A visualization method for temperature field modeling of mountain bridges based on spatiotemporal interpolation and fusion

Abstract

The present invention relates to a method for modeling and visualizing the temperature field of mountain bridges based on spatiotemporal interpolation and fusion, belonging to the technical fields of geographic information systems and three-dimensional spatial modeling. The method comprises: analyzing the heat transfer, heat exchange, and heat radiation patterns of the mountain bridge temperature field, establishing a numerical simulation model of the mountain bridge temperature field, and obtaining temperature field simulation results; performing consistency registration between the temperature field simulation results and the bridge voxel model, and modeling the mountain bridge temperature field using spatiotemporal interpolation and fusion based on the physical characteristics of the temperature difference between the inside and outside of the bridge; and achieving efficient volume rendering and visualization of the mountain bridge temperature field based on ray casting. The present invention can effectively supplement the temperature field simulation results, accurately depicting their spatiotemporal distribution and variation patterns, and can provide key data support for the subsequent intelligent construction and refined management of mountain bridges driven by digital twins.

Description

Technical Field

[0001] The present invention relates to the technical field of geographic information systems and three-dimensional space modeling, in particular to a method for modeling and visualizing a temperature field of a mountain bridge based on time-space interpolation fusion. Background Art

[0002] Bridges are key nodes and hubs that span rivers and lakes, connect canyons and deep ravines, and interconnect transportation infrastructure. With the continuous advancement of my country's infrastructure construction, especially the implementation of the new medium- and long-term railway plan and the Sichuan-Tibet Railway, more and more complex bridge projects will be put into construction and operation in difficult mountainous areas, facing an increasingly rugged and volatile construction environment. Temperature, as a key load in bridge construction, significantly affects the performance of bridge structures. Mountainous environments are particularly characterized by large diurnal temperature differences and rapid temperature changes. Bridges often exhibit rapid surface temperature changes while internal temperature changes are minimal or even unchanged. This significant internal and external temperature difference and uneven distribution can cause displacement or deformation of the bridge structure. Therefore, modeling and visualizing the temperature field of mountain bridges to intuitively reflect the temporal and spatial distribution and variation of the temperature field is of great significance for improving the refined management of bridge construction.

[0003] There are two main approaches to studying bridge temperature fields. One involves installing sensors at specific locations on the bridge to continuously monitor temperature changes. However, this approach is costly for sensor equipment and data acquisition, and only obtains temperature data at limited locations on the bridge surface, making it difficult to detect temperature distribution within the bridge or across the entire bridge. Another approach involves numerically simulating the bridge temperature field through finite element analysis. However, due to the geometric irregularities of bridge components, unstructured networks are typically used to partition the bridge structure. This approach results in sparse temperature field simulation results with uneven distribution in time and space. Furthermore, the approach lacks integration with geographic context, making it difficult to accurately describe bridge temperature variations. Summary of the Invention

[0004] To solve the above-mentioned problems in the existing technology, a temperature field modeling and visualization method for mountain bridges based on spatiotemporal interpolation and fusion is proposed based on the numerical simulation model of the temperature field of mountain bridges and taking into account the physical characteristics of the temperature difference between the inside and outside of the bridge. This method solves the problem of uneven spatiotemporal distribution of temperature simulation results due to the geometric irregularities of bridge component structures.

[0005] A method for modeling and visualizing the temperature field of mountain bridges based on spatiotemporal interpolation fusion includes the following steps:

[0006] Step 1: Analyze the heat transfer, heat exchange, and heat radiation laws of the temperature field of mountain bridges, establish a numerical simulation model of the temperature field of mountain bridges, and obtain temperature field simulation results;

[0007] Step 2: The temperature field simulation results are aligned with the bridge voxel model, and the temperature field of the mountain bridge is modeled using spatiotemporal interpolation fusion based on the physical characteristics of the temperature difference between the inside and outside of the bridge.

[0008] Step 3: Implement efficient volume rendering visualization of the temperature field of mountain bridges based on ray casting.

