Karst geological multi-parameter fusion intelligent deduction and hierarchical early warning system

Abstract

This invention discloses a multi-parameter fusion intelligent extrapolation and hierarchical early warning system for karst geology, relating to the field of karst cave detection technology. It includes: a data acquisition module that generates multi-source standardized data for karst cave detection; a detection evaluation module that generates karst cave detection effect characteristic representations, constructs a karst cave detection effect evaluation and optimization strategy model, and outputs initial detection evaluation results; a multi-parameter fusion extrapolation and correction module that constructs a multi-parameter fusion network model, obtains the initial detection evaluation results, corrects the initial detection evaluation results, and outputs the corrected karst cave detection parameters; and an intelligent early warning module that determines the associated risk between the construction area and the karst cave and outputs an early warning trigger signal. This invention integrates deep learning algorithms with multi-source geological and construction data to construct a karst cave detection evaluation system and a multi-parameter fusion extrapolation model, achieving accurate correction of karst cave parameters and real-time intelligent early warning of construction risks.

Description

Technical Field

[0001] This invention relates to the field of karst cave detection technology, specifically to a multi-parameter fusion intelligent simulation and hierarchical early warning system for karst geology. Background Technology

[0002] As transportation infrastructure construction extends to areas with complex geological conditions, cave detection and pile foundation construction in karst areas have become core aspects of ensuring project quality and safety. Caves are characterized by their hidden distribution, diverse shapes, and complex spatial structures. Traditional cave detection technologies rely heavily on the experience and judgment of geological personnel, lacking a scientific and systematic evaluation system and prediction model, resulting in low detection accuracy and low construction efficiency.

[0003] Although machine learning technology has begun to be applied to the field of cave identification and surveying, it still faces many challenges: existing models mostly rely on a single data source and have insufficient ability to fuse and process multi-parameter data; the scarcity of cave samples and the difficulty in labeling them result in limited generalization ability of the models; noise interference and incompleteness of the detection data further affect the prediction accuracy; traditional pile foundation construction lacks a real-time early warning mechanism, which cannot avoid the construction risks brought by cave areas in a timely manner, resulting in problems such as construction cost overruns and project delays.

[0004] Therefore, there is an urgent need to develop a deep learning system that integrates multi-source data processing, accurate prediction and correction, and intelligent early warning functions to break through existing technological bottlenecks and meet the needs of engineering construction in karst areas for efficient, accurate, and safe construction. Summary of the Invention

[0005] To address the aforementioned technical problems, a multi-parameter fusion intelligent simulation and hierarchical early warning system for karst geology is provided. This technical solution solves the problems mentioned above.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A multi-parameter fusion intelligent extrapolation and hierarchical early warning system for karst geology includes: a data acquisition module, a detection and evaluation module, a multi-parameter fusion extrapolation correction module, and an intelligent early warning module;

[0008] The data acquisition module is used to collect pile foundation construction data and supplement geological data in the karst cave area, and generate multi-source standardized data for karst cave detection.

[0009] The detection and evaluation module is used to extract features and perform correlation analysis based on multi-source standardized data from cave exploration to generate a feature representation of the cave exploration effect; based on the feature representation of the cave exploration effect, a cave exploration effect evaluation and optimization strategy model is constructed, and the initial detection evaluation results are output.

[0010] The multi-parameter fusion inference and correction module is used to construct a multi-parameter fusion network model based on the standardized dataset and the initial detection evaluation results; train the multi-parameter fusion network model using multi-source standardized data of cave detection; infer the relevant geological parameters of the cave based on the trained multi-parameter fusion network model; obtain the initial detection evaluation results; correct the initial detection evaluation results; and output the corrected cave detection parameters.

[0011] The intelligent early warning module is used to collect pile foundation construction status data in real time based on karst cave detection parameters, compare the pile foundation construction status data with the corrected karst cave detection parameters, determine the associated risk between the construction area and the karst cave, and output an early warning trigger signal.

[0012] Preferably, the detection and evaluation module includes:

[0013] The feature processing unit, based on deep belief networks and convolutional neural network variants, performs multi-dimensional feature mining and dimensionality reduction on multi-source standardized data of cave exploration, and extracts feature representations of cave exploration effect.

[0014] The model building unit constructs a model for evaluating and optimizing the effectiveness of karst cave exploration based on the correlation patterns of the characteristics of karst cave exploration. The model is iteratively trained using a gradient descent optimization algorithm combined with a backpropagation mechanism. The model takes the characteristics of karst cave exploration as input and the exploration method suitability score and the reliability coefficient of the exploration results as output targets. The model parameters are continuously optimized until convergence, and the model with stable evaluation capabilities is output.

[0015] The evaluation execution unit inputs the characteristics of the cave exploration effect into the model, and uses the model to quantitatively evaluate the adaptability of the exploration method to the current geological conditions and the reliability of the initial exploration results, forming the initial exploration evaluation results and outputting them.

[0016] The strategy optimization unit, based on the initial detection evaluation results, extracts the shortcomings in the current detection process, provides optimization suggestions for the selection of detection methods and adjustment of borehole layout schemes, and outputs the detection strategy optimization scheme.

[0017] Preferably, the multi-parameter fusion inference and correction module includes:

[0018] The network construction unit combines the image feature extraction advantages of fully convolutional networks with the sequence data processing strengths of long short-term memory networks to build a multi-parameter fusion network model framework adapted to the inference of geological parameters of karst caves, and outputs the basic network model.

[0019] The data adaptation unit receives the multi-source standardized data and initial detection evaluation results of the karst cave exploration, performs format conversion and dimension alignment processing on the data, generates model training adaptation data, and outputs the training adaptation dataset.

[0020] The model training unit takes the training adaptation dataset as input and uses an adaptive learning rate optimization algorithm combined with an early stopping mechanism to iteratively train the multi-parameter fusion network model. With the prediction accuracy of the spatial distribution, morphological characteristics and surrounding geological conditions of the karst cave as the optimization objective, the network weights and bias parameters are continuously adjusted, and the trained multi-parameter fusion inference model is output.

