High-altitude adaptive digital modeling method and system fusing real scene modeling and BIM

Abstract

The application provides a high-altitude adaptive digital modeling method and system fusing real scene modeling and BIM, and belongs to the technical field of digital construction of water conservancy projects. The method comprises the following steps: constructing a BIM elastic parameter field, and expanding component parameters into an elastic parameter space represented by a Gaussian mixture model; constructing a four-dimensional environmental disturbance tensor field based on an implicit neural representation network as a carrier, and outputting a system-level geometric distortion kernel; generating a hypothetical space coverage set covering multiple environmental evolution paths, and determining the optimal estimation of the current environmental disturbance through nearest neighbor search; performing inverse filtering processing on real scene data by using the inverse operator of the optimal geometric distortion kernel, inputting the real scene data after stripping the system distortion into the BIM elastic parameter field for Bayesian updating; and triggering outer loop updating based on the residual error obtained through Bayesian updating, optimizing the network weight and correcting the variance boundary. The system and method are cooperatively operated, the adaptive fusion of real scene data and BIM models in a high-altitude environment is realized, and the modeling accuracy and long-term stability are significantly improved.

Description

Technical Field

[0001] This invention relates to the field of digital construction technology for water conservancy projects, and more specifically, to a high-altitude-adaptive digital modeling method and system that integrates real-scene modeling and BIM. Background Technology

[0002] As water conservancy projects extend to high-altitude areas, the integration of BIM and reality modeling technologies is increasingly being used in construction process management. Existing digital modeling methods are typically built under conventional environmental conditions, with BIM model parameters largely being deterministic values. The collection and processing of reality data are also based on assumptions about the conventional environment. However, in ultra-high-altitude areas above 4000 meters, special environmental conditions such as low air pressure, large temperature differences, and frequent strong winds lead to significant geometric distortions during construction, making conventional modeling methods difficult to adapt effectively. Furthermore, the fusion of BIM models and reality data in existing methods largely relies on manual intervention or post-construction registration, lacking online perception and adaptive compensation mechanisms for environmental disturbances. The integration of multi-source data and the dynamic evolution of model parameters are also insufficient, limiting the effectiveness of digital modeling technology in engineering applications in extreme high-altitude environments. Summary of the Invention

[0003] In view of the above problems, the present invention provides a high-altitude adapted digital modeling method that integrates reality modeling and BIM, the method comprising: A BIM elastic parameter field is constructed, which expands the design geometric parameters of each component in the BIM model from rigid scalars to an elastic parameter space with probability distribution. Each component parameter in the elastic parameter space is characterized by a Gaussian mixture model. The mean of the Gaussian mixture model is the design value of the component, and the variance is dynamically determined based on the historical environmental fluctuation data of the high-altitude area where the component is located. An environmental disturbance field is constructed, and an environmental sensing grid is deployed to collect real-time environmental data of the high-altitude construction site. The real-time environmental data includes temperature offset, air pressure change rate, light intensity gradient and gust vector. A four-dimensional environmental disturbance tensor field is trained using the real-time environmental data as training samples. The four-dimensional environmental disturbance tensor field includes three-dimensional spatial coordinates and time dimension. The output is a system-level geometric distortion kernel corresponding to the spatial location and time. Before real-scene data collection, short-term environmental estimates are obtained, and M high-probability environmental disturbance scenarios are generated. The mean values ​​of each component in the BIM elastic parameter field are positively distorted and rendered through the geometric distortion kernels corresponding to the M environmental disturbance scenarios to generate M combinations of real-scene pre-simulation data, forming a hypothetical space coverage set. Collect real-scene data, extract feature descriptors from the real-scene data, perform nearest neighbor search in the hypothesis space coverage set, match the synthetic real-scene pre-simulation data with the highest similarity to the current real-scene data, and determine the geometric distortion kernel corresponding to the synthetic real-scene pre-simulation data as the optimal estimate of the current environmental disturbance; The real-scene data is inversely filtered using the inverse operator of the optimally estimated geometric distortion kernel to remove system distortions caused by environmental disturbances and obtain environmentally normalized real-scene data. The normalized real-world environmental data is input into the BIM elastic parameter field for Bayesian update, thereby updating the posterior distribution parameters of the Gaussian mixture model of the corresponding component. The residual between the normalized real-world environmental data and the updated mean of the BIM elastic parameter field is calculated. The residuals are clustered according to spatial coordinates and time dimensions to construct a residual tensor. When the mean residual under a certain spatiotemporal cluster continuously exceeds the variance boundary of the current Gaussian mixture model, an outer loop update is triggered: the learnable parameters of the four-dimensional environmental perturbation tensor field are updated using the residual pattern as a new training sample, and the variance of the Gaussian mixture model of the corresponding component is relaxed.

[0004] In addition, the present invention provides a high-altitude adapted digital modeling system that integrates real-scene modeling and BIM, which is used to store and execute the above-mentioned methods.

[0005] Based on the above, this invention constructs a closed-loop collaborative architecture of "environmental perception—distortion pre-simulation—inverse filtering compensation—online update—model evolution." Unlike the "modular patchwork" approach in conventional digital modeling where data acquisition, model building, and disturbance compensation are isolated, this invention establishes a deeply coupled nonlinear gain relationship between its functional modules. Its overall technical effect is not simply the sum of the effects of each module, but is specifically reflected in the following systematic collaborative aspects: First, the data quality and prior information in the forward path mutually enhance each other. This invention provides probabilistic geometric priors through the BIM elastic parameter field, and uses the four-dimensional environmental perturbation tensor field and the hypothesis space coverage set to predict distortion patterns for real-world data. This enables the inverse filtering normalization module to accurately remove environmental noise using inverse operators with structural priors. This process is not a simple "acquisition-filtering" chain, but a deep coupling where prior distribution guides distortion estimation, and distortion estimation ensures filtering quality. High-quality normalized data provides reliable observations for Bayesian updates, while the updated elastic parameters, through residual feedback, inversely supervise the training of the perturbation field, forming a positive data flywheel that becomes more accurate with use.

[0006] Second, the short-term response and long-term evolution are coupled in the closed-loop feedback. This invention mathematically locks the Bayesian update (online correction of component-level parameters) and the outer-loop update (evolution of the global environment model) through variance boundaries. These are not simply parallel "dual processors," but rather a rigorous causal trigger chain is constructed: the spatiotemporal clustering residuals generated by the Bayesian update are the sole criterion for triggering the outer-loop update; the variance boundary after the outer-loop correction, in turn, constrains the confidence allocation of the next round of Bayesian updates. This coupling mechanism enables the system to respond rapidly to instantaneous disturbances on a second-level scale while continuously accumulating environmental knowledge on a weekly scale, fundamentally avoiding the inherent problems of "short-term overfitting and long-term drift" in conventional methods.

[0007] Third, a paradigm shift from passive registration to active pre-adaptation. The hypothesis space pre-modeling module of this invention is not an independent data augmentation tool, but rather forms a closed-loop solution chain of "forward rendering - reverse retrieval" with the feature matching engine. This design transforms the inverse problem, which traditionally requires time-consuming nonlinear iterative optimization frame by frame, into fast nearest neighbor retrieval in a pre-generated hypothesis space. This allows environmental disturbance compensation to no longer rely on repeated trial and error afterward, but rather to achieve distortion pre-computation before the arrival of real-world data. This systemic architectural innovation allows the real-time performance improvement of real-world data processing to directly benefit from the completeness of the hypothesis space coverage and retrieval efficiency, demonstrating the emergent characteristics of the overall architecture being superior to local algorithm optimization.

[0008] In summary, this invention, through data coupling, temporal coupling, and causal coupling among the aforementioned modules, enables the system to possess an adaptive closed-loop capability of "perception-reasoning-action-evolution" in extreme high-altitude environments. It is precisely this synergistic effect generated by deep inter-module linkage that makes the technical effect of this invention far exceed the simple sum of the optimizations of each module individually, providing reliable technical support for the digital construction of water conservancy projects in high-altitude areas. Attached Figure Description

[0009] Fig. 1 This is a flowchart illustrating the method provided in an embodiment of the present invention; Fig. 2 This is a schematic diagram of the system provided by the present invention. Detailed Implementation

[0010] like Figs. 1-2 As shown.