[0009] Furthermore, the step 1 specifically includes:

[0010] Step 101: Set basic assumptions: a) The material density and specific heat capacity of bridge components are uniform and do not change with external conditions; b) The exterior of bridge components is only affected by solar radiation and air convection, while the internal temperature is affected by heat transfer.

[0011] Step 102: establishing basic control equations for mountain bridge temperature field simulation based on basic laws of computational fluid dynamics (including the law of conservation of mass, the law of conservation of momentum, and the law of conservation of energy);

[0012] Step 103, setting initial parameters and boundary conditions of the simulation;

[0013] Step 104 , meshing the bridge geometric model, and using the resulting generated volume mesh model as input data for simulation calculations;

[0014] Step 105: Iteratively solve the discrete equation using a numerical method and output the numerical simulation results of the temperature field of the mountain bridge.

[0015] Furthermore, the step 2 specifically includes:

[0016] Step 201, voxelizing the bridge geometric model to obtain a bridge voxel model;

[0017] Step 202, mapping the temperature field simulation value to the bridge voxel model through point cloud registration;

[0018] In step 203, the data on the consistency of temporal and spatial relationships within the same area are used as metadata for time dimension interpolation and space dimension interpolation, and the aligned temperature field simulation data are processed in two ways: time first and then space, and space first and then time. Finally, the two interpolation results are fused to obtain a comprehensive temperature field model.

[0019] Furthermore, the voxelization process of the bridge geometric model in step 201 is specifically as follows:

[0020] Axis-aligned bounding box acquisition: The axis-aligned bounding box of the bridge geometric model is represented using the maximum-minimum value representation method. By traversing the vertex coordinates of the bridge geometric model's triangular mesh, the minimum and maximum values of the model on the x, y, and z coordinate axes are obtained;

[0021] Seed scanning line scanning: Using a unit cube as a seed to form a straight line parallel to the coordinate axis, the scanning determines the internal and external positional relationship between the voxels on the path and the bridge geometry model; when the unit cube seed and the bridge geometry model intersect or contain each other, the position information of the unit cube seed is recorded;

[0022] Voxel judgment: When the seed scanning line sweeps across the bridge geometric model, it has three positional relationships with the bridge geometric model: the unit cube seed just entering the model, completely entering the model, and just exiting the model; voxel judgment is performed based on the positional relationship. The seed coordinates just entering the model and just exiting the model represent boundary voxels, while the seed coordinates when completely entering the model represent voxels inside the model.

[0023] Furthermore, the step 202 is specifically as follows: using a four-point consistent set algorithm to perform coarse registration on the temperature simulation data and the bridge voxel data, and then using an iterative closest point algorithm to perform fine registration on the point cloud.

[0024] Furthermore, the specific process of using the iterative closest point algorithm to perform precise registration of point clouds is as follows:

[0025] Step 2021: determine the corresponding point pair set; take the temperature simulation data as the source point cloud P and the bridge voxel data as the target point cloud Q, and for all the points in the source point cloud P, Search for the nearest corresponding point in the target point cloud Q Thus forming a point pair set n is the number of point pairs, i is the number of point clouds, and k is the number of iterations;

[0026] Step 2022, according to the formula

[0027] Establish the target error function and calculate the rotation matrix R k and the translation matrix T k ;

[0028] Step 2023, update the source point cloud coordinates; use the rotation matrix R calculated in step 2022 k and the translation matrix T k , for the source point cloud The coordinates are updated to obtain the updated point cloud The update formula is:

[0029]

[0030] Step 2024, determine whether to continue iteration; according to the formula

[0031]

[0032] Calculate the mean square error σ of the iterative transformation k, if the number of iterations reaches the maximum number k max or |σ k+1 -σ k |<ε, where ε is the iterative mean square error threshold, the iterative calculation is stopped; otherwise, k=k+1 is set and the process returns to step 2021 and iterates again;

[0033] Step 2025, align the point cloud; use the best rotation matrix R calculated last time k and the translation matrix T k , change the point cloud coordinates, complete the point cloud registration, and further map the temperature simulation value to the bridge voxel point cloud.