[0021] The correction execution unit inputs the initial detection and evaluation results into the trained multi-parameter fusion inference model. The model then performs quantitative analysis and correction on the deviations of the cave parameters in the initial evaluation results, generating the corrected cave detection parameters.

[0022] Preferably, the intelligent early warning module includes:

[0023] The status acquisition unit collects construction status data such as construction location, drilling speed and torque changes in real time through pile foundation construction monitoring sensors, performs real-time data cleaning and standardization, and generates a real-time status representation of pile foundation construction.

[0024] The dynamic comparison unit, based on the real-time status characterization of the pile foundation construction and the corrected karst cave detection parameters, establishes spatial location matching rules and risk judgment thresholds, performs point-to-point comparison analysis between the construction location and the spatial distribution data of karst caves, and outputs the comparison analysis results.

[0025] The risk assessment unit, based on the comparative analysis results, determines the distance relationship between the construction area and the karst cave and the potential collision risk. When the risk value exceeds the preset threshold, it generates an early warning trigger signal and outputs the early warning trigger signal and the risk assessment result.

[0026] The early warning execution unit receives the early warning trigger signal and simultaneously sends early warning information through three channels: sound and light alarm, construction management platform pop-up window, and management personnel mobile terminal push, outputting multi-channel early warning notifications.

[0027] Preferably, the data acquisition module includes:

[0028] The raw data collection unit collects pile foundation construction data, shallow geological exploration data, deep geological imaging data, and karst development monitoring data in the karst cave area through pile foundation construction monitoring equipment, ground-penetrating radar, cross-hole CT, and micro-motion detection equipment, and outputs multiple types of raw data.

[0029] The data preprocessing unit receives the various types of raw collected data, performs noise reduction using median filtering, completes missing values ​​using linear interpolation, and normalizes the data dimensions to generate standardized multi-source data for cave exploration.

[0030] The data integration unit associates and matches supplementary geological data collected by different detection devices with pile foundation construction data, establishes data traceability tags, and generates an integrated dataset with traceability information.

[0031] Preferably, the multi-parameter fusion inference and correction module further includes:

[0032] The correlation analysis unit receives the training process data from the model training unit, calculates the correlation strength between various monitoring data and cave detection parameters using Pearson correlation coefficient analysis, obtains the influence weights of different data on the detection results, and outputs a data correlation weight matrix. The Pearson correlation coefficient analysis formula is as follows:

[0033]

[0034] In the formula, For monitoring data sequences With cave exploration parameter sequence Pearson correlation coefficient, The number of data samples. For the first One monitoring data point, To monitor the mean of the data series, For the first Parameters for cave detection This represents the mean of the sequence of parameters for cave exploration.

[0035] The deviation correction unit, based on the data association weight matrix, corrects the output results of the multi-parameter fusion inference model, eliminating the influence of single data source deviation on the correction results, and outputs the deviation-corrected cave exploration parameters; the correction formula is as follows:

[0036]

[0037] In the formula, These are the corrected parameters for cave exploration. For the number of data sources, For the first The weights of each data source are taken from the data association weight matrix. For the first Model prediction parameters corresponding to each data source.

[0038] Preferably, the feature processing unit of the detection and evaluation module further includes:

[0039] The feature filtering subunit receives the multi-dimensional feature mining data, uses a recursive feature elimination algorithm to filter the effectiveness of the extracted cave exploration effect feature representation, removes redundant and interfering features, retains the core features that contribute more than a preset threshold to the evaluation of the exploration effect, and outputs the optimized cave exploration effect feature representation.

[0040] The feature verification subunit uses K-fold cross-validation to verify the effectiveness of the optimized core features, ensuring the stability and generalization ability of the features, and outputs a feature verification report.

[0041] Preferably, the intelligent early warning module further includes:

[0042] The risk classification unit receives the risk assessment results and the corrected cave exploration parameters, and establishes a three-level risk classification standard based on the cave size, geological stability, and construction progress requirements. The construction risks are divided into three levels: high, medium, and low, and the risk level classification results are output.

[0043] The tiered early warning unit, based on the risk level classification, triggers early warning mechanisms of different intensities: high-risk level triggers emergency work stoppage early warning, medium-risk level triggers deceleration construction early warning, and low-risk level triggers key monitoring early warning, and outputs tiered early warning execution instructions.

[0044] Preferably, the correction execution unit of the multi-parameter fusion inference correction module further includes:

[0045] The result verification subunit receives the corrected cave detection parameters, calls the supplementary geological verification data in the integrated dataset output by the data acquisition module, and uses the sum of squared errors method to verify the rationality of the correction results. When the verification error is lower than the preset threshold, the correction results are confirmed to be valid, and the verified cave detection parameters are output.

[0046] If the verification error exceeds a preset threshold, the deviation analysis unit records the cause of the deviation and generates a deviation analysis report.

[0047] Preferably, the strategy optimization unit of the detection and evaluation module further includes:

[0048] The scheme verification subunit receives the optimized detection strategy scheme, a historical detection data and verified karst cave detection parameters, verifies the feasibility and effectiveness of the optimized scheme using a simulated construction method, and outputs the scheme verification results.

[0049] The dynamic adjustment unit dynamically adjusts the selection of detection methods and borehole layout based on the scheme verification results, generates the optimal detection strategy adapted to the current geological conditions, and outputs the dynamically adjusted detection execution plan to provide direct guidance for on-site construction.

[0050] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0051] 1. This system integrates deep belief networks and convolutional neural network variants through the detection and evaluation module to perform feature mining and correlation analysis on multi-source standardized data, and constructs a scientific evaluation model for the effectiveness of cave detection. This solves the problems of traditional detection relying on experience and strong subjectivity in evaluation, improves the accuracy of detection and evaluation and detection efficiency, and provides a reliable decision-making basis for the selection of detection methods and the layout of boreholes.

