A CRTS III type track slab construction scheme optimization method and device based on a BIM model

Abstract

The application relates to the technical field of building component management, and relates to a CRTS III type track slab construction scheme optimization method and device based on a BIM model. The method comprises the following steps: acquiring first information, second information and third information; the first information comprises a CRTS III type track slab BIM model, a hoisting equipment BIM model and a bridge BIM model; the second information comprises real-time meteorological data; a digital twin model is constructed according to the first information, the second information and the third information; a hoisting position determination scheme is generated by using Monte Carlo simulation in the digital twin model; real-time pose information of the track slab and attitude information of an operator wearing AR glasses are acquired; and the CRTS III type track slab rough paving construction scheme is optimized according to the hoisting position determination scheme, the real-time pose information of the track slab and the attitude information of the operator wearing AR glasses. The application effectively improves the construction precision and efficiency of the CRTS III type track slab.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of building component management, in particular to a CRTS III type track slab construction scheme optimization method and device based on a BIM model. BACKGROUND

[0002] In the existing CRTS III type track slab construction, the scheme design relies on empiricism and lacks digital support. The hoisting position planning is estimated by manual work, which is prone to cause safety hazards due to collisions between equipment, track slabs and bridge guardrails. The track slab pose monitoring accuracy is insufficient and relies on manual measurement, which is difficult to cope with dynamic construction errors. Moreover, the construction scheme is disconnected with the field conditions and cannot be adjusted in real time in combination with the operator's perspective, resulting in low coarse paving efficiency and poor accuracy. SUMMARY

[0003] The present application aims to provide a CRTS III type track slab construction scheme optimization method and device based on a BIM model to improve the above problems.

[0004] To achieve the above purpose, the present application provides the following technical solutions:

[0005] On the one hand, the present application provides a CRTS III type track slab construction scheme optimization method based on a BIM model, which comprises:

[0006] Obtaining first information, second information and third information, the first information comprising a CRTS III type track slab BIM model, a hoisting equipment BIM model and a bridge BIM model, the second information comprising real-time meteorological data, and the third information comprising a base plate acceptance report;

[0007] Constructing a digital twin model according to the first information, the second information and the third information;

[0008] Generating a hoisting position determination scheme by Monte Carlo simulation in the digital twin model;

[0009] Obtaining real-time pose of the track slab and attitude information of the operator wearing AR glasses;

[0010] Optimizing the CRTS III type track slab coarse paving construction scheme according to the hoisting position determination scheme, the real-time pose of the track slab and the attitude information of the operator wearing AR glasses.

[0011] Secondly, the present application provides a CRTS III type track slab construction scheme optimization device based on a BIM model, which comprises:

[0012] The first acquisition module is configured to acquire first information, second information and third information, the first information comprises a CRTS III type track slab BIM model, a hoisting equipment BIM model and a bridge BIM model, the second information comprises real-time meteorological data, and the third information comprises a base plate acceptance report;

[0013] The first processing module is configured to construct a digital twin model according to the first information, the second information and the third information.

[0014] The second processing module is configured to generate a hoisting position determination scheme by using Monte Carlo simulation in the digital twin model.

[0015] The second acquisition module is configured to acquire real-time pose information of the track slab and pose information of an operator wearing AR glasses.

[0016] The third processing module is configured to optimize a CRTS III type track slab rough paving construction scheme according to the hoisting position determination scheme, the real-time pose information of the track slab and the pose information of the operator wearing AR glasses.

[0017] In a third aspect, an embodiment of the present application provides a CRTS III type track slab construction scheme optimization device based on a BIM model, the device comprising a memory and a processor. The memory is configured to store a computer program; and the processor is configured to execute the computer program to implement the steps of the method.

[0018] In a fourth aspect, an embodiment of the present application provides a readable storage medium, and the readable storage medium stores a computer program. When the computer program is executed by a processor, the steps of the above-mentioned CRTS III type track slab construction scheme optimization method based on a BIM model are implemented.

[0019] The beneficial effects of the present application are as follows:

[0020] The present application integrates the BIM model, the meteorological data and the acceptance report through the digital twin model, provides a precise virtual scene for Monte Carlo simulation, avoids the collision risk of the equipment and the bridge guardrail from the source, solves the safety hidden danger of the traditional manual planning, and realizes the millimeter-level dynamic adjustment in the rough paving process in combination with the real-time pose information of the track slab and the pose information of the operator wearing AR glasses, greatly improves the construction precision, and solves the problem of insufficient adaptation of the static scheme and the dynamic working condition in the traditional construction.

[0021] Other features and advantages of the present application will be illustrated in the following description, and some will become apparent from the description, or will be understood from the practice of the present application. The purposes and other advantages of the present application can be achieved and obtained by the structures specifically pointed out in the written description and the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some of the embodiments of the present application, and therefore should not be considered as limiting the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0023] Figure 1 The figure shows the flowchart of the construction scheme optimization method for CRTS III track slab based on BIM model.

[0024] Figure 2 The figure shows the structure diagram of the construction scheme optimization device for CRTS III track slab based on BIM model.