[0034] Furthermore, the time dimension interpolation adopts cubic spline function for time interpolation; the space dimension interpolation adopts inverse distance weighted method to complete the data of mountain bridge temperature field model in space dimension, and reduces the amount of data used for a single interpolation point by limiting the data influence range.

[0035] Furthermore, the fusion process of the interpolation results is:

[0036] T=λ1T1+λ2T2

[0037] Among them, T is the comprehensive temperature field model, T1 is the temperature field model obtained by time interpolation first and then space interpolation, T2 is the temperature field model obtained by space interpolation first and then time interpolation, λ1 represents the weight coefficient of T1, and λ2 represents the weight coefficient of T2.

[0038] Furthermore, the step 3 is specifically as follows:

[0039] Step 301: constructing a volume texture and a volume bounding box in the CPU based on the bridge temperature field modeling result v=(d, t, c) in step 2; wherein v is a comprehensive temperature field voxel model, d represents the spatial domain, t represents the time domain, and c represents the temperature value;

[0040] In step 302, the constructed volume texture and volume bounding box data are transferred to the GPU, and the GPU is used for ray casting. Through the optimized rendering method of parallel acceleration, early ray termination, and blank voxel jumping, the real-time and efficient rendering of the spatiotemporal data of the mountain bridge temperature field is achieved.

[0041] Beneficial effects of the invention: The present invention proposes a three-dimensional modeling method for the temperature field of mountain bridges that couples numerical simulation with spatiotemporal interpolation. Through the registration of temperature simulation results with bridge voxel models, spatiotemporal interpolation fusion modeling of the temperature field, and temperature field volume rendering based on ray projection, the precise characterization of the spatiotemporal distribution of the temperature field of mountain bridges and efficient visualization rendering are achieved. It can effectively complement the temperature field simulation results, accurately depict its spatiotemporal distribution and change laws, and provide key data support for the subsequent digital twin-driven intelligent construction and refined management of mountain bridges. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is the overall flow chart of the three-dimensional modeling method of bridge temperature field;

[0043] Figure 2 It is the digital simulation calculation process of the temperature field of mountain bridges;

[0044] Figure 3 It is the voxelization process of the bridge geometric model;

[0045] Figure 4 This is the flow chart of spatiotemporal interpolation fusion modeling of mountain bridge temperature field;

[0046] Figure 5 It is a flow chart of temperature field volume rendering based on ray casting. DETAILED DESCRIPTION

[0047] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0048] The "Notice on Comprehensively Promoting the Construction of Realistic 3D China" issued by the General Office of the Ministry of Natural Resources in February 2022 clearly pointed out that real-life 3D is divided into terrain level, city level and component level according to the expression content and level. Among them, component-level real-life 3D focuses on the realistic description and refined expression of city-level real-life 3D, mainly including high-precision geometric modeling, texture directional mapping, perceptual information fusion and attribute behavior simulation, so as to serve the on-demand customization of personalized applications. With regard to the temperature field modeling of mountain bridges, on the one hand, the refinement of the component-level real-life 3D model of bridges can effectively improve the accuracy of numerical simulation of the temperature field. On the other hand, the results of the spatiotemporal modeling of the temperature field can further enrich the semantic information of the real-life 3D model of the bridge, and have important reference opinions on the realistic expression of component-level real-life 3D environmental parameters, thereby exerting greater benefits in the digital bridge construction with multi-professional collaboration.

[0049] Based on this, this embodiment takes mountain bridges as an example and proposes a temperature field modeling and visualization method for mountain bridges based on spatiotemporal interpolation fusion. Figure 1 As shown, the method includes the following steps:

[0050] Step 1: Analyze the heat transfer, heat exchange and heat radiation laws of the temperature field of mountain bridges and establish a numerical simulation model of the solar radiation temperature field of mountain bridges;

[0051] Step 2: The temperature simulation results are aligned and corrected with the bridge voxel model in terms of spatial position, spatial attitude, and spatial scale. Taking into account the physical characteristics of the temperature difference between the inside and outside of the bridge, a 3D modeling method for mountain bridge temperature scenarios is proposed using spatiotemporal interpolation fusion.

