Road and bridge design method and system based on BIM real scene model

Abstract

The invention discloses a road bridge design method and system based on a BIM real scene model, and relates to the technical field of constructional engineering information, and the method comprises the steps: a BIM model carries out multi-objective optimization through a quantum annealing algorithm in combination with a bridge-crossing topology library, synchronously optimizes topological connectivity, structural stress and energy consumption indexes, generates a parameterized component, and feeds back the parameterized component to the BIM model; the model of the BIM model is loaded and optimized based on augmented reality equipment, the model is superposed to a reality scene through space anchoring, and the BIM model is corrected in combination with a conflict monitoring engine and a knowledge graph; the corrected BIM model is deployed to edge computer equipment, a dynamic deviation thermodynamic diagram is generated by scanning a construction surface, a lofting robot is driven to execute coordinate dotting, and meanwhile construction error data is transmitted back; according to the method, the problem that a global optimal solution is difficult to find when a traditional optimization algorithm is used for processing a complex multi-target problem is solved by utilizing an advanced quantum computing technology, and the economical efficiency and the structural safety of bridge design are greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of construction engineering information technology, and in particular to a road and bridge design method based on a BIM real-scene model and a system thereof. Background Art

[0002] In recent years, with the rapid development of building information modeling (BIM) technology, its application in the field of road and bridge design has become increasingly widespread. In existing technologies, BIM models are usually combined with geographic information systems (GIS) and laser scanning technology to achieve the collection and modeling of surface and underground data. However, the existing technology for modeling geological-meteorological coupling effects is still at a static or semi-dynamic stage, lacking accurate quantification of dynamic processes such as land creep and storm runoff. At the same time, traditional BIM models still rely on empirical formulas or heuristic algorithms in multi-objective optimization (such as topological connectivity, structural stress, and energy consumption indicators), making it difficult to achieve rapid convergence to the global optimal solution.

[0003] Although existing road and bridge design methods can simulate and predict various situations during the construction process to a certain extent, they still face two major challenges in practical applications: first, the coupling effect of geological subsidence and meteorological changes is not sufficiently considered, resulting in the lack of sufficient adaptability of the design scheme when responding to natural disasters; second, it is difficult to take into account both structural safety and economy at the same time during the multi-objective optimization process, especially in areas with complex geological conditions, where traditional optimization algorithms find it difficult to find the global optimal solution. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a road and bridge design method based on the BIM real-scene model to solve the problems of insufficient geological and meteorological coupling and difficulty in optimizing both safety and economy.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, the present invention provides a road and bridge design method based on a BIM real-scene model, which includes collecting surface and underground data, and constructing a BIM model including geological settlement and meteorological changes in combination with meteorological parameters; the BIM model is multi-objectively optimized through a quantum annealing algorithm in combination with a cross-bridge topology library, synchronously optimizing topological connectivity, structural stress, and energy consumption indicators, generating parametric components and then feeding them back to the BIM model; loading and optimizing the BIM model based on an augmented reality device, and superimposing it on the real scene through spatial anchoring, correcting the BIM model in combination with a conflict monitoring engine and a knowledge graph; deploying the corrected BIM model to an edge computer device, generating a dynamic deviation heat map by scanning the construction surface and driving a lofting robot to execute coordinate marking, and at the same time transmitting back construction error data; based on the construction error data, adjusting the geological coupling parameters and structural compensation amounts in the BIM model through a parametric rule library, and generating a final design scheme when the construction error data reaches a preset threshold.

[0007] As a preferred solution of the road and bridge design method based on the BIM real-scene model of the present invention, wherein: the surface and underground data include topographic form data, surface cover data, existing structure data, geological parameter data, hidden structure data, and geological physical property data.

[0008] As a preferred solution of the road and bridge design method based on the BIM real-scene model of the present invention, wherein: the steps of collecting surface and underground data and constructing a BIM model including geological settlement and meteorological changes in combination with meteorological parameters are as follows: Collect surface and underground data through unmanned aerial vehicle oblique photography and 3D laser scanners, and associate rainfall and temperature and humidity data of a meteorological monitoring station with a geographic information database to generate a spatio-temporally calibrated multi-source data set; Input the multi-source data set into a parametric rule library, and generate a BIM dynamic settlement model reflecting land creep and rainstorm runoff effects based on the geological-meteorological coupling effect; Input the existing structure data into the BIM dynamic settlement model for historical condition inversion, and generate a BIM model when the settlement prediction value of the BIM dynamic settlement model is consistent with the historical monitoring data.

[0009] As a preferred solution of the road and bridge design method based on the BIM real-scene model of the present invention, wherein: the steps of multi-objectively optimizing the BIM model through a quantum annealing algorithm in combination with a cross-bridge topology library, synchronously optimizing topological connectivity, structural stress, and energy consumption indicators, generating parametric components and then feeding them back to the BIM model are as follows: Based on the geological parameter data in the BIM model, match and load a bridge type data set adapted to the current geological conditions from the cross-bridge topology library; Input the loaded bridge-type dataset into the quantum annealing algorithm. By calculating the multi-objective coupled Hamiltonian that includes topological connectivity, structural stress, and energy consumption indicators, generate a Pareto optimal solution set that meets the construction accuracy constraints through quantum tunneling evolution. Based on the Pareto optimal solution set, generate parametric components of pier families and girder families that adapt to the geological conditions through a parametric modification engine, and inversely associate them with the corresponding components in the BIM model.

[0010] As a preferred solution of the road and bridge design method based on the BIM real-scene model described in the present invention, wherein: optimize the loading of the BIM model based on the augmented reality device, and overlay it on the real scene through spatial anchoring. Combine the conflict monitoring engine and the knowledge graph to correct the BIM model. The specific steps are as follows. Obtain the multi-source heterogeneous data of the BIM model through the augmented reality device, and use the quantum annealing algorithm to dynamically optimize the loading parameters in the BIM model to generate an optimized model data stream. Based on the optimized model data stream, align the BIM model coordinates with the real-time pose data of the augmented reality device through the spatial anchoring engine to complete the three-dimensional overlay of the BIM model on the real scene. During the overlay process, the conflict monitoring engine real-time detects the geometric-topological conflicts between the BIM model components and the physical scene, and triggers a multi-modal warning signal. Based on the multi-modal warning signal, call the engineering rule library and expert experience library stored in the knowledge graph, generate a topological correction instruction for the BIM model, and correct the BIM model.