[0025] Figure 3 The figure shows the structure diagram of the construction scheme optimization device for CRTS III track slab based on BIM model.

[0026] In the figure, 800 is the construction scheme optimization device for CRTS III track slab based on BIM model; 801 is a processor; 802 is a memory; 803 is a multimedia assembly; 804 is an I / O interface; 805 is a communication assembly; 901 is a first acquisition module; 902 is a first processing module; 903 is a second processing module; 904 is a second acquisition module; and 905 is a third processing module. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solutions and advantages of the embodiments of the present application more clear, the following will combine the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, but not all the embodiments. The components of the embodiments of the present application described and shown in the drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0028] It should be noted that: similar labels and letters represent similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of the present application, the terms "first", "second" and the like are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0029] Embodiment 1

[0030] The embodiment provides a CRTS III type track slab construction scheme optimization method based on a BIM model. It can be understood that a scene can be laid in the embodiment, for example, a scene in which a CRTS III type track slab needs to be laid on a bridge.

[0031] Referring to Figure 1 , the method includes steps S1, S2, S3, S4 and S5.

[0032] In step S1, first information, second information and third information are acquired. The first information includes a CRTS III type track slab BIM model, a hoisting device BIM model and a bridge BIM model. The second information includes real-time meteorological data. The third information includes a base plate acceptance report.

[0033] In step S2, a digital twin model is constructed according to the first information, the second information and the third information.

[0034] In the step, the limitation of traditional construction relying on only a single design drawing is broken through. Multi-source data such as BIM models, real-time meteorological data and acceptance reports are integrated to provide comprehensive information support for scheme design. Multi-dimensional data is coupled to construct a digital twin model, digital mapping of the construction scene is realized, and the problem of lack of a virtual rehearsal platform in the traditional scheme is solved.

[0035] In step S3, a hoisting position determination scheme is generated by using Monte Carlo simulation in the digital twin model.

[0036] The step S3 further includes steps S31, S32, S33 and S34, which specifically include the following.

[0037] In step S31, the first information, the second information and the third information are coupled by using a multi-body dynamics engine to obtain a virtual scene.

[0038] In the step, the behavior coupling of objects in the virtual scene is realized by the multi-body dynamics engine, which specifically includes that the movement of the hoisting device is associated with the swing of the track slab in real time, the spatial position relationship between the track slab and the bridge guardrail is dynamically updated with movement, and finally a digital twin model with synchronous behavior is formed to provide a credible virtual test field for subsequent simulation.

[0039] In step S32, a plurality of simulation schemes are randomly generated by using the Monte Carlo method.

[0040] In this step, the input of Monte Carlo sampling includes but is not limited to the lifting point position and the wind speed, and multiple simulations are repeatedly performed in the digital twin model, each simulation generates a potential construction scheme based on randomly sampled input.

[0041] Step S33, the positional relationship between the lifting equipment, the track plate and the bridge guardrail is compared in real time in each simulation scheme by using the collision detection algorithm to obtain an identification result.

[0042] In this step, the spatial distance between the track plate, the bridge guardrail, the lifting equipment and the beam body is monitored in real time, the collision risk is identified, and an identification result of the existing collision risk is obtained.

[0043] Step S34, scoring each simulation scheme according to the identification result to obtain a lifting position determination scheme.

[0044] In this embodiment, the combination of the dynamics of the virtual scene and the randomness of the simulation scheme not only determines the lifting position but also simulates the influence of meteorological factors on the lifting in advance, so that the adaptability of the generated scheme to the dynamic environment is significantly improved, solving the problem of poor adaptability of traditional static schemes to dynamic working conditions on site, and greatly reducing the trial and error cost in the early stage.

[0045] Step S4, acquiring real-time pose of track plate and posture information of operator wearing AR glasses;

[0046] Before the step S4, it further includes steps S41, S42, S43, S44, S45 and S46, which specifically include:

[0047] Step S41, acquiring point cloud information of track plate and point cloud information of base plate;

[0048] Step S42, denoising the point cloud information of the track plate and the point cloud information of the base plate by using the radius filtering algorithm to obtain denoised point cloud information;

[0049] The traditional radius filtering algorithm will misfilter effective point cloud in rainy and snowy weather, resulting in distortion of the triangular mesh model after Poisson reconstruction, therefore, the radius filtering algorithm is optimized in this step to dynamically change the filtering radius based on the turbulence intensity, specifically:

[0050] ;

[0051] In the above formula, represents the adaptive filtering radius, represents the basic filtering radius, Indicates the turbulence intensity, when the turbulence intensity is small, the adaptive filtering radius is small, which can retain more detail points in the stable wind environment; when the turbulence intensity is large, the adaptive filtering radius is large, at this time the point cloud is easy to appear discrete points due to equipment shaking, therefore the filtering range is expanded to eliminate isolated noise points caused by wind-induced vibration.

[0052] Step S43, surface reconstruction is performed on the denoised point cloud by using a Poisson reconstruction algorithm to obtain a triangular mesh model;

[0053] In this step, the track slab boss needs to be labeled in the triangular mesh model, and the labeling content includes position parameters and shape parameters.