[0052] 2. The multi-parameter fusion inference and correction module combines the image feature extraction advantages of fully convolutional networks with the sequence data processing strengths of long short-term memory networks. It quantifies the influence weight of each monitoring data on the detection results through Pearson correlation coefficient analysis, and then eliminates the bias of a single data source through a weighted correction algorithm. This improves the accuracy of cave parameter inference, accurately depicts the spatial distribution, morphological characteristics and surrounding geological conditions of caves, and effectively corrects the initial detection bias.

[0053] 4. This system achieves intelligent operation of the entire process of cave detection, simulation correction, and early warning decision-making through multi-module collaboration, reducing errors caused by human intervention, lowering construction costs, improving construction efficiency, avoiding resource waste caused by blind construction, reducing damage to the surrounding environment, and balancing economic and social benefits.

[0054] 5. The system is compatible with data collected by various devices such as ground-penetrating radar, cross-hole CT, and micro-motion detection, and is adapted to geological scenarios with different degrees of karst development. It can be applied not only to transportation infrastructure projects such as highways and bridges, but also to construction projects in karst areas such as subways and building foundations, providing technical references for similar projects and promoting the technological upgrading of the geological exploration and pile foundation construction industry in karst areas. Attached Figure Description

[0055] Figure 1 This is a flowchart outlining the steps of the present invention.

[0056] Figure 2 This is a system framework diagram of the present invention. Detailed Implementation

[0057] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0058] Reference Figure 1 As shown, a multi-parameter fusion intelligent simulation and hierarchical early warning system for karst geology includes the following steps:

[0059] Step 101: Collect pile foundation construction data and supplementary geological data in the karst cave area through the data acquisition module to generate multi-source standardized data for karst cave detection;

[0060] Step 102: Through the detection and evaluation module, feature extraction and correlation analysis are performed on the multi-source standardized data of cave detection to generate a feature representation of the cave detection effect, construct a cave detection effect evaluation and optimization strategy model, and output the initial detection evaluation results;

[0061] Step 103: Based on the standardized dataset and the initial detection evaluation results, the multi-parameter fusion simulation and correction module constructs and trains a multi-parameter fusion network model to deduce the relevant geological parameters of the karst cave, correct the initial detection evaluation results, and output the corrected karst cave detection parameters.

[0062] Step 104: Collect pile foundation construction status data in real time through the intelligent early warning module, compare it with the corrected karst cave detection parameters, determine the associated risk between the construction area and the karst cave, and output an early warning trigger signal.

[0063] The above approach utilizes multi-device collaborative collection of multi-dimensional geological and construction data to overcome the limitations of single data sources; it mines core data features based on deep learning algorithms to construct a scientific evaluation model, enabling quantitative evaluation of initial detection results; it integrates the advantages of fully convolutional networks and long short-term memory networks to accurately deduce cave parameters and correct deviations; and it combines real-time construction status with dynamic early warning of corrected parameters to solve the problems of low detection accuracy and difficulty in predicting construction risks in traditional methods, effectively improving the accuracy of cave detection and construction safety.

[0064] In some embodiments, the specific implementation of the data acquisition module includes:

[0065] The raw data collection unit collects pile foundation construction data, shallow geological exploration data, deep geological imaging data, and karst development monitoring data in the karst cave area through pile foundation construction monitoring equipment, ground-penetrating radar, cross-hole CT, and micro-motion detection equipment, and outputs multiple types of raw data.

[0066] The data preprocessing unit receives the various types of raw collected data, performs noise reduction using median filtering, completes missing values ​​using linear interpolation, and normalizes the data dimensions to generate standardized multi-source data for cave exploration.

[0067] The data integration unit correlates and matches supplementary geological data collected by different detection devices with pile foundation construction data, establishes data traceability tags, and generates an integrated dataset with traceability information.

[0068] Specifically, the raw data collection unit deploys multiple types of detection and monitoring equipment to form a collaborative acquisition network: pile foundation construction monitoring equipment tracks and records data such as geological conditions, borehole depth, drilling speed, and torque changes during construction, comprehensively capturing construction dynamics; ground-penetrating radar equipment detects shallow geology and obtains reflected wave information of shallow karst cave distribution; cross-hole CT equipment generates imaging data of deep geology through the propagation characteristics of electromagnetic or elastic waves between boreholes, accurately depicting the morphology of deep karst caves; micro-motion detection equipment collects vibration signals within the area to analyze the activity and distribution patterns of karst development. All raw data is recorded in a unified format, including acquisition timestamps, equipment numbers, acquisition location coordinates, and data values, ensuring data traceability. The data preprocessing unit first uses median filtering to denoise the raw data, replacing abnormal and abrupt data by taking the median through a sliding window, and eliminating invalid information caused by factors such as equipment vibration and electromagnetic interference. For missing values ​​in the dataset, based on the continuous missing data, they are supplemented by linear interpolation of adjacent valid data or by combining historical data of the same region and similar geological conditions to ensure the integrity of the dataset. Finally, a normalization method is used to map all data to a unified interval, unify the data units, eliminate the influence of numerical differences between different types of data, and generate multi-source standardized data for cave exploration. The data integration unit uses the collection location coordinates and timestamps as the core association keys to establish data association rules. It matches the pile foundation construction data with supplementary geological data collected by different detection devices to ensure that multi-source data from the same spatial location and time node are associated. Each integrated data point is assigned a unique traceability tag, which includes key information such as equipment number, collection time, and data type, enabling full traceability of data from collection, processing to application. The associated dataset is then subjected to consistency verification to identify and resolve issues such as data conflicts and mismatches. Finally, an integrated dataset with traceability information is generated, providing high-quality data support for subsequent model training and result verification.

[0069] In some embodiments, the specific implementation of the detection and evaluation module includes:

[0070] The feature processing unit, based on deep belief networks and convolutional neural network variants, performs multi-dimensional feature mining and dimensionality reduction on multi-source standardized data of cave exploration, and extracts feature representations of cave exploration effect.