[0011] As a preferred solution of the road and bridge design method based on the BIM real-scene model described in the present invention, wherein: deploy the corrected BIM model to the edge computer device, generate a dynamic deviation heat map by scanning the construction surface and drive the layout robot to perform coordinate marking, and at the same time transmit the construction error data back. The specific steps are as follows. Load the corrected BIM model into the real-time rendering engine of the edge computer device, activate the lidar array to perform three-dimensional point cloud scanning on the construction surface, and generate a three-dimensional point cloud dataset. Based on the three-dimensional point cloud dataset, calculate the geometric deviation of each area of the construction surface through the gradient descent algorithm, and combine with the parameter vector in the BIM model to generate a dynamic deviation heat map covering the construction surface. According to the coordinate distribution of the areas exceeding the preset threshold in the dynamic deviation heat map, call the path planning device to generate a marking sequence instruction for the layout robot, and drive the robotic arm to perform high-precision coordinate marking according to the preset pressure value. Based on the lidar array, real-time collect the offset of the marking position and transmit the error data back to the BIM model correction engine.

[0012] As a preferred solution of the road and bridge design method based on the BIM real - scene model of the present invention, wherein: based on the construction error data, the geological coupling parameters and structural compensation amounts in the BIM model are adjusted through a parametric rule base, and a final design solution is generated when the construction error data reaches a preset threshold. The specific steps are as follows: Through the weight distribution algorithm in the parametric rule base, the construction error data is converted into an adjustment coefficient for the geological coupling parameters and a correction factor for the structural compensation amounts; According to the adjustment coefficient of the geological coupling parameters and the correction factor of the structural compensation amounts, the structural mechanical property parameters of the construction surface nodes are associated, and a dynamically updated error data set is generated; When the dynamically updated error data set meets the preset threshold, the genetic algorithm in the parametric rule base is triggered, and a final design solution including the corrected geological parameters, structural compensation amounts, and construction surface coordinate sets is generated.

[0013] In a second aspect, the present invention provides a road and bridge design system based on a BIM real - scene model, including a data acquisition module, a quantum optimization module, an AR correction module, a scanning and lofting module, and an error adjustment module; The data acquisition module collects surface and underground data and constructs a BIM model including geological settlement and meteorological changes in combination with meteorological parameters; The quantum optimization module performs multi - objective optimization on the BIM model through the quantum annealing algorithm in combination with a cross - bridge - type topology library, synchronously optimizes the topological connectivity, structural stress, and energy consumption indicators, and generates parametric components and then feeds them back to the BIM model; The AR correction module is used to load and optimize the BIM model based on an augmented reality device, overlay it on the real - world scene through spatial anchoring, and correct the BIM model in combination with a conflict monitoring engine and a knowledge graph; The scanning and lofting module deploys the corrected BIM model to an edge computer device, generates a dynamic deviation heat map by scanning the construction surface and drives a lofting robot to perform coordinate marking, and at the same time transmits the construction error data back; The error adjustment module, based on the construction error data, adjusts the geological coupling parameters and structural compensation amounts in the BIM model through a parametric rule base, and generates a final design solution when the construction error data reaches a preset threshold.

[0014] In a third aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and: when the computer program is executed by the processor, any step of the road and bridge design method based on the BIM real - scene model as described in the first aspect of the present invention is implemented.

[0015] In a fourth aspect, the present invention provides a computer - readable storage medium, on which a computer program is stored, and: when the computer program is executed by the processor, any step of the road and bridge design method based on the BIM real - scene model as described in the first aspect of the present invention is implemented.

[0016] The beneficial effects of the present invention are as follows: By using advanced quantum computing technology, the problem that traditional optimization algorithms are difficult to find the global optimal solution when dealing with complex multi-objective problems is solved, greatly improving the economy and structural safety of bridge design. Further, the BIM model is loaded and optimized based on the augmented reality device, and superimposed on the real scene through spatial anchoring. Combining the conflict monitoring engine and the knowledge graph to correct the BIM model realizes the high integration of virtual design and actual construction environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative work, other drawings can also be obtained based on these drawings.

[0018] Figure 1 It is a flowchart of a road and bridge design method based on a BIM real scene model.

[0019] Figure 2 It is a schematic diagram of a road and bridge design system based on a BIM real scene model.

[0020] Figure 3 It is a flowchart of the BIM model optimization process of a road and bridge design method based on a BIM real scene model.

[0021] Figure 4 It is a flowchart of the operation of the AR correction module of a road and bridge design method based on a BIM real scene model. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention will be described in detail below with reference to the drawings of the specification.

[0023] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0024] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that is mutually exclusive with other embodiments.

[0025] Refer toFigures 1 to 4 , which is an embodiment of the present invention. This embodiment provides a road and bridge design method based on a BIM real-scene model, including the following steps: S1: Collect surface and underground data, and construct a BIM model containing geological settlement and meteorological changes in combination with meteorological parameters.

[0026] S1.1: The surface and underground data include topographic morphology data, surface cover data, existing structure data, geological parameter data, concealed structure data, and geological physical property data.

[0027] The specific process includes that drone oblique photography can obtain large-area surface images for generating high-resolution digital terrain models, thereby providing detailed topographic morphology data; at the same time, 3D laser scanners can accurately record the spatial position information of surface covers and existing structures, providing support for the collection of existing structure data. For geological parameter data and geological physical property data, they are obtained through borehole sampling and laboratory analysis. Concealed structure data may need to be detected by means of technical means such as ground-penetrating radar.

[0028] S1.2: Collect surface and underground data through drone oblique photography and 3D laser scanners, and associate the rainfall, temperature, and humidity data of meteorological monitoring stations with the geographic information database to generate a spatio-temporally calibrated multi-source dataset.

[0029] The specific process includes that first, drone oblique photography technology is used to take surface images from multiple angles to obtain detailed topographic morphology data and surface cover data. The drone can fly at different heights and take pictures according to a preset route, ensuring comprehensive data coverage. At the same time, 3D laser scanners are used to accurately record the spatial position information of existing structures, geological parameters, and concealed structures. The working principle is to calculate the distance by emitting laser beams and measuring the time of reflection, thereby generating high-precision point cloud data. The point cloud data not only contains the three-dimensional coordinate information of surface objects but also can reflect geological physical property characteristics.

