals collected by the BeiDou satellite time service system. It performs phase alignment and clock drift compensation on multi-source time signals through clock synchronization protocol and generates timestamps.

[0148] The positioning data fusion unit is used to perform credibility-weighted fusion processing on GPS positioning data and UWB positioning data collected by multi-source positioning devices, calculate dynamic weight coefficients based on signal strength variance, and generate fused positioning coordinates.

[0149] The power attenuation modeling unit is used to perform attenuation modeling on the transmit power and receive power of the acquired UWB positioning device, and convert the power difference through a logarithmic transformation formula to generate signal reliability.

[0150] The component feature encoding unit is used to perform feature encoding processing on the component geometric topology data and material property parameters stored in the BIM system, and uses a hash algorithm to generate irreversible component identifiers.

[0151] The multi-source data association unit is used to perform spatiotemporal association processing on the quality inspection data, timestamps, fused positioning coordinates, signal reliability and component identification collected by the mobile terminal on site, establish a five-dimensional feature mapping relationship, and generate spatiotemporal composite tags.

[0152] Preferably, the joint calibration and offset compensation module 620 provided in this application is configured with the following units:

[0153] The SLAM point cloud matching unit is used to perform real-time SLAM point cloud matching on the fused positioning coordinates of spatiotemporal composite labels. It generates a pose transformation matrix by extracting the spatial correspondence between the on-site scanned point cloud and the feature points of the BIM model and optimizing the rigid body pose transformation parameters.

[0154] The material attenuation integration unit is used to perform time integration processing on the material attenuation characteristic parameters pre-stored in the engineering material database, calculate the cumulative effect of material performance attenuation in combination with the time span of the current construction stage, and generate a dynamic drift weight factor.

[0155] The coordinate transformation fusion unit is used to perform coordinate transformation matrix fusion processing on the pose transformation matrix and dynamic drift weight factor based on the preset digital twin BIM model. It adjusts the vertex spatial coordinates of the digital twin model through weighted interpolation to generate theoretical position data and transformation matrix after drift compensation.

[0156] Preferably, the multi-terminal conflict verification module 630 provided in this application is configured with the following units:

[0157] The spatial clustering conflict identification unit is used to perform spatial clustering processing on theoretical location data, and to identify the location clustering areas of high-density outliers through the density peak detection algorithm, thereby generating potential conflict areas.

[0158] The time conflict marking unit is used to perform sliding window analysis on the timestamps of the spatiotemporal composite label, detect concurrent operation records of multiple terminals within a preset time threshold, and generate time conflict markers.

[0159] The joint verification unit for conflicts is used to jointly verify potential conflict areas and time conflict markers. It calculates arbitration weights based on terminal permission levels and data freshness, generates conflict event priority scores through weighted fusion of spatial overlap and temporal overlap, determines valid conflict events and their spatial coordinate sets based on the score ranking results, and generates arbitration results and conflict location sets.

[0160] Preferably, the incremental module 640 provided in this application is configured with the following units:

[0161] The matrix difference calculation unit is used to perform Frobenius norm difference processing on the transformation matrix, calculate the Euclidean space distance between the current transformation matrix and the historical transformation matrix, and generate the matrix difference.

[0162] The component extraction unit is used to perform threshold-triggered processing on the matrix difference. When the value of the matrix difference exceeds the preset tolerance threshold, the geometric topology data of the corresponding component is extracted to generate a set of modified components.

[0163] The component priority sorting and incremental packet generation unit is used to prioritize the changed component set, calculate the transmission priority score based on the arbitration result and the component security level, generate the component priority sequence by multiplying the security level mapping score by the arbitration weight, and generate component-level incremental packets by lightweight encapsulating the component data in sequence order.

[0164] Preferably, the multi-terminal dynamic supervision interface generation module 650 provided in this application is configured with the following units:

[0165] The multi-terminal adaptive rendering unit is used to perform multi-terminal adaptive rendering of component-level incremental packages. It generates AR-loadable model primitives through lightweight parsing on mobile devices, and generates BIM models for global engineering status monitoring in the command center through full-precision rendering on large screen devices, generating multi-resolution visualization primitives.

[0166] The conflict location annotation unit is used to spatially annotate the set of conflict locations. It overlays semi-transparent warning area markers on the AR interface generated by the mobile 3D rendering engine and overlays heat diffusion effects on the GIS map on the large screen to generate a spatial annotation layer for quality anomalies.