[0052] Step three, high-efficiency visualization of mountain bridge temperature volume rendering was achieved based on ray casting.

[0053] 1. Numerical simulation of temperature field of mountain bridges

[0054] This example performs a numerical simulation of the temperature field for a steel truss suspension bridge in a mountainous area, with the following basic assumptions:

[0055] (1) The material density and specific heat capacity of bridge components are uniform and do not change with changes in external conditions;

[0056] (2) The exterior of bridge components is only affected by solar radiation heat transfer and air convection heat transfer, while the internal temperature is affected by heat transfer;

[0057] The digital simulation calculation process of mountain bridge temperature field is as follows Figure 2 As shown in the figure, first, based on the basic laws of computational fluid dynamics, such as the laws of conservation of mass, momentum, and energy, the basic governing equations for the temperature field simulation of mountain bridges are established. Second, the initial simulation parameters and boundary conditions, such as thermodynamic coefficients, inlet and outlet wind speeds, and temperatures, are set. Next, the bridge geometry model is meshed to generate a volume mesh model as input data for the simulation. Finally, a numerical method is used to iteratively solve the discrete equations and output the numerical simulation results of the mountain bridge temperature field.

[0058] 2. Temperature field modeling based on spatiotemporal interpolation fusion

[0059] 2.1 Spatial consistency registration of simulation results

[0060] Although most temperature simulation results for mountain bridges are presented as scattered points, differences in geometric partitioning and data organization lead to inconsistencies in spatial position, spatial attitude, and spatial scale between the simulation results and the bridge model, making them difficult to directly use for spatiotemporal interpolation and fusion modeling of mountain bridge temperature fields. To address this issue, this paper employs a method of voxelizing the bridge geometric model and using it as a benchmark. The temperature simulation values are then mapped to the bridge voxel model through point cloud registration, serving as the basis for spatiotemporal interpolation and fusion modeling of the temperature field.

[0061] 2.1.1 Voxelization of bridge geometric model:

[0062] The voxelization of the bridge geometric model mainly includes axis-aligned bounding box acquisition, seed scanning line scanning and voxel judgment. The specific process is as follows Figure 3 shown.

[0063] 2.1.2 Registration of simulation model and voxel model

[0064] This embodiment uses the four-point consensus set algorithm to roughly align the temperature simulation data with the bridge voxel data, and then uses the robust iterative closest point algorithm to finely align the point cloud. Let the temperature simulation data be the source point cloud, and record it as P = {p1, p2, ... p m}, m is the number of point clouds, the bridge voxel data is the target point cloud, recorded as Q = {q1,q2,…q n}, n is the number of point clouds, the number of iterations is k, and the mean square error threshold of the iteration is ε , the fine registration process is as follows:

[0065] (1) Determine the corresponding point pair set. For all points in the source point cloud P Search for the nearest corresponding point in the target point cloud Q Thus forming a point pair set n is the number of point pairs;

[0066] (2) According to the formula Establish the target error function and calculate the rotation matrix R k and the translation matrix T k ;

[0067] (3) Update the source point cloud coordinates. The rotation matrix R calculated in step (2) is k and the translation matrix T k , for the source point cloud The coordinates are updated to obtain the updated point cloud The update formula is:

[0068]

[0069] (4) Determine whether to continue iteration. According to the formula

[0070]

[0071] Calculate the mean square error σ of the iterative transformation k , if the number of iterations reaches the maximum number k max or |σ k+1 -σ k |<ε, ε is the iterative mean square error threshold, then stop the iterative calculation, otherwise set k=k+1 and return to step (1) to repeat the iterative process;

[0072] (5) Align the point cloud. The optimal rotation matrix R obtained by the last calculation is used. k and the translation matrix T k , change the point cloud coordinates, complete the point cloud registration, and further map the temperature simulation value to the bridge voxel point cloud.

