Real-time video monitoring system and method for emergency disposal of barrier lake

Abstract

The present invention relates to the technical field of emergency disposal of barrier lakes, specifically to a real-time video monitoring system and method for emergency disposal of barrier lakes, wherein the system comprises a video analysis module, a dam monitoring module, a hydropower station control module, a hydraulic structure analysis module, a water flow simulation module, a geological change identification module, and a risk assessment and response module. In the present invention, accurate monitoring and analysis of the environment and safety status of barrier lakes are achieved by integrating advanced technologies, and technical means such as convolutional neural networks, machine vision algorithms, dynamic programming, structural mechanics models, and computational fluid dynamics models are applied to improve the ability to capture dynamic events and the accuracy of predicting potential risks, which not only improves the real-time and accuracy of monitoring, but also enhances the efficiency and effectiveness of emergency response, significantly improves the comprehensiveness of barrier lake monitoring, the timeliness of early warning, and the scientific nature of emergency disposal, and provides strong support for disaster prevention and mitigation.

Description

Technical Field

[0001] The invention relates to the technical field of emergency disposal of a dammed lake, and in particular to a real-time video monitoring system and method for emergency disposal of a dammed lake. Background Art

[0002] The technical field of emergency response to impounded lakes involves responding to natural disasters, especially the formation and changes of impounded lakes caused by landslides, earthquakes or other geological activities. The core lies in quickly and accurately assessing the risks of impounded lakes in order to carry out effective disaster prevention and emergency response. It is usually necessary to monitor key indicators such as the water level, water movement, and dam stability of the impounded lake, and take measures when necessary, such as drainage, dam reinforcement or evacuation.

[0003] Among them, the real-time video monitoring system for emergency disposal of the barrier lake is a system that uses video monitoring technology to monitor the condition of the barrier lake in real time. The purpose is to provide real-time and accurate data support to help decision makers better understand the current condition of the barrier lake and take quick response measures when necessary. The system usually uses high-resolution video cameras, combined with image processing and data analysis technology, to monitor the water level changes of the barrier lake, the condition of the spillway, the stability of the dam body, etc. in real time. In this way, it aims to achieve the effect of rapid response to natural disasters and reduce potential losses.

[0004] In the traditional emergency monitoring system for landslide lakes, the real-time nature of monitoring data is insufficient, the early warning accuracy is low, and the emergency response efficiency is limited. It mostly relies on manual inspections and simple monitoring equipment, and it is difficult to capture the changes and potential risks of landslide lakes in real time and comprehensively. This limitation leads to delayed identification of risks, thus missing the best time for early warning and emergency response. Emergency response plans often lack pertinence and scientificity, and cannot effectively respond to complex and changeable disaster situations, resulting in poor results of emergency measures and even exacerbating the impact of disasters. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a real-time video monitoring system and method for emergency disposal of a barrier lake.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: a real-time video monitoring system for emergency disposal of a barrier lake, the system comprising a video analysis module, a dam monitoring module, a hydropower station control module, a hydraulic structure analysis module, a water flow simulation module, a geological change identification module, and a risk assessment and response module.

[0007] The video analysis module uses a convolutional neural network to perform dynamic scene analysis based on the video data collected by the camera, combines the time series analysis method to process the video stream, performs dynamic feature capture and real-time content analysis, identifies dynamic changes through a change detection algorithm, and generates video dynamic analysis results;

[0008] The dam monitoring module uses a machine vision algorithm combined with edge detection and image segmentation technology to scan the dam surface based on the video dynamic analysis results, analyze changes and potential cracks, track crack development through a crack tracking algorithm, and generate a dam stability index;

[0009] The hydropower station control module uses dynamic programming and hydraulic models to adjust the hydropower station operation parameters based on the dam stability index and historical hydrological data, balances the power station operation and reservoir safety through real-time feedback control algorithms, and generates control operation strategies;

[0010] The hydraulic structure analysis module uses the structural mechanics model and finite element analysis method to deeply analyze the stress condition of the hydraulic structure based on the video dynamic analysis results, uses the risk assessment algorithm to assess potential safety hazards, and generates a structural safety assessment record;

[0011] The water flow simulation module simulates the water flow behavior of the reservoir based on the video dynamic analysis results and topographic data using a computational fluid dynamics model, predicts the flow rate and direction through fluid dynamics analysis, and generates a water flow behavior prediction;

[0012] The geological change identification module is based on topographic and historical geological data, applies digital image correlation analysis and geological analysis methods to identify geological changes in the area surrounding the barrier lake, and combines geological change detection algorithms to generate geological change detection results;

[0013] The risk assessment and response module uses a multi-factor risk assessment model and emergency response planning algorithm to conduct comprehensive risk assessment and emergency strategy formulation based on control operation strategies, structural safety assessment records, water flow behavior predictions and geological change detection results, and generates a comprehensive risk management plan.

[0014] The present invention has improvements in that the video dynamic analysis results are specifically the identification and classification of key dynamic events, including the tracking of dynamic objects, the detection of abnormal activities and the recording of environmental changes; the dam body stability index specifically refers to the deformation measurement of the dam body surface, the quantification of crack width and length and the analysis of crack development trend; the control operation strategy includes the water regulation plan of the hydropower station, the power generation optimization plan and the emergency response measures; the structural safety assessment record includes the evaluation of structural integrity, the identification of potential risk areas and the division of safety levels; the water flow behavior prediction is specifically the prediction of water flow speed and direction, the evaluation of the impact of water flow on the dam body and the prediction of potential flooding areas; the geological change detection results include the evaluation of geological stability, the identification of potential landslide and collapse areas and the analysis of geological change trends; the comprehensive risk management plan is specifically the division of risk levels, the recommendation of preventive measures and the emergency response plan.

[0015] The present invention is improved in that the video analysis module includes a dynamic capture submodule, a real-time analysis submodule, and a change detection submodule.

[0016] The dynamic capture submodule uses the target detection algorithm to analyze the video frames based on the video data collected by the camera, locates and tracks the moving objects through feature extraction and pattern recognition technology, analyzes the motion relationship between pixels using the optical flow method, identifies the object's motion trajectory, and generates dynamic feature information;

[0017] The real-time analysis submodule uses a long short-term memory network to process time series data based on dynamic feature information, analyzes the behavior trend and event development of objects by learning the temporal pattern of object movement, performs in-depth analysis and event prediction on dynamic scenes, and generates event analysis results;

[0018] The change detection submodule uses a differential image method to compare consecutive video frames at the pixel level based on the event analysis results, analyzes the statistical characteristics and trends of pixel changes through statistical methods, identifies dynamic changes and abnormal behaviors, and generates video dynamic analysis results.

[0019] The present invention is improved in that the dam body monitoring module includes a surface monitoring submodule, a deformation analysis submodule, and a crack tracking submodule.

[0020] The surface monitoring submodule uses image texture analysis based on the video dynamic analysis results, extracts texture features in the image through gray-level co-occurrence matrix analysis, uses the Sobel algorithm to identify image edges, locates changes on the dam surface, and generates surface feature analysis records;

[0021] The deformation analysis submodule is based on surface feature analysis records, applies digital image correlation analysis, compares pixel changes in the same area in a continuous image sequence, combines 3D reconstruction technology, uses stereo vision principles and deep learning methods to build a 3D model of the dam surface, analyzes surface deformation, and generates deformation risk assessment results;

[0022] The crack tracking submodule uses a crack detection algorithm based on the deformation risk assessment results, identifies the crack location and shape through image segmentation technology, performs time series analysis, tracks the crack change trend over time, evaluates crack development, and generates a dam stability index.

[0023] The present invention is improved in that the hydropower station control module includes an energy optimization submodule, a safety adjustment submodule, and a strategy implementation submodule.

[0024] The energy optimization submodule adopts a multi-objective optimization algorithm based on the dam stability index and historical hydrological data, combines power output and water resource utilization, uses an energy demand forecasting model, predicts power demand based on historical data and trend analysis, matches power station energy output and demand, and generates an energy optimization strategy;

[0025] The safety adjustment submodule is based on the energy optimization strategy, uses the hydraulic simulation model to simulate the changes in reservoir water level, flow rate and pressure, combines the real-time feedback control strategy, adjusts the operation of the sluice and generator set in real time according to the monitoring data, and generates a safety adjustment plan;

[0026] The strategy implementation submodule is based on the safety adjustment plan and adopts a data-driven decision analysis method. By analyzing and integrating monitoring data, simulation model output and power plant operation history, it identifies key decision points, optimizes power plant operation strategies, and generates control operation strategies.