[0030] Furthermore, in order to associate the rainfall, temperature, and humidity data of meteorological monitoring stations with a geographic information database, it is first necessary to determine the specific geographic location coordinates of each data collection point. Based on the geographic location coordinates, the nearest meteorological monitoring station is found, and rainfall, temperature, and humidity records matching the data collection time are extracted. For example, if a data collection point is within 5 kilometers of a meteorological monitoring station, the monitoring data of the meteorological monitoring station is used as a reference. Then, using timestamps, the spatial data on the geographic information database is synchronized with the meteorological data to ensure that each piece of geographic information corresponds to meteorological conditions within a specific time period. Through standardized processing procedures such as spatial alignment, time synchronization, data fusion, and quality control, multi-source heterogeneous data such as terrain morphology, surface cover, existing structures, geological parameters, concealed structures, geological physical properties, and meteorological monitoring are integrated into a four-dimensional dataset (X, Y, Z, T) with a unified spatio-temporal reference. The four-dimensional dataset is based on the CGCS2000 coordinate system and the ISO8601 time standard, with a spatial resolution of 10 cm (terrain) / 1 m (geology) and a time resolution of 15 minutes (meteorology) / 1 hour (deformation), providing a parametric input basis that meets the engineering accuracy requirements for constructing a BIM dynamic model that couples geological subsidence and meteorological changes.

[0031] S1.3: Input the multi-source dataset into the parametric rule base and generate a BIM dynamic settlement model that reflects the effects of land creep and storm runoff based on the geological-meteorological coupling effect.

[0032] The specific process includes, first, inputting the multi-source dataset into the parametric rule base. The parametric rule base extracts key parameters (such as rainfall, soil pore water pressure, and shear strength of rock and soil masses) based on the geological-meteorological coupling effect (such as the correlation between rainfall intensity and geological permeability coefficient, and the mapping relationship between soil creep rate and groundwater level change). Second, the parametric rule base analyzes the dynamic settlement influencing factors (such as soil compression coefficient and creep displacement increment) in combination with the effects of storm runoff (such as surface runoff scouring force and runoff infiltration rate). Finally, the dynamic settlement influencing factors are integrated with the geological structure parameters (such as soil layer thickness and elastic modulus of rock and soil) in the BIM model, and the geometric and attribute data of the BIM dynamic settlement model are updated hour by hour through finite element analysis or empirical formulas (such as the modified equation of Terzaghi's consolidation theory).

[0033] S1.4: Input the existing structure data into the BIM dynamic settlement model for historical condition inversion. When the settlement prediction value of the BIM dynamic settlement model is consistent with the historical monitoring data, a BIM model is generated, and the expression is: ; where represents the time variable, represents time The corresponding settlement prediction value, represents the maximum settlement, represents the settlement rate factor, represents the geological coupling factor, represents the mean value of the normal time function, represents the mean value of the normal time function, represents the sensitivity coefficient, represents the dynamic elastic modulus at time represents the initial elastic modulus, represents the error function.

[0034] The specific process includes that the process of inputting the existing structure data into the BIM dynamic settlement model for historical condition inversion is specifically manifested as follows: The existing structure data includes historical settlement monitoring records, geological exploration reports, construction load logs, etc. After the existing structure data is standardized, it is imported into the BIM dynamic settlement model through a data interface; in the BIM dynamic settlement model, the least squares optimization algorithm is used to iteratively adjust , , , , , and other parameters to gradually reduce the error between the settlement prediction value and the historical monitoring data; when the root mean square error between the settlement prediction value and the historical monitoring data is less than the preset threshold, and the preset threshold is determined comprehensively according to the allowable settlement error limit of the project and the accuracy of the monitoring instrument. It is determined that the parameter calibration of the BIM dynamic settlement model has been completed; at this time, the optimized parameter combination is written into the BIM model property set to generate a BIM model with a complete settlement prediction function; in the settlement prediction calculation, the basic law of soil compression deformation is described by an S-shaped curve for the foundation settlement component, the time-dependent characteristics of soil layer consolidation are characterized by a specific mathematical function for the geological correction component, and the long-term evolution of material properties is reflected by the stiffness correction component. The synergistic effect of these three parts completely captures the whole process characteristics of soil settlement over time; the BIM model after parameter optimization can accurately predict the future deformation trend according to the current project status by using the complete settlement prediction function.

[0035] S2: The BIM model performs multi-objective optimization through the quantum annealing algorithm combined with the cross-bridge topology library, synchronously optimizes the topological connectivity, structural stress and energy consumption indicators, and generates parametric components and then feeds them back to the BIM model.

[0036] S2.1: Based on the geological parameter data in the BIM model, match and load the bridge type dataset adapted to the current geological conditions from the cross-bridge topology library.

[0037] The specific process includes first identifying the specific geological parameter data contained in the BIM model, such as geological physical property data, geological parameter data, etc. The geological parameter data reflects the geological characteristics of a specific area, such as soil type, rock formation structure, etc. Next, using the geological parameter data as a query condition, search for the bridge type dataset that meets the current geological conditions in the cross-bridge type topology library. For example, if the geological parameters in a certain area indicate a high groundwater level and soft soil foundation, then look for the bridge structure dataset in the cross-bridge type topology library that is specifically designed for soft soil foundation and has good anti-settlement performance. Ensure that the selected bridge type dataset can meet the requirements of specific geological conditions, thus providing a solid foundation for subsequent multi-objective optimization. Finally, the matched bridge type dataset will be loaded into the BIM model, making the BIM model not only contain detailed geological information but also have a bridge design scheme suitable for the geological conditions.

[0038] S2.2: Input the loaded bridge type dataset into the quantum annealing algorithm. By calculating the multi-objective coupled Hamiltonian containing topological connectivity, structural stress, and energy consumption indicators, generate the Pareto optimal solution set that meets the construction accuracy constraints through quantum tunneling evolution. The expression is: ; where represents the multi-objective coupled Hamiltonian of the bridge type topological structure , represents the bridge type topological structure, represents the measured geological parameter vector, represents the index vector reflecting the geological conditions obtained through the coupled analysis of the geological exploration data and the mechanical properties of the bridge type topological structure , represents the allowable stress of the material, represents the actual stress, represents the reference energy consumption, represents the actual energy consumption, represents the construction accuracy weight coefficient ( ∈[0.1, 1.0]), represents the actual construction error, represents the construction error threshold, represents the standard deviation of the measurement error.