[0073] 2.2 Temporal and spatial interpolation fusion modeling of mountain bridge temperature field

[0074] This embodiment uses the idea of subtraction to perform spatiotemporal interpolation fusion modeling on the temperature field of mountain bridges. The basic idea is to use data with good temporal consistency and spatial relationship consistency in the same area as metadata for time dimension interpolation and space dimension interpolation, respectively. Then, the temperature data is processed in two ways: time first, then space, and space first, then time. Finally, the two interpolation results are fused to form a mountain bridge temperature field model and perform accuracy evaluation, such as Figure 4 shown.

[0075] 2.2.1 Temperature field time dimension interpolation

[0076] In the time dimension, considering the accuracy of interpolation smoothing, this embodiment uses the cubic spline function for time interpolation, which has the characteristics of high accuracy, stable numerical calculation, freely adjustable endpoint conditions, and small amount of calculation. Assume that there is a set of temperature field time series data (x i ,y i ),i∈[1,n],x i is the timestamp, y i is the temperature value, then the interval [x i ,x i+1 The interpolation polynomial of ] is: S i (x) = a i (xx i ) 3 +b i (xx i ) 2 +c i (xx i )+d i ,

[0077] S i (x) The following three conditions are met:

[0078] (1)S i (x i )=y i , i∈[1,n];

[0079] (2)S i (x i ) in each interval [x i ,x i+1 ] is a cubic polynomial;

[0080] (3)S i (x i ) the entire interval [x i ,x n ] has continuous first and second order derivatives, and S'(x i )=S'(x i+1 ), S”(x i )=S”(x i+1 );

[0081] This embodiment selects the “free boundary” constraint, S”(x0)=S”(x n )=0, the unknown parameter a can be solved i , b i , c i and d i , substitute into the polynomial S i The time interpolation curve of the entire temperature field can be obtained in (x).

[0082] 2.2.2 Temperature field spatial dimension interpolation

[0083] In terms of spatial dimension, this embodiment uses the inverse distance weighted method to complete the data of the mountain bridge temperature field model in the spatial dimension. The formula is as follows:

[0084]

[0085] Where λ j Indicates the weight value of point j to point i in the temperature field, d ij Represents the distance from point j to point i, and the power value is usually 2;

[0086] In this example, to improve spatial interpolation efficiency, the data impact range is limited to reduce the amount of data used for each interpolation point. The temperature distribution depth of the bridge towers affected by sunlight in mountainous areas is approximately 0.9m, but the temperature variation within the depth of 0-0.6m is much greater than that within the depth of 0.6-0.9m. The impact depth of the steel structure is 1.0m. Therefore, the temperature data impact ranges for the left and right bank towers and steel truss elements are set to 0.6m and 1.0m, respectively.

[0087] 2.2.3 Spatiotemporal interpolation fusion modeling

[0088] The temperature field model T1 obtained by interpolating time first and space second is fused with the temperature field model T2 obtained by interpolating space first and time second to obtain the comprehensive temperature field model T. The calculation formula is:

[0089] T=λ1T1+λ2T2

[0090] Wherein, λ1 and λ2 represent the weight coefficient of T1 and T2 respectively. In this embodiment, both are set to 0.5.

[0091] 3. Temperature field rendering based on ray casting

[0092] In this embodiment, in order to improve the visualization efficiency of the temperature field of mountain bridges, a ray casting algorithm is used to perform volume rendering of the temperature field. The process is as follows: Figure 5 First, based on the aforementioned spatiotemporal voxel model of the temperature field v = (d, t, c), where d represents the spatial domain, t represents the temporal domain, and c represents the temperature value, a volume texture and bounding box are constructed. This process is completed on the CPU. Second, the constructed volume texture and bounding box data are transferred to the GPU, where ray casting is performed. Furthermore, optimized rendering methods such as parallel acceleration, early ray termination, and blank voxel skipping are employed to achieve real-time and efficient rendering of the spatiotemporal temperature field data of mountain bridges.