[0027] The present invention is improved in that the hydraulic structure analysis module includes a mechanical modeling submodule, a risk assessment submodule, and an integrity inspection submodule.

[0028] The mechanical modeling submodule uses elastic mechanics theory to model the hydraulic structure based on the video dynamic analysis results, including determining the geometric parameters and material properties of the structure, applying basic mechanics equations to simulate the response of the structure under various loads, and generating structural mechanics analysis records;

[0029] The risk assessment submodule analyzes the stress and strain state of the structure based on the structural mechanics analysis record using the finite element analysis method, divides the structure into multiple small elements, calculates the behavior of each element under stress, evaluates the stability of the entire structure, and generates stress and strain assessment results;

[0030] The integrity inspection submodule uses structural integrity analysis technology based on stress-strain assessment results, integrates external environmental factors and historical data of the structure, conducts a comprehensive assessment of the integrity and safety hazards of the structure, and generates a structural safety assessment record.

[0031] The present invention is improved in that the water flow simulation module includes a flow rate analysis submodule, a simulation modeling submodule, and a behavior prediction submodule.

[0032] The velocity analysis submodule simulates reservoir water flow behavior based on video dynamic analysis results and topographic data, applies fluid dynamics theory, analyzes water flow characteristics including flow velocity and flow direction, and predicts water flow behavior by calculating the relationship between flow velocity and pressure field according to the basic physical laws of water flow, generating water flow characteristic analysis conclusions;

[0033] The simulation modeling submodule uses a computational fluid dynamics model to simulate water flow based on the conclusions of water flow characteristic analysis, simulates the movement and interaction of water flow through numerical methods, and generates water flow dynamic simulation results by combining the dynamic changes of water flow under various hydrological and meteorological conditions;

[0034] The behavior prediction submodule uses predictive analysis technology to perform long-term predictions of water flow behavior based on the results of water flow dynamic simulation. It combines historical data and model outputs, analyzes trends through statistical methods, predicts future changes in flow rate and direction, and generates water flow behavior predictions.

[0035] The present invention is improved in that the geological change identification module includes a ground monitoring submodule, a change analysis submodule, and a geological assessment submodule.

[0036] The ground monitoring submodule is based on topographic and historical geological data and uses remote sensing image processing technology to perform preliminary capture of geological features, including enhancement and feature extraction of remote sensing images, identification of basic morphology and changes of the surface, and generation of preliminary records of geological features;

[0037] The change analysis submodule uses digital image correlation analysis based on the preliminary record of geological features to conduct comparative analysis on the changes in surface morphology, identify key geological change points by tracking the time series changes of geological features, and generate geological change trend analysis results;

[0038] The geological assessment submodule conducts an in-depth assessment of the captured geological changes based on the geological change trend analysis results and combines geological analysis methods to determine their impact on the surrounding areas of the barrier lake and generate geological change detection results.

[0039] The present invention is improved in that the risk assessment and response module includes a risk analysis submodule, a strategy planning submodule, and a response coordination submodule.

[0040] The risk analysis submodule uses a multi-factor risk assessment model to integrate and analyze data based on control operation strategies, structural safety assessment records, water flow behavior predictions, and geological change detection results, assess the impact and interaction of multiple risk factors, and generate a comprehensive risk analysis record;

[0041] The strategy planning submodule applies the emergency response planning algorithm based on the comprehensive risk analysis records to formulate response strategies for various risk situations, including preventive measures and emergency response plans, and generates risk response strategy plans;

[0042] The response coordination submodule optimizes and coordinates the predetermined emergency response measures based on the risk response strategy plan, adopts decision tree analysis and resource optimization allocation algorithm, evaluates the effects and results of differential response strategies, configures and utilizes key resources and personnel, and generates a comprehensive risk management plan.

[0043] The real-time video monitoring method for emergency disposal of a barrier lake is implemented based on the real-time video monitoring system for emergency disposal of a barrier lake, and comprises the following steps:

[0044] S1: Based on the video data collected by the camera, the target detection algorithm is used to analyze the video frames, and the moving objects are located and tracked through feature extraction and pattern recognition technology. The long short-term memory network is used to process the time series data, and the behavior trends and event developments are analyzed. The statistical characteristics and trends of pixel changes are analyzed through statistical methods, and dynamic changes and abnormal behaviors are identified to generate video dynamic analysis results.

[0045] S2: Based on the video dynamic analysis results, the dam surface is analyzed using image texture analysis and Sobel algorithm, texture features are extracted through gray-level co-occurrence matrix analysis, changes are identified, digital image correlation analysis and three-dimensional reconstruction technology are applied to construct a three-dimensional model of the dam surface, surface deformation is analyzed, crack change trends are tracked through crack detection algorithms, and dam stability indicators are generated;

[0046] S3: Based on the dam stability index and historical hydrological data, a multi-objective optimization algorithm is used in combination with an energy demand forecasting model to match the power station energy output with demand, a hydraulic simulation model is used to simulate changes in reservoir water level, flow rate and pressure, and a real-time feedback control strategy is used to adjust operating parameters and generate a control operation strategy;

[0047] S4: Based on the dynamic analysis results of the video, the elastic mechanics theory and finite element analysis method are used to perform mechanical modeling and stress-strain state analysis on the hydraulic structure, and the fluid dynamics theory and computational fluid dynamics model are used to analyze the water flow behavior, including flow velocity and flow direction, to evaluate the impact on the structure, and generate structural safety assessment records and water flow characteristics analysis conclusions;

[0048] S5: Based on topographic and historical geological data, remote sensing image processing technology is used to capture geological features, and digital image correlation analysis is used to identify geological change trends. Combined with geological analysis methods, the captured geological changes are deeply evaluated to determine their impact on the surrounding areas of the barrier lake and generate geological change detection results;

[0049] S6: Based on the control operation strategy, structural safety assessment records, water flow characteristics analysis conclusions and geological change detection results, use multi-factor risk assessment models and data-driven decision analysis methods to conduct risk assessment and emergency strategy formulation to generate a comprehensive risk management plan.

[0050] Compared with the prior art, the advantages and positive effects of the present invention are:

[0051] In the present invention, accurate monitoring and analysis of the environment and safety status of the barrier lake are achieved by integrating advanced technologies. Technical means such as convolutional neural networks, machine vision algorithms, dynamic programming, structural mechanics models, computational fluid dynamics models, etc. are applied to improve the ability to capture dynamic events and the accuracy of predicting potential risks. This not only improves the real-time and accuracy of monitoring, but also enhances the efficiency and effectiveness of emergency response, significantly improves the comprehensiveness of barrier lake monitoring, the timeliness of early warning and the scientific nature of emergency response, and provides strong support for disaster prevention and mitigation. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a module diagram of a real-time video monitoring system for emergency disposal of a barrier lake proposed by the present invention;

[0053] Figure 2 A schematic diagram of a video analysis module in a real-time video monitoring system for emergency disposal of a barrier lake proposed by the present invention;

[0054] Figure 3 A schematic diagram of a dam monitoring module in a real-time video monitoring system for emergency disposal of a barrier lake proposed by the present invention;

[0055] Figure 4 A schematic diagram of a hydropower station control module in a real-time video monitoring system for emergency disposal of a barrier lake proposed by the present invention;

[0056] Figure 5 A schematic diagram of a hydraulic structure analysis module in a real-time video monitoring system for emergency disposal of a barrier lake proposed by the present invention;

[0057] Figure 6 A schematic diagram of a water flow simulation module in a real-time video monitoring system for emergency disposal of a barrier lake proposed by the present invention;

[0058] Figure 7 A schematic diagram of a geological change identification module in a real-time video monitoring system for emergency disposal of a barrier lake proposed by the present invention;

[0059] Figure 8 A schematic diagram of a risk assessment and response module in a real-time video monitoring system for emergency response to a barrier lake proposed by the present invention;

[0060] Fig. 9The present invention provides a flow chart of a real-time video monitoring method for emergency disposal of a barrier lake. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0062] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating positions or positional relationships, are based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0063] Example

[0064] See also Figures 1 to 8 , the present invention provides a technical solution: a real-time video monitoring system for emergency disposal of a barrier lake, the system includes a video analysis module, a dam monitoring module, a hydropower station control module, a hydraulic structure analysis module, a water flow simulation module, a geological change identification module, and a risk assessment and response module;

[0065] The video analysis module uses convolutional neural networks to analyze dynamic scenes based on video data collected by cameras, combines time series analysis methods to process video streams, capture dynamic features and analyze real-time content, and uses change detection algorithms to identify dynamic changes and generate video dynamic analysis results.