[0071] The model building unit constructs a model for evaluating and optimizing cave exploration effectiveness based on the correlation patterns of cave exploration effect characteristics. It uses a gradient descent optimization algorithm combined with backpropagation for iterative training, taking cave exploration effect characteristics as input and exploration method suitability score and exploration result reliability coefficient as output targets. The model parameters are continuously optimized until convergence, outputting a cave exploration effectiveness evaluation and optimization strategy model with stable evaluation capabilities. The evaluation execution unit inputs cave exploration effect characteristics into the model, quantitatively evaluates the suitability of the exploration method to current geological conditions and the reliability of initial exploration results, and outputs the initial exploration evaluation results.

[0072] Based on the initial detection evaluation results, the strategy optimization unit extracts the shortcomings in the current detection process and provides targeted optimization suggestions for the selection of detection methods and adjustment of borehole layout schemes, outputting an optimized detection strategy scheme. Specifically, the feature processing unit uses a combination of Deep Belief Network (DBN) and a variant of Convolutional Neural Network (CNN) algorithm for multi-dimensional feature mining: the DBN network mines deep nonlinear correlation features in the data through unsupervised pre-training and supervised fine-tuning; the CNN variant algorithm extracts local spatial features of the data through a combination of convolutional and pooling layers, and simultaneously solves the gradient vanishing problem in deep networks through mechanisms such as residual connections, thereby enhancing feature extraction capabilities. For the high-dimensional features obtained through mining, principal component analysis (PCA) is used for dimensionality reduction, retaining principal components that reflect the core patterns of the data, simplifying the computational complexity of subsequent models, and finally extracting the feature representation of the cave detection effect.

[0073] Based on the correlation patterns of cave exploration effect feature representations, the model building unit is designed as a neural network model framework comprising an input layer, hidden layers, and an output layer. The input layer receives the cave exploration effect feature representations, the hidden layer is responsible for further feature transformation and fusion, and the output layer outputs the exploration method suitability score and the reliability coefficient of the exploration results. Model training employs a stochastic gradient descent optimization algorithm combined with backpropagation to dynamically adjust the learning rate. The loss function uses mean squared error, and the model parameters are optimized by calculating the difference between the model's predicted values ​​and the actual values. During training, when the loss function value stabilizes within a reasonable range and no longer decreases significantly, the model is considered converged, and a cave exploration effect evaluation and optimization strategy model with stable evaluation capabilities is output.

[0074] The evaluation execution unit organizes the initial exploration data, including the type of exploration method used, original exploration parameters, and preliminary exploration conclusions, and converts it into a data format consistent with the model input format. The converted initial exploration data is then input into the trained evaluation model. Based on its built-in evaluation logic and parameters, the model quantitatively analyzes the suitability of the exploration method to the current geological conditions and the reliability of the initial exploration results, outputting a method suitability score and a reliability coefficient for the exploration results, thus forming the initial exploration evaluation results. The strategy optimization unit deeply analyzes the initial exploration evaluation results, combining feature correlation patterns and model evaluation logic to identify shortcomings in the current exploration process, such as insufficient ability of the exploration method to detect deep caves and unreasonable borehole spacing leading to blurred cave boundary delineation. For the identified shortcomings, and considering the technical requirements of cave exploration and the characteristics of geological conditions, targeted optimization suggestions are formulated, such as adjusting exploration equipment parameters to improve deep exploration capabilities and optimizing the location and spacing of boreholes to improve cave boundary resolution. These optimization suggestions are summarized to form a complete exploration strategy optimization plan, providing clear guidance for the improvement of subsequent exploration work.

[0075] In some embodiments, the specific implementation of the multi-parameter fusion inference and correction module includes:

[0076] The network construction unit combines the image feature extraction advantages of fully convolutional networks with the sequence data processing strengths of long short-term memory networks to build a multi-parameter fusion network model framework adapted to the inference of geological parameters of karst caves, and outputs the basic network model; the data adaptation unit receives the multi-source standardized data of karst cave exploration and the initial exploration evaluation results, performs format conversion and dimension alignment processing on the data, generates model training adaptation data, and outputs the training adaptation dataset.

[0077] The model training unit takes the training adaptation dataset as input and uses an adaptive learning rate optimization algorithm combined with an early stopping mechanism to iteratively train the multi-parameter fusion network model. The optimization goal is to continuously adjust the network weights and bias parameters with the prediction accuracy of the spatial distribution, morphological features and surrounding geological conditions of the karst cave as the optimization objective, and outputs the trained multi-parameter fusion inference model.

[0078] The correction execution unit inputs the initial detection and evaluation results into the trained multi-parameter fusion inference model, and uses the model to quantitatively analyze and correct the deviation of the cave parameters in the initial evaluation results, generating the corrected cave detection parameters.

[0079] Specifically, the network construction unit builds a fully convolutional network (FCN) submodule and a long short-term memory network (LSTM) submodule: the FCN submodule extracts the spatial morphological features of the karst cave through a combination of convolutional and deconvolutional layers, and the deconvolutional layer achieves feature map upsampling to restore spatial resolution; the LSTM submodule processes the temporal monitoring data during pile foundation construction through a gating mechanism to capture the temporal correlation features of the data; a feature fusion layer is set to concatenate and fuse the spatial features extracted by the FCN submodule and the temporal features extracted by the LSTM submodule to generate a fused feature vector, thus completing the basic network model adapted for the inference of karst cave geological parameters. The data adaptation unit receives multi-source standardized data from karst cave exploration and initial exploration evaluation results, converts the initial exploration evaluation results into numerical vectors consistent with the standardized data format; for the dimensional differences between the two types of data, zero-padding or truncation is used for dimension alignment to ensure that all data are vectors of fixed dimensions; the dimension-aligned dataset is randomly shuffled and divided into training and validation sets to generate model training adaptation data, and the training adaptation dataset is output.

[0080] The model training unit uses an adaptive learning rate optimization algorithm to iteratively train the multi-parameter fusion network model, with the prediction accuracy of cave spatial distribution, morphological features, and surrounding geological conditions as the optimization objective. An early stopping mechanism is set up, with the prediction accuracy of the validation set as the monitoring indicator. When the prediction accuracy of the validation set no longer improves, training is stopped to avoid model overfitting. The changes in model parameters are recorded in real time during training, and the trained multi-parameter fusion inference model is finally output.