[0093] The main innovation of this invention is that it takes the numerical simulation model of the temperature field of mountain bridges as the basis, takes into account the physical characteristics of the temperature difference between the inside and outside of the bridge, and proposes a three-dimensional modeling method for mountain bridge temperature scenes that integrates spatiotemporal interpolation and fusion, solving the problem of uneven spatiotemporal distribution of temperature simulation results due to the geometric irregularities of bridge component structures. In addition, the ray casting algorithm is used to efficiently render the bridge temperature field modeling results in the geographic scene. On the one hand, it can provide key data support for the multi-field coupling modeling of mountain bridges driven by digital twins, serve the intelligent construction and refined management of bridges, and effectively improve the construction quality of bridges in complex and dangerous mountainous areas. On the other hand, it can further enrich the semantic information of the real-life three-dimensional model of the bridge, and provide important reference opinions for the realistic expression of component-level real-life three-dimensional environmental parameters, thereby exerting greater benefits in the digital bridge construction of multi-disciplinary collaboration.

[0094] In the description of the embodiments of the present invention, the terms "first," "second," "third," and "fourth" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly specifying the number of the technical features indicated. Therefore, a feature specified as "first," "second," "third," or "fourth" may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "plurality" means two or more.

[0095] In describing the embodiments of the present invention, it should be understood that "-" and "~" represent a range between two values, and the range includes the endpoints. For example, "AB" represents a range greater than or equal to A and less than or equal to B. "A~B" represents a range greater than or equal to A and less than or equal to B.

[0096] In the description of the embodiments of the present invention, the term "and / or" in this embodiment is simply a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this embodiment generally indicates that the associated objects are in an "or" relationship.

[0097] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for modeling and visualizing the temperature field of mountain bridges based on spatiotemporal interpolation fusion, characterized by: The following steps are involved: Step 1: Analyze the heat transfer, heat exchange, and heat radiation laws of the temperature field of the mountain bridge, establish a numerical simulation model of the temperature field of the mountain bridge, and obtain the temperature field simulation results, including: Step 101: Set basic assumptions: a. The material density and specific heat capacity of the bridge components are uniform and do not change with changes in external conditions; b. The exterior of the bridge components is only affected by solar radiation heat transfer and air convection heat transfer, and the internal temperature is affected by heat transfer; Step 102: Establish the basic control equations for the temperature field simulation of the mountain bridge based on the basic laws of computational fluid dynamics; Step 103: Set the initial parameters and boundary conditions of the simulation; Step 104: Mesh the bridge geometric model and use the resulting generated volume mesh model as input data for the simulation calculation; Step 105: Iteratively solve the discrete equations using a numerical method and output the numerical simulation results of the temperature field of the mountain bridge; Step 2: align the temperature field simulation results with the bridge voxel model, and use spatiotemporal interpolation fusion to model the temperature field of the mountain bridge based on the physical characteristics of the temperature difference between the inside and outside of the bridge. The method includes: step 201: voxelize the bridge geometric model to obtain the bridge voxel model; step 202: map the temperature field simulation value to the bridge voxel model through point cloud registration; step 203: use the data of the consistency of the temporal and spatial relationships in the same area as metadata for time dimension interpolation and space dimension interpolation, and process the registered temperature field simulation data in two ways: time first then space and space first then time. Finally, the two interpolation results are fused to obtain a comprehensive temperature field model. Step 3: Implement efficient volume rendering visualization of the temperature field of mountain bridges based on ray casting.

