Geotechnical engineering geological disaster prediction method and system based on deep learning
By collecting and extracting features from multiple sources, combined with convolutional neural networks and interpretability enhancement algorithms, the problem of prediction accuracy in areas lacking data has been solved, improving the accuracy and transparency of the geotechnical engineering geological disaster prediction system and optimizing system performance.
Patent Information
- Application Number
- CN202510994708.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-18
- Publication Date
- 2025-11-04
AI Technical Summary
Existing deep learning-based geotechnical engineering geological hazard prediction systems show decreased prediction accuracy in areas lacking data, and the poor interpretability of deep learning models affects user trust and application.
It employs multi-source data acquisition, data preprocessing, and feature extraction modules, combined with convolutional neural networks and long short-term memory networks for training, and integrates interpretability enhancement algorithms and decision support modules. It also improves model transparency through tools such as decision trees and heatmaps.
It improves the model's prediction accuracy and system stability, enhances users' understanding and trust in the model's decision-making process, and optimizes system performance.
Smart Images
Figure CN120894616A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of geological disaster prediction, in particular to a rock-soil engineering geological disaster prediction method and system based on deep learning. BACKGROUND
[0002] The core structure of the rock-soil engineering geological disaster prediction method and system based on deep learning includes data acquisition and preprocessing, feature extraction and selection, deep learning model training, and disaster prediction and result output. First, relevant data in rock-soil engineering, such as soil properties, groundwater level, and weather changes, are collected through sensors, remote sensing technology, and other means, and the data is preprocessed, such as denoising and normalization, to ensure the accuracy of the data. Then, key influencing factors such as terrain slope and soil type are extracted from the data through feature extraction and selection to optimize model input. Then, the data is trained using a deep neural network, and the model learns complex nonlinear relationships through iterative optimization, enabling it to predict future geological disasters. Finally, the system analyzes new input data based on the trained model and outputs the prediction results of the disaster, usually through a visual interface to display to the user and provide support for related decisions, such as disaster risk area delineation and early warning mechanisms.
[0003] However, despite the strong potential of this system in rock-soil engineering geological disaster prediction, there are still some obvious defects. The accuracy of the system is highly dependent on large-scale, high-quality historical data, and in some areas with few geological disasters or where data is difficult to obtain, data scarcity can lead to decreased prediction accuracy. Deep learning models themselves have the problem of poor interpretability, although they can provide high-precision predictions, the decision-making process is often difficult for engineers or decision-makers to understand, which can affect users' trust and application of the system. SUMMARY
[0004] To address the shortcomings of the prior art, the present application provides a rock-soil engineering geological disaster prediction method and system based on deep learning, which solves the problem of decreased prediction accuracy caused by data scarcity, and the problem of the decision-making process being difficult for engineers or decision-makers to understand, affecting users' trust and application of the system.
[0005] To achieve the above purpose, the present application is implemented by the following technical solutions: a rock-soil engineering geological disaster prediction method and system based on deep learning, comprising:
[0006] A data acquisition module, a data preprocessing module, a feature extraction module, a deep learning model module, and a prediction output module;
[0007] The data acquisition module uses remote sensing technology, geological exploration equipment, and sensors to collect soil properties, weather data, groundwater level, and terrain slope multidimensional data.
[0008] The data preprocessing module denoises, normalizes and fills in missing data of the collected raw data;
[0009] The feature extraction module extracts key features with geological disaster prediction significance through data mining algorithms;
[0010] The deep learning model module uses convolutional neural networks or long short-term memory networks to train and predict the processed data;
[0011] The prediction output module generates a geological disaster prediction report based on the output of the deep learning model, outputting the time, location and type of the disaster.
[0012] Preferably, the deep learning model module uses a convolutional neural network structure to capture spatial features in geological data.
[0013] Preferably, the visualization display includes decision trees, heat maps, sensitivity analysis graphs, etc., to help users understand the model's prediction basis, and the system also includes a decision support module that provides engineers and decision-makers with a visual display of the model's decision-making process through an explainability-enhanced algorithm.
[0014] Preferably, the deep learning model module uses a multi-task learning strategy to jointly predict different types of geological disasters, improving the model's generalization ability.
[0015] A deep learning-based geotechnical engineering geological disaster prediction method, comprising:
[0016] a. Obtain the function modules, data flow and processing procedures of the deep learning-based geotechnical engineering geological disaster prediction system;
[0017] b. According to the working characteristics and workload of the function modules, construct a virtual environment for performance testing and set performance indicators for testing, including system response time, processing speed, memory consumption, CPU / GPU usage, etc.;
[0018] c. Construct representative load scenarios by simulating data flow and user behavior, including but not limited to multi-user concurrent requests, large-scale data processing, long-running, etc.;
[0019] d. Configure parameters for deep learning model training and inference, including learning rate, batch size, training rounds, to reflect the computational complexity of the model;
[0020] e. Simulate the performance of the system under different loads using performance testing tools and collect system response time, throughput, resource consumption data;
[0021] f. Calculate the bottleneck of system performance using a mathematical model based on time complexity and space complexity calculation formula:
[0022] T = O(f(n)) T = O(f(n)) T = O(f(n))
[0023] Where T is the system response time, f(n) is the computational complexity of model training and inference, and n is the scale of input data.