[0081] The correction execution unit inputs the cave parameters from the initial exploration and evaluation results into the trained multi-parameter fusion inference model. Based on the correlation patterns learned during training, the model calculates the deviation between the initial parameters and the model inference results, quantifying the magnitude and distribution of the deviation. Based on the deviation quantification analysis results and combined with the actual characteristics of the cave geological conditions, the cave parameters in the initial exploration and evaluation results are corrected parameter by parameter, such as adjusting the cave center coordinates, correcting the cave size, and supplementing the stability description of the surrounding rock layers, so that the corrected parameters are more in line with the actual geological conditions, generating the corrected cave exploration parameters.

[0082] In some embodiments, the specific implementation of the intelligent early warning module includes:

[0083] The status acquisition unit collects construction status data such as construction location, drilling speed and torque changes in real time through pile foundation construction monitoring sensors, performs real-time data cleaning and standardization, and generates a real-time status representation of pile foundation construction.

[0084] Based on the real-time status characterization of the pile foundation construction and the corrected karst cave detection parameters, the dynamic comparison unit establishes spatial location matching rules and risk judgment thresholds, performs point-to-point comparison and analysis between the construction location and the spatial distribution data of karst caves, and outputs the comparison and analysis results.

[0085] Based on the comparative analysis results, the risk assessment unit determines the distance relationship between the construction area and the karst cave and the potential collision risk. When the risk value exceeds the preset threshold, it generates an early warning trigger signal and outputs the early warning trigger signal and risk assessment results.

[0086] The early warning execution unit receives the early warning trigger signal and simultaneously sends early warning information through three channels: sound and light alarm, construction management platform pop-up window, and management personnel mobile terminal push, outputting multi-channel early warning notification.

[0087] Specifically, the status acquisition unit deploys monitoring equipment such as GPS positioning sensors, drilling speed sensors, and torque sensors to collect construction status data such as construction location, drilling speed, and torque changes in real time. The collected construction status data is cleaned in real time to remove outliers and avoid data distortion caused by factors such as sensor failure. The cleaned data is standardized to a uniform range using a normalization method to generate a feature vector containing construction location, drilling speed, and torque changes, which is the real-time status representation of pile foundation construction.

[0088] Based on the corrected karst cave detection parameters, the dynamic comparison unit clarifies key information such as the spatial distribution range and boundary contour coordinates of the karst caves, and establishes spatial location matching rules: the Euclidean distance formula is used to calculate the shortest distance between the construction location and the karst cave boundary, clarifying the relative positional relationship between the construction location and the karst cave; combined with engineering construction specifications and geological conditions, risk judgment thresholds are set to divide different risk ranges; the construction location coordinates in the real-time status representation of pile foundation construction are compared point-to-point with the karst cave spatial distribution data to calculate the shortest distance and output the comparison analysis results.

[0089] The risk assessment unit, combining the scale of the karst cave with geological stability, establishes a risk assessment index system. It uses a weighted summation method to calculate the construction risk value, quantifying the degree of construction risk. A risk assessment threshold is set; when the calculated risk value exceeds this threshold, a construction risk is identified, an early warning trigger signal is generated, and the early warning trigger signal and risk assessment result are output. Upon receiving the early warning trigger signal and risk assessment result, the early warning execution unit activates a multi-channel early warning notification mechanism: the audible and visual alarm activates flashing lights and buzzer alerts, reminding personnel at the construction site to be aware of the risk; the construction management platform displays an early warning window showing detailed information such as the early warning level, risk area, and the relative relationship between the construction location and the karst cave; early warning notifications are pushed to the management personnel's mobile app, ensuring relevant personnel receive timely risk alerts; and key information such as the early warning trigger time, early warning level, risk area, and handling status are recorded simultaneously, providing a basis for subsequent risk tracing.

[0090] In some embodiments, the specific implementation of the correlation analysis unit and the deviation correction unit of the multi-parameter fusion inference correction module includes:

[0091] The correlation analysis unit receives the training process data from the model training unit, uses the Pearson correlation coefficient analysis method to calculate the correlation strength between various monitoring data and cave detection parameters, obtains the influence weight of different data on the detection results, and outputs the data correlation weight matrix; the deviation correction unit corrects the output results of the multi-parameter fusion inference model based on the data correlation weight matrix, eliminates the influence of single data source deviation on the correction results, and outputs the deviation-corrected cave detection parameters.

[0092] Specifically, the correlation analysis unit extracts the correspondence data between monitoring data and cave detection parameters during model training. It uses Pearson correlation coefficient analysis to calculate the correlation strength between various monitoring data and cave detection parameters, clarifying the influence of different monitoring data on the cave detection results. Influence weights are assigned based on the absolute value of the correlation coefficients, generating a data correlation weight matrix that clearly presents the contribution of each data source to the detection results. The deviation correction unit extracts the preliminary output results of the multi-parameter fusion model, i.e., the predicted values ​​of cave detection parameters corresponding to each data source. It calls the data correlation weight matrix and uses a weighted correction algorithm to correct the preliminary output results, fusing the prediction results from each data source to eliminate the influence of single data source deviations on the corrected results, improving the accuracy of the detection parameters, and outputting the deviation-corrected cave detection parameters.

[0093] In some embodiments, the specific implementation of the subunit of the feature processing unit of the detection and evaluation module includes:

[0094] The feature filtering subunit receives the multi-dimensional feature mining data and uses a recursive feature elimination algorithm to filter the effectiveness of the extracted cave exploration effect feature representations, removing redundant and interfering features, retaining the core features that contribute significantly to the evaluation of the exploration effect, and outputting the optimized cave exploration effect feature representations. The feature verification subunit uses the K-fold cross-validation method to verify the effectiveness of the optimized core features, ensuring the stability and generalization ability of the features, and outputs a feature verification report.