[0066] The dam monitoring module uses machine vision algorithms combined with edge detection and image segmentation technology to scan the dam surface based on the video dynamic analysis results, analyze changes and potential cracks, track crack development through crack tracking algorithms, and generate dam stability indicators;

[0067] The hydropower station control module uses dynamic programming and hydraulic models to adjust the hydropower station operation parameters based on dam stability indicators and historical hydrological data, balances power station operation and reservoir safety through real-time feedback control algorithms, and generates control operation strategies;

[0068] The hydraulic structure analysis module uses the structural mechanics model and finite element analysis method to deeply analyze the stress condition of the hydraulic structure based on the video dynamic analysis results, and uses the risk assessment algorithm to assess potential safety hazards and generate structural safety assessment records;

[0069] The water flow simulation module uses computational fluid dynamics models to simulate reservoir water flow behavior based on video dynamic analysis results and topographic data, predicts flow velocity and direction through fluid dynamics analysis, and generates water flow behavior predictions;

[0070] The geological change identification module uses digital image correlation analysis and geological analysis methods based on topographic and historical geological data to identify geological changes in the area around the barrier lake, and combines geological change detection algorithms to generate geological change detection results;

[0071] The risk assessment and response module uses a multi-factor risk assessment model and emergency response planning algorithm to conduct comprehensive risk assessment and emergency strategy formulation based on control operation strategies, structural safety assessment records, water flow behavior predictions and geological change detection results, and generate a comprehensive risk management plan.

[0072] The results of video dynamic analysis specifically include the identification and classification of key dynamic events, including the tracking of dynamic objects, the detection of abnormal activities, and the recording of environmental changes. The dam stability index specifically refers to the deformation measurement of the dam surface, the quantification of crack width and length, and the analysis of crack development trends. The control operation strategy includes the water regulation plan of the hydropower station, the power generation optimization plan and the emergency response measures. The structural safety assessment record includes the evaluation of structural integrity, the identification of potential risk areas, and the classification of safety levels. The water flow behavior prediction specifically includes the prediction of water flow speed and direction, the assessment of the impact of water flow on the dam body, and the prediction of potential flooding areas. The geological change detection results include the assessment of geological stability, the identification of potential landslide and collapse areas, and the analysis of geological change trends. The comprehensive risk management plan specifically includes the classification of risk levels, the recommendation of preventive measures, and the emergency response plan.

[0073] In the video analysis module, a convolutional neural network (CNN) is used to perform dynamic scene analysis on video data collected by the camera. Specifically, the CNN structure extracts spatial features in the video through multi-level convolutional layers and pooling layers, and then uses a fully connected layer to merge and classify features. Combined with time series analysis, such as long short-term memory networks (LSTM), the temporal relationships in the video stream are processed to capture dynamic features. Change detection algorithms, such as background subtraction, are used to identify dynamic changes in the video, such as object movement or environmental changes. Through these steps, the module not only captures and parses real-time content, but also generates recognition and classification results for key dynamic events, such as dynamic object tracking, abnormal activity detection, and environmental change recording.

[0074] In the dam monitoring module, the dam surface is scanned based on the video dynamic analysis results, combined with machine vision algorithms, edge detection and image segmentation technology. Edge detection algorithms, such as the Canny algorithm, are used to identify the edges and contours of the dam surface, while image segmentation technologies, such as threshold segmentation, are used to distinguish different areas of the dam. The crack tracking algorithm further tracks the development of cracks, and through quantitative analysis of crack width and length, generates dam stability indicators, including deformation measurement of the dam surface and crack development trend analysis, providing an important basis for the maintenance and repair of the dam.

[0075] In the hydropower station control module, dynamic programming and hydraulic models are used to adjust the hydropower station operation parameters based on dam stability indicators and historical hydrological data. Dynamic programming is used to optimize long-term operation strategies to ensure maximum water resource utilization while ensuring power station operation. Hydraulic models, such as the Navier-Stokes equations, are used to simulate and predict water flow behavior to ensure safe management of reservoir water level and flow. The real-time feedback control algorithm adjusts the operating parameters based on the model output, balances power station operation and reservoir safety, and generates control operation strategies, including water volume regulation plans and power generation optimization plans.

[0076] In the hydraulic structure analysis module, based on the video dynamic analysis results, the structural mechanics model and finite element analysis method are used to deeply analyze the stress condition of the hydraulic structure. The structural mechanics model provides basic mechanical calculations, such as stress and strain analysis, while finite element analysis is used for more complex structural behavior simulations, such as local stress concentration and vibration analysis. The risk assessment algorithm further evaluates potential safety hazards, generates structural safety assessment records through structural integrity evaluation, potential risk area identification and safety level classification, and provides a scientific basis for the maintenance and improvement of hydraulic structures.

[0077] In the water flow simulation module, computational fluid dynamics (CFD) models are used to simulate reservoir water flow behavior based on video dynamic analysis results and topographic data. The CFD model predicts water flow velocity, direction, and pressure distribution by solving a set of fluid dynamics equations, such as the Navier-Stokes equations. Combined with topographic data, the model can accurately predict the impact of water flow on the dam body and potential flooding areas, generate water flow behavior forecasts, and provide important references for reservoir operation and emergency preparedness.

[0078] In the geological change identification module, digital image correlation analysis and geological analysis methods are used to identify geological changes in the area around the barrier lake, combining topographic and historical geological data. Digital image correlation analysis identifies topographic changes by comparing topographic images of different periods, while geological analysis deeply studies the changes in stratum structure and rock properties. The geological change detection algorithm integrates these analysis results, evaluates geological stability, identifies potential landslides and collapse areas, and generates geological change detection results, providing a scientific basis for geological disaster prevention and response.

[0079] In the risk assessment and response module, comprehensive risk assessment is conducted using a multi-factor risk assessment model and an emergency response planning algorithm, combining control operation strategies, structural safety assessment records, water flow behavior predictions, and geological change detection results. The multi-factor risk assessment model combines various data and prediction results to assess the overall risk level of the barrier lake, while the emergency response planning algorithm formulates response strategies based on the risk assessment results, including preventive measures and emergency response plans, and generates a comprehensive risk management plan to provide guidance for the safety management and emergency response of the barrier lake.

[0080] The video analysis module includes a dynamic capture submodule, a real-time analysis submodule, and a change detection submodule.

[0081] The dynamic capture submodule uses the target detection algorithm to analyze the video frames based on the video data collected by the camera, locates and tracks the moving objects through feature extraction and pattern recognition technology, and uses the optical flow method to analyze the motion relationship between pixels, identify the object's motion trajectory, and generate dynamic feature information;

[0082] The process of locating and tracking moving objects through feature extraction and pattern recognition technology has the following relationship:

[0083] ;

[0084] in, Indicates at time t The set of targets detected in the image, represents the image frame at time t, Representing images The number of regions divided above, Representing images No. n Regions, Indicates area The feature vector extracted from represents the confidence of the output of the classifier applied to the feature vector, Represents an indicator function, which is 1 when the internal condition is met, otherwise it is 0; Represents the classification threshold. When the confidence exceeds the classification threshold, the target is considered to be detected;

[0085] The optical flow method is used to analyze the motion relationship between pixels and identify the motion trajectory of objects. The corresponding process has the following relationship:

[0086] ;

[0087] in, Indicates location The optical flow vector of Indicates the coordinate position, represents the number of surrounding pixels, Relative to the coordinates The specific location of the surrounding pixels, represents the partial derivative, Indicates the image frame at coordinates The gradient in dimension, represents the gradient of the image frame in the time dimension, Indicates a time interval;

