A construction simulation method and system for steel box basket arch bridge based on digital twin

Abstract

The present invention provides a steel box basket arch bridge construction simulation method and system based on digital twin, wherein the method comprises: step 1: obtaining three-dimensional point cloud data of steel box arch ribs; step 2: constructing a three-dimensional visualization point cloud model of steel box arch segments according to the three-dimensional point cloud data; step 3: obtaining the automatic semantic recognition result of the BIM model; step 4: determining the feature mapping mechanism of the three-dimensional visualization point cloud model to the BIM model according to the automatic semantic recognition result; step 5: performing simulated construction of the steel box basket arch bridge according to the feature mapping mechanism. The present invention provides a steel box basket arch bridge construction simulation method and system based on digital twin, constructing a three-dimensional visualization point cloud model of the steel box arch segments, and then determining the feature mapping mechanism of the three-dimensional visualization point cloud model to the BIM model according to the automatic semantic recognition result of the BIM model, and performing simulated construction based on the feature mapping mechanism, thereby improving the control accuracy and quality of subsequent construction.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge engineering, and in particular to a construction simulation method and system for a steel box basket arch bridge based on digital twins. Background Art

[0002] The steel box basket arch bridge is a bridge type that combines the steel box structure and the basket arch design. It usually has high aesthetic value and structural efficiency. However, the arch ribs of the main bridge of this bridge type are basket arches, and the arch ribs and arch beam joints are all special-shaped spatial structures, which are difficult to install and splice. The splicing quality is directly related to the bridge line shape of the arch ribs. The bridge line shape directly affects the stress of the arch ribs and is an important indicator of construction quality.

[0003] The invention patent with application number: CN201810047182.6 discloses a simulation method for steel bridge deck pavement construction based on BIM technology, wherein the method includes: 1) data collection, including construction site geographic information, three-dimensional dimensions of steel bridges and their bridge decks, and construction plans for steel bridge deck pavement projects; 2) building a construction sub-model based on the BIM platform, including a construction site sub-model, a steel bridge sub-model, a steel bridge deck pavement layer sub-model, and a construction machinery sub-model; 3) obtaining construction process information from the construction plan of the steel bridge deck pavement project, and importing the construction sub-model data and construction process information into the construction simulation platform to assemble the construction model; 4) making a simulation animation for steel bridge deck pavement construction based on the construction model. The above method utilizes the BIM platform to add a time dimension on the basis of three-dimensional visual modeling. By inputting the construction plan into the construction simulation platform, the feasibility of the construction plan can be verified intuitively and quickly, which helps to improve the efficiency of pavement construction and reduce the error rate.

[0004] However, the above-mentioned existing technology directly establishes the construction sub-model based on the BIM platform, and there is a deviation in the information link or association between different components or sub-models of the BIM model, which leads to inaccurate or lost information transmission and thus mapping relationship errors. Furthermore, subsequent construction control is also inaccurate.

[0005] In view of this, there is an urgent need for a steel box basket arch bridge construction simulation method and system based on digital twin to at least solve the above-mentioned shortcomings. Summary of the invention

[0006] One of the purposes of the present invention is to provide a construction simulation method and system for a steel box basket arch bridge based on digital twins, obtain the three-dimensional point cloud data of the steel box arch ribs and construct a three-dimensional visualization point cloud model of the steel box arch segment. Automatic semantic recognition is performed on the BIM model, and according to the automatic semantic recognition results, a feature mapping mechanism from the three-dimensional visualization point cloud model to the BIM model is determined. Based on the feature mapping mechanism, the simulated construction of the steel box basket arch bridge is performed, thereby improving the control accuracy and quality of subsequent construction.

[0007] An embodiment of the present invention provides a digital twin-based steel box basket arch bridge construction simulation method, comprising:

[0008] Step 1: Obtain the 3D point cloud data of the processed steel box arch rib;

[0009] Step 2: Construct a 3D visualization point cloud model of the steel box arch segment based on the 3D point cloud data;

[0010] Step 3: Obtain the automatic semantic recognition results of the BIM model;

[0011] Step 4: Determine the feature mapping mechanism from the 3D visualization point cloud model to the BIM model based on the automatic semantic recognition results;

[0012] Step 5: According to the feature mapping mechanism, simulate the construction of the steel box basket arch bridge.

[0013] Preferably, step 1: obtaining three-dimensional point cloud data of the processed steel box arch ribs includes:

[0014] Scan the processed steel box arch ribs with a 3D laser scanner to obtain global point cloud data;

[0015] The global point cloud data is subjected to point cloud denoising, point cloud simplification, point cloud registration and point cloud stitching to determine the three-dimensional point cloud data.

[0016] Preferably, step 3: obtaining the automatic semantic recognition result of the BIM model includes:

[0017] Determine multiple BIM models based on digital twin technology;

[0018] Automatic semantic recognition is performed on the model feature surfaces of the BIM model to obtain constraint conditions, which are used as automatic semantic recognition results. The constraint conditions include: model feature surfaces, model feature lines and model feature points.

