Intelligent big data analysis and prediction method and system based on deep learning

Abstract

The invention provides an intelligent big data analysis and prediction method and system based on deep learning, and the system comprises a physical field sensing layer, an edge calculation layer, a cloud intelligent analysis layer and an emergency response layer, achieves the synchronous collection of multi-dimensional parameters, such as deformation, seepage and stress, of a dam body through a three-dimensional monitoring network constructed by a plurality of high-precision sensors, and achieves the real-time monitoring of the dam body. The edge layer carries out real-time data preprocessing and a dynamic risk assessment model, can identify correlation characteristics across physical fields, predicts a risk conduction path, and realizes conversion from passive monitoring to active early warning. And through multi-channel early warning information pushing and automatic control instruction issuing, the emergency response efficiency is greatly improved. Meanwhile, the three-dimensional visualization platform visually presents professional analysis results, the decision support capacity is remarkably improved, the system has remarkable advantages in the aspects of early warning timeliness, analysis accuracy, management efficiency and the like, and an intelligent solution is provided for hydraulic engineering safety management.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence and big data analysis technology, and in particular to an intelligent big data analysis and prediction method and system based on deep learning, which realizes intelligent assessment and risk warning of the health status of water conservancy facilities through multiple sensor data and learning models. Background Art

[0002] Water conservancy projects are core infrastructure for ensuring national water resource utilization and flood control safety. However, current safety monitoring technologies typically focus on single indicators (such as seepage pressure and displacement), overlooking the chain reactions between multiple factors such as seepage, cracks, and stress. For example, abnormal seepage pressure can cause cracks in the dam to expand, often resulting in an alarm only after the danger has escalated. Furthermore, data from different monitoring systems (such as seepage pressure monitoring and deformation monitoring) is stored in a decentralized manner, lacking a unified temporal and spatial coordinate reference. This results in risk analysis relying on manual experience, which can easily lead to misjudgments or omissions.

[0003] Therefore, there is an urgent need for an intelligent big data analysis and prediction method and system based on deep learning of multi-dimensional data fusion and dynamic intelligent analysis. Summary of the Invention

[0004] In view of this, the present invention proposes an intelligent big data analysis and prediction method and system based on deep learning. The specific technical solutions are as follows:

[0005] The intelligent big data analysis and prediction method and system based on deep learning include a physical field perception layer, an edge computing layer, a cloud-based intelligent analysis layer and an emergency response layer. The multi-physical field perception layer includes a sensor network that integrates strain gauges, piezometers, GNSS positioning base stations, fiber grating sensors, and underwater robots to achieve synchronous collection of multiple key parameters such as water level, seepage, deformation, stress, and vibration. The edge computing layer is used for data processing and anomaly monitoring. The cloud-based intelligent analysis layer captures the correlation characteristics of hydraulic structures in the temporal and spatial dimensions. The emergency response layer triggers differentiated response strategies based on risk levels and supports multi-channel alarms such as SMS, voice, and APP push.

[0006] The intelligent big data analysis and prediction method based on deep learning includes the following steps:

[0007] Step 1: Collect multi-dimensional data such as dam deformation, seepage, and stress through a multi-type sensor network to provide a unified data foundation for subsequent analysis;

[0008] Step 2: Capture the key features in the data, paying special attention to the mutual influence between different physical quantities. Through feature fusion, various monitoring data are transformed into comprehensive indicators with engineering significance.

[0009] Step 3: Identify potential risk areas, analyze risk transmission paths, and finally assess the probability of risk occurrence and development trends. Model training uses a multi-objective optimization strategy to ensure that the prediction results are both accurate and stable, and can provide early warning of various engineering risks;

[0010] Step 4: Implement differentiated response measures based on risk level. Warning information is delivered in real time through multiple channels and supports automated emergency response. The system has continuous learning capabilities, continuously optimizing the accuracy and timeliness of warnings through actual operational feedback.

[0011] Furthermore, step five also includes a three-dimensional visualization platform to intuitively display the project status and risk distribution, providing managers with a convenient interactive experience and comprehensive decision-making basis.

[0012] Furthermore, in step one, fiber grating sensors and micro-electromechanical piezometers are installed at key locations of the dam body, and Beidou monitoring stations are deployed to obtain millimeter-level deformation data. Underwater robots are equipped with multi-beam sonar to regularly inspect the reservoir bottom terrain.