[0095] Specifically, the feature selection subunit employs a recursive feature elimination algorithm, using the prediction accuracy of the cave exploration effect evaluation model as the evaluation metric, to progressively eliminate features with low contribution. Based on the remaining features, the model is retrained, and the above process is repeated until all retained features significantly contribute to the exploration effect evaluation, ultimately yielding an optimized cave exploration effect feature representation containing the core features. The feature verification subunit uses K-fold cross-validation to verify the effectiveness of the optimized core features: the multi-source standardized dataset for cave exploration is divided into multiple mutually exclusive subsets, with one subset selected sequentially as the validation set and the remaining subsets as the training set. The cave exploration effect evaluation model is trained and validated based on the core features; training and validation are repeated multiple times using different partitioning methods, analyzing the model's prediction accuracy, stability, and other metrics to determine the stability and generalization ability of the core features; the verification process and results are compiled, and a feature verification report is generated to provide a basis for model optimization and subsequent applications.

[0096] In some embodiments, the risk classification unit and the graded early warning unit of the intelligent early warning module are specifically implemented as follows:

[0097] The risk classification unit receives the risk assessment results and the corrected cave exploration parameters, and establishes a three-level risk classification standard based on the cave size, geological stability, and construction progress requirements. The construction risks are divided into three levels: high, medium, and low, and the risk level classification results are output.

[0098] Based on the risk level classification, the graded early warning unit triggers early warning mechanisms of different intensities: high-risk level triggers emergency work stoppage early warning, medium-risk level triggers construction slowdown early warning, and low-risk level triggers key monitoring early warning, and outputs graded early warning execution instructions.

[0099] Specifically, the risk grading unit establishes a three-level risk grading standard by combining the size of the karst cave, geological stability, and construction progress requirements:

[0100] High risk: The construction risk value reaches a high level, and the karst cave is large or the geology is unstable, or the construction progress is urgent, which may cause serious safety accidents such as cave collapse and subsidence.

[0101] Medium risk: The construction risk value is at a moderate level. The karst cave is of moderate size or the geology is relatively stable. The construction progress is normal. There are local construction risks, but the impact range is limited.

[0102] Low risk: The construction risk value is low, the karst cave is small in scale and the geology is stable, the construction schedule is relaxed, and the construction risk is low. Based on the above criteria, the risk assessment results and the corrected karst cave detection parameters are comprehensively analyzed to classify the construction risk into corresponding levels, and the risk level classification results are output.

[0103] The tiered early warning unit triggers different levels of early warning mechanisms based on the risk level classification results:

[0104] High-risk level: Activate emergency work stoppage warning, send stop order to pile foundation construction equipment, prohibit further drilling; simultaneously notify site management personnel to organize the evacuation of construction personnel to a safe area, and arrange professional personnel to conduct further investigation and assessment of the karst cave and surrounding geological conditions;

[0105] Medium risk level: Activate the construction slowdown warning, reduce drilling speed, increase monitoring frequency, and track construction status and geological changes in real time; require on-site technical personnel to supervise the entire process and prepare for emergency response;

[0106] Low-risk level: Initiate key monitoring and early warning, maintain normal construction pace, continuously monitor construction status and changes in karst cave parameters, and generate monitoring reports regularly; if the risk level escalates, immediately switch to the corresponding early warning mechanism. Output graded early warning execution instructions to guide the construction site in taking appropriate risk response measures to ensure construction safety.

[0107] In some embodiments, the subunit of the correction execution unit of the multi-parameter fusion inference correction module is specifically implemented as follows:

[0108] The result verification subunit receives the corrected cave exploration parameters, calls the supplementary geological verification data in the integrated dataset output by the data acquisition module, and uses the sum of squared errors method to verify the rationality of the correction results. When the verification error is below the reasonable range, the correction results are confirmed to be valid, and the verified cave exploration parameters are output. If the deviation analysis unit finds that the verification error exceeds the reasonable range, it records the cause of the deviation and generates a deviation analysis report.

[0109] Specifically, the result verification subunit receives the corrected cave exploration parameters and calls upon supplementary geological verification data from the integrated dataset output by the data acquisition module, including borehole core data, on-site geological survey records, and third-party testing reports. This data directly reflects the actual situation of the cave and serves as the true basis for verification. The reasonableness of the correction results is verified using the sum of squared errors method. By calculating the difference between the corrected cave exploration parameters and the actual parameters in the verification data, the accuracy of the correction results is evaluated. When the verification error is below a reasonable range, it indicates that the corrected parameters are highly consistent with the actual geological conditions, confirming the validity of the correction results, and outputting the verified cave exploration parameters. If the verification error exceeds a reasonable range, the deviation analysis unit is triggered. The deviation analysis unit thoroughly investigates the causes of deviations, analyzing them from three dimensions: data acquisition, model training, and geological conditions. The data acquisition dimension includes insufficient sensor accuracy, inappropriate data preprocessing methods, and missing data in certain areas. The model training dimension includes insufficient training samples, unreasonable model network structure, and improper algorithm parameter settings. The geological conditions dimension includes complex and variable cave morphology, abrupt changes in geological structure, and interference from the surrounding environment. A detailed record of the specific causes, scope of impact, and severity of the deviations is generated, providing a reference for subsequent model optimization, data acquisition strategy adjustments, or improvement of geological exploration plans.

[0110] In some embodiments, the subunit of the strategy optimization unit of the detection evaluation module is specifically implemented as follows: the scheme verification subunit receives the detection strategy optimization scheme, combines historical detection data and qualified karst cave detection parameters, and uses a simulation construction method to verify the feasibility and effectiveness of the optimization scheme, and outputs the scheme verification results; the dynamic adjustment unit dynamically adjusts the selection of detection methods and borehole layout scheme based on the scheme verification results, generates the optimal detection strategy adapted to the current geological conditions, and outputs the dynamically adjusted detection execution scheme to provide direct guidance for on-site construction.