[0019] Preferably, automatic semantic recognition of model feature surfaces of the BIM model includes:

[0020] Obtain empirical identification results of BIM models;

[0021] Cluster the empirical recognition results to determine the types of recognition results;

[0022] Determine the data index label according to the type of recognition result;

[0023] Get the data index node;

[0024] According to the data index label and the content label of the data index node, the manual identification experience data is indexed;

[0025] Based on deep learning technology and semantic extraction technology, the automatic semantic recognition model is determined according to manual recognition experience data;

[0026] Automatic semantic recognition is performed on the model feature surfaces of the BIM model according to the automatic semantic recognition model.

[0027] Preferably, step 4: determining a feature mapping mechanism from a 3D visualization point cloud model to a BIM model according to the automatic semantic recognition result, comprises:

[0028] Determine key geometric features of 3D point cloud data;

[0029] Based on the multi-point spline curve fitting algorithm, the fitting spline curve is determined according to the key geometric features;

[0030] Extract geometric parameters from spline curves;

[0031] According to the geometric parameters, an inverse model is constructed;

[0032] Obtaining a first comparison verification result of the inverse model and the three-dimensional point cloud data;

[0033] Determine a target inverse model according to the first comparison verification result;

[0034] Import the target reverse model into the preset finite element analysis platform to obtain a three-dimensional finite element model;

[0035] Based on the automatic semantic recognition results and the three-dimensional finite element model, the feature mapping mechanism from the three-dimensional finite element model to the BIM model is determined.

[0036] Preferably, determining the target inverse model according to the first comparison verification result includes:

[0037] If the first comparison verification result is that the inverse model reaches the preset fitting degree, the corresponding inverse model is used as the target inverse model;

[0038] If the first comparison verification result is that the inverse model does not reach the preset fitting degree, the spline curve is adjusted to obtain a reshaped inverse model;

[0039] When the second comparison verification result between the reshaped inverse model and the three-dimensional point cloud data reaches a preset fit, the corresponding reshaped inverse model is used as the target inverse model.

[0040] Preferably, adjusting the spline curve includes:

[0041] Parse the spline curve and obtain the current spline curve parameters;

[0042] Determine the parameter type of the current spline curve parameters, which include: control point position, control point number and spline tension;

[0043] Determining a first parameter adjustment direction according to the first comparison verification result;

[0044] Determine whether the adjustment direction of the first parameter is comprehensive;

[0045] If the first parameter adjustment direction is comprehensive, adjust the current spline curve parameters according to the first parameter adjustment direction;

[0046] If the first parameter adjustment direction is not comprehensive, determine the parameter type to be adjusted and use it as the target parameter type;

[0047] Traversing each target parameter type in turn, randomly adjusting the current spline curve parameters of the target parameter type currently being traversed, and obtaining random adjustment data;

[0048] Determine the process model based on random adjustment data;

[0049] Obtaining a third comparison verification result of the process model and the three-dimensional point cloud data;

[0050] Determine a comparison result of the first comparison verification result and the third comparison verification result;

[0051] Determine a second parameter adjustment direction of the target parameter type currently being traversed according to the comparison result and the random adjustment direction of the random adjustment data;

[0052] When all target parameter types are traversed, the first parameter adjustment direction and the second parameter adjustment direction are used as the third parameter adjustment direction, and the current spline curve parameters are adjusted according to the third parameter adjustment direction.

[0053] Preferably, step 5: performing simulation construction of a steel box basket arch bridge according to a feature mapping mechanism, including:

[0054] According to the feature mapping mechanism, the assembly process of the steel box basket arch bridge in the BIM environment is determined;

[0055] According to the assembly process, simulated construction of steel box basket arch bridge is carried out.

[0056] An embodiment of the present invention provides a digital twin-based steel box basket arch bridge construction simulation method, which also includes:

[0057] In the process of simulating the construction of the steel box basket arch bridge according to the assembly process, the construction plan is obtained;

[0058] Among them, in the process of simulating the construction of the steel box basket arch bridge according to the assembly process, the construction plan is obtained, including:

[0059] According to the assembly process, determine the assembly process sequence;

[0060] Traverse the assembly processes in the assembly process sequence from the beginning to the end, and take the assembly process currently being traversed as the target assembly process;

[0061] Determine the construction solution set according to the target assembly process and the preset construction solution library;

[0062] Determine the target solution characteristics of the construction sub-solutions in the construction solution set;

[0063] Obtain construction condition information;

[0064] Build a condition evaluation template based on construction condition information;

[0065] Determine the extraction score of the construction sub-scheme based on the target scheme characteristics and condition evaluation template;

[0066] Determine the construction plan, extract the construction sub-plan with the highest score, and conduct simulated assembly;

[0067] If the simulated assembly is successful, the corresponding construction sub-plan will be used as the target sub-plan;

[0068] If the simulated assembly fails, the corresponding construction sub-scheme in the construction scheme set is eliminated, and the remaining construction sub-schemes in the construction scheme set are simulated and assembled in order from high to low in terms of extraction scores until the simulated assembly succeeds, and the corresponding construction sub-scheme when the simulated assembly succeeds is used as the target sub-scheme;

[0069] When all assembly processes in the assembly process sequence are traversed, the target sub-plans are integrated to obtain the construction plan.