[0054] Step S44, the CRTS III type track slab BIM model is imported into the triangular mesh model, and a collision detection algorithm is used to calculate the minimum distance between the track slab boss and the base plate groove;

[0055] In this step, the gap between the inner wall of the groove and the side surface of the boss, and the gap between the bottom surface of the groove and the top surface of the boss are detected, and the sampling density is one detection point per 10 mm, when the minimum distance is less than 0, it is judged as a collision risk; when the minimum distance is greater than or equal to 0 and less than or equal to 5 mm, it is judged as a safe range; when the minimum distance is greater than or equal to 5, it is judged as that the gap is too large and may cause insufficient stability of the track slab after rough paving.

[0056] Step S45, the pre-offset coordinates of the track slab are calculated with the maximum safety margin and the minimum adjustment amount as the target;

[0057] In this step, the objective function requires that the minimum gap between the groove and the boss is stabilized at 2-3 mm (lateral) and 2.5-3.5 (vertical), which can avoid collision and reduce the workload of subsequent fine adjustment; the constraint condition is that the lateral adjustment amount and the longitudinal adjustment amount are less than 20 mm, the rotation angle around the Z axis is less than 1°, and the optimal pre-offset amount can be determined by iterative search using particle swarm optimization, so as to obtain the pre-offset coordinates.

[0058] Step S46, the pre-offset coordinates of the track slab are corrected according to real-time meteorological information to obtain the rough paving initial pose of the track slab.

[0059] In this step, feature extraction is performed according to real-time meteorological information to obtain a feature vector, the feature vector includes average wind speed, wind speed standard deviation, turbulence intensity and main wind direction; historical wind field information is obtained; an LSTM network is established and the feature vector and the historical wind field information are sent to the LSTM network, and a wind field prediction value is output; the wind-resistant pre-compensation amount is calculated based on the wind field prediction value, and it needs to be noted that the calculation process of the wind-resistant pre-compensation amount is as follows:

[0060] ;

[0061] in the above formula, represents the wind resistance coefficient, represents the wind resistance coefficient, represents the predicted wind speed included in the wind field prediction value, represents the predicted wind speed included in the wind field prediction value, represents the turbulence intensity.

[0062] In the embodiment, the optimal rough paving starting pose of the track slab can be obtained by combining the pre-bias coordinates with the wind resistance pre-compensation amount, so as to ensure the accuracy of the initial position of the CRTS III track slab rough paving, reduce the subsequent adjustment amount, and lay a foundation for efficient construction. In addition, by combining with dynamic factors such as weather for correction, not only the collision risk is avoided, the safety and efficiency are balanced, but also the stability of the track slab in the dynamic working condition is improved, the secondary adjustment caused by environmental interference is reduced, and the precise benchmark for subsequent AR guided rough paving is provided, thereby indirectly shortening the overall construction period.

[0063] The step S4 further includes a step S47, a step S48, a step S49, a step S410 and a step S411, which specifically include:

[0064] The step S47 includes acquiring multi-modal sensor data.

[0065] In this step, the multi-modal sensor data includes point cloud data collected by one micro-laser radar arranged at each corner of the bottom of the track slab, which is mainly used to capture the transverse / longitudinal position characteristics of the boss on the track slab; the inclination and angular acceleration of the track slab collected by the IMU inertial measurement unit installed at the center of the track slab, which is used to reflect the attitude change of the plate body; and image information collected by the camera installed on both sides of the track slab.

[0066] The step S48 includes preprocessing the multi-modal sensor data to obtain preprocessed multi-modal sensor data.

[0067] In this step, the preprocessing of the multi-modal sensor data includes filtering the point cloud data and using Kalman filtering to eliminate noise of the IMU data.

[0068] The step S49 includes performing pose solving on the preprocessed multi-modal sensor data respectively to obtain pose solving results of each sensor.

[0069] The step S410 includes performing weighted fusion on the pose solving results of each sensor by using a federated Kalman filtering algorithm to obtain fused pose solving results.

[0070] In this step, the dynamic allocation of weights specifically includes: determining the weather condition according to real-time meteorological data; and allocating different weights to each sensor according to the weather condition, wherein when the weather is sunny, the laser radar and the camera are allocated higher weights; when the weather is windy, the IMU (Inertial Measurement Unit) is allocated a higher weight; and when the weather is strong light, the camera is allocated a lower weight.

[0071] Step S411, calculating according to the rough paving initial pose of the track slab and the fused pose calculation result to obtain the real-time pose of the track slab.

[0072] In this embodiment, a high-precision and dynamic perception system for the real-time pose of the track slab is constructed, breaking through the limitations of traditional single sensor or static measurement. Through multi-modal sensor data fusion and real-time calculation, combined with the weighted fusion strategy of federal Kalman filter, continuous and accurate monitoring of the track slab pose is realized, solving the problem of insufficient precision caused by inconsistent sensor data, measurement lag and environmental interference in dynamic construction, providing reliable dynamic pose basis for real-time adjustment of rough paving construction, ensuring that subsequent deviation correction and scheme optimization based on AR can accurately interface with the site conditions, supporting the dual improvement of construction precision and efficiency from the perception level.