[0011] This technical solution provides a high-altitude-adaptive digital modeling method and system that integrates reality modeling and BIM. The technical details are explained in detail below using a specific application scenario.

[0012] This paper uses a high-altitude reservoir project as an example for illustration. Located at an altitude of approximately 4500 meters, the project features an asphalt concrete core gravel dam with a crest elevation of 4470.00 meters and a maximum height of 39.3 meters. The technical solution of this invention will be described in detail below, taking into account the actual needs for terrain data acquisition, BIM model construction, and environmental disturbance compensation during the dam construction process.

[0013] I. Constructing a BIM Flexible Parameter Field During the dam construction preparation phase of this reservoir project, a BIM flexible parameter field was first constructed. This BIM flexible parameter field is stored on the project team's server, and its physical carrier is an industrial-grade workstation equipped with 64GB of memory and an NVIDIA A4000 graphics processor, running Autodesk Revit 2023 and a custom flexible parameter field management plugin.

[0014] The core data structure of the BIM elastic parameter field extends the design geometric parameters of each component in the BIM model from rigid scalars to an elastic parameter space with a probability distribution. Specifically, for each structural component of the dam, such as a certain fill layer of the asphalt core wall with a design thickness of 0.3 meters, this component is not stored as a fixed value of 0.3 meters in the elastic parameter field, but rather as a Gaussian mixture model. The mean of this Gaussian mixture model is set to the design value of the component, i.e., 0.3 meters; the variance is dynamically determined based on historical environmental fluctuation data of the high-altitude region where the component is located.

[0015] Taking the area where the reservoir project is located as an example, the variance determination process is as follows: First, the construction area is classified by altitude, and the 4470-meter elevation area where the project is located is designated as an ultra-high altitude area. Historical environmental fluctuation data is collected for the historical construction period at this altitude level (e.g., the same period from May to November in the past 5 years). The fluctuation amplitude statistics, such as the peak-to-peak value of temperature fluctuations, and the fluctuation frequency statistics, such as the number of daily temperature fluctuations, are calculated for each altitude level. Using the fluctuation amplitude statistics and fluctuation frequency statistics as input, a preset environmental fluctuation intensity index calculation function is called to generate the environmental fluctuation intensity index. This environmental fluctuation intensity index is positively correlated with the fluctuation amplitude statistics and fluctuation frequency statistics; that is, the larger the fluctuation amplitude and the more frequent the fluctuations, the higher the index value. For each altitude level, using the environmental fluctuation intensity index corresponding to that altitude level as an index, a pre-configured variance mapping table is called to obtain the variance coefficient matching the environmental fluctuation intensity index. This variance mapping table is a lookup table pre-fitted based on historical measured data from similar high-altitude projects. The horizontal axis represents the range of environmental fluctuation intensity indices, and the vertical axis represents the corresponding variance coefficients. Each component is assigned this variance coefficient according to its altitude level, which serves as the variance of the component's Gaussian mixture model. For example, for an asphalt core wall component located in a high-altitude area at the dam crest, if the variance coefficient obtained from the table is 0.010~0.025, then the variance of the component's Gaussian mixture model is 0.010~0.025, corresponding to a standard deviation of approximately 0.055~0.122 meters for a design thickness of 0.3 meters.

[0016] In this way, each component parameter in the BIM elastic parameter field is characterized by a Gaussian mixture model. The mean reflects the design intent, and the variance quantifies the uncertainty of the high-altitude environment fluctuations on the component's geometric parameters.

[0017] II. Constructing an Environmental Disturbance Field At the construction site of the reservoir project, an environmental sensing grid was deployed to collect real-time environmental data. This grid consists of multiple sensor nodes, including temperature sensors, air pressure sensors, light intensity sensors, and triaxial anemometers distributed across different elevations within the dam site area. The spatial coordinates of the sensor nodes were calibrated using the construction coordinate system. Each node collected data at a preset sampling frequency, for example, every 10 seconds. The collected data included temperature shift, air pressure change rate, light intensity gradient, and gust vectors. All sensor nodes transmitted real-time environmental data wirelessly to a relay gateway located at the project site. The relay gateway then uploaded the data via a 4G industrial router to an environmental disturbance field construction module deployed on a cloud server.

[0018] A four-dimensional environmental perturbation tensor field is trained using real-time environmental data as training samples. This four-dimensional environmental perturbation tensor field includes three-dimensional spatial coordinates and a time dimension, and its output is the system-level geometric distortion kernel corresponding to the spatial location and time. The specific construction method is as follows: the spatial location coordinates of each sensor node in the environmental sensing grid are combined with the data acquisition timestamp to form a four-dimensional input vector. The temperature offset, air pressure change rate, light intensity gradient, and gust vector collected by the sensor node are concatenated to form the environmental feature tensor. A learnable implicit neural representation network is constructed as the carrier network for the four-dimensional environmental perturbation tensor field. This implicit neural representation network adopts a multi-layer fully connected network structure, for example, 8 fully connected layers, each containing 256 neurons. The activation function uses a sinusoidal activation function to better fit high-frequency details. The network takes the four-dimensional input vector as input, and after mapping through the multi-layer fully connected network, outputs the local geometric distortion kernel parameters at that spatiotemporal location. The local geometric distortion kernel parameters include translational distortion components, rotational distortion components, and anisotropic scaling distortion components along the three coordinate axes, totaling nine parameters.

[0019] The training of this implicit neural representation network uses the registration residual between the mean geometric data at the corresponding spatial location in the BIM elastic parameter field and the real-world data collected at the same spatiotemporal location as the supervision signal. The supervision signal is sourced through two modes: cold start and incremental update. In cold start mode, since the four-dimensional environmental perturbation tensor field has not yet been trained, an identity mapping is used as the initial system-level geometric distortion kernel. This identity mapping is an identity transformation that does not change any coordinate component of a spatial point; that is, for any input spatial coordinates, the output coordinates are exactly the same as the input coordinates. In this case, inverse filtering degenerates into a no-op, and the real-world data is directly used as the environment-normalized real-world data in subsequent Bayesian updates. The residual obtained after the Bayesian update is used as the initial registration residual dataset. This initial registration residual dataset is then used as the supervision signal to perform initial iterative optimization of the weight parameters of the implicit neural representation network, generating the initial training version of the four-dimensional environmental perturbation tensor field. In cold start mode, the supervision signal is the residual obtained after Bayesian update. The translational distortion loss term in the joint loss function is composed of the coordinate offset component of this residual, while the rotational distortion loss term and scaling distortion loss term are composed of the deformation component of this residual. In incremental update mode, the supervision signal is switched to the registration residual. The weight parameters of the implicit neural representation network are iteratively optimized by minimizing the joint loss function, enabling the network to generalize and output the geometric distortion kernel corresponding to the environmental perturbation based on the input four-dimensional spatiotemporal coordinates. After the initial iterative optimization of the four-dimensional environmental perturbation tensor field in cold start mode is completed, it automatically switches to incremental update mode. After the iterative optimization converges, the implicit neural representation network is saved as a four-dimensional environmental perturbation tensor field, and the forward propagation output of this network serves as the system-level geometric distortion kernel.

[0020] III. Generating the Hypothesis Space Coverage Set Before collecting real-world data, short-term environmental forecasts are obtained. Specifically, a high-resolution local numerical weather prediction model is invoked, using real-time environmental data sequences collected by environmental sensor grids within a preset timeframe before the current moment as the initial input. This model is then integrated over time, outputting predicted environmental parameters for multiple time points within a preset future window as the short-term environmental forecast. The short-term environmental forecast includes temperature forecast offset, air pressure forecast rate of change, light intensity gradient, and gust forecast vector. A perturbation parameter configurator is configured to overlay a preset probability distribution function onto each environmental parameter at each time point of the short-term environmental forecast, generating multiple sets of equally probable forecast members around the forecast. Each set of equally probable forecast members contains one possible combination of environmental parameters at the same time point. The variance of the preset probability distribution function is negatively correlated with the confidence level of the short-term environmental forecast, which is determined by the historical forecast accuracy of the high-resolution local numerical weather prediction model. All equally probable forecast members at all time points are arranged and combined along the time dimension to generate M high-probability environmental perturbation scenarios covering multiple environmental evolution paths. The value of M can be, for example, 50 to 500, and can be determined based on computing resources and scene coverage requirements.