[0111] Specifically, the scheme verification subunit receives the optimized detection strategy scheme, including suggestions for adjusting detection methods and optimizing borehole layout; it collects historical detection data under similar geological conditions in the same area, and combines it with verified karst cave detection parameters to build a simulated construction environment; based on historical data and current karst cave parameters, it constructs a geological simulation model, and substitutes the detection methods, borehole locations, and detection parameters from the optimized scheme into the model to simulate the entire detection process; it records indicators such as data acquisition quality, detection result accuracy, and construction efficiency during the simulated detection process, compares and analyzes them with the detection scheme before optimization, evaluates the performance of the optimized scheme in terms of feasibility and effectiveness, and outputs the scheme verification results. The dynamic adjustment unit dynamically adjusts the selection of detection methods and borehole layout based on the scheme verification results: if the verification results show that the optimized scheme is completely feasible, the scheme is directly adopted; if it is partially feasible, targeted adjustments are made to address existing problems, such as adjusting the detection equipment model, optimizing the borehole position and spacing, and correcting the detection parameter settings; if the scheme is not feasible, the initial detection evaluation results and feature correlation patterns are re-analyzed, a new optimized scheme is formulated and verified; finally, the optimal detection strategy adapted to the current geological conditions is generated, clarifying key contents such as detection methods, borehole layout, equipment parameters, and operation procedures, and outputting the dynamically adjusted detection execution plan to provide direct and specific guidance for on-site construction.

[0112] Reference Figure 2 This invention provides a multi-parameter fusion intelligent simulation and hierarchical early warning system for karst geology, comprising:

[0113] The data acquisition module is used to: collect pile foundation construction data and supplement geological data in the karst cave area, and generate multi-source standardized data for karst cave detection;

[0114] The detection and evaluation module is used to: extract and analyze features based on multi-source standardized data from cave exploration to generate a feature representation of the cave exploration effect; construct a cave exploration effect evaluation and optimization strategy model based on the feature representation of the cave exploration effect, and output the initial detection evaluation results;

[0115] The multi-parameter fusion inference and correction module is used to: construct a multi-parameter fusion network model based on the standardized dataset and the initial detection evaluation results; train the multi-parameter fusion network model using multi-source standardized data of cave detection; infer the relevant geological parameters of the cave based on the trained multi-parameter fusion network model, obtain the initial detection evaluation results, correct the initial detection evaluation results, and output the corrected cave detection parameters.

[0116] The intelligent early warning module is used to: collect pile foundation construction status data in real time based on karst cave detection parameters, compare the pile foundation construction status data with the corrected karst cave detection parameters, determine the associated risk between the construction area and the karst cave, and output an early warning trigger signal.

[0117] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A multi-parameter fusion intelligent simulation and hierarchical early warning system for karst geology, characterized in that, include: The system includes a data acquisition module, a detection and evaluation module, a multi-parameter fusion and deduction correction module, and an intelligent early warning module. The data acquisition module is used to collect pile foundation construction data and supplement geological data in the karst cave area, and generate multi-source standardized data for karst cave detection. The detection and evaluation module is used to perform feature extraction and correlation analysis based on multi-source standardized data from cave detection, and to generate a feature representation of the cave detection effect. Based on the characterization of cave exploration effect, a cave exploration effect evaluation and optimization strategy model is constructed, and the initial exploration evaluation results are output. The multi-parameter fusion inference and correction module is used to construct a multi-parameter fusion network model based on the standardized dataset and the initial detection and evaluation results; The multi-parameter fusion network model is trained using multi-source standardized data from cave exploration. Based on the trained multi-parameter fusion network model, the geological parameters related to the cave are extrapolated to obtain the initial exploration evaluation results. The initial exploration evaluation results are then corrected, and the corrected cave exploration parameters are output. The intelligent early warning module is used to collect pile foundation construction status data in real time based on karst cave detection parameters, compare the pile foundation construction status data with the corrected karst cave detection parameters, determine the associated risk between the construction area and the karst cave, and output an early warning trigger signal.

2. The karst geology multi-parameter fusion intelligent extrapolation and hierarchical early warning system according to claim 1, characterized in that, The detection and evaluation module includes: The feature processing unit, based on deep belief networks and convolutional neural network variants, performs multi-dimensional feature mining and dimensionality reduction on multi-source standardized data of cave exploration, and extracts feature representations of cave exploration effect. The model building unit constructs a model for evaluating and optimizing the effectiveness of karst cave exploration based on the correlation patterns of the characteristics of karst cave exploration. The model is iteratively trained using a gradient descent optimization algorithm combined with a backpropagation mechanism. The model takes the characteristics of karst cave exploration as input and the exploration method suitability score and the reliability coefficient of the exploration results as output targets. The model parameters are continuously optimized until convergence, and the model with stable evaluation capabilities is output. The evaluation execution unit inputs the characteristics of the cave exploration effect into the model, and uses the model to quantitatively evaluate the adaptability of the exploration method to the current geological conditions and the reliability of the initial exploration results, forming the initial exploration evaluation results and outputting them. The strategy optimization unit, based on the initial detection evaluation results, extracts the shortcomings in the current detection process, provides optimization suggestions for the selection of detection methods and adjustment of borehole layout schemes, and outputs the detection strategy optimization scheme.

3. The karst geology multi-parameter fusion intelligent extrapolation and hierarchical early warning system according to claim 2, characterized in that, The multi-parameter fusion inference and correction module includes: The network construction unit combines the image feature extraction advantages of fully convolutional networks with the sequence data processing strengths of long short-term memory networks to build a multi-parameter fusion network model framework adapted to the inference of geological parameters of karst caves, and outputs the basic network model. The data adaptation unit receives the multi-source standardized data and initial detection evaluation results of the karst cave exploration, performs format conversion and dimension alignment processing on the data, generates model training adaptation data, and outputs the training adaptation dataset. The model training unit takes the training adaptation dataset as input and uses an adaptive learning rate optimization algorithm combined with an early stopping mechanism to iteratively train the multi-parameter fusion network model. With the prediction accuracy of the spatial distribution, morphological characteristics and surrounding geological conditions of the karst cave as the optimization objective, the network weights and bias parameters are continuously adjusted, and the trained multi-parameter fusion inference model is output. The correction execution unit inputs the initial detection and evaluation results into the trained multi-parameter fusion inference model. The model then performs quantitative analysis and correction on the deviations of the cave parameters in the initial evaluation results, generating the corrected cave detection parameters.