[0073] Step S5, optimizing the CRTS III type track slab rough paving construction scheme according to the hoisting position determination scheme, the real-time pose of the track slab and the attitude information of the AR glasses worn by the operator.

[0074] The step S5 further includes steps S51, S52, S53, S54, S55 and S56, which specifically include:

[0075] Step S51, extracting key deviation parameters of the track slab boss and groove according to the real-time pose;

[0076] Step S52, converting the three-dimensional design coordinates of the base plate groove into three-dimensional coordinates in the local coordinate system of the track slab to obtain first coordinate information;

[0077] In this step, the three-dimensional design coordinates of the base plate groove are based on the world coordinate system, therefore, they need to be adjusted to the local coordinate system of the track slab to eliminate the interference of line curves (horizontal curve, vertical curve) and slope on positioning. It should be noted that the world coordinate system takes the starting point of the line as the origin, the line direction as the X axis, the perpendicular direction of the line as the Y axis, and the vertical direction as the Z axis. The local coordinate system of the track slab takes the geometric center of the track slab as the origin, the length direction of the plate body as the X axis, the width direction as the Y axis, and the thickness direction as the Z axis.

[0078] Step S53, calculating a perspective projection matrix according to the attitude information of the AR glasses worn by the operator and the camera intrinsic parameter;

[0079] In this step, the camera extrinsic parameter is determined according to the pose information of the operator wearing the AR glasses, and then the perspective projection matrix is calculated according to the camera extrinsic parameter and the camera intrinsic parameter.

[0080] Step S54, mapping the first coordinate information to a two-dimensional plane according to the perspective projection matrix to obtain second coordinate information;

[0081] The step S54 further includes steps S541, S542, S543, S544, S545 and S546, which specifically include:

[0082] Step S541, at least four markers are preset at the edge of the base plate groove;

[0083] In this step, at least four markers need to be set to ensure the stability of the solution.

[0084] Step S542, the real scene coordinates of each marker are identified to obtain third coordinate information;

[0085] Step S543, the third coordinate information is compared with the design coordinate information corresponding to each preset marker to obtain deviation information;

[0086] In this step, the deviation information includes a horizontal coordinate difference and a vertical coordinate difference, wherein the horizontal coordinate difference is denoted as , and the vertical coordinate difference is denoted as It can be understood that (x, y) represents the real scene coordinate, and (x', y') represents the design coordinate. , ,

[0087] Step S544, constructing a target equation according to the deviation information;

[0088] In this step, the optimal perspective projection matrix adjustment parameter is found to minimize the sum of squares of deviations of all markers, i.e.

[0089] ;

[0090] Step S545, solving the target equation by using the least square method to obtain the adjustment parameter;

[0091] In this step, since the adjustment amount is very small, the deviation value can be approximated as a linear function of the adjustment parameter by Taylor expansion, thereby constructing a linear relationship between the deviation and the perspective projection matrix adjustment parameter to form an over-determined equation set, and then the least square method is used to solve the over-determined equation set to obtain the adjustment parameter. ​​

[0092] Step S546, adjusting the perspective projection matrix according to the adjustment parameter to obtain an adjusted perspective projection matrix.

[0093] Since the CRTS III type track slab construction requires extremely high precision, micron-level precision needs to be ensured, therefore, the embodiment optimizes the perspective projection matrix by presetting the markers and comparing the deviation of the real scene coordinates and the design coordinates, and solving the adjustment parameter by the least square method, ensures the pixel-level alignment of the virtual groove profile rendered in the AR glasses and the real scene of the base plate, and constructs a dynamic calibration mechanism of virtual and real fusion, which not only avoids the virtual and real misalignment problem caused by camera pose drift and environmental interference in the traditional AR mapping, but also ensures the micron-level precision of the construction.

[0094] Step S55, rendering and drawing a closed contour line in the AR glasses according to the second coordinate information to obtain a virtual profile of the groove, and the virtual profile is displayed in the AR glasses;

[0095] Step S56, marking the key deviation parameters in the AR glasses.

[0096] In this step, pixel-level alignment of the virtual groove profile and the real scene of the base plate is realized in the AR glasses display screen, which ensures that the virtual profile accurately covers the physical position of the groove in the real scene. At the same time, based on the real-time pose deviation matrix, the key deviation parameters of the track slab boss and groove are extracted, including X-direction lateral deviation, Y-direction longitudinal deviation, Z-direction elevation difference, and rotation angle deviation around Z-axis, which are dynamically presented by the following ways:

[0097] Numerical marking: the deviation value is displayed in red numbers beside the virtual profile (such as "X:+8mm" "Y:-5mm"), with 2 decimal places, and the refresh frequency is synchronized with the deviation matrix;

[0098] Arrow indication: the adjustment direction is indicated by a dynamic arrow (the length of the arrow is positively correlated with the deviation value, such as an 8mm long arrow corresponding to an 8mm deviation), and the arrow color gradually changes with the deviation size (green ≤5mm, yellow 5-10mm, red >10mm);

[0099] Region highlighting: when the deviation in a certain direction is out of limit (such as X-direction >10mm), the corresponding virtual profile edge flashes a red warning light strip, triggering the vibration prompt of the AR glasses.