[0021] After obtaining M high-probability environmental disturbance scenarios, M sets of data are generated to form a hypothetical spatial coverage set of real-scene pre-simulation data. The specific process is as follows: Obtain the standard geometric data set corresponding to the mean values ​​of each component in the BIM elastic parameter field. This standard geometric data set includes the standard spatial coordinate data and standard dimension data of each component, such as the design centerline coordinates, thickness, and width of each filling layer of the asphalt core wall. Apply the geometric distortion kernels corresponding to each of the M environmental disturbance scenarios to the standard geometric data set. Each geometric distortion kernel independently performs one forward distortion rendering operation. Forward distortion rendering involves applying spatial coordinate offset perturbations to the standard spatial coordinate data and scale perturbations to the standard dimension data, transforming the standard geometric data set into a distorted geometric data set matching the environmental disturbance scenario. Using each distorted geometric data set as input, a pre-trained neural radiation field network is invoked to perform volume rendering forward propagation. This neural radiation field network is pre-trained using multi-view images as training samples and is used to render 3D geometric data into 2D image data, outputting the synthetic real-scene pre-simulation data corresponding to the distorted geometric data set. The synthetic real-scene pre-simulation data corresponding to each of the M environmental disturbance scenarios are collected to form a hypothetical space coverage set.

[0022] IV. Real-world data acquisition and optimal estimation matching During the dam construction of this reservoir project, the control data acquisition terminal simultaneously collected real-time point-to-point cloud data and real-time image data from the high-altitude construction site according to a preset multimodal acquisition strategy. The data acquisition terminal is a multi-rotor UAV with a lightweight carbon fiber fuselage, equipped with a low-temperature enhanced lithium battery and windproof propellers, and carrying a LiDAR and a full-frame aerial survey camera. The multimodal acquisition strategy includes the terminal's flight speed, scanning frequency, and image sampling interval. These parameters are dynamically adjusted based on the spatial distortion gradient of the geometric distortion kernel corresponding to the current environmental disturbance scene. The larger the spatial distortion gradient, the more severe the geometric deformation caused by the current environmental disturbance. In this case, the flight speed is lower, the scanning frequency is higher, and the image sampling interval is smaller to ensure that sufficiently dense data is acquired in areas of severe deformation. For example, in the core area of ​​the dam site, the flight altitude is set to 150 meters, the flight speed is 8 meters per second, the LiDAR scanning frequency is set to 200,000 points per second, and the image sampling interval is set to one image every 2 seconds.

[0023] The acquired real-world point cloud data and real-world image data are spatiotemporally aligned, ensuring that data acquired at the same time share the same spatial coordinate indices and timestamps, generating multimodal real-world data frames. Joint feature extraction is performed on these multimodal real-world data frames: the real-world point cloud data is input into a point cloud local spectral feature encoder to extract local geometric spectral feature vectors of the point cloud; this encoder, for example, employs a three-layer ensemble abstract encoder based on the PointNet++ architecture. The real-world image data is input into an illumination-invariant texture feature encoder to extract texture feature vectors of the image; this encoder, for example, employs a texture feature extraction backbone network based on ResNet-50. The local geometric spectral feature vectors and texture feature vectors are then concatenated across modalities to generate a multidimensional feature descriptor.

[0024] Using the feature descriptor as the query vector, the cosine similarity between the query vector and the feature descriptors that combine to form the real-scene pre-simulation data is calculated in the hypothesis space coverage set. The synthetic real-scene pre-simulation data with the highest cosine similarity is determined as the matching result, and the environmental disturbance scene corresponding to the synthetic real-scene pre-simulation data is determined as the optimal estimate of the current environmental disturbance.

[0025] V. Inverse Filtering and Environmental Normalization An inverse filtering operator is constructed based on the optimally estimated geometric distortion kernel. This inverse filtering operator is the inverse mapping function of the optimally estimated geometric distortion kernel. Real-world data is input into the inverse filtering operator to perform deconvolution. During the deconvolution operation, based on the spatial coordinate indices of each data point in the real-world data, the corresponding distortion components are retrieved from the optimally estimated geometric distortion kernel for point-by-point inverse mapping. The distortion components are then subtracted from the real-world data point by point, outputting environmentally normalized real-world data.

[0026] VI. Bayesian Update Normalized real-world environmental data is input into the BIM elastic parameter field for Bayesian updating. Using the normalized real-world environmental data as observations, and the mean and variance of the Gaussian mixture model of the corresponding component in the BIM elastic parameter field as the prior mean and variance, a pre-defined Bayesian update formula is called to calculate the posterior parameters. In this Bayesian update formula, the posterior mean equals the weighted average of the prior mean and the observations, with the weights negatively correlated with the prior variance (i.e., the larger the prior variance, the greater the weight of the observations in the update); the posterior variance equals the harmonic mean of the prior variance and the variance of the observations. The posterior mean and posterior variance parameters of the Gaussian mixture model of the component are output, completing the online updating of the BIM elastic parameter field.

[0027] VII. Residual Calculation and Outer Loop Update The residuals between the normalized real-world data and the updated mean of the BIM elastic parameter field are calculated, and the residuals are clustered according to spatial coordinates and time dimensions. When the mean residual of a certain spatiotemporal cluster continuously exceeds the variance boundary of the current Gaussian mixture model, an outer loop update is triggered. "Continuously exceeding" is defined, for example, as the mean residual being greater than the upper limit of the variance boundary for five consecutive sampling periods. The outer loop update includes: using this residual pattern as a new training sample to update the learnable parameters of the four-dimensional environmental perturbation tensor field, i.e., performing additional gradient descent optimization on the weight parameters of the implicit neural representation network; and simultaneously relaxing the variance of the corresponding component's Gaussian mixture model, for example, increasing the upper limit of variance to 1.5 times the standard deviation corresponding to the current residual pattern. Through this outer loop update mechanism, the system can continuously adapt to the constantly changing environmental perturbation patterns in high-altitude environments, achieving adaptive accuracy maintenance and model evolution.

[0028] The dynamic determination of the variance of a Gaussian mixture model has been detailed in Part I with engineering examples, including the collection of historical environmental fluctuation data, the calculation of fluctuation amplitude and frequency statistics, the generation of the environmental fluctuation intensity index, the lookup process of the variance mapping table, and the basis for setting the upper and lower limits of the variance coefficients. It will not be repeated here.

[0029] Assuming the generation of spatial covering sets The specific processes for obtaining short-term environmental forecasts, generating high-probability environmental disturbance scenarios, and rendering positive distortion and neural radiation fields have been detailed in Part III. This section supplements the following implementation details: After generating high-probability environmental disturbance scenarios, the weight of each scenario can be adjusted based on the historical retrieval matching accuracy of the feature descriptors. Specifically, the forecast error of the forecast model for the same region and forecast timeframe over the past 30 days is statistically analyzed, and the root mean square of the forecast error is used as the confidence measure. New scenarios of the same type corresponding to historical scenarios with matching accuracy higher than a preset accuracy threshold are assigned a confidence weight higher than the preset probability density benchmark value. Taking this reservoir project as an example, if historical records show that a certain type of environmental disturbance scenario was successfully matched within the past week and the residual error after inverse filtering is small, the confidence weight of this type of scenario is increased. Using this confidence weight, 200 high-probability environmental disturbance scenarios are resampled, so that the proportion of scenarios with a confidence weight higher than the probability density benchmark value (i.e., 1 / 200) in the hypothesis space coverage set exceeds the preset coverage proportion threshold (e.g., 60%). This resampling process assumes that the spatial coverage set can be more concentrated on high-probability environmental perturbation patterns under limited computational resources, thus improving matching efficiency. The overall process of the multimodal acquisition strategy, the spatiotemporal alignment processing method, the cross-modal feature descriptor extraction method, and the specific implementation of nearest neighbor retrieval have been detailed in Part IV above. This section provides a supplementary example of the specific implementation of dynamically adjusting the acquisition parameters according to the spatial distortion gradient.