[0070] An embodiment of the present invention provides a digital twin-based steel box basket arch bridge construction simulation system, comprising:

[0071] The 3D point cloud data acquisition subsystem is used to acquire the 3D point cloud data of the processed steel box arch ribs;

[0072] The model building subsystem is used to build a 3D visual point cloud model of the steel box arch segment based on the 3D point cloud data;

[0073] Automatic semantic recognition subsystem, used to obtain automatic semantic recognition results of BIM models;

[0074] A feature mapping mechanism determination subsystem is used to determine the feature mapping mechanism from the 3D visualization point cloud model to the BIM model based on the automatic semantic recognition result;

[0075] The simulation construction subsystem is used to simulate the construction of the steel box basket arch bridge according to the feature mapping mechanism.

[0076] The beneficial effects of the present invention are:

[0077] The present invention obtains the three-dimensional point cloud data of the steel box arch rib and constructs a three-dimensional visualization point cloud model of the steel box arch segment. The BIM model is automatically semantically recognized, and according to the automatic semantic recognition result, the feature mapping mechanism of the three-dimensional visualization point cloud model to the BIM model is determined. Based on the feature mapping mechanism, the simulated construction of the steel box basket arch bridge is carried out, which improves the control accuracy and quality of the subsequent construction.

[0078] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures specifically pointed out in the present application documents.

[0079] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0081] Figure 1 Schematic diagram of a steel box basket arch bridge construction simulation method based on digital twin in an embodiment of the present invention;

[0082] Figure 2 It is a schematic diagram of a steel box basket arch bridge construction simulation system based on digital twin in an embodiment of the present invention. DETAILED DESCRIPTION

[0083] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0084] The embodiment of the present invention provides a construction simulation method of a steel box basket arch bridge based on digital twin, such as Figure 1 As shown, including:

[0085] Step 1: Obtain the 3D point cloud data of the processed steel box arch rib; wherein the steel box arch rib is: a closed box-shaped structure processed and manufactured by the staff and used to bear the load of the steel box basket arch bridge under construction; the 3D point cloud data is: a full-range scan of the steel box arch rib is performed to obtain point cloud data reflecting the global characteristics of the arch rib, and then the point cloud data reflecting the global characteristics of the arch rib is processed and analyzed, and an efficient point cloud denoising, simplification, registration and splicing method is established to obtain the processed point cloud data in the same coordinate system;

[0086] Step 2: Construct a 3D visualization point cloud model of the steel box arch segment based on the 3D point cloud data; the steel box arch segment is a part of the steel box arch bridge and a constructible unit of the bridge. After being prefabricated in the factory, it is transported to the construction site for assembly; the 3D visualization point cloud model is: a 3D model of the steel box arch segment is intuitively displayed using the 3D point cloud data;

[0087] Step 3: Obtain the automatic semantic recognition result of the BIM model; wherein the automatic semantic recognition result is obtained based on the automatic semantic recognition technology, and the recognized contents are: the characteristic surface, characteristic line and characteristic point of the BIM model; the characteristic surface is: the geometric characteristic surface in the BIM model, such as: wall surface, floor slab, roof, beam and column and other structural elements; the characteristic line is: the characteristic line automatically extracted from the recognized geometric characteristic surface, such as: edge line and center line; the characteristic point is: identifying and extracting key geometric points, such as: corner point, intersection point or preset reference point;

[0088] Step 4: Determine the feature mapping mechanism of the 3D visualization point cloud model to the BIM model based on the automatic semantic recognition result; wherein the feature mapping mechanism is a process of establishing a corresponding relationship between the geometric features of the 3D visualization point cloud model and the automatic semantic recognition result of the BIM model;

[0089] Step 5: According to the feature mapping mechanism, simulate the construction of the steel box basket arch bridge. When the simulated construction of the steel box basket arch bridge is carried out according to the feature mapping mechanism, the BIM model corresponding to the steel box arch segment is determined according to the mapping relationship in the feature mapping mechanism for simulated assembly, so as to realize the simulated construction of the steel box basket arch bridge, which is used for subsequent verification of the construction plan and optimization of the construction process.

[0090] The working principle and beneficial effects of the above technical solution are:

[0091] This application obtains the 3D point cloud data of the steel box arch rib and constructs a 3D visualization point cloud model of the steel box arch segment. Automatic semantic recognition is performed on the BIM model, and based on the automatic semantic recognition results, the feature mapping mechanism from the 3D visualization point cloud model to the BIM model is determined. Based on the feature mapping mechanism, the simulated construction of the steel box basket arch bridge is performed, which improves the control accuracy and quality of subsequent construction.