[0039] The specific process includes: First, it is necessary to ensure that the bridge type dataset contains detailed bridge type topological structure information and related geological parameter vectors. The bridge type dataset reflects the performance of a specific bridge design under different geological conditions. Next, in the quantum annealing algorithm, based on parameters such as the bridge type topological structure, geological parameter vector, allowable stress of materials, and benchmark energy consumption, a multi-objective coupled Hamiltonian containing topological connectivity, structural stress, and energy consumption indicators is calculated. For example, for a specific bridge type design, according to its topological structure characteristics and stress distribution under the geological conditions, combined with the difference between the actual energy consumption and the benchmark energy consumption, the comprehensive performance of the bridge type design under the construction accuracy constraints can be quantitatively evaluated.

[0040] Furthermore, the quantum annealing algorithm evolves and calculates through the quantum tunneling effect, and generates a Pareto optimal solution set that meets the construction accuracy constraint conditions. Through the quantum tunneling effect, the quantum annealing algorithm can search for the global optimal solution or a solution set close to the global optimal solution in a complex energy landscape. By using the multi-objective coupled Hamiltonian in the quantum annealing algorithm, combined with parameters such as the construction accuracy weight coefficient, actual construction error, and standard deviation of measurement error, the bridge type topological structure is optimized to meet the construction accuracy requirements. Once a Pareto optimal solution set that meets the construction accuracy constraints is found, it indicates that a set of design solutions that achieve the best balance in topological connectivity, structural stress, and energy consumption indicators has been found.

[0041] S2.3: Based on the Pareto optimal solution set, generate parametric components of pier families and girder families that adapt to geological conditions through a parametric modification engine, and inversely associate them with the corresponding components in the BIM model.

[0042] Furthermore, first, it is necessary to utilize the various feasible design solution combinations provided in the Pareto optimal solution set. Each solution represents a different balance point in terms of topological connectivity, structural stress, and energy consumption indicators. For each optimal solution, according to key factors such as the corresponding geological parameter vector, allowable stress of materials, and actual stress, adjust the design parameters of the piers and girders to generate parametric components of pier families and girder families that meet the requirements of specific geological conditions. For example, under a certain specific geological condition, if the optimal solution shows that higher structural strength is required, then correspondingly increase the design dimensions of the piers and girders or select higher-strength materials.

[0043] Next, in order to reverse-associate the generated parametric components of the pier family and the main girder family with the corresponding components in the BIM model, it is first necessary to ensure that each parametric component has clear spatial position information and attribute labels. This includes but is not limited to the three-dimensional coordinates, geometric shapes, and material properties of the components. Then, using the geographical location coordinates and time stamps in the BIM model, the newly generated parametric components are accurately mapped back to the positions of the corresponding bridge components in the original BIM model. The BIM model is updated so that the piers and main girders can reflect the design scheme optimized based on the Pareto optimal solution set. Finally, this reverse association not only ensures the accuracy and real-time nature of the BIM model.

[0044] S3: Optimize the loading of the BIM model based on the augmented reality device and overlay it onto the real-world scene through spatial anchoring, and correct the BIM model in combination with the conflict monitoring engine and the knowledge graph.

[0045] S3.1: Obtain the multi-source heterogeneous data of the BIM model through the augmented reality device, and dynamically optimize the loading parameters in the BIM model using the quantum annealing algorithm to generate the optimized model data stream.

[0046] The specific process includes that first, it is necessary to use the augmented reality device to capture the three-dimensional spatial information in the environment and various data types included in the BIM model. The augmented reality device can identify and extract multi-source heterogeneous data such as terrain morphology data, surface cover data, and existing structure data in the BIM model, and overlay it with the live-shot on-site images. For example, at a specific bridge construction site, the augmented reality device can capture the actual positions of the piers and main girders and their surrounding environment information, and at the same time match it with the corresponding data in the BIM model, thereby generating a multi-source heterogeneous data set containing detailed construction environment and design information.

[0047] Furthermore, based on the multi-source heterogeneous data obtained by the augmented reality device, determine the specific loading parameters of the BIM model in actual applications, such as spatial position, geometric shape, and material properties. Then, input the specific loading parameters into the quantum annealing algorithm. For example, for the design parameters of a specific bridge component, adjust its geometric dimensions and material properties to achieve the best structural performance and energy consumption efficiency. The quantum annealing algorithm can efficiently search for the optimal solution in the complex energy landscape and finally generate the optimized model data stream. This process not only improves the adaptability and accuracy of the BIM model in actual applications but also ensures that the design scheme can achieve the best performance during the construction process.

[0048] S3.2: Based on the optimized model data stream, align the BIM model coordinates with the real-time pose data of the augmented reality device through the spatial anchoring engine to complete the three-dimensional overlay of the BIM model onto the real-world scene.

[0049] The specific process includes that first, it is necessary to use the augmented reality device to capture the three-dimensional spatial information of the site and obtain the real-time pose data, including the position and orientation of the three-dimensional spatial information. The real-time pose data provides the accurate position and orientation of the augmented reality device in the physical space. Then, the spatial anchoring engine is used to match the coordinate information in the BIM model with the real-time pose data provided by the augmented reality device. For example, at a bridge construction site, the augmented reality device can provide its specific position and viewing direction relative to a fixed point, and the spatial anchoring engine adjusts the positions and orientations of the corresponding components in the BIM model according to the provided information to ensure their spatial consistency.

[0050] Furthermore, once the BIM model coordinates are successfully aligned with the real-time pose data of the augmented reality device, the BIM model can be superimposed and displayed in the actual construction environment in three dimensions through the augmented reality device. Specifically, the augmented reality device dynamically adjusts and renders the BIM model according to the real-time captured environmental images and spatial anchor points, so that the virtual bridge structure can accurately appear in the actual position. For example, at the construction site, the augmented reality device can display the design forms of the bridge piers and main girders in real time and their relationships with the existing terrain and other structures, helping engineers intuitively check whether the design scheme conforms to the actual situation.

[0051] S3.3: During the superposition process, the conflict monitoring engine detects the geometric-topological conflicts between the BIM model components and the physical scene in real time and triggers multi-modal warning signals.

[0052] The specific process includes that during the superposition process, the conflict monitoring engine detects the geometric-topological differences between the BIM model components and the point cloud data of the physical scene obtained by lidar scanning by comparing them in real time. When the actual positions or forms of the BIM model components and the physical scene exceed the preset threshold, it is determined that there is a geometric conflict. The preset threshold is determined based on engineering specification requirements and the accuracy of measurement equipment and is used to define the acceptable deviation range. At the same time, the inconsistency between the component connection relationship and the actual topological structure of the physical scene is detected through graph theory analysis methods. For example, the beam-column joints in the BIM model are rigidly connected while the actual scanning shows gaps.