[0030] The spatial distortion gradient is obtained by calculating the spatial derivatives of the distortion components at various spatial locations within the geometric distortion kernel, reflecting the severity of environmental disturbances as they change with spatial location. Taking a typical construction day of the reservoir project as an example, the flight altitude in the core area of ​​the dam site is set at 150 meters. When the spatial distortion gradient is low, the flight speed is 10 meters per second. When the spatial distortion gradient increases, the flight speed is dynamically reduced to 6 meters per second, the lidar scanning frequency increases from 150,000 points per second to 250,000 points per second, and the image sampling interval is shortened from one image every 3 seconds to one image every 1.5 seconds. The larger the spatial distortion gradient, the more severe the geometric deformation caused by the current environmental disturbance changes with location. At this time, the flight speed is reduced, the scanning frequency is increased, and the sampling interval is decreased to ensure that sufficiently dense data is obtained in areas of severe deformation. The construction of the inverse filtering operator and the specific implementation of the deconvolution operation have been detailed in Part 5 above. After the above inverse filtering processing, this embodiment further performs residual distortion testing on the filtering effect to evaluate whether the currently optimally estimated geometric distortion kernel fully expresses the real environmental disturbance.

[0031] The specific method for residual distortion verification is as follows: Residual distortion components that were not completely subtracted after point-by-point reverse mapping of the real-scene data using an inverse operator are extracted. These residual distortion components are then spatially registered according to the spatial coordinate index of the real-scene data, generating a geometric distortion residual field with the same spatial dimension as the real-scene data. Each residual component in the geometric distortion residual field contains residual offsets and residual scaling along the three coordinate axes at that spatial location. The residual field energy spectral density of this geometric distortion residual field is calculated. The residual offsets and residual scaling at each spatial location are squared, summed, and then integrated along the spatial dimension to obtain the total residual energy value of the geometric distortion residual field. The total residual energy value is compared with a preset residual energy activation threshold (e.g., 0.01~0.10 square meters). If the total residual energy value is lower than the residual energy activation threshold, it indicates that the deviation between the currently optimally estimated geometric distortion kernel and the actual environmental disturbance is within an acceptable range, and the geometric distortion kernel remains unchanged. If the total residual energy value is higher than the residual energy activation threshold, it indicates that there are unexpressed distortion modes between the currently optimal estimated geometric distortion kernel and the actual environmental disturbance. At this point, the alternative kernel generation process is triggered (see the "Alternative Kernel Generation Process" section below for details), and a better geometric distortion kernel is reselected from the hypothesis space coverage set. The specific calculation formula and posterior parameter derivation for Bayesian updates have been detailed in Part VI above. In this embodiment, according to the formula described in that section, using normalized real-world environmental data as observations, and the mean and variance of the Gaussian mixture model of the corresponding component in the BIM elastic parameter field as the prior mean and variance, the posterior mean and posterior variance are calculated, and the results are written back to the BIM elastic parameter field storage module. Through the above Bayesian update process, the BIM elastic parameter field can continuously absorb new observation data, making the model parameters increasingly closer to the actual working conditions under high-altitude environmental conditions. Short-term environmental prediction value acquisition and high-probability environmental disturbance scenario generation. The process for obtaining short-term environmental forecasts and the methods for generating high-probability environmental disturbance scenarios have been detailed in Part III above. This section supplements the specific time nodes and parameter settings for this project: Every morning at 6:30, the environmental disturbance field construction module automatically starts, calling the high-resolution local numerical forecasting model deployed on the project's server through a data interface. The model uses the real-time environmental data sequence collected by the environmental sensor grid within the 72 hours prior to the current moment as the initial field, drives the model integration after four-dimensional variational assimilation, and outputs the environmental parameter forecast values ​​at 3-hour intervals for the next 48 hours.

[0032] After the forecast values ​​are generated, the perturbation parameter configurator is configured as described in Part III above to expand them into multiple equally probable forecast members. For each environmental parameter at each time node, random samples are taken from its corresponding probability distribution function. Then, the sampled values ​​of all environmental parameters at all time nodes are arranged and combined along the time dimension to generate 200 high-probability environmental perturbation scenarios. Taking temperature as an example, if 5 sets of sampled values ​​are generated at each time node, 17 time nodes will form 85 temperature sampling sequences. Similarly, other environmental parameters are sampled and combined.

[0033] Based on this, the weight of each scene is adjusted according to the historical retrieval matching accuracy of the feature descriptors. The historical retrieval matching accuracy is recorded in the system log, and its calculation method is the degree of agreement between the environmental normalized real-world data obtained by inverse filtering after matching each type of environmental disturbance scene in the past 7 days and the prior mean of the BIM elastic parameter field. Scene types with matching accuracy higher than the preset accuracy threshold are judged as high-confidence types, and are given a confidence weight (e.g., 3 times) higher than the probability density benchmark value (i.e., 1 / 200) during the resampling process. The preset coverage ratio threshold is set to 60%. Through the above resampling process, it is assumed that the spatial coverage set is more concentrated in the environmental disturbance patterns that occur with high probability. The specific methods for residual distortion detection (including the extraction of residual distortion components, the construction of geometric distortion residual field, the calculation of residual field energy spectral density, and the comparison of total residual energy value with activation threshold) have been detailed in the "Inverse Filtering Normalization Process" section and will not be repeated here. Here, we only add that if the total residual energy value is higher than the residual energy activation threshold, the replacement kernel generation process is triggered, as detailed in the "Replacement Kernel Generation Process" below. Replacement Kernel Generation Process: The goal of the replacement kernel generation process is to select a geometric distortion kernel from the hypothesis space coverage set that better matches the current real-world data as a replacement. The specific execution steps are as follows.

[0034] The first step is to remove the geometric distortion kernels corresponding to the currently matched synthetic reality pre-simulation data from the hypothesis space coverage set to avoid repeatedly selecting the same kernel. All remaining geometric distortion kernels are then sorted from high to low according to the cosine similarity between their corresponding synthetic reality pre-simulation data and the feature descriptors of the reality data.

[0035] The second step involves selecting the top K geometric distortion kernels as a candidate replacement kernel set. K is a preset positive integer, and in this embodiment, K is set to 5. The value of K is chosen based on the following: if K is too small, the candidate set may not contain the optimal replacement kernel; if K is too large, the subsequent computational workload will increase significantly. Through engineering practice, a K value of 5 achieves a good balance between search completeness and computational efficiency. The real-world data is then input into the inverse operators corresponding to the five candidate replacement kernels to perform inverse filtering, generating five sets of candidate environment-normalized real-world data.

[0036] The third step involves calculating the likelihood between the five sets of candidate environment normalized reality data and the prior mean of the Gaussian mixture model of the corresponding components in the BIM elastic parameter field. The likelihood is calculated as follows: for each set of candidate environment normalized reality data, the weighted Euclidean distance between it and the prior mean is calculated, with the weight being the reciprocal of the prior variance; a smaller distance indicates a higher likelihood. The candidate replacement kernel corresponding to the candidate environment normalized reality data with the highest likelihood is determined as the updated optimal estimate of the geometric distortion kernel.

[0037] The fourth step involves re-executing the inverse filtering process using the inverse operator of the updated optimally estimated geometric distortion kernel. This outputs updated environmental normalized real-world data, which replaces the original inverse filtering result and participates in subsequent Bayesian updates. Simultaneously, the generation record of this replacement kernel is written to the system log for later analysis of the evolution of current environmental disturbance patterns.

[0038] The construction principles of the four-dimensional environmental perturbation tensor field, network definition, and the specific principles of the two training modes, cold start and incremental update, have been detailed in Part II above and will not be repeated here. This section supplements the specific implementation parameters and training details of this network in this project.

[0039] The implicit neural representation network has an eight-layer fully connected structure, with 256 neurons in each layer. The activation function is a sinusoidal activation function, and the total number of network weight parameters is approximately 500,000. The network takes four-dimensional spatiotemporal coordinates as input (the first three dimensions are the X, Y, and Z axis coordinates in the construction coordinate system, and the fourth dimension is a timestamp) and outputs local geometric distortion kernel parameters at that spatiotemporal location (including translation, rotation, and anisotropic scaling distortion components along the three coordinate axes, totaling nine parameters).