4. The karst geology multi-parameter fusion intelligent extrapolation and hierarchical early warning system according to claim 3, characterized in that, The intelligent early warning module includes: The status acquisition unit collects construction status data such as construction location, drilling speed and torque changes in real time through pile foundation construction monitoring sensors, performs real-time data cleaning and standardization, and generates a real-time status representation of pile foundation construction. The dynamic comparison unit, based on the real-time status characterization of the pile foundation construction and the corrected karst cave detection parameters, establishes spatial location matching rules and risk judgment thresholds, performs point-to-point comparison analysis between the construction location and the spatial distribution data of karst caves, and outputs the comparison analysis results. The risk assessment unit, based on the comparative analysis results, determines the distance relationship between the construction area and the karst cave and the potential collision risk. When the risk value exceeds the preset threshold, it generates an early warning trigger signal and outputs the early warning trigger signal and the risk assessment result. The early warning execution unit receives the early warning trigger signal and simultaneously sends early warning information through three channels: sound and light alarm, construction management platform pop-up window, and management personnel mobile terminal push, outputting multi-channel early warning notifications.

5. The karst geology multi-parameter fusion intelligent extrapolation and hierarchical early warning system according to claim 4, characterized in that, The data acquisition module includes: The raw data collection unit collects pile foundation construction data, shallow geological exploration data, deep geological imaging data, and karst development monitoring data in the karst cave area through pile foundation construction monitoring equipment, ground-penetrating radar, cross-hole CT, and micro-motion detection equipment, and outputs multiple types of raw data. The data preprocessing unit receives the various types of raw collected data, performs noise reduction using median filtering, completes missing values ​​using linear interpolation, and normalizes the data dimensions to generate standardized multi-source data for cave exploration. The data integration unit associates and matches supplementary geological data collected by different detection devices with pile foundation construction data, establishes data traceability tags, and generates an integrated dataset with traceability information.

6. The karst geology multi-parameter fusion intelligent extrapolation and hierarchical early warning system according to claim 5, characterized in that, The multi-parameter fusion inference and correction module also includes: The correlation analysis unit receives the training process data from the model training unit, calculates the correlation strength between various monitoring data and cave detection parameters using Pearson correlation coefficient analysis, obtains the influence weights of different data on the detection results, and outputs a data correlation weight matrix. The Pearson correlation coefficient analysis formula is as follows: ； In the formula, For monitoring data sequences With cave exploration parameter sequence Pearson correlation coefficient, The number of data samples. For the first One monitoring data point, To monitor the mean of the data series, For the first Parameters for cave detection This represents the mean of the sequence of parameters for cave exploration. The deviation correction unit, based on the data association weight matrix, corrects the output results of the multi-parameter fusion inference model, eliminating the influence of single data source deviation on the correction results, and outputs the deviation-corrected cave exploration parameters; the correction formula is as follows: ； In the formula, These are the corrected parameters for cave exploration. For the number of data sources, For the first The weights of each data source are taken from the data association weight matrix. For the first Model prediction parameters corresponding to each data source.

7. The karst geology multi-parameter fusion intelligent extrapolation and hierarchical early warning system according to claim 6, characterized in that, The feature processing unit of the detection and evaluation module further includes: The feature filtering subunit receives the multi-dimensional feature mining data, uses a recursive feature elimination algorithm to filter the effectiveness of the extracted cave exploration effect feature representation, removes redundant and interfering features, retains the core features that contribute more than a preset threshold to the evaluation of the exploration effect, and outputs the optimized cave exploration effect feature representation. The feature verification subunit uses K-fold cross-validation to verify the effectiveness of the optimized core features, ensuring the stability and generalization ability of the features, and outputs a feature verification report.

8. The karst geology multi-parameter fusion intelligent extrapolation and hierarchical early warning system according to claim 7, characterized in that, The intelligent early warning module also includes: The risk classification unit receives the risk assessment results and the corrected cave exploration parameters, and establishes a three-level risk classification standard based on the cave size, geological stability, and construction progress requirements. The construction risks are divided into three levels: high, medium, and low, and the risk level classification results are output. The tiered early warning unit, based on the risk level classification, triggers early warning mechanisms of different intensities: high-risk level triggers emergency work stoppage early warning, medium-risk level triggers deceleration construction early warning, and low-risk level triggers key monitoring early warning, and outputs tiered early warning execution instructions.

9. The karst geology multi-parameter fusion intelligent extrapolation and hierarchical early warning system according to claim 8, characterized in that, The correction execution unit of the multi-parameter fusion inference correction module also includes: The result verification subunit receives the corrected cave detection parameters, calls the supplementary geological verification data in the integrated dataset output by the data acquisition module, and uses the sum of squared errors method to verify the rationality of the correction results. When the verification error is lower than the preset threshold, the correction results are confirmed to be valid, and the verified cave detection parameters are output. If the verification error exceeds a preset threshold, the deviation analysis unit records the cause of the deviation and generates a deviation analysis report.

10. A multi-parameter fusion intelligent extrapolation and hierarchical early warning system for karst geology according to claim 9, characterized in that, The strategy optimization unit of the detection and evaluation module also includes: The scheme verification subunit receives the optimized detection strategy scheme, a historical detection data and verified karst cave detection parameters, verifies the feasibility and effectiveness of the optimized scheme using a simulated construction method, and outputs the scheme verification results. The dynamic adjustment unit dynamically adjusts the selection of detection methods and borehole layout based on the scheme verification results, generates the optimal detection strategy adapted to the current geological conditions, and outputs the dynamically adjusted detection execution plan to provide direct guidance for on-site construction.

Images