[0053] Furthermore, when a conflict is detected, multi-modal warning signals are generated according to the type and degree of the conflict. Geometric conflicts trigger visual warnings, highlighting the deviation area in the BIM model and marking specific information; topological conflicts generate structured reports, detailing the inconsistent positions and types; for complex conflict situations, audio warnings are issued synchronously. All warning information contains accurate time and space identifiers to ensure that problems can be traced and located.

[0054] S3.4: Based on the multimodal warning signals, call the engineering rule library and expert experience library stored in the knowledge graph, generate topology correction instructions for the BIM model, and correct the BIM model.

[0055] Furthermore, the engineering rule library is a collection of standardized engineering design and construction specifications, and the expert experience library is a collection of accumulated expert practical experiences and solutions. The two work together to provide a scientific basis and optimization suggestions for generating topology correction instructions for the BIM model. First, it is necessary to analyze the specific content of the multimodal warning signals, including information such as conflict types, three-dimensional spatial positions, and severity levels. For example, when a geometric conflict is detected between a pier and existing underground pipelines, the multimodal warning signal will contain a detailed description of the conflict, such as the exact coordinates and the affected range of the conflict. Then, search for corresponding engineering rules and expert experiences in the knowledge graph. The knowledge graph stores a large amount of data on structural design, construction methods, and solutions to common problems, and can provide targeted suggestions for different types of conflicts.

[0056] Specifically, once a matching rule is found, such as a method for handling underground pipeline conflicts, specific topology correction instructions will be generated based on the matching rule. For example, if the rule suggests adjusting the position of the pier to avoid underground pipelines, the generated topology correction instructions will include detailed adjustment parameters, such as new three-dimensional coordinate values and necessary geometric dimension changes. The topology correction instructions are then applied to the corresponding components in the BIM model and directly drive BIM software (such as Revit or Tekla) to perform the correction of the BIM model.

[0057] S4: Deploy the corrected BIM model to the edge computer device, generate a dynamic deviation heat map by scanning the construction surface and drive the layout robot to perform coordinate marking, and at the same time transmit back the construction error data.

[0058] S4.1: Load the corrected BIM model into the real-time rendering engine of the edge computer device, activate the lidar array to perform three-dimensional point cloud scanning on the construction surface, and generate a three-dimensional point cloud data set.

[0059] The specific process includes ensuring that the edge computer device has sufficient processing power and storage space to support the data volume and complexity of the BIM model. Through a data transmission interface, such as a network connection or a physical storage medium, the corrected BIM model file is transmitted to the edge computer device. Once the file transmission is completed, the real-time rendering engine will read and analyze the error data and convert it into visual three-dimensional structural information. For example, at a bridge construction site, the corrected BIM model contains the latest position adjustment information of the piers and main girders. The real-time rendering engine can quickly convert the updated design parameters into visual images for on-site engineers to view and verify.

[0060] Furthermore, the process of activating the lidar array to perform three-dimensional point cloud scanning on the construction surface and generating a three-dimensional point cloud dataset relies on the high-precision measurement ability of lidar technology. After deploying the lidar array at the construction site, the lidar equipment is started through the control software to begin scanning the working area of the construction site. The lidar equipment emits laser beams and receives the reflected pulse echo signals, and calculates the distance of the target object based on the time difference of the returned pulse echo signals, thereby constructing detailed three-dimensional point cloud data. For example, during bridge construction, the lidar array can comprehensively scan the construction surface of the pier foundation to capture every detail feature. After the original data is processed and integrated, a three-dimensional point cloud dataset containing accurate coordinate information is formed. The three-dimensional point cloud dataset not only reflects the actual terrain conditions of the construction surface but also provides basic data support for subsequent analysis.

[0061] S4.2: Based on the three-dimensional point cloud dataset, calculate the geometric deviation of each area of the construction surface through the gradient descent algorithm, and combine with the parameter vector in the BIM model to generate a dynamic deviation heat map covering the construction surface. The expression is: ; where represents the geometric deviation of each area of the construction surface, represents the total number of measurement points divided on the construction surface, represents the three-dimensional coordinate data of the actual construction point cloud obtained by laser scanning, represents the dynamic rotation matrix, represents the BIM model, represents the theoretical design coordinate value of the corresponding component in the BIM model, represents the theoretical coordinates of the BIM model through the rotation matrix coordinate values after spatial transformation, represents the translation vector, local point cloud density standard deviation ( ∈[0.1,1.0]), represents the dynamic weight of the angular deviation, represents the normal vector of the scanned point cloud (∥ ∥ = 1), represents the theoretical normal vector of the BIM model (∥ ∥ = 1), represents the curvature difference coefficient, represents the principal curvature of the scanned point cloud, represents the principal curvature of the BIM model, represents the stiffness gradient suppression factor, represents the node stiffness matrix gradient, represents the non-linear activation function, Indicates The function sensitivity parameter Indicates the real-time force data

[0062] The specific process includes: after deploying the lidar array at the construction site, the 3D laser scanner performs 3D scanning on the operation area of the construction site to obtain high-precision point cloud data. The lidar measures the spatial position of the target object by emitting laser pulses and receiving the reflected signals, forming a 3D coordinate data set containing millions of measurement points. The 3D coordinate data set completely records the actual geometric shape and spatial position of the construction surface. Based on the 3D point cloud data obtained by scanning, data preprocessing is first carried out, including noise filtering and outlier removal, to ensure the data quality. Then, the processed 3D point cloud data is spatially registered with the theoretical design values in the BIM model, and the optimal spatial transformation relationship is found through an optimization algorithm and the best matching state is achieved. After the registration is completed, the actual measurement data is compared with the theoretical design values in the BIM model point by point, and the geometric deviation amount at each position is calculated. The geometric deviation amount at each position comprehensively considers factors such as position offset, angle difference, and surface shape change, and comprehensively reflects the construction quality status.

[0063] Furthermore, according to the calculated geometric deviation amount, the construction area is divided into regular grid cells, and the distribution characteristics of the deviation amount are statistically analyzed within each cell. Through the color scale coding technology, different ranges of deviation amounts are converted into corresponding colors for display, forming an intuitive dynamic deviation heat map. In the dynamic deviation heat map, cold colors are used to represent the areas that meet the requirements, and warm colors are used to represent the areas that exceed the allowable deviation, realizing the visual presentation of the construction quality status. The dynamic deviation heat map is dynamically refreshed as the scanned data is updated, and it reflects the latest state of the construction surface in real time, providing a decision-making basis for quality control.