[0167] The drift deviation visualization unit is used to perform drift deviation analysis on theoretical position data and measured position data collected in real time by the field mobile terminal. It generates a scalar field to quantify the position deviation between the model and the entity through Euclidean distance calculation, and generates a multi-gradient heat map through Gaussian smoothing and chromatographic mapping to generate the drift deviation visualization layer.

[0168] The multi-terminal interface fusion unit is used to perform multi-terminal synchronous fusion processing of multi-resolution visualization primitives, quality anomaly spatial annotation layers and drift deviation visualization layers. It generates an AR supervision interface that supports gesture interaction on mobile devices, a management console interface with a data panel on PCs, a command and decision interface with multi-viewport linkage on large screens, and generates a dynamic supervision interface.

[0169] In one embodiment, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described method for quality supervision of road and bridge engineering based on multi-terminal collaboration of mobile Internet.

[0170] In one embodiment, this application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method for quality supervision of road and bridge engineering based on multi-terminal collaboration via mobile Internet.

[0171] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0172] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The components described as separate parts may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0173] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in this application, and these should all be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for quality supervision of road and bridge engineering based on multi-terminal collaboration via mobile internet, characterized in that, Includes the following steps: S1: Spatiotemporal alignment and feature fusion are performed on the road and bridge engineering quality inspection data collected by the on-site mobile terminal, the timing signal collected by the Beidou satellite timing system, the positioning data collected by the multi-source positioning device, and the component information stored in the BIM system to generate spatiotemporal composite tags. S2: Perform SLAM-Kalman joint calibration on the spatiotemporal composite label, combine the material attenuation characteristic parameters pre-stored in the engineering material database, perform dynamic position offset compensation on the preset digital twin BIM model, and generate theoretical position data and transformation matrix after drift compensation. S3: Perform multi-terminal operation conflict verification on the theoretical location data and the timestamp in the spatiotemporal composite label, and generate arbitration results and conflict location set based on spatial distance threshold and time window overlap analysis; S4: Calculate the difference magnitude of the transformation matrix, filter the changed components according to the priority determined by the arbitration result, and generate a component-level incremental package; S5: Perform multi-resolution rendering on the component-level incremental package, and spatially label it in conjunction with the set of conflict locations to generate a dynamic supervision interface that can be simultaneously displayed on the mobile AR interface, the PC management platform, and the large-screen command center. The dynamic supervision interface is used to display the rendered BIM model 3D structure, the labeled quality anomaly areas, and the drift deviation heatmap between the theoretical and measured positions.

2. The method according to claim 1, characterized in that, S1 includes: S11: Perform time base unification processing on the timing signals collected by the BeiDou satellite timing system, perform phase alignment and clock drift compensation on the multi-source timing signals through the clock synchronization protocol, and generate timestamps; S12: Perform credibility-weighted fusion processing on GPS positioning data and UWB positioning data collected by multi-source positioning devices, calculate dynamic weight coefficients based on signal strength variance, and generate fused positioning coordinates; S13: Perform attenuation modeling on the transmit and receive power of the acquired UWB positioning device, convert the power difference using a logarithmic transformation formula, and generate signal reliability. S14: Perform feature encoding processing on the component geometric topology data and material property parameters stored in the BIM system, and use a hash algorithm to generate irreversible component identifiers; S15: Perform spatiotemporal correlation processing on the quality inspection data collected by the on-site mobile terminal, the timestamp, the fused positioning coordinates, the signal reliability, and the component identifier to establish a five-dimensional feature mapping relationship and generate a spatiotemporal composite label.

3. The method according to claim 1, characterized in that, S2 includes: S21: Perform SLAM real-time point cloud matching on the fused positioning coordinates of the spatiotemporal composite label, and generate a pose transformation matrix by extracting the spatial correspondence between the on-site scanned point cloud and the feature points of the BIM model and optimizing the rigid body pose transformation parameters. S22: Perform time integration processing on the material attenuation characteristic parameters pre-stored in the engineering material database, calculate the cumulative effect of material performance attenuation in combination with the time span of the current construction stage, and generate a dynamic drift weight factor. S23: Based on the preset digital twin BIM model, the pose transformation matrix and the dynamic drift weight factor are fused by coordinate transformation matrix. The vertex spatial coordinates of the digital twin model are adjusted by weighted interpolation to generate theoretical position data and transformation matrix after drift compensation.