[0100] In the embodiment, a dynamic adjustment system of virtual-real fusion is constructed, which breaks through the limitations of information transmission lag and large deviation of manual judgment in traditional rough paving construction. By extracting key deviation parameters, coordinate conversion, perspective projection mapping and AR rendering, the three-dimensional space deviation of the track slab and the base plate is converted into intuitive virtual contour and quantitative parameters under the perspective of the operator, realizing accurate transmission and real-time feedback of construction information. The application enables the operator to dynamically correct the rough paving deviation based on the virtual guidance in the AR glasses, solves the information fault problem of "design coordinates-site adjustment" in traditional construction, converts abstract three-dimensional deviation into concrete visual prompts, significantly improves the efficiency and accuracy of rough paving adjustment, and provides key technical support for dynamic adaptation of the rough paving link of the CRTS III track slab.

[0101] The step S5 further comprises a step S57, a step S58, a step S59 and a step S510, which specifically comprise:

[0102] The step S57 comprises obtaining a first space range, a second space range and a third space range, wherein the first space range comprises a hoisting initial position to a safe operation radius, the second space range comprises a base plate to a low-altitude docking point, and the third space range comprises the safe operation radius to a ground-air docking point.

[0103] In this step, a specific embodiment is that the safe operation radius is set at a distance of 5 m from the base plate, and the low-altitude docking point is set at a distance of 1.5 m from the base plate. When the track slab is lowered to the low-altitude docking point, the deceleration motion is started, and the fine docking is performed. The application breaks through the one-size-fits-all planning mode of the traditional lifting path, and adapts to the operation requirements of different stages through space range subdivision, solving the problem that a single speed control cannot balance efficiency and safety.

[0104] The step S58 comprises determining a speed control curve according to the first space range, the second space range and the third space range.

[0105] In this step, acceleration motion is performed in the first space range, deceleration motion is performed in the second space range, and uniform motion is performed in the third space range, so as to generate a corresponding speed change curve and realize dynamic adaptation of the lifting speed.

[0106] The step S59 comprises generating a path point sequence according to the speed control curve.

[0107] In this step, the continuous speed curve is discretized into specific path point coordinates to provide quantifiable reference nodes for the action execution of the lifting device, so as to ensure that the lifting device moves accurately along the preset trajectory, avoid path deviation caused by speed curve execution deviation, and improve the executability of the scheme.

[0108] Step S510, according to the path point sequence, analysis is performed to obtain an analysis result, the analysis result including a motion parameter of the hoisting device.

[0109] In the embodiment, in the first spatial range, the gradual increase in speed is used to avoid the violent shaking caused by the starting impact; in the second spatial range, the uniform speed is used to maintain the stable motion state and reduce the inertial disturbance; and in the third spatial range, the smooth deceleration is used to reduce the end swing amplitude. In the form of speed gradient control, the track slab swing can be maximally inhibited, the mechanical mode and the control error can be reduced, and the stability and the construction efficiency in the dynamic environment can be further improved.

[0110] After the step S5, the step S6, the step S7 and the step S8 are further included, and specifically include:

[0111] Step S6, the track slab joint and the bottom gap after rough paving are scanned to construct a three-dimensional model of the gap;

[0112] In this step, a three-dimensional laser scanner is used to perform omnidirectional scanning on the track slab joint (the gap between adjacent track slabs) and the bottom gap between the track slab and the base slab after rough paving, high-density point cloud data is obtained, and through point cloud splicing and noise reduction processing, a three-dimensional model of the gap is reconstructed to intuitively present the spatial distribution form of the gap.

[0113] Step S7, the flow path resistance of the self-compacting concrete is calculated through fluid dynamics simulation calculation on the three-dimensional model of the gap to generate a resistance thermal map;

[0114] In this step, the three-dimensional model of the gap is imported into a fluid dynamics simulation software, the material parameters of the self-compacting concrete are set, the flow process of the concrete in the gap is simulated, the flow resistance of different regions (for example, the resistance is large at the narrow gap and the resistance is small at the open area) is calculated, and the resistance distribution is visualized in the form of a thermal map (the deeper the color, the greater the resistance), so as to identify the high-resistance areas prone to incomplete pouring.

[0115] Step S8, different grouting strategies are adopted for different regions according to the resistance thermal map.

[0116] In this step, for the high-resistance areas in the thermal map (such as the narrow track slab joint and the uneven bottom gap), high-pressure grouting, multi-point supplementary grouting or adjustment of the grouting port position and other strategies are adopted; for the low-resistance areas, conventional pressure grouting is adopted to ensure uniform filling of the concrete. Through the differential grouting strategy, the self-compacting concrete is ensured to fully flow and densely fill in the entire gap, the connection strength of the track slab and the base slab is improved, and the structural stability hidden danger caused by insufficient grouting is avoided.