[0064] S4.3: According to the coordinate distribution of the area exceeding the preset threshold in the dynamic deviation heat map, call the path planning device to generate the dotting sequence instruction for the lofting robot, and drive the robotic arm to perform high-precision coordinate dotting according to the preset pressure value.

[0065] Furthermore, it is first necessary to determine the specific location of the area. The preset threshold area is set based on engineering specifications and quality standards. For example, if the allowable deviation range of a certain construction surface is ±5 mm, then any area exceeding this range will be regarded as an area exceeding the preset threshold. Its function is to identify the positions with large deviations during the construction process for precise correction. On the dynamic deviation heat map, the areas exceeding the preset threshold will be displayed in a specific color or mark for quick positioning.

[0066] Specifically, the equipment deployed at the construction site is a 3D laser scanner (such as industrial-grade lidar equipment like the FARO Focus series or Leica BLK360). After performing 3D scanning on the designated operation area through the equipment deployed at the construction site and obtaining high-precision point cloud data, the coordinates of the out-of-limit areas are identified by analyzing the dynamic deviation heat map (for example, areas where the pier foundation deviation exceeds ±5 mm). The path planning equipment (such as the Trimble Site Positioning System or Topcon LN-150 navigation controller) analyzes the optimal movement path of the layout robot based on the coordinate information and the distribution of on-site obstacles. The role of the path planning equipment is to generate navigation instructions containing the 3D coordinates of the target points and the optimal access sequence (such as the movement path from point A→B→C), ensuring that the layout robot (such as the Leica iCON robot or Hilti Jaibot) can reach each point to be corrected efficiently and accurately to perform the layout operation.

[0067] Furthermore, the process of driving the robotic arm to perform high-precision coordinate marking depends on the marking sequence instructions received by the layout robot. The robotic arm on the layout robot moves to the specified coordinates according to the instructions and performs the marking operation. For example, when correcting the pier foundation, the robotic arm will accurately move to the points in the area where the deviation exceeds the preset threshold according to the instructions, and then perform marking with a specified pressure value (such as 100 Newtons) to ensure that each point is accurately corrected.

[0068] S4.4: Real-time collect the offset of the marking position based on the lidar array and transmit the error data back to the BIM model correction engine.

[0069] The specific process includes that the lidar array conducts three-dimensional scanning with millimeter-level accuracy on the area of the construction surface where the dotting has been completed (for example, the FARO Focus laser scanner has an acquisition accuracy of ±2 mm), and obtains the actual coordinate point cloud data containing dotting marks; through the ICP point cloud registration algorithm, the scanned point cloud is spatially matched with the theoretical design values in the BIM model, and the three-dimensional Euclidean distance between the center position of each dotting mark and the corresponding design coordinate is analyzed as the position offset (for example, the deviation in the X direction is +3.2 mm, and the deviation in the Y direction is -1.8 mm); the offset data is packed in the ISO 6709 coordinate standard format and transmitted in real time to the BIM model correction engine through the 5G network; after receiving the data, the BIM model correction engine first verifies whether the offset exceeds the threshold allowed by the construction specification (for example, ±5 mm specified in the "Technical Specifications for Highway Bridge and Culvert Construction" JTG / T 3650-2020), and automatically marks the over-limit points as a red warning status; then it calls the weight distribution algorithm in the parametric rule library to convert the offset data into correction parameters for the coordinates of the BIM model components (for example, the coordinates of the beam control points need to be adjusted 2.1 mm in the positive X-axis direction); finally, the generated correction instructions are written into the BIM model through the IFC format interface to complete the iterative update of the model coordinates, and the updated model is immediately synchronized to all terminal devices.

[0070] S5: Based on the construction error data, adjust the geological coupling parameters and structural compensation amounts in the BIM model through the parametric rule library, and generate the final design scheme when the construction error data reaches the preset threshold.

[0071] S5.1: Through the weight distribution algorithm in the parametric rule library, convert the construction error data into the adjustment coefficient of the geological coupling parameter and the correction factor of the structural compensation amount. The expression is: ; Among them, represents the adjustment coefficient of the geological coupling parameter, represents the total number of construction error types, represents the index of the construction error type, represents the th class of construction error in the dynamic weight coefficient in the weight distribution calculation, represents the error attenuation factor, represents the th class of construction error, represents the correction factor of the structural compensation amount, represents the total amount of data of the structural displacement and stress and strain monitoring parameters collected in real time by the on-site sensors, represents the serial number index of the monitoring data, represents the th structural deformation data value obtained from the represents the The time node of the next monitoring Indicates the mean of the normal distribution Indicates the standard deviation of the normal distribution Indicates the Average value of the data of the th monitoring Indicates the settlement rate control factor Indicates the S-shaped threshold response function Indicates the compensation amount deviation threshold

[0072] The specific process includes that the weight distribution algorithm in the parameterized rule base converts the construction error data into the adjustment coefficient of the geological coupling parameter and the correction factor of the structural compensation amount. First, for various construction error data, the weight distribution algorithm classifies and statistics according to the error type, calculates the weighted influence value according to the absolute value of the error type and the preset weight coefficient. Different error types correspond to different weight coefficients, reflecting the difference in the influence degree on the geological conditions. The preset weight coefficient is a parameter preset according to the relative influence degree of various construction errors on the project quality (such as the dimension error weight is 0.6, and the position error weight is 0.4) and the project specification requirements. Its function is to quantify the contribution ratio of different error types in the adjustment of geological coupling parameters. At the same time, the weight distribution algorithm processes the structural deformation data obtained from on-site monitoring, analyzes the distribution characteristics of the deformation value changing with time, and combines the normal distribution parameters to evaluate the deformation development trend. On this basis, the weight distribution algorithm comprehensively combines the construction error influence value and the monitoring data analysis result, and through specific mathematical transformations and function processing, finally outputs the adjustment coefficient of the geological coupling parameter and the correction factor of the structural compensation amount. The adjustment coefficient of the geological coupling parameter reflects the comprehensive influence degree of the construction error on the geological conditions, and the correction factor of the structural compensation amount quantifies the correction requirements of the structural response characteristics reflected by the monitoring data on the compensation demand. The entire conversion process strictly follows the principles of engineering mechanics and material properties to ensure that the output parameters can accurately guide the optimization and adjustment of construction parameters.