[0092] In one embodiment, step 1: obtaining three-dimensional point cloud data of the processed steel box arch ribs includes:

[0093] The processed steel box arch rib is scanned by a 3D laser scanner to obtain global point cloud data; wherein the global point cloud data is: a collection of point cloud data of the steel box arch rib obtained by scanning from different angles by a 3D laser scanner;

[0094] The global point cloud data is subjected to point cloud denoising, point cloud simplification, point cloud registration and point cloud splicing to determine the three-dimensional point cloud data. Point cloud denoising is to use a denoising algorithm to identify and remove unnecessary data points in the global point cloud data; point cloud simplification is to reduce the number of data points in the point cloud to improve the efficiency of subsequent processing; point cloud registration is to align the scanned data into a unified coordinate system when scanning the steel box arch rib from different positions; point cloud splicing is to merge multiple registered point cloud data blocks into a single, continuous point cloud model.

[0095] The working principle and beneficial effects of the above technical solution are:

[0096] This application introduces a three-dimensional laser scanner to scan the steel box arch ribs to obtain global point cloud data, performs point cloud denoising, point cloud simplification, point cloud registration and point cloud stitching on the global point cloud data, determines the three-dimensional point cloud data, and improves the acquisition accuracy and processing efficiency of the three-dimensional point cloud data.

[0097] In one embodiment, step 3: obtaining the automatic semantic recognition result of the BIM model includes:

[0098] Based on the digital twin technology, multiple BIM models are determined; wherein the BIM model is: a building information model constructed based on the digital twin technology;

[0099] Automatic semantic recognition is performed on the model feature surfaces of the BIM model to obtain constraint conditions, which are used as the automatic semantic recognition results. The constraint conditions include: model feature surfaces, model feature lines and model feature points. Among them, model feature surfaces are: the surfaces that constitute the building entities in the building information model; automatic semantic recognition is: allowing the computer to automatically recognize and understand the semantics of the BIM model; model feature lines are: the lines that constitute the edges of the building in the building information model; model feature points are: the points that define the specific locations of the building in the BIM model.

[0100] The working principle and beneficial effects of the above technical solution are:

[0101] This application introduces automatic semantic recognition to determine the constraint conditions of the model feature surface of the BIM model, and uses the constraint conditions as the automatic semantic recognition result, thereby improving the efficiency of subsequent model matching.

[0102] In one embodiment, automatically semantically identifying a model feature surface of a BIM model includes:

[0103] Obtaining an empirical recognition result of the BIM model; wherein the empirical recognition result is: a result obtained by manually recognizing and classifying the BIM model;

[0104] Clustering the empirical recognition results to determine the recognition result types; wherein the recognition result types are grouping categories obtained by grouping the empirical recognition results according to the similarity of the results, such as feature line recognition results, and feature point recognition results;

[0105] Determine the data index label according to the type of recognition result; wherein the data index label is: a label for identifying and quickly retrieving big data;

[0106] Obtaining a data index node; wherein the data index node is: a big data communication node;

[0107] According to the data index tag and the content tag of the data index node, the manual identification experience data is indexed; wherein the content tag is: the data content identifier provided by the data index node;

[0108] Based on deep learning technology and semantic extraction technology, the automatic semantic recognition model is determined according to manual recognition experience data; the dynamic semantic recognition model is: an AI model that automatically recognizes the characteristic surfaces in the BIM model;

[0109] Automatic semantic recognition is performed on the model feature surfaces of the BIM model according to the automatic semantic recognition model.

[0110] The working principle and beneficial effects of the above technical solution are:

[0111] This application clusters the empirical recognition results of manual recognition of the acquired BIM model to determine the type of recognition result. According to the type of recognition result, the data index label is determined. The manual recognition empirical data is indexed according to the data index label and the content label of the data index node. Deep learning technology and semantic extraction technology are introduced to train the automatic semantic recognition model based on the manual recognition empirical data, and the model feature surface of the BIM model is automatically semantically recognized based on the automatic semantic recognition model, which is more intelligent and accurate.

[0112] In one embodiment, step 4: determining a feature mapping mechanism from a 3D visualization point cloud model to a BIM model based on the automatic semantic recognition result, includes:

[0113] Determine the key geometric features of the 3D point cloud data; wherein the key geometric features are: geometric information in the 3D point cloud data, such as: edges, corners and planes;

[0114] Based on the multi-point spline curve fitting algorithm, according to the key geometric features, a fitting spline curve is determined; wherein the spline curve is: a smooth curve generated according to the multi-point spline curve fitting algorithm and the key geometric features;

[0115] Extracting geometric parameters from a spline curve; where geometric parameters are parameters that determine the shape and characteristics of the curve, such as control points, tension, and smoothness;

[0116] According to the geometric parameters, a reverse model is constructed; wherein the reverse model is: a three-dimensional geometric model constructed in CAD software according to the geometric parameters;

[0117] Obtaining a first comparison verification result of the inverse model and the three-dimensional point cloud data; wherein the first comparison verification result is: a model deviation of the three-dimensional model corresponding to the inverse model and the three-dimensional point cloud data;

[0118] According to the first comparison and verification result, a target inverse model is determined; wherein the target inverse model is: an inverse model used for constructing a three-dimensional finite element model;

[0119] Importing the target reverse model into a preset finite element analysis platform to obtain a three-dimensional finite element model; wherein the preset finite element analysis platform is: FEA software;

[0120] Based on the automatic semantic recognition results and the three-dimensional finite element model, the feature mapping mechanism from the three-dimensional finite element model to the BIM model is determined.