[0088] The real-time analysis submodule uses long short-term memory network to process time series data based on dynamic feature information. It analyzes the behavior trend and event development of objects by learning the temporal pattern of object movement, conducts in-depth analysis and event prediction of dynamic scenes, and generates event analysis results. The corresponding process has the following relationship:

[0089] ;

[0090] in, Indicates time t The LSTM unit output is denote the weight matrices of the input layer and the hidden layer respectively, represents the bias vector, represents the activation function;

[0091] Based on the event analysis results, the change detection submodule uses the differential image method to compare the continuous video frames at the pixel level, analyzes the statistical characteristics and trends of pixel changes through statistical methods, identifies dynamic changes and abnormal behaviors, and generates video dynamic analysis results. The corresponding process has the following relationship:

[0092] ;

[0093] in, Indicates time t and t −1 difference image, represents the number of pixels in the image, Indicates at time t The image of p pixel value;

[0094] In the dynamic capture submodule, the target detection algorithm is executed to analyze the video frames through the video data format collected by the camera. The target detection algorithm, such as YOLO or SSD, first divides the video frame into multiple regions and predicts the existence of objects and their bounding boxes in each region. Through feature extraction techniques, such as HOG (Histogram of Oriented Gradients) or CNN (Convolutional Neural Network), the algorithm extracts the shape, texture and other features of the object from each region, and then classifies and identifies these features in combination with pattern recognition technology. Next, the optical flow method, a motion estimation technology based on pixel changes, is used to analyze and calculate the movement of each pixel over time, thereby identifying and tracking the movement trajectory of the object. In this process, the algorithm generates the movement trajectory information of the object by calculating the displacement of pixels between adjacent frames. Finally, the dynamic capture submodule generates dynamic feature information, including the position, movement trajectory and speed of the object, providing basic data for subsequent in-depth analysis.

[0095] In the real-time analysis submodule, the long short-term memory network (LSTM) is used to process time series data through dynamic feature information. LSTM learns the temporal pattern of object movement through its unique gating mechanism, which can effectively handle the dependency problem in long time series. Specifically, the LSTM unit controls the inflow, retention and outflow of information through the input gate, forget gate and output gate, so as to learn the movement pattern and behavior trend of the object. By performing time series analysis on the dynamic features extracted from continuous frames, LSTM can identify and predict the changing trend of object behavior, such as changes in walking paths or the occurrence of abnormal behavior. This submodule finally generates event analysis results, including the classification of object behavior and the prediction of future development, providing accurate pre-information for change detection.

[0096] In the change detection submodule, the differential image method is used to compare consecutive video frames at the pixel level based on the event analysis results. The differential image method captures the changes in the picture by calculating the differences in pixels at the same position between consecutive frames. Statistical methods, such as grayscale histogram analysis, further perform statistical feature and trend analysis on these pixel-level changes to identify dynamic changes and abnormal behaviors in the scene. This method can effectively detect rapidly changing scenes, such as sudden objects or environmental changes. Through this series of processes, the change detection submodule generates video dynamic analysis results, which include the identification and classification of key dynamic events and the detection of abnormal activities, providing important security guarantees for video surveillance systems.

[0097] The dam monitoring module includes surface monitoring submodule, deformation analysis submodule and crack tracking submodule.

[0098] The surface monitoring submodule uses image texture analysis based on the video dynamic analysis results, extracts texture features in the image through gray-level co-occurrence matrix analysis, uses the Sobel algorithm to identify image edges, locates changes on the dam surface, and generates surface feature analysis records. The process of extracting texture features in the image through gray-level co-occurrence matrix analysis has the following relationship:

[0099] ;

[0100] in, Representing images I The texture feature vector of represents the elements in the gray-level co-occurrence matrix, Indicates the number of gray levels;

[0101] The Sobel algorithm is used to identify the edge of the image and locate the changes on the dam surface. The corresponding process has the following relationship:

[0102] ;

[0103] in, Represents the image edge information extracted using the Sobel algorithm;

[0104] The process of generating surface feature analysis records corresponds to the following relationship:

[0105] ;

[0106] in, Represents a surface feature analysis record generated by combining texture features and edge information;

[0107] The deformation analysis submodule is based on surface feature analysis records and applies digital image correlation analysis. By comparing the pixel changes in the same area in the continuous image sequence, combined with 3D reconstruction technology, the 3D model of the dam surface is constructed using stereo vision principles and deep learning methods to analyze surface deformation and generate deformation risk assessment results.

[0108] The method of applying digital image correlation analysis, comparing the pixel changes in the same area in the continuous image sequence, combining 3D reconstruction technology, using stereo vision principles and deep learning methods to build a 3D model of the dam surface, analyzing the surface deformation, and generating deformation risk assessment results specifically includes the following steps:

[0109] Extract two consecutive images from the surface feature analysis record to calculate the displacement field between the images. The corresponding process has the following relationship:

[0110] ;

[0111] in, Represents two consecutive images, represents the displacement distance, Indicates displacement The correlation coefficient at Indicates that the first image is at position The gray value at Indicates that the second image is at position The gray value at Represent the average grayscale values ​​of the first image and the second image respectively, Respectively represent the width and height of the image;

[0112] The DIC calculation of the displacement field is performed to obtain the optimal displacement. The corresponding process has the following relationship:

[0113] ;

[0114] in, represents the optimal displacement;

[0115] The optimal displacement is used to calculate the depth value for 3D reconstruction. The corresponding process has the following relationship:

[0116] ;

[0117] in, Indicates location The depth value at represents the focal length of the camera, represents the baseline distance of the binocular camera, represents a tiny constant that prevents division by zero;

[0118] Based on the deformation risk assessment results, the crack tracking submodule uses a crack detection algorithm and image segmentation technology to identify the location and shape of cracks, conduct time series analysis, track the change trend of cracks over time, evaluate crack development, and generate dam stability indicators.

[0119] Based on the deformation risk assessment results, the crack detection algorithm is used to identify the crack location and shape through image segmentation technology, conduct time series analysis, track the change trend of cracks over time, evaluate the development of cracks, and generate the dam stability index. The specific steps include:

[0120] The crack position and shape are identified by image segmentation technology. The crack detection process corresponding to image segmentation has the following relationship:

[0121] ;

[0122] in, Indicates location Is it a crack? represents the crack threshold;

[0123] To calculate the crack width, the corresponding process has the following relationship:

[0124] ;

[0125] in, Indicates time t The average width of the cracks, represents the number of sample points of cracks, Indicates time t Time a The width of the sample points;

[0126] Calculate the crack length, the corresponding process has the following relationship:

[0127] ;

[0128] in, Indicates time t The total length of the crack, represents the spacing of each sample point, Indicates time t Time a Whether the sample point belongs to a crack;

[0129] The convolutional neural network is used to extract features from the three-dimensional model and obtain the feature map. The corresponding process has the following relationship:

[0130] ;

[0131] in, represents the feature map, represents the rectified linear unit, represents the convolution operation, represents the convolution kernel weight, Represents the bias of the convolutional neural network;

[0132] Taking the feature map as input, RNN is used to predict the crack shape time series. The corresponding process has the following relationship:

[0133] ;

[0134] in, It shows the change trend of cracks over time. represents the bias vector, The weight matrix representing the input features to the hidden state;

[0135] The location, shape, width, length of the cracks and their changing trends over time constitute the dam stability index, and the corresponding process has the following relationship:

[0136] ;

[0137] in, It represents the stability index of the dam.

[0138] In the surface monitoring submodule, image texture analysis technology is used to process the dam surface image based on the video dynamic analysis results. This process first involves analyzing the texture features in the image using the gray level co-occurrence matrix (GLCM). GLCM depicts texture characteristics such as contrast, uniformity, and entropy by calculating the relative frequency between pixels in the image and their neighboring pixels, thereby highlighting the texture pattern of the image. Subsequently, the Sobel algorithm is used for edge detection, which identifies the edge position in the image by calculating the spatial gradient of pixel brightness. This combination not only locates changes on the dam surface, such as fine cracks or depressions, but also generates surface feature analysis records that describe the state of the dam surface in detail, providing key data for subsequent deformation analysis and crack monitoring.