[0073] S5.2: According to the adjustment coefficient of the geological coupling parameter and the correction factor of the structural compensation amount, associate with the structural mechanical property parameters of the construction surface node, and generate a dynamically updated error data set.

[0074] The specific process includes using the adjustment coefficient of the geological coupling parameter to correct the original structural mechanical property parameters to ensure that they can adapt to the new geological conditions. Similarly, the correction factor of the structural compensation amount is also used to adjust the related structural mechanical property parameters, such as increasing the foundation thickness of a specific area or enhancing the material strength, so as to ensure the stability and safety of the structure during actual construction.

[0075] Furthermore, based on the adjusted structural mechanics performance parameters, information such as the actual stress conditions and deformation data of each node on the construction surface are collected through real-time monitoring means (such as a sensor network). For example, on a specific construction surface node, the installed sensors can record the displacement and stress change data of the node during construction in real time. The displacement and stress change data are integrated into a dynamically updated error dataset for further analysis and optimization. For example, if the actual displacement of a certain node exceeds the adjusted theoretical value range, it will be recorded as new error data and added to the dynamically updated error dataset. This continuous data collection and update mechanism ensures that the BIM model can timely reflect the real situation of the construction site, providing reliable data support for subsequent design optimization and construction adjustment. Eventually, a closed-loop management from construction error data collection to dynamic adjustment and then to feedback optimization is achieved.

[0076] S5.3: When the dynamically updated error dataset meets the preset threshold, trigger the genetic algorithm in the parametric rule base and generate a final design solution containing the corrected geological parameters, structural compensation amounts, and construction surface coordinate sets.

[0077] The specific process includes that the preset threshold is preset based on engineering specifications and quality standards, such as the allowable maximum deviation range or specific structural performance indicators. Its function is to set an acceptable error limit to ensure that the actual performance during construction does not deviate too far from the design requirements, thus ensuring the safety and stability of the final structure. When the dynamically updated error dataset meets the preset threshold, it means that the error in the current construction state has reached the level that requires optimization and adjustment.

[0078] Specifically, the parametric rule base is a collection containing various engineering parameters and their adjustment rules, used to automatically calculate and generate an optimized design solution according to the input data (such as the error dataset), which contains key information such as the corrected geological parameters, structural compensation amounts, and construction surface coordinate sets. Input the actual deviation values recorded in the error dataset into the parametric rule base, and use swarm intelligence optimization technology to globally search and optimize the error dataset. For example, during the construction of a bridge, if the actual displacement of a certain pier foundation part exceeds the preset threshold, use swarm intelligence optimization technology to comprehensively optimize the geological parameters and structural compensation amounts in the involved area. By simulating the natural selection process, swarm intelligence optimization technology can iteratively find the optimal solution, thereby adjusting the geological coupling parameters, structural compensation amounts, and construction surface coordinate sets to better conform to the actual construction conditions. Eventually, after multiple iterative optimizations, a final design solution integrating the latest geological parameters, accurate structural compensation amounts, and accurate construction surface coordinate sets is generated. This method not only improves the adaptability and accuracy of the design solution but also ensures that the final construction result can meet the design requirements and quality standards.

[0079] This embodiment also provides a road and bridge design system based on a BIM real-scene model, including: a data acquisition module, a quantum optimization module, an AR correction module, a scanning and lofting module, and an error adjustment module; The data acquisition module collects surface and underground data, and constructs a BIM model including geological settlement and meteorological changes in combination with meteorological parameters; The quantum optimization module performs multi-objective optimization on the BIM model through the quantum annealing algorithm in combination with a cross-bridge type topology library, synchronously optimizes the topological connectivity, structural stress, and energy consumption indicators, and generates parametric components and then feeds them back to the BIM model; The AR correction module is used to load and optimize the BIM model based on an augmented reality device, and superimpose it on the real scene through spatial anchoring, and correct the BIM model in combination with a conflict monitoring engine and a knowledge graph; The scanning and lofting module deploys the corrected BIM model to an edge computer device, generates a dynamic deviation heat map by scanning the construction surface and drives the lofting robot to perform coordinate marking, and at the same time transmits back the construction error data; The error adjustment module adjusts the geological coupling parameters and structural compensation amounts in the BIM model based on the construction error data through a parametric rule library, and generates a final design scheme when the construction error data reaches a preset threshold.

[0080] This embodiment also provides a computer device, which is applicable to the situation of the road and bridge design method based on the BIM real-scene model, including: a memory and a processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the road and bridge design method based on the BIM real-scene model as proposed in the above embodiment.

[0081] This computer device can be a terminal. This computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of this computer device is used to provide computing and control capabilities. The memory of this computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of this computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen of this computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of this computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the computer device housing, or an external keyboard, touchpad, or mouse, etc.

[0082] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the method for road and bridge design based on the BIM real - scene model proposed in the above - mentioned embodiment; the storage medium can be implemented by any type of volatile or non - volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, abbreviated as SRAM), electrically erasable programmable read - only memory (Electrically Erasable Programmable Read - Only Memory, abbreviated as EEPROM), erasable programmable read - only memory (Erasable Programmable Read Only Memory, abbreviated as EPROM), programmable read - only memory (Programmable Red - Only Memory, abbreviated as PROM), read - only memory (Read - Only Memory, abbreviated as ROM), magnetic memory, flash memory, magnetic disk or optical disc.

[0083] In summary, the present invention: uses advanced quantum computing technology to solve the problem that traditional optimization algorithms are difficult to find the global optimal solution when dealing with complex multi - objective problems, greatly improving the economic efficiency and structural safety of bridge design. Further, the BIM model is loaded and optimized based on the augmented reality device, and is superimposed on the real - world scene through spatial anchoring. By combining the conflict monitoring engine and the knowledge graph to correct the BIM model, a high degree of integration of virtual design and actual construction environment is achieved.

[0084] It should be noted that the above - mentioned embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A road and bridge design method based on a BIM real-scene model, characterized in that: including, collecting surface and underground data, and constructing a BIM model including geological subsidence and meteorological changes by combining meteorological parameters; The BIM model is multi-objectively optimized by the quantum annealing algorithm in combination with a cross-bridge topology library, synchronously optimizing the topological connectivity, structural stress, and energy consumption indicators, and generating parametric components and then feeding them back to the BIM model; loading and optimizing the BIM model based on an augmented reality device, and overlaying it onto the real scene through spatial anchoring, and correcting the BIM model in combination with a conflict monitoring engine and a knowledge graph; deploying the corrected BIM model to an edge computer device, generating a dynamic deviation heat map by scanning the construction surface and driving a lofting robot to perform coordinate marking, and at the same time transmitting back construction error data; adjusting the geological coupling parameters and structural compensation amounts in the BIM model based on the construction error data through a parametric rule library, and generating a final design plan when the construction error data reaches a preset threshold.