[0121] The working principle and beneficial effects of the above technical solution are:

[0122] This application introduces a multi-point spline curve fitting algorithm, and obtains a fitted spline curve based on the key geometric features of the determined three-dimensional point cloud data. The parameters that determine the shape and characteristics of the spline curve are extracted and input into the CAD software to build an inverse model. The inverse model and the three-dimensional point cloud data are compared and verified to obtain a first comparison verification result, and the target inverse model is determined according to the first comparison verification result. The target inverse model is input into the FEA software to obtain a three-dimensional finite element model, and the coordination of the scanned model to the three-dimensional finite element model is achieved, thereby improving the accuracy of the three-dimensional finite element model. The feature mapping mechanism is then determined based on the automatic semantic recognition results of the BIM model and the three-dimensional finite element model, and the determination of the feature mapping mechanism is more appropriate.

[0123] In one embodiment, adjusting the spline curve includes:

[0124] If the first comparison verification result is that the inverse model reaches a preset degree of fit, the corresponding inverse model is used as the target inverse model; wherein the preset degree of fit is manually preset;

[0125] If the first comparison verification result is that the inverse model does not reach the preset fitting degree, the spline curve is adjusted to obtain a reshaped inverse model; the reshaped inverse model is: after the spline curve is adjusted, a three-dimensional geometric model is constructed according to the geometric parameters of the adjusted spline curve;

[0126] When the second comparison verification result between the reshaped inverse model and the three-dimensional point cloud data reaches a preset degree of fit, the corresponding reshaped inverse model is used as the target inverse model; wherein the second comparison verification result is: the model deviation between the reshaped inverse model and the three-dimensional model of the three-dimensional point cloud data.

[0127] The working principle and beneficial effects of the above technical solution are:

[0128] This application introduces a preset degree of fit. When the first comparison verification result reaches the degree of fit, the corresponding inverse model is directly used as the target inverse model. Otherwise, the spline curve is adjusted to construct a reshaped inverse model. When the second comparison verification result of the reshaped inverse model and the three-dimensional point cloud data reaches the preset degree of fit, the corresponding reshaped inverse model is used as the target inverse model. The determination efficiency of the target inverse model is higher.

[0129] In one embodiment, adjusting the spline curve includes:

[0130] Parse the spline curve and obtain the current spline curve parameters; the current spline curve parameters are: variables that define the shape and position of the spline curve, such as: control point coordinates, curve tension, etc.;

[0131] Determine the parameter type of the current spline curve parameter, the parameter types include: control point position, control point number and spline tension; wherein, the control point position is: the coordinate of each control point in the spline curve in space; the control point number is: the total number of control points in the spline curve; the spline tension is: the parameter controlling the local curvature of the spline curve;

[0132] Determine the first parameter adjustment direction according to the first comparison verification result; wherein the first parameter adjustment direction is: the adjustment direction of the current spline curve sub-parameter of a certain parameter type that can be directly determined according to the first comparison verification result, for example: if there is a model volume deviation, the first parameter adjustment direction of the number of control points is: increase the number of control points;

[0133] Determine whether the adjustment direction of the first parameter is comprehensive; wherein, determining whether the adjustment direction of the first parameter is comprehensive is: whether the adjustment direction of the current spline curve word parameter of all parameter types can be directly determined according to the first comparison verification result, if so, it is comprehensive, otherwise it is not comprehensive;

[0134] If the first parameter adjustment direction is comprehensive, adjust the current spline curve parameters according to the first parameter adjustment direction;

[0135] If the first parameter adjustment direction is not comprehensive, determine the parameter type to be adjusted and use it as the target parameter type;

[0136] Traversing each target parameter type in turn, randomly adjusting the current spline curve parameter of the target parameter type currently being traversed, and obtaining random adjustment data; wherein the random adjustment is: randomly selecting the adjustable direction of the current spline curve parameter of the target parameter type currently being traversed to make an adjustment of any size;

[0137] Determine a process model according to the random adjustment data; wherein the process model is: a three-dimensional model reversely constructed according to the spline curve parameters determined by the random adjustment data;

[0138] Obtaining a third comparison verification result of the process model and the three-dimensional point cloud data; wherein the third comparison verification result is: a model deviation of the three-dimensional model corresponding to the process model and the three-dimensional point cloud data;

[0139] Determine a comparison result of the first comparison verification result and the third comparison verification result; wherein the comparison result is: a deviation difference between a model deviation of the first comparison verification result and a model deviation of the third comparison verification result;

[0140] Determine the second parameter adjustment direction of the target parameter type currently being traversed according to the comparison result and the random adjustment direction of the random adjustment data; wherein, if the first comparison verification result is greater than the third comparison verification result, the random adjustment direction is the second parameter adjustment direction; and if the first comparison verification result is less than or equal to the third comparison verification result, the opposite direction of the random adjustment direction is the second parameter adjustment direction;

[0141] When all target parameter types are traversed, the first parameter adjustment direction and the second parameter adjustment direction are used as the third parameter adjustment direction, and the current spline curve parameters are adjusted according to the third parameter adjustment direction.