[0024] g. Perform statistical analysis on the performance data generated during simulation, draw performance curve and calculate peak load, average response time, maximum throughput;
[0025] h. According to the performance test results, identify possible performance bottlenecks in the system, including memory overflow and excessive consumption of computing resources;
[0026] i. By comparing the simulation results with the actual application scenarios, optimize the parameter configuration of the model, such as adjusting the learning rate and optimizing the batch size of the training data, to improve the performance of the model;
[0027] j. After model optimization, perform performance testing again and compare the performance changes before and after optimization to verify the performance improvement of the system;
[0028] k. Based on the performance test results of simulation, generate performance report and output to engineers and decision makers, report including performance indicators, optimization suggestions and system stability analysis;
[0029] l. According to the performance report, further optimize the system and propose feasible improvement scheme.
[0030] Preferably, the learning rate of the deep learning model training in step d is set as:
[0031]
[0032] Where Ir is the learning rate, β is the decay coefficient, and t is the current training round.
[0033] Preferably, the batch size and training round of the deep learning model in step d are set as:
[0034] batch size = min(max batch size, n)
[0035]
[0036] Where max batch size is the maximum batch size and n is the total amount of training data.
[0037] Preferably, the mathematical model formula in step f is based on space complexity and memory consumption calculation:
[0038] S = O(m*n)
[0039] Where S is the memory consumption, m is the feature dimension of each data sample, and n is the total amount of data.
[0040] The present application provides a deep learning-based geotechnical engineering geological disaster prediction method and system. The following beneficial effects are provided:
[0041] The deep learning-based geotechnical engineering geological disaster prediction method and system ensure the accuracy and reliability of the original data by using multi-source data acquisition and optimizing the data preprocessing and feature extraction module, and improve the quality of model training. In particular, the deep learning model module uses a convolutional neural network structure to capture spatial features in geological data, which can automatically learn more complex patterns, thereby improving the accuracy of the prediction. In addition, the system integrates an explainability enhancement algorithm, combined with decision trees, heat maps, and sensitivity analysis graphs, and other visualization tools to help users understand the model decision-making process, and improve the transparency of the system and the trust of users.
[0042] By constructing a virtual environment to simulate multi-user concurrent requests, large-scale data processing, and other load scenarios, and combining the training parameter configuration of the deep learning model, the response time, throughput, memory consumption, and other key performance indicators of the system are comprehensively tested. Further, a mathematical model based on time complexity and space complexity is used to calculate the system performance bottleneck, thereby providing a basis for optimization. The optimized model parameter configuration, such as adjusting the learning rate and optimizing the batch size, effectively improves the calculation efficiency of the model and the performance of the system. In addition, the generated performance report provides detailed optimization suggestions for engineers and decision-makers, which helps to improve the stability and long-term performance of the system. BRIEF DESCRIPTION OF DRAWINGS
[0043] Figure 1 The present application is an internal structure diagram. DETAILED DESCRIPTION
[0044] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.
[0045] Embodiment one
[0046] As Figure 1As shown, the embodiment of the present application provides a deep learning-based geotechnical engineering geological disaster prediction system, which comprises a data acquisition module, a data preprocessing module, a feature extraction module, a deep learning model module and a prediction output module. The deep learning model module adopts a convolutional neural network structure to capture the spatial features in the geological data. Visualization display includes decision tree, heat map, sensitivity analysis chart, etc. to help users understand the prediction basis of the model. The system also includes a decision support module. The decision support module provides a visual display of the model decision-making process for engineers and decision-makers through the explainability enhancement algorithm of the integrated model. The deep learning model module uses a multi-task learning strategy to jointly predict different types of geological disasters to improve the generalization ability of the model.
[0047] The data acquisition module uses remote sensing technology, geological exploration equipment and sensors to collect multi-dimensional data such as soil properties, meteorological data, groundwater level and terrain slope.
[0048] The data preprocessing module denoises, normalizes and fills in missing data for the collected raw data.
[0049] The feature extraction module extracts key features that are significant for geological disaster prediction through data mining algorithms.
[0050] The deep learning model module uses a convolutional neural network or a long short-term memory network to train and predict the processed data.
[0051] The prediction output module generates a geological disaster prediction report based on the output of the deep learning model, outputting information such as the time, location and type of the disaster.