[0139] In the deformation analysis submodule, based on the surface feature analysis records, digital image correlation analysis technology is applied to analyze the deformation of the dam surface. This technology identifies surface changes by comparing pixel changes in the same area in a continuous image sequence. In addition, a three-dimensional model of the dam surface is constructed by combining three-dimensional reconstruction technology, especially stereo vision principles and deep learning methods. During the three-dimensional reconstruction process, the algorithm calculates the depth information of each pixel by analyzing multi-angle images to generate an accurate three-dimensional representation of the dam surface. Such analysis not only accurately depicts the surface deformation of the dam, such as bulges or collapses, but also generates deformation risk assessment results, which are crucial for preventing dam damage and guiding maintenance work.

[0140] In the crack tracking submodule, crack detection algorithms are used to monitor cracks in the dam body based on the deformation risk assessment results. The algorithm uses image segmentation techniques, such as threshold segmentation or region growing, to identify the location and shape of cracks in the image. A time series analysis is then performed to track the trend of cracks over time, such as the increase or decrease in crack width and length. Through this continuous monitoring, the crack tracking submodule can accurately assess the development of cracks and generate dam stability indicators. These indicators not only provide detailed information on cracks, but also provide key data for predicting and preventing potential structural failures of the dam body.

[0141] The hydropower station control module includes energy optimization submodule, safety adjustment submodule, and strategy implementation submodule.

[0142] The energy optimization submodule uses a multi-objective optimization algorithm based on dam stability indicators and historical hydrological data, combines power output and water resource utilization, uses an energy demand forecasting model, predicts power demand based on historical data and trend analysis, matches power station energy output and demand, and generates an energy optimization strategy. The corresponding process has the following relationship:

[0143] ;

[0144] in, Indicates at time t The dam stability index is represents the power generation vector of the power station, Indicates The power generation of a power station, Indicates at time t Historical hydrological data, They represent the weight parameters corresponding to the dam stability index and historical hydrological data, Indicates at time t Energy optimization strategies, Indicates The weight of a power station, Indicates at time t No. Water usage, represents two different tuning parameters, represents the number of power stations, Indicates the number of water resource use types;

[0145] The safety adjustment submodule is based on the energy optimization strategy and uses the hydraulic simulation model to simulate the changes in reservoir water level, flow rate and pressure. It combines the real-time feedback control strategy to adjust the operation of the sluice gate and generator set in real time according to the monitoring data and generate a safety adjustment plan.

[0146] The strategy implementation submodule is based on the safety adjustment plan and adopts a data-driven decision analysis method. By analyzing and integrating monitoring data, simulation model output and power plant operation history, it identifies key decision points, optimizes power plant operation strategies, and generates control operation strategies.

[0147] In the energy optimization submodule, a multi-objective optimization algorithm is used to formulate an energy optimization strategy based on the dam stability index and historical hydrological data. The algorithm takes the maximization of power output and the efficiency of water resource utilization as the goal, and comprehensively considers the operating cost and environmental impact of the power station. First, the energy demand forecasting model is used to predict future power demand in combination with historical power consumption data and socio-economic development trends. These forecasts are performed through time series analysis, such as the autoregressive moving average model (ARMA) or the seasonally decomposed time series forecasting method (STL). Then, the algorithm calculates the optimal operating parameters of the power station, such as power generation and reservoir water level, based on power demand and available water resources. The multi-objective optimization algorithm, in the process, balances the stability of power supply and the rational use of water resources to generate an energy optimization strategy. This strategy not only ensures that the needs of the power market are met, but also improves the economic benefits and environmental sustainability of the hydropower station.

[0148] In the safety adjustment submodule, a hydraulic simulation model is used to simulate the changes in water level, flow rate and pressure of the reservoir based on the energy optimization strategy. The simulation model accurately simulates the hydrodynamic behavior of the reservoir based on the basic principles of hydraulics, such as the Navier-Stokes equations. Combined with real-time feedback control strategies, such as proportional-integral-derivative (PID) controllers, the operation of the sluice gates and generator sets is adjusted in real time based on monitoring data. This approach can quickly respond to changes in hydrological conditions and ensure the safe operation of the reservoir. Based on real-time monitoring data such as water level and flow rate, the PID controller adjusts the sluice gate opening and the load of the generator set to maintain the safe water level of the reservoir and optimize the power generation efficiency. Finally, this submodule generates a safety adjustment plan to ensure that the hydropower station can effectively prevent reservoir safety risks while ensuring power supply.

[0149] In the strategy implementation submodule, a data-driven decision analysis method is used to optimize the power plant operation strategy based on the safety adjustment plan. This method identifies key decision points, such as the best time for power plant operation and the optimal load distribution, by analyzing and integrating monitoring data, simulation model outputs, and power plant operation history. Data-driven analysis methods, such as cluster analysis or principal component analysis (PCA), help identify patterns and trends in the data and guide the power plant's operation strategy. In this process, the decision analysis method evaluates the benefits and risks of different operation plans to ensure that the implementation of the strategy meets the needs of the energy market and does not threaten the safety of the hydropower station. Finally, this submodule generates a control operation strategy, which provides scientific operation guidance for the hydropower station and ensures the stability of power supply and the safe operation of the hydropower station.

[0150] The hydraulic structure analysis module includes mechanical modeling submodule, risk assessment submodule, and integrity inspection submodule.

[0151] The mechanical modeling submodule uses elastic mechanics theory to model the hydraulic structure based on the video dynamic analysis results, including determining the geometric parameters and material properties of the structure, applying the basic equations of mechanics to simulate the response of the structure under various loads, and generating structural mechanics analysis records;

[0152] The risk assessment submodule uses the finite element analysis method to analyze the stress and strain state of the structure based on the structural mechanics analysis records. By dividing the structure into multiple small elements, the behavior of each element under stress is calculated to evaluate the stability of the entire structure and generate stress and strain assessment results;

[0153] Based on the stress-strain assessment results, the integrity inspection submodule applies structural integrity analysis technology, integrates external environmental factors and historical data of the structure, conducts a comprehensive assessment of the integrity and safety hazards of the structure, and generates a structural safety assessment record.

[0154] In the mechanical modeling submodule, the hydraulic structure is modeled using the theory of elastic mechanics based on the video dynamic analysis results. First, the geometric parameters and material properties of the structure, such as size, shape, and elastic modulus of the material, are determined. Then the basic equations of mechanics, such as stress-strain relationships and elastic theory equations, are applied to simulate the response of the structure under various loads. This includes the analysis of external forces such as gravity, water pressure, and wind loads. By establishing a mathematical model, the response of the structure under these loads is calculated, and its displacement, stress, and strain under different conditions are analyzed. The generated structural mechanics analysis record describes in detail the behavior of the structure under various loads, such as deformation and stress distribution, which is essential for evaluating the stability of the structure and improving its design.

[0155] In the risk assessment submodule, the stress-strain state of the structure is analyzed using the finite element analysis method based on the structural mechanics analysis record. This method calculates the behavior of these elements under stress by dividing the structure into multiple small elements and assuming that each element is continuous and uniform. Finite element software, such as ANSYS or ABAQUS, is used for this process, providing an accurate simulation of complex structures. In each element, stress and strain are calculated according to physical equations and transferred throughout the structure. In this way, the stability of the entire structure can be evaluated, and possible weaknesses and areas of damage risk can be identified. The generated stress-strain assessment results reveal the risks and problems that the structure may face, providing a key basis for the maintenance and reinforcement of the structure.

[0156] In the integrity inspection submodule, based on the stress-strain assessment results, the structural integrity analysis technology is applied to conduct a comprehensive assessment of the integrity of the structure. This includes comprehensive consideration of external environmental factors such as temperature, humidity, chemical corrosion and historical data of the structure, such as previous maintenance records and inspection results. Structural integrity analysis is performed through techniques such as crack detection, corrosion assessment and material aging analysis. During this process, various parts of the structure are carefully examined to identify potential safety hazards and weaknesses. The generated structural safety assessment record includes a detailed evaluation of the structural integrity and a prediction of potential future risks, which is essential to ensure the long-term safe operation of the structure and plan maintenance work.

[0157] The water flow simulation module includes flow velocity analysis submodule, simulation modeling submodule, and behavior prediction submodule.