[0040] In the iterative optimization phase, an adaptive moment estimation optimizer was used, with a learning rate set to 0.1% and a batch size of 32 four-dimensional spatiotemporal coordinate samples. Convergence was defined as the loss value on the validation set no longer decreasing after 20 consecutive training epochs. After convergence, the implicit neural representation network was saved as a four-dimensional environmental perturbation tensor field, and the forward propagation output of this network served as the system-level geometric distortion kernel. After the network completed the initial iterative optimization in cold-start mode, it automatically switched to incremental update mode, continuously using new real-world data to incrementally update the network during subsequent construction. Dynamic historical environment database and outer ring update management were implemented. During the construction of this reservoir project, data records generated each time an outer ring update was triggered were stored in the dynamic historical environment database. This database was deployed on the project's server as a relational database. Each data record contains the following fields: the spatiotemporal clustering index that triggered the outer loop update, which identifies the spatial region and time period in which the residual deviation occurred; the mean residual under the spatiotemporal cluster, which is the arithmetic mean of the residuals of all data points within the cluster; the environmental disturbance scenario identifier corresponding to the spatiotemporal cluster, used to locate the environmental disturbance scenario matched by the system when the outer loop update is triggered; and the correction amount of the learnable parameters of the four-dimensional environmental disturbance tensor field after the outer loop update, which is the difference vector of the network weight parameters before and after this update.

[0041] The environmental disturbance scenario identifiers in the data records generated by the outer ring update are associated with historical data records under the same spatiotemporal clusters stored in the dynamic historical environment database. The association method is as follows: using the spatiotemporal cluster index as the primary key, all historical outer ring update records within the same spatial region and similar time period are queried, and the environmental disturbance scenario identifiers in each record are extracted. The trend of the mean residual value under this spatiotemporal cluster as a function of the frequency of environmental disturbance scenarios is calculated. Specifically, the number of times each environmental disturbance scenario occurs under this cluster, and the mean residual value corresponding to each occurrence, are statistically analyzed, and a relationship curve between the mean residual value and the frequency of scenario occurrence is fitted. The environmental fitness coefficient corresponding to this spatiotemporal cluster is generated based on the slope of this relationship curve. In this invention, the environmental fitness coefficient is a quantitative indicator characterizing the adaptability of a spatial region to a specific type of environmental disturbance; a higher value indicates a stronger resistance to similar disturbances after multiple outer ring updates. The environmental adaptability coefficient and the mean residual are positively correlated with the rate of decrease of the frequency of occurrence of environmental disturbance scenarios. That is, as similar scenarios occur repeatedly, the faster the mean residual decreases, the stronger the adaptability and the higher the value of the environmental adaptability coefficient.

[0042] The variance boundary of the Gaussian mixture model is decomposed into two independent parameters: the lower variance bound and the upper variance bound. The lower variance bound is determined by Bayesian updates, and its physical meaning is the minimum level of uncertainty in the model parameters under the constraints of the current observation data. The lower variance bound monotonically decreases as the residuals decrease, meaning that the lower limit of uncertainty in the model parameters decreases as the observed data becomes increasingly consistent with the model predictions. The upper variance bound is determined by outer-loop updates based on the environmental fitness coefficient, and its physical meaning is the maximum allowable range of uncertainty in the model parameters. The upper variance bound monotonically increases as the environmental fitness coefficient increases, meaning that the stronger the environmental fitness, the richer the environmental disturbance patterns experienced by the region, and the larger the upper variance bound needs to be to accommodate new disturbance patterns that may emerge in the future.

[0043] The actual variance is determined by calling the dynamic equilibrium value calculation function. This function takes the lower and upper bounds of variance as input and the actual variance as output. The actual variance is the weighted geometric mean of the lower and upper bounds. The weighted geometric mean is calculated by taking the logarithm of both the lower and upper bounds, multiplying them by their respective weighting factors, summing them, and then taking the exponent. The weighting factors are determined by the cumulative confidence of the fitness coefficient within the current spatiotemporal cluster. The cumulative confidence is positively correlated with the number of historical outer ring updates within that spatiotemporal cluster; that is, the more historical updates, the more reliable the estimation of the fitness coefficient, and the higher the weighting factor value.

[0044] The actual variance is used as the prior variance of the Gaussian mixture model in the next round of Bayesian updates, and also as a benchmark threshold for judging whether the residual exceeds the variance boundary. When the lower variance limit exceeds the upper variance limit, it indicates a logical contradiction between the determinism of the model parameters and the allowable fluctuation range, triggering the variance boundary violation protection mechanism. This mechanism forcibly assigns the actual variance to the arithmetic mean of the lower and upper variance limits, and updates the upper variance limit with this arithmetic mean, ensuring that the lower variance limit is again lower than or equal to the upper variance limit, thus ensuring that subsequent Bayesian updates and outer loop updates can proceed normally.

[0045] Online construction of the inverse operator. Before performing inverse filtering on the real-world data using the inverse operator with the optimally estimated geometric distortion kernel, the inverse operator needs to be constructed online. The inverse operator is constructed based on a pre-trained learnable implicit neural representation network, which is the carrying network of the four-dimensional environmental perturbation tensor field. Its forward propagation path realizes the mapping from four-dimensional spatiotemporal coordinates to local geometric distortion kernel parameters.

[0046] Based on the forward propagation path of the implicit neural representation network, a reverse mapping network is constructed. In this invention, the reverse mapping network is a neural network topologically symmetrical to the forward propagation path of the implicit neural representation network. Its function is to map spatial coordinates distorted by environmental disturbances back to the original, undistorted spatial coordinates. The reverse mapping network and the implicit neural representation network share weight parameters; essentially, they are two uses of the same network in different propagation directions. The forward propagation direction of the implicit neural representation network is from spatiotemporal coordinates to distortion parameters, while the propagation direction of the reverse mapping network is from distorted geometric data to the original, undistorted coordinates. The input to the reverse mapping network is the distorted geometric data output by the optimally estimated geometric distortion kernel, i.e., the coordinate values ​​of each spatial point in the real-world data after forward mapping by the geometric distortion kernel. The output is the original, undistorted spatial coordinates corresponding to these distorted geometric data.

[0047] The loss function configured for the inverse mapping network is the inverse reconstruction loss. The inverse reconstruction loss is defined as follows: The original distortion-free spatial coordinates output by the inverse mapping network are input again into the implicit neural representation network for a forward propagation, resulting in reconstructed distorted geometric data. The mean square error between this reconstructed distorted geometric data and the original distorted geometric data input to the inverse mapping network is then calculated. The physical meaning of this loss function is that the distortion-free coordinates output by the inverse mapping network should enable the implicit neural representation network to regenerate the same distorted coordinates as its input. In other words, the inverse mapping network and the implicit neural representation network constitute an autoencoder structure, with each acting as an encoder and decoder. By minimizing the inverse reconstruction loss, the inverse mapping network learns an accurate inverse mapping relationship.

[0048] The calculation of the inverse reconstruction loss is performed each time an inverse operator needs to be constructed. Taking the inverse filtering process of this reservoir project as an example, the four-dimensional spatiotemporal coordinates corresponding to the currently optimally estimated geometric distortion kernel are used as input conditions. The distorted geometric data output by the geometric distortion kernel is used as the target to be reconstructed. The forward propagation of the inverse mapping network is performed to obtain the predicted undistorted coordinates. Then, the predicted undistorted coordinates are input into the implicit neural representation network to obtain the reconstructed distorted geometric data. The mean square error between the reconstructed distorted geometric data and the original distorted geometric data is calculated. If the mean square error is lower than a preset threshold, the current inverse mapping network is considered to have sufficient inverse mapping accuracy.

[0049] In practical applications, the real-time forward propagation results of the inverse mapping network are not directly used as the inverse operator. Instead, a weight freeze copy mechanism is employed. Specifically, the weight parameters of the implicit neural representation network at the current time step are copied to form a weight freeze copy. In this paper, the weight freeze copy refers to a network instance whose parameter values ​​are identical to the main network but are no longer updated during the main network's training. The four-dimensional spatiotemporal coordinates corresponding to the optimally estimated geometric distortion kernel are input into this weight freeze copy for a forward propagation, yielding the local geometric distortion kernel parameters at that spatiotemporal location. The parameter matrix is ​​then inverted to obtain the inverse operator. Since the parameters of the weight freeze copy remain unchanged during this inverse filtering process, the inverse operator is deterministic within this processing cycle and will not fluctuate due to updates to the main network's parameters, thus ensuring the stability of the inverse filtering process.