[0052] A deep learning-based geotechnical engineering geological disaster prediction method, comprising:
[0053] a. Obtain the functional modules, data flow and processing flow of the deep learning-based geotechnical engineering geological disaster prediction system.
[0054] b. According to the working characteristics and working load of the functional modules, construct a virtual environment for performance testing, and set performance indicators for testing, including system response time, processing speed, memory consumption, CPU / GPU usage, etc.
[0055] c. Construct representative load scenarios by simulating data flow and user behavior, including but not limited to multi-user concurrent requests, large-scale data processing, long-time running, etc.
[0056] d. Configure the parameters for deep learning model training and inference, including learning rate, batch size, training rounds, to reflect the computational complexity of the model. The learning rate for deep learning model training in step d is set to:
[0057]
[0058] where Ir is the learning rate, β is the decay coefficient, t is the current training round, and the batch size and training round of the deep learning model in step d are set as:
[0059] batch size = min(max batch size, n)
[0060]
[0061] where max batch size is the maximum batch size, and n is the total amount of training data.
[0062] e. Simulate the performance of the system under different loads using performance testing tools, and collect system response time, throughput, and resource consumption data.
[0063] f. Calculate the system performance bottlenecks using mathematical models based on time complexity and space complexity calculation formulas:
[0064] T = O(f(n)) T = O(f(n)) T = O(f(n))
[0065] where T is the system response time, f(n) is the computational complexity of model training and inference, and n is the size of the input data. The mathematical model calculation formula in step f is based on space complexity and memory consumption calculation:
[0066] S = O(m·n)
[0067] where S is the memory consumption, m is the feature dimension of each data sample, and n is the total amount of data.
[0068] g. Perform statistical analysis on the performance data generated during simulation, draw performance curve graphs, and calculate peak load, average response time, and maximum throughput.
[0069] h. Based on the performance test results, identify possible performance bottlenecks in the system, including memory overflow and excessive consumption of computing resources.
[0070] i. Optimize the model's parameter configuration by comparing simulation results with actual application scenarios, such as adjusting the learning rate and optimizing the batch size of training data, to improve model performance.
[0071] j. After model optimization, perform performance testing again and compare the performance changes before and after optimization to verify the performance improvement of the system.
[0072] k. Based on the performance test results of simulation, generate a performance report and output it to engineers and decision-makers, including performance indicators, optimization suggestions, and system stability analysis.
[0073] l. Based on the performance report, further optimization of the system is conducted, and feasible improvement plans are proposed.
[0074] Comparative Example
[0075] To verify the effectiveness and advantages of the deep learning-based geotechnical engineering geological disaster prediction system of the present invention, two different examples are designed for comparison testing. One is the example of the present invention, and the other is the example of the traditional shallow learning-based geological disaster prediction system. By comparing the prediction accuracy, system performance and stability of the two, the technical advantages of the present invention are evaluated.
[0076] Example 2: Traditional Shallow Learning-Based System
[0077] System Configuration:
[0078] Data Acquisition Module: Same as Example 1.
[0079] Data Preprocessing Module: Same as Example 1.
[0080] Feature Extraction Module: Simple feature selection algorithm is used to manually select some static features such as soil type and precipitation.
[0081] Shallow Learning Model Module: Traditional shallow learning models such as support vector machines or decision trees are used for disaster prediction.
[0082] No Visual Support Module: No algorithm or visualization display for explainability enhancement is integrated.
[0083] Prediction Accuracy:
[0084] Due to the simplicity of the model and the lack of spatial feature capture by deep learning models, the prediction accuracy is 78%, and the error rate is 22%. Especially when dealing with complex geological disaster types, the accuracy decreases significantly.
[0085] Performance Test Results:
[0086] When processing large-scale data, the system has a long response time of 6.7 seconds, a throughput of 200 records per second, a CPU usage of 95%, and a memory consumption of 5GB. The performance is unstable under heavy load.
[0087] System Stability:
[0088] After 48 hours of continuous operation, the system has problems of memory overflow and excessive consumption of computing resources, and needs to be restarted regularly for maintenance. After optimization, the system performance cannot be effectively improved.
[0089] Comparative Analysis
[0090] Prediction accuracy:
[0091] The deep learning model of the embodiment improves the generalization ability of the model through convolutional neural network and multi-task learning strategy, and can effectively process various types of geological disasters. In contrast, the traditional system uses a shallow learning model and is difficult to accurately capture complex geological data features, resulting in low prediction accuracy.
[0092] Performance and stability:
[0093] The embodiment performs more stably in large-scale data processing and multi-user concurrent scenarios, has shorter response time and less resource consumption. The traditional system has longer response time and higher resource consumption when the load is high, and the system stability is poor, which needs frequent maintenance.
[0094] System optimization:
[0095] Through performance testing and optimization, the embodiment realizes significant improvement of system performance, including optimization of response time and memory consumption. The traditional system cannot be effectively optimized, and the performance improvement is limited.