[0117] Embodiment 2

[0118] As Figure 2As shown, the embodiment provides a CRTS III type track slab construction scheme optimization device based on a BIM model. The device includes a first acquisition module 901, a first processing module 902, a second processing module 903, a second acquisition module 904, and a third processing module 905, which specifically include:

[0119] The first acquisition module 901 is configured to acquire first information, second information, and third information. The first information includes a CRTS III type track slab BIM model, a hoisting equipment BIM model, and a bridge BIM model. The second information includes real-time meteorological data. The third information includes a base plate acceptance report.

[0120] The first processing module 902 is configured to construct a digital twin model according to the first information, the second information, and the third information.

[0121] The second processing module 903 is configured to generate a hoisting position determination scheme in the digital twin model by using Monte Carlo simulation.

[0122] The second acquisition module 904 is configured to acquire real-time pose information of a track slab and pose information of an operator wearing AR glasses.

[0123] The third processing module 905 is configured to optimize a CRTS III type track slab rough paving construction scheme according to the hoisting position determination scheme, the real-time pose information of the track slab, and the pose information of the operator wearing AR glasses.

[0124] In one specific embodiment of the present disclosure, the second processing module includes a first processing unit, a second processing unit, a third processing unit, and a fourth processing unit, which specifically include:

[0125] The first processing unit is configured to couple the first information, the second information, and the third information by using a multi-body dynamics engine to obtain a virtual scene.

[0126] The second processing unit is configured to randomly generate a plurality of simulation schemes by using a Monte Carlo method.

[0127] The third processing unit is configured to compare the position relationship of the hoisting equipment, the track slab, and the bridge guardrail in each simulation scheme in real time by using a collision detection algorithm to obtain an identification result.

[0128] The fourth processing unit is configured to score each simulation scheme according to the identification result to obtain a hoisting position determination scheme.

[0129] In an embodiment of the present disclosure, the second acquisition module comprises a first acquisition unit, a fifth processing unit, a sixth processing unit, a seventh processing unit, an eighth processing unit, and a ninth processing unit, which specifically comprise:

[0130] The first acquisition unit is configured to acquire point cloud information of the track slab and point cloud information of the base slab.

[0131] The fifth processing unit is configured to denoise the point cloud information of the track slab and the point cloud information of the base slab by using a radius filtering algorithm to obtain denoised point cloud information.

[0132] The sixth processing unit is configured to perform surface reconstruction on the denoised point cloud by using a Poisson reconstruction algorithm to obtain a triangular mesh model.

[0133] The seventh processing unit is configured to import a CRTS III type track slab BIM model into the triangular mesh model and calculate the minimum distance between the track slab boss and the base slab groove by using a collision detection algorithm.

[0134] The eighth processing unit is configured to calculate the pre-offset coordinates of the track slab with the goal of maximizing the safety margin and minimizing the adjustment amount.

[0135] The ninth processing unit is configured to correct the pre-offset coordinates of the track slab according to real-time meteorological information to obtain the coarse paving initial pose of the track slab.

[0136] In an embodiment of the present disclosure, the second acquisition module comprises a second acquisition unit, a tenth processing unit, an eleventh processing unit, a twelfth processing unit, and a thirteenth processing unit, which specifically comprise:

[0137] The second acquisition unit is configured to acquire multi-modal sensor data.

[0138] The tenth processing unit is configured to pre-process the multi-modal sensor data to obtain pre-processed multi-modal sensor data.

[0139] The eleventh processing unit is configured to perform pose solving on the pre-processed multi-modal sensor data respectively to obtain pose solving results of each sensor.

[0140] The twelfth processing unit is configured to perform weighted fusion on the pose solving results of each sensor by using a federated Kalman filtering algorithm to obtain fused pose solving results.

[0141] The thirteenth processing unit is configured to calculate the real-time pose of the track slab according to the coarse paving initial pose of the track slab and the fused pose solving results.

[0142] It should be noted that the specific manners in which the various modules perform operations have been described in detail in the embodiments relating to the method, and will not be described in detail here.

[0143] Embodiment 3:

[0144] Corresponding to the above method embodiments, this embodiment also provides a CRTS III type track slab construction scheme optimization equipment based on a BIM model. The CRTS III type track slab construction scheme optimization equipment based on a BIM model described below can be correspondingly referred to the CRTS III type track slab construction scheme optimization method based on a BIM model described above.