[0013] Furthermore, the second step adopts a 3D dilated convolutional network, sets a multi-scale convolution kernel with an expansion rate of [1, 2, 3], and combines it with a spatial attention module to adaptively focus on high-risk areas.

[0014] The above technical solution has the following beneficial effects:

[0015] The present invention uses a three-dimensional monitoring network constructed by a variety of high-precision sensors to achieve the synchronous collection of multi-dimensional parameters such as dam deformation, seepage, and stress. The edge layer performs real-time data preprocessing, and the cloud deploys an innovative 3D void convolutional network combined with an attention mechanism to ensure both response speed and analysis depth. The dynamic risk assessment model can identify correlation features across physical fields, predict risk transmission paths, and achieve a transition from passive monitoring to active early warning. The efficiency of emergency response is greatly improved by pushing early warning information through multiple channels and issuing automated control instructions. At the same time, the three-dimensional visualization platform intuitively presents professional analysis results, significantly improving decision-making support capabilities, giving the system significant advantages in terms of early warning timeliness, analysis accuracy, and management efficiency, providing an intelligent solution for the safety management of water conservancy projects. DETAILED DESCRIPTION

[0016] The following is a clear and complete description of the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present invention.

[0017] The sensing layer in this invention is composed of a variety of sensors, including GNSS equipment for measuring deformation, piezometers for monitoring seepage, and fiber optic sensors for detecting stress. These sensors are distributed at key locations on the dam and collect various physical parameters in real time.

[0018] The edge computing layer, deployed at the monitoring site, is responsible for preliminary processing of sensor data, including basic analysis tasks such as data cleaning and outlier detection, thereby alleviating computing pressure on the cloud. The intelligent analysis layer, deployed in the cloud, uses deep learning models to conduct in-depth analysis of data from the edge layer. This layer can discover the inherent connections between different parameters and identify potential risk patterns. The response layer: Based on the analysis results, it automatically triggers corresponding early warning mechanisms, notifies relevant personnel through various means, and initiates emergency measures according to pre-set plans.

[0019] The system continuously collects multi-dimensional monitoring data such as deformation, seepage, and stress through a network of sensors deployed throughout the dam. To ensure data quality, a unified timestamp and spatial coordinate system is adopted, and a three-dimensional convolutional neural network is used to process the collected data. The network has multi-scale perception capabilities and can capture local details and overall trends simultaneously. A specially designed attention mechanism enables the network to automatically focus on key areas and establish a comprehensive assessment model that not only determines the current status but also predicts future development trends. The model takes into account the interaction of multiple risk factors, can simulate the risk transmission path, and implement a graded response based on the risk level. Low-risk situations only record logs, medium-risk situations trigger manual inspections, and high-risk situations immediately activate emergency plans. All warning information is transmitted in real time through multiple channels.

[0020] The specific operations are as follows:

[0021] The sensor network is optimally deployed at key locations of the dam body. A monitoring unit is arranged every 20 meters at important locations such as the dam crest, abutment and foundation of the concrete gravity dam. Each unit is equipped with a fiber Bragg grating strain gauge with a range of ±1500με and an accuracy of ±2με for stress monitoring, two vibrating wire piezometers with a range of 0-1MPa and an accuracy of 0.1%FS for seepage monitoring, and a Beidou high-precision monitoring station with a horizontal accuracy of 2mm+0.5ppm for deformation monitoring.

[0022] An additional distributed fiber optic temperature measurement system with a spatial resolution of 0.5 meters is deployed on the water-facing side of the dam. Two autonomous underwater robots are also deployed to conduct multi-beam terrain scanning of the reservoir area every month to obtain 5cm×5cm high-precision terrain data.

[0023] The edge computing layer utilizes an industrial-grade edge computing gateway, one deployed at each monitoring section. It features a built-in real-time data quality detection algorithm based on the 3σ criterion and a lightweight YOLOv4-tiny anomaly detection model, achieving 85 FPS of real-time processing power. The cloud platform, built on a four-socket A100 GPU server cluster, deploys a 3D dilated convolutional neural network with a [1,2,3] dilation rate, combined with a spatial attention module and a physical constraint loss function. The model is trained using the AdamW optimizer with an initial learning rate of 3e-4 and a batch size of 32. After 200 epochs of training, convergence is achieved using a cosine annealing schedule.