[0096] Interpretability and decision support:
[0097] The embodiment has integrated explainability enhancement algorithm and decision support module, through visual display and decision tree tools, so that engineers and decision makers can more clearly understand the decision-making process of the model, thereby improving the usability and trust of the system. The traditional system lacks such support, resulting in low user trust in model prediction results.
[0098] Conclusion
[0099] Through the test results of the comparative embodiment, it is proved that the geotechnical engineering geological disaster prediction system based on deep learning provided by the present application has significant advantages in prediction accuracy, system performance, stability and interpretability compared with the traditional shallow learning system. This system is more suitable for use in actual complex geotechnical engineering applications.
[0100] Although embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and alterations can be made without departing from the principles and spirit of the present application, the scope of which is defined by the appended claims and their equivalents.
Claims
1. A deep learning-based geotechnical engineering geological hazard prediction system, characterized in that, include: The system includes a data acquisition module, a data preprocessing module, a feature extraction module, a deep learning model module, and a prediction output module. The data acquisition module uses remote sensing technology, geological exploration equipment and sensors to collect multi-dimensional data such as soil properties, meteorological data, groundwater level and topographic slope. The data preprocessing module performs noise reduction and normalization on the collected raw data, and fills in missing data. The feature extraction module extracts key features that are significant for geological disaster prediction using data mining algorithms. The deep learning model module uses convolutional neural networks or long short-term memory networks to train and predict the processed data; The prediction output module generates a geological disaster prediction report based on the output of the deep learning model, and outputs information on the time, location, and type of the disaster.
2. The geotechnical engineering geological hazard prediction system based on deep learning according to claim 1, characterized in that: The deep learning model module employs a convolutional neural network structure to capture spatial features in geological data.
3. The geotechnical engineering geological hazard prediction system based on deep learning according to claim 1, characterized in that: The visualization includes decision trees, heatmaps, sensitivity analysis charts, etc., to help users understand the basis of the model's predictions. The system also includes a decision support module, which provides engineers and decision-makers with a visualization of the model's decision-making process by integrating model interpretability enhancement algorithms.
4. The geotechnical engineering geological hazard prediction system based on deep learning according to claim 1, characterized in that: The deep learning model module uses a multi-task learning strategy to jointly predict different types of geological disasters, thereby improving the model's generalization ability.
5. A deep learning-based method for predicting geological hazards in geotechnical engineering, characterized in that, include: a. Obtain the functional modules, data flow, and processing flow of a deep learning-based geotechnical engineering geological disaster prediction system; b. Based on the working characteristics and workload of the functional modules, construct a virtual environment for performance testing and set the performance indicators for testing, including system response time, processing speed, memory consumption, CPU / GPU utilization, etc. c. By simulating data flow and user behavior, construct representative load scenarios, including but not limited to multi-user concurrent requests, large-scale data processing, and long-term operation; d. Configure parameters for deep learning model training and inference, including learning rate, batch size, and training epochs, to reflect the computational complexity of the model; e. Simulate the system's performance under different loads using performance testing tools, and collect data on system response time, throughput, and resource consumption; f. Use a mathematical model to calculate the bottleneck of system performance, the mathematical model being based on formulas for calculating time and space complexity: T=O(f(n))T=O(f(n))T=O(f(n)) Where T is the system response time, f(n) is the computational complexity of model training and inference, and n is the size of the input data; g. Perform statistical analysis on the performance data generated during the simulation, plot performance curves, and calculate peak load, average response time, and maximum throughput; h. Based on the performance test results, identify potential performance bottlenecks in the system, including memory overflow and excessive consumption of computing resources; i. Optimize the model's parameter configuration by comparing simulation results with actual application scenarios; j. After model optimization, conduct performance testing again and compare the performance changes before and after optimization; k. Based on the simulated performance test results, generate a performance report and output it to engineers and decision-makers. The report includes performance metrics, optimization suggestions, and system stability analysis.
1. Based on the performance report, further optimize the system and propose feasible improvement plans.
6. The method for predicting geological hazards in geotechnical engineering based on deep learning according to claim 5, characterized in that: The learning rate for training the deep learning model in step d is set as follows: Where Ir is the learning rate, β is the decay coefficient, and t is the current training epoch.
7. The method for predicting geological hazards in geotechnical engineering based on deep learning according to claim 5, characterized in that: The batch size and training epochs of the deep learning model described in step d are set as follows: batch size=min(max batch size,n) Where max batch size is the maximum batch size, and n is the total amount of training data.
8. The method for predicting geological hazards in geotechnical engineering based on deep learning according to claim 5, characterized in that: The mathematical model calculation formula described in step f is based on space complexity and memory consumption: S = O(m·n) Where S represents memory consumption, m represents the feature dimension of each data sample, and n represents the total amount of data.