[0145] Figure 3 is a block diagram of a CRTS III type track slab construction scheme optimization equipment 800 based on a BIM model according to an exemplary embodiment. As shown in Figure 3 the CRTS III type track slab construction scheme optimization equipment 800 based on a BIM model can include a processor 801, a memory 802. The CRTS III type track slab construction scheme optimization equipment 800 based on a BIM model can also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0146] The processor 801 is configured to control overall operation of the BIM model based CRTS III type track slab construction scheme optimization device 800 to complete all or part of the steps of the above-mentioned BIM model based CRTS III type track slab construction scheme optimization method. The memory 802 is configured to store various types of data to support the operation of the BIM model based CRTS III type track slab construction scheme optimization device 800, which can include, for example, instructions for any application or method operating on the BIM model based CRTS III type track slab construction scheme optimization device 800, and application-related data, such as contact data, sent and received messages, pictures, audio, video, and the like. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk. The multimedia component 803 can include a screen and an audio component. The screen can be a touch screen, for example, and the audio component is configured to output and / or input audio signals. For example, the audio component can include a microphone configured to receive external audio signals. The received audio signals can be further stored in the memory 802 or transmitted through the communication component 805. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, which can be a keyboard, a mouse, a button, and the like. The buttons can be virtual buttons or physical buttons. The communication component 805 is configured to enable wired or wireless communication between the BIM model based CRTS III type track slab construction scheme optimization device 800 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, near field communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 805 can include a Wi-Fi module, a Bluetooth module, an NFC module.

[0147] In an example embodiment, the BIM model based CRTS III track slab construction scheme optimization device 800 can be implemented by one or more Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor or other electronic elements for executing the above-mentioned BIM model based CRTS III track slab construction scheme optimization method.

[0148] In another example embodiment, a computer readable storage medium including program instructions is also provided, which when executed by a processor, implement the steps of the above-mentioned BIM model based CRTS III track slab construction scheme optimization method. For example, the computer readable storage medium can be the above-mentioned memory 802 including program instructions, which can be executed by the processor 801 of the BIM model based CRTS III track slab construction scheme optimization device 800 to complete the above-mentioned BIM model based CRTS III track slab construction scheme optimization method.

[0149] Embodiment 4:

[0150] Corresponding to the above method embodiments, in this embodiment, a readable storage medium is also provided, which can be referred to the above-mentioned BIM model based CRTS III track slab construction scheme optimization method.

[0151] A readable storage medium, on which a computer program is stored, the computer program being executed by a processor to implement the steps of the above-mentioned BIM model based CRTS III track slab construction scheme optimization method of the method embodiments.

[0152] The readable storage medium can be specifically a U disk, a mobile hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and various readable storage media that can store program codes.

[0153] The above merely describes the preferred embodiments of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0154] The above merely describes the preferred embodiments of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0154] The above merely describes the preferred embodiments of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A CRTS III track slab construction scheme optimization method based on a BIM model, characterized in that, The method comprises the following steps: obtaining first information, second information and third information, wherein the first information comprises a CRTS III track slab BIM model, a hoisting equipment BIM model and a bridge BIM model, the second information comprises real-time meteorological data, and the third information comprises a base plate acceptance report; constructing a digital twin model according to the first information, the second information and the third information; generating a hoisting position determination scheme by using Monte Carlo simulation in the digital twin model; obtaining real-time pose information of the track slab and posture information of an operator wearing AR glasses; optimizing a CRTS III track slab rough paving construction scheme according to the hoisting position determination scheme, the real-time pose information of the track slab and the posture information of the operator wearing AR glasses, comprising: extracting key deviation parameters of a track slab boss and a groove according to the real-time pose information; converting three-dimensional design coordinates of a base plate groove into three-dimensional coordinates in a local coordinate system of the track slab to obtain first coordinate information; calculating a perspective projection matrix according to the posture information of the operator wearing AR glasses and camera intrinsic parameters; mapping the first coordinate information to a two-dimensional plane according to the perspective projection matrix to obtain second coordinate information; rendering and drawing a closed contour line in the AR glasses according to the second coordinate information to obtain a virtual contour of the groove, and the virtual contour is displayed in the AR glasses; annotating the key deviation parameters in the AR glasses.

2. The BIM model-based CRTS III type track slab construction scheme optimization method according to claim 1, characterized in that, The method for generating a hoisting position determination scheme by using Monte Carlo simulation in the digital twin model comprises the following steps: coupling the first information, the second information and the third information by using a multi-body dynamics engine to obtain a virtual scene; randomly generating a plurality of simulation schemes by using a Monte Carlo method; real-time comparing the positional relationship among hoisting equipment, track slabs and bridge guardrails in each simulation scheme by using a collision detection algorithm to obtain an identification result; scoring each simulation scheme according to the identification result to obtain a hoisting position determination scheme.

3. The BIM model-based CRTS III type track slab construction scheme optimization method according to claim 1, characterized in that, Before obtaining the real-time pose information of the track slab, the method comprises the following steps: obtaining point cloud information of the track slab and point cloud information of the base plate; denoising the point cloud information of the track slab and the point cloud information of the base plate by using a radius filtering algorithm to obtain denoised point cloud information; surface reconstructing the denoised point cloud by using a Poisson reconstruction algorithm to obtain a triangular mesh model; importing the CRTS III track slab BIM model into the triangular mesh model and calculating the minimum distance between the track slab boss and the base plate groove by using a collision detection algorithm; calculating pre-offset coordinates of the track slab with the maximum safety margin and the minimum adjustment as the target; correcting the pre-offset coordinates of the track slab according to real-time meteorological information to obtain a rough paving starting pose of the track slab.