[0142] The working principle and beneficial effects of the above technical solution are:

[0143] The present application determines the parameter type of the current spline curve parameter of the spline curve, and determines the first parameter adjustment direction according to the first comparison verification result. If the first parameter adjustment direction is comprehensive, it can be adjusted directly according to the first parameter adjustment direction. If the first parameter adjustment direction is not comprehensive, the parameter type corresponding to the first parameter adjustment direction in the parameter type is eliminated to obtain the target parameter type. Traverse each target parameter type in turn, and randomly adjust the current spline curve parameter of the target parameter type currently being traversed to obtain random adjustment data. Obtain a process model based on the random adjustment data. Obtain the model deviation of the three-dimensional model corresponding to the process model and the three-dimensional point cloud data (the third comparison verification result). Compare the first comparison verification result with the third comparison verification result to obtain the comparison result. Determine the second parameter adjustment direction of the target parameter type currently being traversed according to the comparison result. When all target parameter types are traversed, the first parameter adjustment direction and the second parameter adjustment direction are used together as the third parameter adjustment direction, and the current spline curve parameters are adjusted based on the third parameter adjustment direction, thereby improving the adjustment rate of the spline curve.

[0144] In one embodiment, step 5: performing simulated construction of a steel box basket arch bridge according to a feature mapping mechanism, including:

[0145] According to the feature mapping mechanism, the assembly process of the steel box basket arch bridge in the BIM environment is determined; the assembly process is: the process of assembling the simulation models of the prefabricated parts of the steel box basket arch bridge in the BIM environment according to the design requirements;

[0146] According to the assembly process, the simulated construction of the steel box basket arch bridge is carried out. Among them, the simulated construction of the steel box basket arch bridge is: using computer simulation technology to simulate the construction process corresponding to the assembly process.

[0147] The working principle and beneficial effects of the above technical solution are:

[0148] Based on the feature mapping mechanism, this application determines the process of assembling the simulation model of each prefabricated component of the steel box basket arch bridge in the BIM environment according to the design requirements and simulates the corresponding construction process, so as to avoid construction errors in the subsequent construction process and reduce costs.

[0149] The embodiment of the present invention provides a construction simulation method of a steel box basket arch bridge based on digital twin, and further includes:

[0150] In the process of simulating the construction of the steel box basket arch bridge according to the assembly process, a construction plan is obtained; wherein the construction plan is: an actual construction plan corresponding to the best simulated construction process simulated by the simulated construction process;

[0151] Among them, in the process of simulating the construction of the steel box basket arch bridge according to the assembly process, the construction plan is obtained, including:

[0152] According to the assembly process, an assembly process sequence is determined; wherein the assembly process sequence is: a sequence obtained by sorting the assembly processes according to the order of assembly;

[0153] Traverse the assembly processes in the assembly process sequence from the beginning to the end, and take the assembly process currently being traversed as the target assembly process;

[0154] According to the target assembly process and the preset construction solution library, a construction solution set is determined; wherein the preset construction solution library stores a set of implementation plans corresponding to multiple work processes; the construction solution set is: a collection of solutions that can achieve the target assembly process;

[0155] Determine the target solution characteristics of the construction sub-solutions in the construction solution set; wherein the construction sub-solutions are: the set elements in the construction solution set; the target solution characteristics are: the characteristic representation of the construction sub-solutions, such as: what equipment is dispatched to perform what operation, and what personnel are dispatched to perform what operation;

[0156] Obtain construction condition information; the construction condition information includes: conditions for the construction of the steel box basket arch bridge, such as: what kind of construction equipment to configure and what kind of construction personnel to deploy;

[0157] According to the construction condition information, a condition evaluation template is constructed; wherein the condition evaluation template is a template for generating a quantitative score of the suitability of the condition in comparison with the characteristics of the target scheme;

[0158] Determine the extraction score of the construction sub-scheme based on the target scheme characteristics and condition evaluation template;

[0159] Determine the construction plan, extract the construction sub-plan with the highest score, and conduct simulated assembly;

[0160] If the simulated assembly is successful, the corresponding construction sub-plan will be used as the target sub-plan;

[0161] If the simulated assembly fails, the corresponding construction sub-scheme in the construction scheme set is eliminated, and the remaining construction sub-schemes in the construction scheme set are simulated and assembled in order from high to low in terms of extraction scores until the simulated assembly succeeds, and the corresponding construction sub-scheme when the simulated assembly succeeds is used as the target sub-scheme;

[0162] When all assembly processes in the assembly process sequence are traversed, the target sub-plans are integrated to obtain the construction plan.