[0050] When the weight parameters of the implicit neural representation network change via the outer loop update, the parameters of the main network are updated. At this point, the parameters in the weight-frozen copy are no longer consistent with the main network and need to be updated synchronously. The update method is as follows: the weight parameters updated by the outer loop are refrozen in the weight-frozen copy of the inverse mapping network, replacing the original old parameters. Then, based on the new weight-frozen copy, a forward propagation is performed again, that is, the four-dimensional spatiotemporal coordinates corresponding to the currently best estimated geometric distortion kernel are re-inputted to obtain the updated local geometric distortion kernel parameters and re-invert them, thereby updating the inverse operator. This synchronous update mechanism ensures that the inverse operator always remains consistent with the latest environmental perturbation field model, reflecting the new knowledge introduced by the outer loop update.

[0051] Through the aforementioned online construction and synchronous update mechanism, the inverse operator no longer requires explicit pre-construction or offline computation. Instead, it is generated in real time in a lightweight manner, relying on the continuous representation capability of the implicit neural representation network and the stable execution mechanism of weighted frozen copies. This mechanism demonstrates good adaptability in the practical application of this reservoir project. The reconstruction of the inverse operator after the outer loop update can be completed within seconds, significantly outperforming the traditional pointwise numerical inversion method.

[0052] Assume the spatial overlay set is continuously updated. During the construction of the reservoir dam, it is assumed that the spatial overlay set is not generated once and remains fixed, but is continuously updated in a rolling manner. To achieve this rolling update, when generating each set of composite reality simulation data, the system simultaneously generates a timestamp and a validity period label. The timestamp records the generation time of the composite reality simulation data, and the validity period label specifies the duration within which the data is considered valid.

[0053] The duration of the validity label is determined by the confidence level of the short-term environmental forecast. The confidence level is calculated based on the historical forecast accuracy of the high-resolution local numerical weather prediction model. Specifically, it is calculated by statistically analyzing the root mean square error between the forecast and observed values ​​for the same region and forecast lead time over a past period. The smaller the root mean square error, the higher the confidence level. Higher confidence levels indicate stronger reliability of the forecast, and the resulting environmental disturbance scenarios will remain valuable for a longer period, thus extending the validity label duration accordingly. Conversely, lower confidence levels result in a shorter validity label duration. For example, in a typical forecast for this reservoir project, if the high-resolution local numerical weather prediction model assesses a high confidence level for the 48-hour forecast, the validity label duration is set to 12 hours; if the confidence level is moderate, the validity label duration is shortened to 6 hours. The validity period of the label also takes into account the environmental changes during the construction period. In seasons with large temperature differences between day and night, environmental conditions change drastically, so the validity period is appropriately shortened.

[0054] After each nearest neighbor search of real-scene data is completed, the system records the matching similarity value and the actual environmental parameter sequence corresponding to the current real-scene data. The matching similarity value is the cosine similarity between the feature descriptor and the feature descriptor of the successfully matched synthetic real-scene pre-simulation data. This value reflects the degree of closeness between the scene in the hypothesis space coverage set and the real environment. The actual environmental parameter sequence is extracted from the real-time environmental data collected by the environmental sensing grid at the time of real-scene data acquisition, including the measured values ​​of temperature offset, air pressure change rate, and gust vector corresponding to the acquisition time. The actual environmental parameter sequence is compared item by item with the environmental disturbance scene parameters corresponding to the successfully matched synthetic real-scene pre-simulation data in the hypothesis space coverage set to calculate the environmental parameter deviation. The environmental parameter deviation includes temperature prediction deviation, air pressure prediction deviation, and gust prediction deviation. Temperature prediction deviation is the difference between the measured temperature offset and the preset temperature offset in the scene; air pressure prediction deviation is the difference between the measured air pressure change rate and the preset air pressure change rate in the scene; gust prediction deviation is the difference between the measured gust vector and the preset gust vector in the scene along each coordinate axis.

[0055] The calculated environmental parameter deviations are fed back to the high-resolution local numerical weather prediction model via a data interface. These deviations serve as error correction signals input to the model's data assimilation module. The data assimilation module treats the deviations as the observation increments at the current moment and incorporates these increments into the model's current state field using Kalman filtering or three-dimensional variational assimilation methods. This drives the model to update its internal integral parameters, enabling it to more accurately reflect the actual environmental evolution patterns in the next forecast round. This closed-loop feedback mechanism allows the numerical weather prediction model to continuously self-correct during actual implementation, gradually improving the forecast accuracy for high-altitude local environments.

[0056] When the deviation of environmental parameters exceeds the preset simulation failure threshold, it indicates that the deviation between the scene in the current hypothesis space coverage set and the real environment has exceeded the acceptable range, and the reliability of the matching results decreases. The simulation failure threshold is set based on the following: when the deviation exceeds this threshold, the posterior variance of the Bayesian update obtained after inverse filtering based on the matching results of this scene will increase significantly, indicating that the contradiction between the observed data and the model prior cannot be resolved by conventional updates. Taking this reservoir project as an example, the simulation failure threshold for temperature prediction deviation is set to 5 degrees Celsius, and the threshold for air pressure prediction deviation is set to 300 Pascals. If the temperature prediction deviation exceeds 5 degrees Celsius or the air pressure prediction deviation exceeds 300 Pascals, a forced refresh of the hypothesis space coverage set is triggered.

[0057] The forced refresh process is as follows: First, based on the updated short-term environmental estimates from the high-resolution local numerical weather prediction model, a new batch of synthetic real-scene simulation data is generated in the manner described above. Then, all existing data in the hypothesis space coverage set that have exceeded their validity period are removed and replaced with the newly generated synthetic real-scene simulation data. Existing data that have not exceeded their validity period are retained and, together with the new data, constitute the updated hypothesis space coverage set. Through the above validity period management and forced refresh mechanism, the hypothesis space coverage set can dynamically evolve with the rapid changes in the high-altitude environment, always maintaining sufficient coverage of the real-scene features that may occur under the current environmental conditions, providing a reliable retrieval space for subsequent real-scene data matching.

[0058] This embodiment also provides a system that is deployed on a construction site and consists of multiple functional modules that work together to complete the entire process from data acquisition and environmental disturbance compensation to model updates.

[0059] The overall system architecture of the high-altitude adaptable digital modeling system that integrates real-scene modeling and BIM provided in this embodiment is physically deployed in two parts: the site end and the project department end. The site end equipment is deployed in the dam construction area of ​​the reservoir project, and the project department end equipment is deployed in the computer room of the project department. The two ends communicate with each other through a combination of wired and wireless networks.

[0060] The system comprises a data acquisition front-end, an environmental disturbance field construction module, a BIM elastic parameter field storage module, a hypothesis space pre-simulation module, a feature matching engine, an inverse filtering normalization module, a Bayesian update module, an outer loop evolution module, and a power supply unit. The data acquisition front-end is a field-end device, while the remaining modules are deployed on an industrial-grade high-performance workstation at the project site. Each software module runs as a service process within the workstation's operating system, and data exchange between modules occurs through shared memory and message queues.

[0061] The data acquisition front-end comprises an environmental sensing grid deployed at the high-altitude construction site and a multimodal real-scene acquisition terminal. The environmental sensing grid consists of multiple sensor nodes, distributed across different elevations within the dam site area. Each node integrates a temperature sensor, a barometric pressure sensor, a light intensity sensor, and a three-axis anemometer. The spatial coordinates of the sensor nodes are calibrated using the construction coordinate system. The nodes are networked via LoRa wireless communication, and the collected real-time environmental data is aggregated to a relay gateway located in the center of the dam site area. The multimodal real-scene acquisition terminal is a multi-rotor drone with a lightweight carbon fiber fuselage, equipped with a lidar and aerial survey camera, capable of simultaneously acquiring real-world cloud data and real-world image data. The drone is equipped with an onboard computer for flight control, data acquisition, and initial compression and storage.