4. The BIM model-based CRTS III type track slab construction scheme optimization method according to claim 1, characterized in that, The method for obtaining the real-time pose information of the track slab comprises the following steps: obtaining multi-modal sensor data; preprocessing the multi-modal sensor data to obtain preprocessed multi-modal sensor data; respectively solving the pose of the preprocessed multi-modal sensor data to obtain the pose solving result of each sensor; The pose calculation results of each sensor are fused by a federal Kalman filtering algorithm to obtain a fused pose calculation result; The real-time pose of the track slab is obtained by calculating the initial pose of the coarse paving of the track slab and the fused pose calculation result.

5. The BIM model-based CRTS III type track slab construction scheme optimization method according to claim 1, characterized in that, The CRTS III type track slab coarse paving construction scheme is optimized according to the hoisting position determination scheme, the real-time pose of the track slab and the posture information of the operator wearing the AR glasses, including: A first space range, a second space range and a third space range are obtained, the first space range includes a hoisting initial position to a safe operation radius, the second space range includes a base plate to a low-altitude docking point, and the third space range includes the safe operation radius to a ground-air docking point; A speed control curve is determined according to the first space range, the second space range and the third space range; A path point sequence is generated according to the speed control curve; An analysis result is obtained by analyzing the path point sequence, the analysis result includes the action parameters of the hoisting equipment.

6. A CRTS III type track slab construction scheme optimization device based on a BIM model, characterized in that, It includes: The first acquisition module is used to obtain first information, second information and third information, the first information includes a CRTS III type track slab BIM model, a hoisting equipment BIM model and a bridge BIM model, the second information includes real-time weather data, and the third information includes a base plate acceptance report; The first processing module is used to construct a digital twin model according to the first information, the second information and the third information; The second processing module is used to generate a hoisting position determination scheme by Monte Carlo simulation in the digital twin model; The second acquisition module is used to obtain the real-time pose of the track slab and the posture information of the operator wearing the AR glasses; The third processing module is used to optimize the CRTS III type track slab coarse paving construction scheme according to the hoisting position determination scheme, the real-time pose of the track slab and the posture information of the operator wearing the AR glasses, including: The key deviation parameters of the track slab boss and the groove are extracted according to the real-time pose; The three-dimensional design coordinates of the base plate groove are converted into three-dimensional coordinates in the local coordinate system of the track slab to obtain first coordinate information; The perspective projection matrix is calculated according to the posture information of the operator wearing the AR glasses and the camera internal parameter; The first coordinate information is mapped to a two-dimensional plane according to the perspective projection matrix to obtain second coordinate information; The virtual contour of the groove is obtained by rendering and drawing a closed contour line in the AR glasses according to the second coordinate information, and the virtual contour is displayed in the AR glasses; The key deviation parameters are marked in the AR glasses.

7. The BIM model-based CRTS III type track slab construction scheme optimization device according to claim 6, characterized in that, The second processing module includes: The first processing unit is used to couple the first information, the second information and the third information by using a multi-body dynamics engine to obtain a virtual scene; The second processing unit is used to randomly generate a plurality of simulation schemes by using the Monte Carlo method; The third processing unit is used to compare the position relationship of the hoisting equipment, the track slab and the bridge guardrail in each simulation scheme in real time by using a collision detection algorithm to obtain an identification result; A fourth processing unit is configured to score each simulation scheme according to the identification result to obtain a lifting position determination scheme.

8. The BIM model-based CRTS III type track slab construction scheme optimization device according to claim 6, characterized in that, Before the second acquisition module, the method comprises: A first acquisition unit is configured to acquire point cloud information of a track slab and point cloud information of a base slab. A fifth processing unit is configured to denoise the point cloud information of the track slab and the point cloud information of the base slab by using a radius filtering algorithm to obtain denoised point cloud information. A sixth processing unit is configured to perform surface reconstruction on the denoised point cloud by using a Poisson reconstruction algorithm to obtain a triangular mesh model. A seventh processing unit is configured to import a CRTS III type track slab BIM model into the triangular mesh model and calculate a minimum distance between a track slab boss and a base slab groove by using a collision detection algorithm. An eighth processing unit is configured to calculate pre-offset coordinates of the track slab with the maximum safety margin and the minimum adjustment as the target. A ninth processing unit is configured to correct the pre-offset coordinates of the track slab according to real-time meteorological information to obtain a coarse paving initial pose of the track slab.

9. The BIM model-based CRTS III type track slab construction scheme optimization device according to claim 6, characterized in that, The second acquisition module comprises: A second acquisition unit is configured to acquire multi-modal sensor data. A tenth processing unit is configured to pre-process the multi-modal sensor data to obtain pre-processed multi-modal sensor data. An eleventh processing unit is configured to perform pose calculation on the pre-processed multi-modal sensor data respectively to obtain pose calculation results of each sensor. A twelfth processing unit is configured to perform weighted fusion on the pose calculation results of each sensor by using a federated Kalman filtering algorithm to obtain fused pose calculation results. A thirteenth processing unit is configured to calculate a real-time pose of the track slab according to the coarse paving initial pose of the track slab and the fused pose calculation results.

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