2. The method for designing roads and bridges based on the BIM real-scene model according to claim 1, wherein: The surface and underground data include topographic morphology data, surface cover data, existing structure data, geological parameter data, hidden structure data, and geological physical property data.

3. The road and bridge design method based on the BIM real scene model according to claim 2, wherein: The steps of collecting surface and underground data and constructing a BIM model including geological subsidence and meteorological changes by combining meteorological parameters are as follows: collecting surface and underground data through drone oblique photography and 3D laser scanners, and associating rainfall and temperature and humidity data from a meteorological monitoring station to a geographic information database to generate a spatio-temporally calibrated multi-source dataset; inputting the multi-source dataset into a parametric rule library, and generating a BIM dynamic settlement model reflecting land creep and rainstorm runoff effects based on the geological-meteorological coupling effect; inputting the existing structure data into the BIM dynamic settlement model for historical condition inversion, and generating a BIM model when the settlement prediction value of the BIM dynamic settlement model is consistent with the historical monitoring data.

4. The method for designing roads and bridges based on the BIM real-scene model according to claim 3, wherein: The steps of the BIM model being multi-objectively optimized by the quantum annealing algorithm in combination with a cross-bridge topology library, synchronously optimizing the topological connectivity, structural stress, and energy consumption indicators, and generating parametric components and then feeding them back to the BIM model are as follows: matching and loading a bridge type dataset adapted to the current geological conditions from the cross-bridge topology library based on the geological parameter data in the BIM model; inputting the loaded bridge type dataset into the quantum annealing algorithm, and generating a Pareto optimal solution set that meets the construction accuracy constraints through quantum tunneling evolution by calculating a multi-objective coupling Hamiltonian including topological connectivity, structural stress, and energy consumption indicators; generating parametric components of pier families and girder families adapted to the geological conditions based on the Pareto optimal solution set through a parametric modification engine, and reversely associating them to the corresponding components in the BIM model.

5. The road and bridge design method based on the BIM real scene model according to claim 4, characterized in that: The steps of loading and optimizing the BIM model based on an augmented reality device, and overlaying it onto the real scene through spatial anchoring, and correcting the BIM model in combination with a conflict monitoring engine and a knowledge graph are as follows: obtaining multi-source heterogeneous data of the BIM model through an augmented reality device, and dynamically optimizing the loading parameters in the BIM model by using the quantum annealing algorithm to generate an optimized model data stream; Based on the optimized model data stream, the BIM model coordinates are aligned with the real-time pose data of the augmented reality device through the spatial anchoring engine, and the 3D BIM model is superimposed on the real scene; During the superimposition process, the geometric-topological conflicts between the BIM model components and the physical scene are detected in real time by the conflict monitoring engine, and a multimodal warning signal is triggered; Based on the multimodal warning signal, the engineering rule library and the expert experience library stored in the knowledge graph are called to generate topological correction instructions for the BIM model and correct the BIM model.

6. The method for designing roads and bridges based on the BIM real-scene model according to claim 5, wherein: Deploy the corrected BIM model to the edge computer device, generate a dynamic deviation heat map by scanning the construction surface and drive the lofting robot to perform coordinate marking, and at the same time transmit the construction error data back. The specific steps are as follows: Load the corrected BIM model into the real-time rendering engine of the edge computer device, activate the lidar array to perform 3D point cloud scanning on the construction surface, and generate a 3D point cloud data set; Based on the 3D point cloud data set, calculate the geometric deviation of each area of the construction surface through the gradient descent algorithm, and combine with the parameter vector in the BIM model to generate a dynamic deviation heat map covering the construction surface; According to the coordinate distribution of the area exceeding the preset threshold in the dynamic deviation heat map, call the path planning device to generate a dotting sequence instruction for the lofting robot, and drive the robotic arm to perform high-precision coordinate dotting according to the preset pressure value; Based on the lidar array, the offset of the dotting position is collected in real time, and the error data is transmitted back to the BIM model correction engine.

7. The road and bridge design method based on the BIM real scene model according to claim 6, characterized in that: Based on the construction error data, adjust the geological coupling parameters and structural compensation in the BIM model through the parametric rule library, and generate the final design plan when the construction error data reaches the preset threshold. The specific steps are as follows: Through the weight distribution algorithm in the parametric rule library, convert the construction error data into an adjustment coefficient for the geological coupling parameter and a correction factor for the structural compensation; According to the adjustment coefficient of the geological coupling parameter and the correction factor of the structural compensation, associate with the structural mechanics performance parameters of the construction surface nodes, and generate a dynamically updated error data set; When the dynamically updated error data set meets the preset threshold, trigger the genetic algorithm in the parametric rule library, and generate the final design plan including the corrected geological parameters, structural compensation and construction surface coordinate set.

8. A road and bridge design system based on a BIM real-scene model, based on the road and bridge design method based on a BIM real-scene model according to any one of claims 1 to 7, characterized in that: Including a data acquisition module, a quantum optimization module, an AR correction module, a scanning and lofting module, and an error adjustment module; The data acquisition module collects surface and underground data, and constructs a BIM model including geological settlement and meteorological changes in combination with meteorological parameters; The quantum optimization module performs multi-objective optimization on the BIM model through the quantum annealing algorithm combined with the cross-bridge topology library, synchronously optimizes the topological connectivity, structural stress and energy consumption indicators, and generates parametric components and then feedbacks them to the BIM model; The AR correction module is used to load and optimize the BIM model based on the augmented reality device, superimpose it on the real scene through spatial anchoring, and correct the BIM model in combination with the conflict monitoring engine and the knowledge graph; The scanning lofting module deploys the corrected BIM model to the edge computer device, generates a dynamic deviation heat map by scanning the construction surface and drives the lofting robot to perform coordinate marking, and at the same time transmits back the construction error data; The error adjustment module adjusts the geological coupling parameters and structural compensation amounts in the BIM model through a parametric rule base based on the construction error data, and generates a final design plan when the construction error data reaches a preset threshold.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the road and bridge design method based on the BIM real-scene model according to 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 the processor, it implements the steps of the road and bridge design method based on the BIM real-scene model according to any one of claims 1 to 7.

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