[0062] The environmental disturbance field construction module communicates with the data acquisition front-end. Its software process runs on a workstation and receives real-time environmental data forwarded from a relay gateway via a 4G industrial router. Internally, the module maintains a learnable implicit neural representation network as the carrier network for the four-dimensional environmental disturbance tensor field. This network takes four-dimensional spatiotemporal coordinates as input and outputs the local geometric distortion kernel parameters at that spatiotemporal location. The module includes a training submodule, responsible for iteratively optimizing the weight parameters of the implicit neural representation network using real-time environmental data as training samples and the registration residuals fed back from the BIM elastic parameter field as supervision signals. The training submodule supports both cold start and incremental update modes, automatically switching to incremental update mode after a cold start.

[0063] The BIM Elastic Parameter Field Storage Module stores the spatial elastic parameters of each structural component, characterized by a Gaussian mixture model. The physical storage medium for this module is a solid-state drive array built into the workstation, and the data is organized as a relational database. Each structural component has a record in the database, containing a component identifier, spatial location index, mean parameter, and variance parameter from the Gaussian mixture model. The mean parameter represents the design geometric value of the component, while the variance parameter is dynamically determined based on historical environmental fluctuation data from the high-altitude region where the component is located. The module provides parameter read / write interfaces for the Bayesian update module and the outer-loop evolution module to update or query the elastic parameters of each component.

[0064] The hypothetical spatial pre-simulation module is connected to both the environmental disturbance field construction module and the BIM elastic parameter field storage module. Internally, it includes a scene generation submodule and a rendering submodule. The scene generation submodule acquires short-term environmental estimates and generates multiple high-probability environmental disturbance scenarios using a disturbance parameter configurator. The rendering submodule reads the standard geometric data set corresponding to the mean values ​​of each component from the BIM elastic parameter field storage module, applies the geometric distortion kernel corresponding to each scenario to the standard geometric data set to perform forward distortion rendering, and then calls a pre-trained neural radiation field network to render the distorted geometric data set into synthetic real-scene pre-simulation data. All synthetic real-scene pre-simulation data are aggregated to form a hypothetical spatial coverage set, which is stored in the workstation's cache for retrieval by the feature matching engine.

[0065] The feature matching engine communicates with the data acquisition front-end and the hypothesis space pre-simulation module. Internally, the engine includes a feature extraction submodule and a retrieval submodule. During runtime, the feature extraction submodule receives real-world point cloud data and real-world image data from the data acquisition front-end. It extracts local geometric spectral feature vectors and texture feature vectors using a point cloud local spectral feature encoder and an illumination-invariant texture feature encoder, respectively, and concatenates them to generate a cross-modal feature descriptor. The retrieval submodule uses this feature descriptor as the query vector, calculates the cosine similarity between it and each combined real-world pre-simulation data feature descriptor in the hypothesis space coverage set, and determines the geometric distortion kernel corresponding to the synthetic real-world pre-simulation data with the highest cosine similarity as the optimal estimate of the current environmental perturbation. This optimal estimate is then output to the inverse filtering and normalization module.

[0066] The inverse filtering and normalization module communicates with the feature matching engine. Internally, it includes an inverse operator construction submodule and an inverse filtering execution submodule. The inverse operator construction submodule uses the optimally estimated geometric distortion kernel as a benchmark to construct its inverse mapping function as the inverse filtering operator. The inverse filtering execution submodule takes real-world point cloud data and real-world image data as input, substitutes them into the inverse filtering operator to perform deconvolution operations, and calls the corresponding distortion components from the geometric distortion kernel according to the spatial coordinate index of each data point for point-by-point inverse mapping. It then subtracts the distortion components from the data point by point, generating environment-normalized real-world data and outputting it to the Bayesian update module.

[0067] The Bayesian update module communicates with the inverse filtering normalization module and the BIM elastic parameter field storage module. Internally, it includes a parameter reading submodule, a posterior calculation submodule, and a write-back submodule. The parameter reading submodule reads the prior mean and prior variance of the Gaussian mixture model of the corresponding component from the environmental normalized real-world data in the BIM elastic parameter field storage module. The posterior calculation submodule uses the environmental normalized real-world data as observations and calls a preset Bayesian update formula to calculate the posterior mean and posterior variance parameters. The write-back submodule writes the calculated posterior parameters into the record of the corresponding component in the BIM elastic parameter field storage module, completing the online update of the BIM elastic parameter field.

[0068] The outer loop evolution module communicates with the Bayesian update module and the environmental perturbation field construction module. Internally, it includes a residual calculation submodule, a clustering submodule, and a triggering submodule. The residual calculation submodule calculates the residual between the normalized real-world environmental data and the updated mean of the BIM elastic parameter field. The clustering submodule clusters the residuals according to spatial coordinates and time dimensions, generating a spatiotemporal clustering index. The triggering submodule monitors the mean residual under each spatiotemporal cluster. When it continuously exceeds the variance boundary of the current Gaussian mixture model, it triggers the outer loop update. Using this residual pattern as a new training sample, it calls the training submodule of the environmental perturbation field construction module to update the learnable parameters of the four-dimensional environmental perturbation tensor field. Simultaneously, it sends a variance correction command to the BIM elastic parameter field storage module to relax the variance boundary of the corresponding component's Gaussian mixture model.

[0069] The power supply unit and communication links are interconnected. The power supply unit, consisting of two redundant uninterruptible power supplies (UPS), is located at both the field and project sites. The field-side UPS provides regulated power to the environmental sensor grid, relay gateway, and drone battery charging station. The project-side UPS provides uninterrupted power to the workstations, network switches, and servers, automatically switching to backup battery power in case of a main power outage to ensure uninterrupted system operation. The communication links between the data acquisition front-end and the environmental disturbance field construction module include wired and wireless communication links. The relay gateway of the environmental sensor grid connects to the project workstation via wired Ethernet, while the multimodal real-scene acquisition terminal interacts with the workstation via wireless communication. Data transfer between software modules occurs through message queues and shared memory within the workstation.

[0070] It should be further explained that the residual tensor is a multidimensional array, whose dimensions include three-dimensional spatial coordinate indices and time dimension indices. Each array element stores the residual vector at that spatiotemporal location. This tensor has the same spatial resolution as the real-world data, and the time dimension is organized according to the date and shift of the construction log, serving as the input data for the outer ring update trigger condition.

[0071] This invention, using a reservoir project as an application scenario, fully presents the technical details of the entire process, from BIM elastic parameter field construction, environmental disturbance field training, hypothesis space coverage set generation, real-scene data acquisition and feature matching, inverse filtering normalization, Bayesian update to outer-loop update. The above steps form a closed-loop iterative relationship: the BIM elastic parameter field provides prior geometric information; the environmental disturbance field quantifies the impact of the high-altitude environment on construction geometric accuracy; the hypothesis space coverage set provides a retrieval space for rapid matching of real-scene data; inverse filtering removes environmental distortion, making the real-scene data usable for Bayesian update; Bayesian update enables online correction of component-level parameters; and outer-loop update continuously optimizes the expressive power of the environmental disturbance field using accumulated residual patterns. The signal flow between each step is clear, the data format is unified, and a complete adaptive loop is formed.

[0072] Regarding the specific values ​​of each parameter, the values ​​given in the above embodiments are all based on the actual construction conditions of the reservoir project and the statistical experience of similar high-altitude projects. In practical applications, those skilled in the art can make adaptive adjustments to the above values ​​according to factors such as the altitude, climate characteristics, and performance of construction equipment of the specific project. The adjusted values ​​still fall within the protection scope of this invention.

[0073] The above embodiments are merely preferred embodiments of the present invention under specific engineering conditions and are not intended to limit the scope of protection of the present invention. Any equivalent changes or modifications made to the module structure, parameter configuration, step sequence, or data format in the above embodiments within the scope of the technical concept and claims of the present invention should be considered to fall within the scope of protection of the present invention. For digital construction scenarios of water conservancy projects, transportation infrastructure projects, or industrial and civil building projects in other high-altitude areas, those skilled in the art, after reading this specification, can apply the methods and systems of the present invention to corresponding engineering practices without creative effort; such applications should also be considered equivalent embodiments covered by the present invention.