4. The method according to claim 1, characterized in that, S3 includes: S31: Perform spatial clustering processing on the theoretical location data, identify the location clustering areas of high-density anomalies through the density peak detection algorithm, and generate potential conflict areas; S32: Perform sliding window analysis on the timestamp of the spatiotemporal composite tag, detect concurrent operation records of multiple terminals within a preset time threshold, and generate time conflict markers; S33: Perform joint verification processing on the potential conflict area and the time conflict marker, calculate the arbitration weight based on the terminal permission level and data freshness, generate a conflict event priority score by weighted fusion of spatial overlap and temporal overlap, determine the valid conflict events and their spatial coordinate set according to the score ranking result, and generate the arbitration result and conflict location set.

5. The method according to claim 4, characterized in that, The formula for calculating the priority score of the conflict event is as follows: in, Prioritize conflict events and score them. Terminal type weights, This is the freshness decay coefficient. , For spatiotemporal factor weights, , The area of ​​spatial overlap. For the duration of time overlap, The total area is This refers to the duration of the time window.

6. The method according to claim 1, characterized in that, S4 includes: S41: Perform Frobenius norm difference processing on the transformation matrix, calculate the Euclidean space distance between the current transformation matrix and the historical transformation matrix, and generate the matrix difference quantity; S42: Perform threshold triggering processing on the matrix difference. When the value of the matrix difference exceeds the preset tolerance threshold, extract the geometric topology data of the corresponding component and generate a set of changed components. S43: Perform priority sorting on the changed component set, calculate the transmission priority score based on the arbitration result and the component security level, generate a component priority sequence by multiplying the security level mapping score and the arbitration weight, and generate a component-level incremental packet by lightweight encapsulating the component data in sequence order.

7. The method according to any one of claims 1-6, characterized in that, S5 includes: S51: Perform multi-terminal adaptive rendering on the component-level incremental package, generate AR loadable model primitives through lightweight parsing on the mobile terminal, and generate a BIM model for global engineering status monitoring in the command center through full-precision rendering on the large screen terminal, generating multi-resolution visualization primitives. S52: Spatial labeling of the set of conflict locations; overlaying semi-transparent warning area markers onto the AR interface generated by the mobile terminal 3D rendering engine; overlaying heat diffusion effect onto the GIS map on the large screen terminal; generating a spatial labeling layer for quality anomalies. S53: Perform drift deviation analysis on the theoretical position data and the measured position data collected in real time by the on-site mobile terminal, generate a scalar field to quantify the position deviation between the model and the entity by Euclidean distance calculation, and generate a multi-gradient heat map by Gaussian smoothing and chromatographic mapping to generate a drift deviation visualization layer. S54: Perform multi-terminal synchronous fusion processing on the multi-resolution visualization primitive, the quality anomaly spatial annotation layer and the drift deviation visualization layer, generate an AR supervision interface that supports gesture interaction on the mobile terminal, generate a management console interface with a data panel on the PC terminal, generate a command and decision interface with multi-viewport linkage on the large screen terminal, and generate a dynamic supervision interface.

8. A road and bridge engineering quality supervision system based on multi-terminal collaboration via mobile internet, characterized in that, The system includes: The multi-source data spatiotemporal fusion module is used to perform spatiotemporal alignment and feature fusion on road and bridge engineering quality inspection data collected by on-site mobile terminals, timing signals collected by the Beidou satellite timing system, positioning data collected by multi-source positioning devices, and component information stored in the BIM system to generate spatiotemporal composite tags. The joint calibration and offset compensation module is used to perform SLAM-Kalman joint calibration on the spatiotemporal composite label, and combine the material attenuation characteristic parameters pre-stored in the engineering material database to perform dynamic position offset compensation on the preset digital twin BIM model, generating theoretical position data and transformation matrix after drift compensation. The multi-terminal conflict verification module is used to perform multi-terminal operation conflict verification between the theoretical location data and the timestamp in the spatiotemporal composite label, and generate arbitration results and conflict location set based on spatial distance threshold and time window overlap analysis. An incremental module is constructed to calculate the difference magnitude of the transformation matrix, filter the changed components according to the priority determined by the arbitration result, and generate a component-level incremental package; The multi-terminal dynamic supervision interface generation module is used to perform multi-resolution rendering processing on the component-level incremental package, and to perform spatial annotation in combination with the conflict location set to generate a dynamic supervision interface that can be displayed simultaneously on the mobile AR interface, the PC management platform, and the large screen command center. The dynamic supervision interface is used to display the rendered BIM model 3D structure, the annotated quality anomaly areas, and the drift deviation heat map between the theoretical and measured positions.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

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