[0158] The velocity analysis submodule uses fluid dynamics theory to simulate reservoir water flow behavior based on video dynamic analysis results and topographic data, analyzes water flow characteristics including flow velocity and flow direction, and predicts water flow behavior by calculating the relationship between flow velocity and pressure field based on the basic physical laws of water flow, generating water flow characteristic analysis conclusions;

[0159] The simulation modeling submodule uses computational fluid dynamics models to simulate water flow based on the conclusions of water flow characteristics analysis. It simulates the movement and interaction of water flow through numerical methods, combines the dynamic changes of water flow under various hydrological and meteorological conditions, and generates water flow dynamic simulation results.

[0160] The behavior prediction submodule uses predictive analysis technology to make long-term predictions of water flow behavior based on the results of water flow dynamic simulation. It combines historical data and model outputs, analyzes trends through statistical methods, predicts future changes in flow rate and direction, and generates water flow behavior predictions.

[0161] In the flow rate analysis submodule, fluid dynamics theory is used to simulate the behavior of reservoir water flow through video dynamic analysis results and topographic data. In this process, video data provides visual information of water flow, while topographic data depicts the geographical features of the bottom and surrounding of the reservoir. Based on these data, fluid dynamics models, such as the Navier-Stokes equations, are used to calculate the velocity and pressure field of water flow. These equations describe the changes in fluid particle velocity and force conditions, and can accurately predict the behavior of water flow under different conditions. For example, the model can calculate the flow rate and direction at a specific water level and sluice opening. In this way, the submodule generates conclusions of water flow characteristics analysis, which describe in detail the velocity, direction, and possible vortex or turbulent areas of water flow in the reservoir, providing important information for reservoir management and risk prevention.

[0162] In the simulation modeling submodule, based on the conclusions of the water flow characteristics analysis, a computational fluid dynamics (CFD) model is used to simulate the water flow. The CFD model simulates the movement and interaction of water flows through numerical methods such as the finite difference method or the finite volume method. The model takes into account a variety of hydrological and meteorological conditions, such as rainfall, evaporation rate and temperature changes, and their impact on water flow behavior. During the simulation process, the reservoir is divided into multiple calculation units, and the water flow behavior of each unit is calculated according to the physical equations and interacts with the behavior of the adjacent units. The generated water flow dynamic simulation results reveal the dynamic changes of water flow under different conditions, such as the increase in flow velocity and change in flow direction during floods, providing a scientific basis for emergency management and flood control planning of reservoirs.

[0163] In the behavior prediction submodule, based on the results of water flow dynamic simulation, the prediction analysis technology is used to make long-term predictions of water flow behavior. By combining historical data and model output, statistical methods such as regression analysis or time series analysis are used to analyze water flow trends. For example, the flow velocity data of the past few years are analyzed, combined with climate change trends, to predict future changes in flow velocity and direction. This approach takes into account the impact of seasonal changes, long-term climate trends, and extreme weather events. The generated water flow behavior forecast provides valuable information for the long-term planning and risk management of reservoirs, such as predicting possible future flood events and water level changes, thereby guiding reservoir managers to take appropriate measures.

[0164] The geological change identification module includes ground monitoring submodule, change analysis submodule and geological assessment submodule.

[0165] The ground monitoring submodule uses remote sensing image processing technology to capture the initial geological features based on topographic and historical geological data, including enhancing and extracting features from remote sensing images, identifying the basic morphology and changes of the surface, and generating preliminary records of geological features.

[0166] The change analysis submodule uses digital image correlation analysis based on the preliminary records of geological features to conduct comparative analysis of changes in surface morphology, identify key geological change points by tracking the time series changes of geological features, and generate geological change trend analysis results;

[0167] The geological assessment submodule conducts an in-depth assessment of the captured geological changes based on the results of geological change trend analysis and combines geological analysis methods to determine its impact on the areas surrounding the barrier lake and generate geological change detection results.

[0168] In the ground monitoring submodule, the geological features are initially captured by integrating topographic and historical geological data using remote sensing image processing technology. Specifically, high-resolution satellite images and historical geological maps are used as data input, and image enhancement algorithms (such as histogram equalization and sharpening) are used to improve image quality and increase the recognizability of geological features. Next, feature extraction algorithms (such as Sobel edge detection or Gaussian mixture models) are used to identify basic surface morphology and changes. In this process, feature extraction algorithms refine surface lines and textures, which helps to capture geological structures more accurately. Finally, the generated preliminary record of geological features includes surface morphology maps and change indicator reports, providing basic data for subsequent analysis.

[0169] In the change analysis submodule, the digital image correlation analysis method is used to compare and analyze the changes in surface morphology. This process first uses image registration technology (such as feature-based registration algorithm) based on the preliminary record of geological features to ensure that images at different time points are spatially aligned. Subsequently, correlation analysis (such as mutual information algorithm) is applied to compare and analyze the registered images to capture subtle changes in surface morphology. Through time series analysis (using dynamic time warping algorithm), key geological change points are tracked. This process can accurately identify geological change trends, and the generated geological change trend analysis results provide a scientific basis for geological assessment.

[0170] In the geological assessment submodule, based on the results of geological change trend analysis, the captured geological changes are deeply evaluated in combination with geological analysis methods. This process uses statistical analysis and model simulation techniques (such as Monte Carlo simulation) to evaluate the potential impact of geological changes on the surrounding areas of the barrier lake. By integrating model simulation and historical geological event data, the assessment submodule is able to generate detailed geological change detection results. These results include not only the probabilistic assessment of geological changes, but also the impact prediction of different geological change scenarios, providing important support for geological disaster prevention and control.

[0171] The risk assessment and response module includes risk analysis submodule, strategy planning submodule, and response coordination submodule.

[0172] The risk analysis submodule uses a multi-factor risk assessment model to integrate and analyze data based on control operation strategies, structural safety assessment records, water flow behavior predictions, and geological change detection results, evaluates the impact and interaction of multiple risk factors, and generates a comprehensive risk analysis record;

[0173] The strategy planning submodule applies the emergency response planning algorithm based on the comprehensive risk analysis records to formulate response strategies for various risk situations, including preventive measures and emergency response plans, and generate risk response strategy plans;

[0174] The response coordination submodule is based on the risk response strategy plan, and uses decision tree analysis and resource optimization allocation algorithm to optimize and coordinate the predetermined emergency response measures, evaluate the effectiveness and results of differential response strategies, configure and utilize key resources and personnel, and generate a comprehensive risk management plan.

[0175] In the risk analysis submodule, a multi-factor risk assessment model is used to integrate and analyze data from control operation strategies, structural safety assessment records, water flow behavior predictions, and geological change detection results. The model first converts various types of data into a unified numerical format, for example, converting text records into numerical indicators, and standardizing water flow predictions and geological change data into risk scores. Then, the Bayesian network or decision tree method is used to evaluate and analyze the interdependence and influence between these data to reveal potential risk associations. Through this process, the model can identify key risk factors and predict their impact on system safety. The generated comprehensive risk analysis record describes in detail the impact and interaction of each risk factor, providing a scientific basis for subsequent strategy planning.

[0176] In the strategy planning submodule, based on the comprehensive risk analysis records, the emergency response planning algorithm is applied to formulate response strategies for various risk situations. In this process, the algorithm combines the risk prediction model and the historical case library, and selects the most effective preventive measures and emergency response plans through heuristic search and optimization techniques (such as genetic algorithms). This process includes simulation and effect evaluation of response strategies under different risk scenarios to ensure the practicality and effectiveness of the strategies. The final risk response strategy plan lists in detail the specific operational steps and resource allocation suggestions for various risk situations, providing a detailed guide for actual emergency response.

[0177] In the response coordination submodule, decision tree analysis and resource optimization allocation algorithms are used to optimize and coordinate response measures based on risk response strategy solutions. Through decision tree analysis, the possible results of different emergency response solutions are evaluated to identify the optimal response path. Resource optimization allocation algorithms (such as linear programming) are used to effectively allocate key resources and personnel to ensure that resources are optimally utilized in emergency situations. This process not only optimizes the efficiency and effectiveness of emergency response, but also evaluates the actual application effects of various solutions by simulating different response strategies. The final comprehensive risk management plan details the response coordination strategy and resource allocation under different risk situations, providing important support for achieving efficient risk management and emergency response.