Claims

1. A high-altitude adapted digital modeling method integrating real-scene modeling and BIM, characterized in that, The method includes: Construct a BIM elastic parameter field, which extends the design geometric parameters of BIM components into a probability distribution space characterized by a Gaussian mixture model, and the distribution parameters are dynamically determined based on historical high-altitude environmental data. An environmental perturbation field is constructed by collecting real-time environmental data, including temperature, air pressure, light intensity, and gusts, through an environmental sensing grid. A four-dimensional perturbation tensor field is trained based on the real-time environmental data to output a system-level geometric distortion kernel. Short-term environmental estimates are obtained to generate multiple environmental disturbance scenarios. The BIM elastic parameter field is then positively distorted and rendered through the geometric distortion kernel corresponding to each scenario to generate multiple sets of real-scene pre-simulation data, forming a hypothetical space coverage set. Collect real-world data and extract feature descriptors. Perform nearest neighbor search in the hypothesis space coverage set, match the synthetic real-world pre-simulation data with the highest similarity, and determine the corresponding geometric distortion kernel as the optimal estimate of the current environmental disturbance. The real-scene data is inversely filtered using the inverse operator of the optimally estimated geometric distortion kernel to obtain environmentally normalized real-scene data. The normalized real-world environmental data is input into the BIM elastic parameter field for Bayesian update, thereby updating the distribution parameters of the corresponding components. The residuals between the normalized real-world environmental data and the updated BIM elastic parameter field are calculated and spatiotemporal clustering is performed. When the mean of the clustering residuals continuously exceeds the variance boundary, the outer loop update is triggered to update the parameters of the four-dimensional perturbation tensor field with the residual pattern and relax the corresponding variance boundary.

2. The high-altitude adaptive digital modeling method integrating real-scene modeling and BIM as described in claim 1, characterized in that, The dynamic determination of the variance of the Gaussian mixture model based on historical environmental fluctuation data includes: Collect environmental fluctuation data during the historical construction period according to altitude level; calculate the fluctuation amplitude and frequency at each altitude level to generate an environmental fluctuation intensity index; obtain the corresponding variance coefficient by looking up the index in a table, and assign the variance coefficient according to the altitude of the component.

3. The high-altitude adaptive digital modeling method integrating real-scene modeling and BIM as described in claim 1, characterized in that, The generated M-groups form a hypothetical spatial coverage set composed of real-scene pre-simulation data, including: Obtain the standard geometric data of each component; apply the geometric distortion kernel corresponding to each environmental disturbance scene to the standard geometric data, apply coordinate offset and scale scaling perturbation to obtain distorted geometric data; call the pre-trained neural radiation field network to render each distorted geometric data into synthetic real scene pre-playback data, and collect the rendering results of all scenes.

4. The high-altitude adaptive digital modeling method integrating real-scene modeling and BIM as described in claim 1, characterized in that, The process of collecting real-world data and extracting feature descriptors for nearest neighbor retrieval includes: The point cloud and real-world image are acquired simultaneously according to a multimodal acquisition strategy. The acquisition strategy dynamically adjusts the acquisition parameters based on the spatial distortion gradient of the distortion kernel. The point cloud and the image are spatiotemporally aligned to generate a multimodal data frame. The local geometric spectral features of the point cloud and the texture features of the image are extracted and concatenated into a feature descriptor. The cosine similarity between the descriptor and each synthetic data in the hypothesis space is calculated, and the optimal estimated scene is determined by the highest similarity.

5. The high-altitude adaptive digital modeling method integrating reality modeling and BIM according to claim 1, characterized in that, The inverse filtering and Bayesian update include: The real-scene data is deconvolved point-by-point using the inverse mapping function of the optimally estimated geometric distortion kernel to subtract the distortion components and obtain normalized real-scene data of the environment. Using this normalized data as the observation value, the posterior mean and variance of the corresponding components are calculated according to the Bayesian update formula to complete the online update.

6. The high-altitude adaptive digital modeling method integrating reality modeling and BIM according to claim 1, characterized in that, The process of obtaining short-term environmental forecasts and generating M high-probability environmental disturbance scenarios includes: The local numerical weather prediction model is invoked to predict environmental parameters for future periods using the current real-time environmental data sequence as the initial field. Multiple sets of equally probable forecast members are generated by superimposing probability distributions on each forecast value and arranging them along time to obtain multiple environmental evolution scenarios. The weights of each scenario are adjusted according to the historical retrieval matching accuracy, and the scenarios are resampled to ensure that the coverage ratio of high-confidence scenarios meets the standard.

7. The high-altitude adaptive digital modeling method integrating reality modeling and BIM according to claim 1, characterized in that, The inverse filtering process also includes: Extract the residual distortion components that were not removed after inverse filtering, generate the geometric distortion residual field and calculate its total residual energy; if the total residual energy is lower than the threshold, keep the current distortion kernel; if it is higher than the threshold, remove the current kernel from the hypothesis space, select the top K candidate kernels according to similarity, re-inverse filter each kernel and calculate the likelihood with the prior mean, select the kernel with the highest likelihood as the updated optimal estimate, and re-inverse filter it.

8. The high-altitude adaptive digital modeling method integrating reality modeling and BIM according to claim 1, characterized in that, The training yields a four-dimensional environmental perturbation tensor field, including: Using the spatiotemporal coordinates of sensor nodes as input, an implicit neural representation network is constructed to output local geometric distortion kernel parameters. The registration residual between the BIM elastic parameter field and the real scene data is used as a supervision signal, and two modes, cold start and incremental update, are set. A joint loss function including translation, rotation and scaling distortion losses is constructed, and the network parameters are iteratively optimized. After the cold start converges, the incremental update mode is switched to save the optimized network as the four-dimensional environmental perturbation tensor field.

9. The high-altitude adaptive digital modeling method integrating reality modeling and BIM according to claim 1, characterized in that, Also includes: Build a dynamic historical environment database to store records of each outer ring update; Calculate the environmental adaptability coefficient based on historical records under the same type of spatiotemporal clustering; The variance boundary of the Gaussian mixture model is divided into a lower bound determined by Bayesian updates and an upper bound determined by outer loop updates based on the fitness coefficient. The actual variance is calculated using the weighted geometric mean and used as the prior variance for the next round, and as the criterion for judging residual exceedance; when the lower limit exceeds the upper limit, the arithmetic mean of the two is taken and the upper limit is updated.

10. A high-altitude adapted digital modeling system integrating real-scene modeling and BIM, the system being used to store and execute the method described in any one of claims 1-9, characterized in that, include: The data acquisition front end includes an environmental sensing grid deployed at a high-altitude construction site and a multimodal real-scene acquisition terminal. The environmental sensing grid is used to collect real-time environmental data, and the multimodal real-scene acquisition terminal is used to simultaneously collect real-scene cloud data and real-scene image data. The environmental disturbance field construction module is used to construct a four-dimensional environmental disturbance tensor field using the real-time environmental data as training samples. The tensor field takes four-dimensional spatiotemporal coordinates as input and local geometric distortion kernel parameters at that location as output. The BIM elastic parameter field storage module is used to store the space of elastic parameters of each component represented by a Gaussian mixture model. The mean is the design value, and the variance is dynamically determined based on historical environmental fluctuation data. The hypothetical spatial pre-simulation module is used to obtain short-term environmental forecasts and generate multiple environmental disturbance scenarios. The component mean of the BIM elastic parameter field is forward rendered through the geometric distortion kernel of each scenario to generate multiple sets of real-scene pre-simulation data to form a hypothetical spatial coverage set. The feature matching engine is used to extract cross-modal feature descriptors of the real-world cloud and real-world images, and to search for matches in the hypothesis space coverage set. The geometric distortion kernel corresponding to the matched synthetic data is determined as the optimal estimate of the current environmental disturbance. The inverse filtering and normalization module is used to perform inverse filtering on the real-world cloud and real-world image using the inverse operator of the optimally estimated geometric distortion kernel to generate environment-normalized real-world data. The Bayesian update module is used to update the posterior distribution parameters of the Gaussian mixture model with the normalized real-world environmental data as observations, and write them back to the BIM elastic parameter field storage module. The outer ring evolution module is used to calculate the residual between the normalized real-world environmental data and the updated BIM elastic parameter field and cluster them according to the spatiotemporal dimension. When the mean of the clustering residuals continues to exceed the variance boundary, the outer ring update is triggered to update the learnable parameters of the four-dimensional environmental perturbation tensor field with the residual mode and correct the variance boundary. The power supply unit is connected to the above modules and is used to supply power to the system.

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