[0163] The working principle and beneficial effects of the above technical solution are:

[0164] This application determines the assembly process sequence, determines the target assembly process being traversed and obtains the construction plan set of the target assembly process, and characterizes the construction sub-plans in the construction plan set to obtain the target plan characteristics. The extraction score of the construction sub-plan is determined based on the target plan characteristics and the condition evaluation template constructed based on the construction condition information. The construction sub-plans in the construction plan set are simulated assembled in order from high to low in terms of the extraction score, and the corresponding construction sub-plan when the simulated assembly is successful is determined as the target sub-plan. When all the assembly processes in the assembly process sequence are traversed, the target sub-plan is integrated to obtain the construction plan, while taking into account the construction condition suitability and simulation implementation feasibility of the construction sub-plan, thereby improving the suitability of the construction plan.

[0165] The embodiment of the present invention provides a steel box basket arch bridge construction simulation system based on digital twin, such as Figure 2 As shown, including:

[0166] The three-dimensional point cloud data acquisition subsystem 1 is used to acquire the three-dimensional point cloud data of the processed steel box arch rib;

[0167] Model building subsystem 2, used to build a three-dimensional visual point cloud model of the steel box arch segment according to the three-dimensional point cloud data;

[0168] Automatic semantic recognition subsystem 3, used to obtain automatic semantic recognition results of the BIM model;

[0169] A feature mapping mechanism determination subsystem 4 is used to determine a feature mapping mechanism from a three-dimensional visualization point cloud model to a BIM model according to the automatic semantic recognition result;

[0170] The simulation construction subsystem 5 is used to simulate the construction of the steel box basket arch bridge according to the feature mapping mechanism.

[0171] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A construction simulation method for a steel box basket arch bridge based on digital twin, characterized in that: include: Step 1: Obtain the 3D point cloud data of the processed steel box arch rib; Step 2: Construct a 3D visualization point cloud model of the steel box arch segment based on the 3D point cloud data; Step 3: Obtain the automatic semantic recognition results of the BIM model; Step 4: Determine the feature mapping mechanism from the 3D visualization point cloud model to the BIM model based on the automatic semantic recognition results; Step 5: According to the feature mapping mechanism, simulate the construction of the steel box basket arch bridge; Step 3: Obtain the automatic semantic recognition results of the BIM model, including: Determine multiple BIM models based on digital twin technology; Automatically semantically identify the model feature surface of the BIM model, obtain constraint conditions, and use the constraint conditions as the automatic semantic recognition results. The constraint conditions include: model feature surface, model feature line and model feature point; The automatic semantic recognition of the model feature surface of the BIM model includes: Obtain empirical identification results of BIM models; Cluster the empirical recognition results to determine the types of recognition results; Determine the data index label according to the type of recognition result; Get the data index node; According to the data index label and the content label of the data index node, the manual identification experience data is indexed; Based on deep learning technology and semantic extraction technology, the automatic semantic recognition model is determined according to manual recognition experience data; Automatic semantic recognition is performed on the model feature surface of the BIM model according to the automatic semantic recognition model; The step 4: determining a feature mapping mechanism from a 3D visualization point cloud model to a BIM model according to the automatic semantic recognition result, including: Determine key geometric features of 3D point cloud data; Based on the multi-point spline curve fitting algorithm, the fitting spline curve is determined according to the key geometric features; Extract geometric parameters from spline curves; According to the geometric parameters, an inverse model is constructed; Obtaining a first comparison verification result of the inverse model and the three-dimensional point cloud data; Determine a target inverse model according to the first comparison verification result; Import the target reverse model into the preset finite element analysis platform to obtain a three-dimensional finite element model; According to the automatic semantic recognition results and the three-dimensional finite element model, the feature mapping mechanism from the three-dimensional finite element model to the BIM model is determined.

2. A digital twin-based steel box basket arch bridge construction simulation method as claimed in claim 1, characterized in that: Step 1: Obtain the 3D point cloud data of the processed steel box arch rib, including: Scan the processed steel box arch ribs with a 3D laser scanner to obtain global point cloud data; The global point cloud data is subjected to point cloud denoising, point cloud simplification, point cloud registration and point cloud stitching to determine the three-dimensional point cloud data.

3. A digital twin-based steel box basket arch bridge construction simulation method as claimed in claim 1, characterized in that: According to the first comparison and verification result, determining the target reverse model includes: If the first comparison verification result is that the inverse model reaches the preset fitting degree, the corresponding inverse model is used as the target inverse model; If the first comparison verification result is that the inverse model does not reach the preset fitting degree, the spline curve is adjusted to obtain a reshaped inverse model; When the second comparison verification result between the reshaped inverse model and the three-dimensional point cloud data reaches a preset fit, the corresponding reshaped inverse model is used as the target inverse model.