[0178] See also Fig. 9 The video real-time monitoring method for emergency disposal of barrier lake includes the following steps:

[0179] S1: Based on the video data collected by the camera, the target detection algorithm is used to analyze the video frames, and the moving objects are located and tracked through feature extraction and pattern recognition technology. The long short-term memory network is used to process the time series data, and the behavior trends and event developments are analyzed. The statistical characteristics and trends of pixel changes are analyzed through statistical methods, and dynamic changes and abnormal behaviors are identified to generate video dynamic analysis results.

[0180] S2: Based on the video dynamic analysis results, the dam surface is analyzed using image texture analysis and Sobel algorithm. Texture features are extracted through gray-level co-occurrence matrix analysis to identify changes. Digital image correlation analysis and three-dimensional reconstruction technology are used to construct a three-dimensional model of the dam surface, analyze surface deformation, track crack change trends through crack detection algorithms, and generate dam stability indicators.

[0181] S3: Based on the dam stability index and historical hydrological data, a multi-objective optimization algorithm is used in combination with an energy demand forecasting model to match the power station energy output with demand. A hydraulic simulation model is used to simulate the changes in reservoir water level, flow rate and pressure. The operation parameters are adjusted in combination with a real-time feedback control strategy to generate a control operation strategy.

[0182] S4: Based on the video dynamic analysis results, the elastic mechanics theory and finite element analysis method are used to carry out mechanical modeling and stress-strain state analysis of the hydraulic structure. The fluid dynamics theory and computational fluid dynamics model are used to analyze the water flow behavior, including flow velocity and direction, to evaluate the impact on the structure, and generate structural safety assessment records and water flow characteristics analysis conclusions;

[0183] S5: Based on topographic and historical geological data, remote sensing image processing technology is used to capture geological features, and digital image correlation analysis is used to identify geological change trends. Combined with geological analysis methods, the captured geological changes are deeply evaluated to determine their impact on the surrounding areas of the barrier lake and generate geological change detection results;

[0184] S6: Based on the control operation strategy, structural safety assessment records, water flow characteristics analysis conclusions and geological change detection results, use multi-factor risk assessment models and data-driven decision analysis methods to conduct risk assessment and emergency strategy formulation to generate a comprehensive risk management plan.

[0185] In step S1, the video data is deeply analyzed through the target detection algorithm and long short-term memory network, which realizes the real-time monitoring and early warning of dynamic changes in the barrier lake area. This enables managers to detect and respond to potential abnormal situations, such as landslides or sudden changes in water flow, in a timely manner, greatly reducing the risk of disasters.

[0186] In step S2, the surface of the dam is carefully inspected using image texture analysis and 3D reconstruction technology, which can effectively identify and track cracks and other structural changes. This provides important information for the maintenance and repair of the dam, and helps prevent disasters caused by structural failure.

[0187] In step S3, the multi-objective optimization algorithm is combined with the application of the energy demand forecasting model to optimize the matching of the power plant's energy output and demand. In this way, the effective supply of energy is guaranteed and the risks caused by improper operation are reduced.

[0188] Step S4 provides a comprehensive mechanical status assessment for hydraulic structures through the application of elastic mechanics theory and finite element analysis. This helps to timely identify potential structural problems, prevent possible structural damage, and ensure the safety and stability of the structure.

[0189] In step S5, the combination of remote sensing technology and geological analysis methods has enabled us to gain a deeper understanding of the geological changes in the areas surrounding the barrier lake, improve the early warning capabilities for geological disasters, and reduce the impact of natural disasters on the environment and human activities.

[0190] In step S6, the application of multi-factor risk assessment models and data-driven decision analysis methods provides a scientific basis for the formulation of comprehensive risk management plans. This not only improves the efficiency of responding to emergencies, but also ensures that more accurate and effective decisions can be made in emergency situations.

[0191] In summary, this real-time video monitoring method can comprehensively enhance the emergency response capabilities of barrier lakes, effectively prevent and mitigate potential disasters, ensure regional safety, and optimize resource utilization and operational management.

[0192] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A real-time video monitoring system for emergency disposal of barrier lake, characterized in that: The system includes a video analysis module, a dam monitoring module, a hydropower station control module, a hydraulic structure analysis module, a water flow simulation module, a geological change identification module, and a risk assessment and response module. The video analysis module uses a convolutional neural network to perform dynamic scene analysis based on the video data collected by the camera, combines the time series analysis method to process the video stream, performs dynamic feature capture and real-time content analysis, identifies dynamic changes through a change detection algorithm, and generates video dynamic analysis results; The dam monitoring module uses a machine vision algorithm combined with edge detection and image segmentation technology to scan the dam surface based on the video dynamic analysis results, analyze changes and potential cracks, track crack development through a crack tracking algorithm, and generate a dam stability index; The hydropower station control module uses dynamic programming and hydraulic models to adjust the hydropower station operation parameters based on the dam stability index and historical hydrological data, balances the power station operation and reservoir safety through real-time feedback control algorithms, and generates control operation strategies; The hydraulic structure analysis module uses the structural mechanics model and finite element analysis method to deeply analyze the stress condition of the hydraulic structure based on the video dynamic analysis results, uses the risk assessment algorithm to assess potential safety hazards, and generates a structural safety assessment record; The water flow simulation module simulates the water flow behavior of the reservoir based on the video dynamic analysis results and topographic data using a computational fluid dynamics model, predicts the flow rate and direction through fluid dynamics analysis, and generates a water flow behavior prediction; The geological change identification module is based on topographic and historical geological data, applies digital image correlation analysis and geological analysis methods to identify geological changes in the area surrounding the barrier lake, and combines geological change detection algorithms to generate geological change detection results; The risk assessment and response module uses a multi-factor risk assessment model and emergency response planning algorithm to conduct comprehensive risk assessment and emergency strategy formulation based on control operation strategies, structural safety assessment records, water flow behavior predictions and geological change detection results, and generates a comprehensive risk management plan; The video dynamic analysis results specifically include the identification and classification of key dynamic events, including the tracking of dynamic objects, the detection of abnormal activities, and the recording of environmental changes; the dam body stability index specifically refers to the deformation measurement of the dam body surface, the quantification of crack width and length, and the analysis of crack development trends; the control operation strategy includes the water regulation plan, power generation optimization plan and emergency response measures of the hydropower station; the structural safety assessment record includes the evaluation of structural integrity, the identification of potential risk areas, and the classification of safety levels; the water flow behavior prediction specifically includes the prediction of water flow speed and direction, the evaluation of the impact of water flow on the dam body, and the prediction of potential flooding areas; the geological change detection results include the evaluation of geological stability, the identification of potential landslide and collapse areas, and the analysis of geological change trends; the comprehensive risk management plan specifically includes the classification of risk levels, the recommendation of preventive measures, and the emergency response plan; The video analysis module includes a dynamic capture submodule, a real-time analysis submodule, and a change detection submodule; The dynamic capture submodule uses the target detection algorithm to analyze the video frames based on the video data collected by the camera, locates and tracks the moving objects through feature extraction and pattern recognition technology, and uses the optical flow method to analyze the motion relationship between pixels, identify the object's motion trajectory, and generate dynamic feature information; Among them, the moving object is located and tracked by feature extraction and pattern recognition technology, and the corresponding process has the following relationship: ； in, Indicates at time t The set of targets detected in the image, represents the image frame at time t, Represents an image frame The number of regions divided above, Represents an image frame No. n Regions, Indicates area The feature vector extracted from represents the confidence of the output of the classifier applied to the feature vector, Represents an indicator function, which is 1 when the internal condition is met, otherwise it is 0; Represents the classification threshold. When the confidence exceeds the classification threshold, the target is considered to be detected; The optical flow method is used to analyze the motion relationship between pixels and identify the motion trajectory of objects. The corresponding process has the following relationship: ； in, Indicates location The optical flow vector of Indicates the coordinate position, represents the number of surrounding pixels, Relative to the coordinates The specific location of the surrounding pixels, represents the partial derivative, Indicates the image frame at coordinates The gradient in dimension, represents the gradient of the image frame in the time dimension, Indicates a time interval; The crack tracking submodule uses a crack detection algorithm based on the deformation risk assessment results, identifies the crack location and shape through image segmentation technology, performs time series analysis, tracks the crack change trend over time, evaluates crack development, and generates a dam stability index; Based on the deformation risk assessment results, the crack detection algorithm is used to identify the crack location and shape through image segmentation technology, conduct time series analysis, track the change trend of cracks over time, evaluate the development of cracks, and generate the dam stability index. The specific steps include: The crack position and shape are identified by image segmentation technology. The crack detection process corresponding to image segmentation has the following relationship: ； in, Indicates location Is it a crack? represents the crack threshold; To calculate the crack width, the corresponding process has the following relationship: ； in, Indicates time t The average width of the cracks, represents the number of sample points of cracks, Indicates time t Time a The width of the sample points; Calculate the crack length, the corresponding process has the following relationship: ； in, Indicates time t The total length of the crack, represents the spacing of each sample point, Indicates time t Time a Whether the sample point belongs to a crack; The convolutional neural network is used to extract features from the three-dimensional model and obtain the feature map. The corresponding process has the following relationship: ； in, represents the feature map, represents the rectified linear unit, represents the convolution operation, represents the convolution kernel weight, Represents the bias of the convolutional neural network; Taking the feature map as input, RNN is used to predict the crack shape time series. The corresponding process has the following relationship: ； in, It shows the change trend of cracks over time. represents the bias vector, The weight matrix representing the input features to the hidden state; The location, shape, width, length of the cracks and their changing trends over time constitute the dam stability index, and the corresponding process has the following relationship: ； in, It indicates the stability index of the dam body; The hydropower station control module includes an energy optimization submodule, a safety adjustment submodule, and a strategy implementation submodule. The energy optimization submodule adopts a multi-objective optimization algorithm based on the dam stability index and historical hydrological data, combines power output and water resource utilization, uses an energy demand forecasting model, predicts power demand based on historical data and trend analysis, matches power station energy output and demand, and generates an energy optimization strategy. The corresponding process has the following relationship: ； in, Indicates at time t The dam stability index is represents the power generation vector of the power station, Indicates The power generation of a power station, Indicates at time t Historical hydrological data, They represent the weight parameters corresponding to the dam stability index and historical hydrological data, Indicates at time t Energy optimization strategies, Indicates The weight of a power station, Indicates at time t No. Water usage, represents two different tuning parameters, represents the number of power stations, Indicates the number of water use types; The safety adjustment submodule is based on the energy optimization strategy, uses the hydraulic simulation model to simulate the changes in reservoir water level, flow rate and pressure, combines the real-time feedback control strategy, adjusts the operation of the sluice and generator set in real time according to the monitoring data, and generates a safety adjustment plan; The strategy implementation submodule is based on the safety adjustment plan and adopts a data-driven decision analysis method. By analyzing and integrating monitoring data, simulation model output and power plant operation history, it identifies key decision points, optimizes power plant operation strategies, and generates control operation strategies.