[0024] The early warning system has a three-level response mechanism: when the risk index R<5, a log record is generated and pushed to the management platform; when 5≤R<8, a drone inspection is automatically triggered and the technical team is notified; when R≥8, the emergency plan is immediately activated, and an emergency warning message is sent to the relevant responsible persons via the 5GURLLC network (end-to-end latency <20ms). The system has also developed a three-dimensional visualization platform based on WebGL2.0, which supports real-time rendering and interactive query of 2 million point cloud data. Managers can view the dam status and risk heat map at any time through PC or mobile APP. During the implementation of the entire system, special attention should be paid to the lightning protection of sensors, the redundant design of communication networks, and the heat dissipation of edge computing equipment to ensure stable operation in harsh outdoor environments. After the system is launched, it will undergo a three-month trial run, during which the model will be fine-tuned once a week. It will enter the formal operation stage after the prediction accuracy reaches more than 90%.

[0025] In actual deployment, the system adopts a modular design, facilitating adjustments based on specific project requirements. The sensor network is optimized according to the dam's structural characteristics, ensuring coverage of all critical locations. Edge computing equipment utilizes industrial-grade hardware to withstand harsh field environments. The cloud-based analysis platform utilizes a distributed architecture to ensure efficient computing. The system features a specially designed visualization interface that intuitively displays dam status and risk distribution in three dimensions. Interactive operations allow managers to gain in-depth insights and support decision-making.

[0026] The above describes the basic principles and main features of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only illustrate the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the invention to be protected. The scope of protection of the invention is defined by the attached claims and their equivalents.

Claims

1. An intelligent big data analysis and prediction method and system based on deep learning, characterized by: It includes a physical field perception layer, an edge computing layer, a cloud-based intelligent analysis layer and an emergency response layer. The multi-physical field perception layer includes a sensor network, integrated strain gauges, piezometers, GNSS positioning base stations, fiber grating sensors, and underwater robots to achieve synchronous collection of multiple key parameters such as water level, seepage, deformation, stress, and vibration. The edge computing layer is used for data processing and anomaly monitoring. The cloud-based intelligent analysis layer captures the correlation characteristics of hydraulic structures in the time and space dimensions. The emergency response layer triggers differentiated response strategies according to the risk level and supports multi-channel alarms such as SMS, voice, and APP push.

2. An intelligent big data analysis and prediction method based on deep learning, characterized by: The following steps are involved: Step 1: Collect multi-dimensional data such as dam deformation, seepage, and stress through a multi-type sensor network to provide a unified data foundation for subsequent analysis; Step 2: Capture the key features in the data, paying special attention to the mutual influence between different physical quantities. Through feature fusion, various monitoring data are transformed into comprehensive indicators with engineering significance. Step 3: Identify potential risk areas, analyze risk transmission paths, and finally assess the probability of risk occurrence and development trends. Model training uses a multi-objective optimization strategy to ensure that the prediction results are both accurate and stable, and can provide early warning of various engineering risks; Step 4: Take differentiated response measures based on the degree of risk. Early warning information is pushed in real time through multiple channels and supports automated emergency response. The system has continuous learning capabilities and continuously optimizes the accuracy and timeliness of early warnings through actual operation feedback.

3. The intelligent big data analysis and prediction method based on deep learning according to claim 2 is characterized in that: The step five also includes a three-dimensional visualization platform to intuitively display the project status and risk distribution, providing managers with a convenient interactive experience and comprehensive decision-making basis.

4. The intelligent big data analysis and prediction method based on deep learning according to claim 2 is characterized in that: In the step 1, fiber grating sensors and micro-electromechanical piezometers are installed at key locations of the dam body, and Beidou monitoring stations are deployed to obtain millimeter-level deformation data. Underwater robots are equipped with multi-beam sonar to regularly inspect the reservoir bottom terrain.

5. The intelligent big data analysis and prediction method based on deep learning according to claim 2 is characterized in that: The second step uses a 3D dilated convolutional network, sets a multi-scale convolution kernel with an expansion rate of [1, 2, 3], and combines it with a spatial attention module to adaptively focus on high-risk areas.