4. A digital twin-based steel box basket arch bridge construction simulation method as claimed in claim 3, characterized in that: Adjust spline curves, including: Parse the spline curve and obtain the current spline curve parameters; Determine the parameter type of the current spline curve parameters, which include: control point position, control point number and spline tension; Determining a first parameter adjustment direction according to the first comparison verification result; Determine whether the adjustment direction of the first parameter is comprehensive; If the first parameter adjustment direction is comprehensive, adjust the current spline curve parameters according to the first parameter adjustment direction; If the first parameter adjustment direction is not comprehensive, determine the parameter type to be adjusted and use it as the target parameter type; Traversing each target parameter type in turn, randomly adjusting the current spline curve parameters of the target parameter type currently being traversed, and obtaining random adjustment data; Determine the process model based on random adjustment data; Obtaining a third comparison verification result of the process model and the three-dimensional point cloud data; Determine a comparison result of the first comparison verification result and the third comparison verification result; Determine a second parameter adjustment direction of the target parameter type currently being traversed according to the comparison result and the random adjustment direction of the random adjustment data; When all target parameter types are traversed, the first parameter adjustment direction and the second parameter adjustment direction are used as the third parameter adjustment direction, and the current spline curve parameters are adjusted according to the third parameter adjustment direction.

5. A digital twin-based steel box basket arch bridge construction simulation method as claimed in claim 1, characterized in that: Step 5: According to the feature mapping mechanism, simulate the construction of the steel box basket arch bridge, including: According to the feature mapping mechanism, the assembly process of the steel box basket arch bridge in the BIM environment is determined; According to the assembly process, simulated construction of steel box basket arch bridge is carried out.

6. A digital twin-based steel box basket arch bridge construction simulation method as claimed in claim 5, characterized in that: Also includes: In the process of simulating the construction of the steel box basket arch bridge according to the assembly process, the construction plan is obtained; Among them, in the process of simulating the construction of the steel box basket arch bridge according to the assembly process, the construction plan is obtained, including: According to the assembly process, determine the assembly process sequence; Traverse the assembly processes in the assembly process sequence from the beginning to the end, and take the assembly process currently being traversed as the target assembly process; Determine the construction solution set according to the target assembly process and the preset construction solution library; Determine the target solution characteristics of the construction sub-solutions in the construction solution set; Obtain construction condition information; Build a condition evaluation template based on construction condition information; Determine the extraction score of the construction sub-scheme based on the target scheme characteristics and condition evaluation template; Determine the construction plan, extract the construction sub-plan with the highest score, and conduct simulated assembly; If the simulated assembly is successful, the corresponding construction sub-plan will be used as the target sub-plan; If the simulated assembly fails, the corresponding construction sub-scheme in the construction scheme set is eliminated, and the remaining construction sub-schemes in the construction scheme set are simulated and assembled in order from high to low in terms of extraction scores until the simulated assembly succeeds, and the corresponding construction sub-scheme when the simulated assembly succeeds is used as the target sub-scheme; When all assembly processes in the assembly process sequence are traversed, the target sub-plans are integrated to obtain the construction plan.

7. A digital twin-based steel box basket arch bridge construction simulation system, characterized in that: include: The 3D point cloud data acquisition subsystem is used to acquire the 3D point cloud data of the processed steel box arch ribs; The model building subsystem is used to build a 3D visual point cloud model of the steel box arch segment based on the 3D point cloud data; Automatic semantic recognition subsystem, used to obtain automatic semantic recognition results of BIM models; A feature mapping mechanism determination subsystem is used to determine the feature mapping mechanism from the 3D visualization point cloud model to the BIM model based on the automatic semantic recognition result; The simulation construction subsystem is used to simulate the construction of the steel box basket arch bridge according to the feature mapping mechanism; The automatic semantic recognition subsystem obtains the automatic semantic recognition result of the BIM model, including: Determine multiple BIM models based on digital twin technology; Automatically semantically identify the model feature surface of the BIM model, obtain constraint conditions, and use the constraint conditions as the automatic semantic recognition results. The constraint conditions include: model feature surface, model feature line and model feature point; The automatic semantic recognition of the model feature surface of the BIM model includes: Obtain empirical identification results of BIM models; Cluster the empirical recognition results to determine the types of recognition results; Determine the data index label according to the type of recognition result; Get the data index node; According to the data index label and the content label of the data index node, the manual identification experience data is indexed; Based on deep learning technology and semantic extraction technology, the automatic semantic recognition model is determined according to manual recognition experience data; Automatic semantic recognition is performed on the model feature surface of the BIM model according to the automatic semantic recognition model; The feature mapping mechanism determination subsystem determines the feature mapping mechanism from the 3D visualization point cloud model to the BIM model according to the automatic semantic recognition result, including: Determine key geometric features of 3D point cloud data; Based on the multi-point spline curve fitting algorithm, the fitting spline curve is determined according to the key geometric features; Extract geometric parameters from spline curves; According to the geometric parameters, an inverse model is constructed; Obtaining a first comparison verification result of the inverse model and the three-dimensional point cloud data; Determine a target inverse model according to the first comparison verification result; Import the target reverse model into the preset finite element analysis platform to obtain a three-dimensional finite element model; According to the automatic semantic recognition results and the three-dimensional finite element model, the feature mapping mechanism from the three-dimensional finite element model to the BIM model is determined.

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