2. The method for modeling and visualizing the temperature field of mountain bridges based on spatiotemporal interpolation and fusion according to claim 1 is characterized in that: The voxelization process of the bridge geometric model in step 201 is specifically as follows: Axis-aligned bounding box acquisition: The axis-aligned bounding box of the bridge geometric model is represented using the maximum-minimum value representation method. By traversing the vertex coordinates of the bridge geometric model's triangular mesh, the minimum and maximum values of the model on the x, y, and z coordinate axes are obtained; Seed scanning line scanning: Using a unit cube as a seed to form a straight line parallel to the coordinate axis, the internal and external positional relationship between the voxels on the path and the bridge geometry model is determined by scanning; When the unit cube seed and the bridge geometry model are in an intersecting or containing relationship, the position information of the unit cube seed is recorded; Voxel judgment: When the seed scanning line sweeps across the bridge geometric model, it has three positional relationships with the bridge geometric model: the unit cube seed just entering the model, completely entering the model, and just exiting the model; voxel judgment is performed based on the positional relationship. The seed coordinates when just entering the model and just exiting the model represent the boundary voxels, and the seed coordinates when completely entering the model represent the voxels inside the model.

3. The method for modeling and visualizing the temperature field of mountain bridges based on spatiotemporal interpolation fusion according to claim 1 is characterized in that: The step 202 specifically includes: using a four-point consistent set algorithm to perform a rough registration on the temperature simulation data and the bridge voxel data, and then using an iterative closest point algorithm to perform a fine registration on the point cloud.

4. The method for modeling and visualizing the temperature field of mountain bridges based on spatiotemporal interpolation fusion according to claim 3 is characterized in that: The specific process of using the iterative closest point algorithm to perform precise registration of point clouds is as follows: Step 2021: determine the corresponding point pair set; take the temperature simulation data as the source point cloud P and the bridge voxel data as the target point cloud Q, and for all the points in the source point cloud P, Search for the nearest corresponding point in the target point cloud Q Thus forming a point pair set n is the number of point pairs, i is the number of point clouds, and k is the number of iterations; Step 2022, according to the formula Establish the target error function and calculate the rotation matrix R k and the translation matrix T k ; Step 2023, update the source point cloud coordinates; use the rotation matrix R calculated in step 2022 k and the translation matrix T k , for the source point cloud The coordinates are updated to obtain the updated point cloud The update formula is: Step 2024, determine whether to continue iteration; according to the formula Calculate the mean square error σ of the iterative transformation k , if the number of iterations reaches the maximum number k max or |σ k+1 -σ k |<ε, where ε is the iterative mean square error threshold, the iterative calculation is stopped; otherwise, k=k+1 is set and the process returns to step 2021 and iterates again; Step 2025, align the point cloud; use the best rotation matrix R calculated last time k and the translation matrix T k , change the point cloud coordinates, complete the point cloud registration, and further map the temperature simulation value to the bridge voxel point cloud.

5. The method for modeling and visualizing the temperature field of mountain bridges based on spatiotemporal interpolation and fusion according to claim 1 is characterized in that: The time dimension interpolation adopts cubic spline function to perform time interpolation; the space dimension interpolation adopts inverse distance weighted method to complete the data of mountain bridge temperature field model in space dimension, and reduces the amount of data used for a single interpolation point by limiting the data influence range.

6. The method for modeling and visualizing the temperature field of mountain bridges based on spatiotemporal interpolation and fusion according to claim 1 is characterized in that: The fusion process of the interpolation results is: T=λ1T1+λ2T2 Among them, T is the comprehensive temperature field model, T1 is the temperature field model obtained by time interpolation first and then space interpolation, T2 is the temperature field model obtained by space interpolation first and then time interpolation, λ1 represents the weight coefficient of T1, and λ2 represents the weight coefficient of T2.

7. The method for modeling and visualizing the temperature field of mountain bridges based on spatiotemporal interpolation and fusion according to claim 1 is characterized in that: The step 3 is specifically as follows: Step 301: constructing a volume texture and a volume bounding box in the CPU based on the result v = (d, t, c) of the bridge temperature field modeling in step 2; wherein v is a comprehensive temperature field voxel model, d represents the spatial domain, t represents the time domain, and c represents the temperature value; In step 302, the constructed volume texture and volume bounding box data are transferred to the GPU, and the GPU is used for ray casting. Through the optimized rendering method of parallel acceleration, early ray termination, and blank voxel jumping, the real-time and efficient rendering of the spatiotemporal data of the mountain bridge temperature field is achieved.

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