2. The real-time video monitoring system for emergency disposal of barrier lake according to claim 1 is characterized by: The real-time analysis submodule uses a long short-term memory network to process time series data based on dynamic feature information, analyzes the behavior trend and event development of objects by learning the temporal pattern of object movement, performs in-depth analysis and event prediction on dynamic scenes, and generates event analysis results. The corresponding process has the following relationship: ； in, Indicates time t The LSTM unit output is denote the weight matrices of the input layer and the hidden layer respectively, represents the bias vector, represents the activation function; The change detection submodule uses the differential image method to compare the continuous video frames at the pixel level based on the event analysis results, analyzes the statistical characteristics and trends of pixel changes through statistical methods, identifies dynamic changes and abnormal behaviors, and generates video dynamic analysis results. The corresponding process has the following relationship: ； in, Indicates time t and t- 1 difference image, represents the number of pixels in the image, Indicates at time t The image frame p pixel values.

3. The real-time video monitoring system for emergency disposal of barrier lake according to claim 2 is characterized by: The dam monitoring module includes a surface monitoring submodule, a deformation analysis submodule, and a crack tracking submodule. The surface monitoring submodule uses image texture analysis based on the video dynamic analysis results, extracts texture features in the image through gray-level co-occurrence matrix analysis, uses the Sobel algorithm to identify image edges, locates changes on the dam surface, and generates surface feature analysis records; Among them, the process of extracting texture features in the image through gray level co-occurrence matrix analysis has the following relationship: ； in, Representing images I The texture feature vector of represents the elements in the gray-level co-occurrence matrix, Indicates the number of gray levels; The Sobel algorithm is used to identify image edges and locate changes on the dam surface. The corresponding process has the following relationship: ； in, Represents the image edge information extracted using the Sobel algorithm; The process of generating surface feature analysis records corresponds to the following relationship: ； in, Represents a surface feature analysis record generated by combining texture features and edge information.

4. The real-time video monitoring system for emergency disposal of barrier lake according to claim 3 is characterized by: The deformation analysis submodule is based on surface feature analysis records, applies digital image correlation analysis, compares pixel changes in the same area in a continuous image sequence, combines 3D reconstruction technology, uses stereo vision principles and deep learning methods to build a 3D model of the dam surface, analyzes surface deformation, and generates deformation risk assessment results; Among them, the method of applying digital image correlation analysis, comparing the pixel changes in the same area in the continuous image sequence, combining 3D reconstruction technology, using stereo vision principles and deep learning methods to build a 3D model of the dam surface, analyzing the surface deformation, and generating deformation risk assessment results specifically includes the following steps: Extract two consecutive images from the surface feature analysis record to calculate the displacement field between the images. The corresponding process has the following relationship: ； in, Represents two consecutive images, represents the displacement distance, Indicates displacement The correlation coefficient at Indicates that the first image is at position The gray value at Indicates that the second image is at position The gray value at Represent the average grayscale values ​​of the first image and the second image respectively, Respectively represent the width and height of the image; The DIC calculation of the displacement field is performed to obtain the optimal displacement. The corresponding process has the following relationship: ； in, represents the optimal displacement; The optimal displacement is used to calculate the depth value for 3D reconstruction. The corresponding process has the following relationship: ； in, Indicates location The depth value at represents the focal length of the camera, represents the baseline distance of the binocular camera, Represents a tiny constant that prevents division by zero.

5. The real-time video monitoring system for emergency disposal of barrier lake according to claim 1 is characterized by: The hydraulic structure analysis module includes a mechanical modeling submodule, a risk assessment submodule, and an integrity inspection submodule. The mechanical modeling submodule uses elastic mechanics theory to model the hydraulic structure based on the video dynamic analysis results, including determining the geometric parameters and material properties of the structure, applying basic mechanics equations to simulate the response of the structure under various loads, and generating structural mechanics analysis records; The risk assessment submodule analyzes the stress and strain state of the structure based on the structural mechanics analysis record using the finite element analysis method, divides the structure into multiple small elements, calculates the behavior of each element under stress, evaluates the stability of the entire structure, and generates stress and strain assessment results; The integrity inspection submodule uses the structural integrity analysis technology based on the stress-strain assessment results, integrates external environmental factors and historical data of the structure, conducts a comprehensive assessment of the integrity and safety hazards of the structure, and generates a structural safety assessment record; The water flow simulation module includes a flow rate analysis submodule, a simulation modeling submodule, and a behavior prediction submodule. The velocity analysis submodule simulates reservoir water flow behavior based on video dynamic analysis results and topographic data, applies fluid dynamics theory, analyzes water flow characteristics including flow velocity and flow direction, and predicts water flow behavior by calculating the relationship between flow velocity and pressure field according to the basic physical laws of water flow, generating water flow characteristic analysis conclusions; The simulation modeling submodule uses a computational fluid dynamics model to simulate water flow based on the conclusions of water flow characteristic analysis, simulates the movement and interaction of water flow through numerical methods, and generates water flow dynamic simulation results by combining the dynamic changes of water flow under various hydrological and meteorological conditions; The behavior prediction submodule uses the prediction analysis technology to make long-term predictions of water flow behavior based on the results of water flow dynamic simulation, combines historical data and model output, analyzes trends through statistical methods, predicts future changes in flow velocity and direction, and generates water flow behavior predictions; The geological change identification module includes a ground monitoring submodule, a change analysis submodule, and a geological assessment submodule. The ground monitoring submodule is based on topographic and historical geological data and uses remote sensing image processing technology to perform preliminary capture of geological features, including enhancement and feature extraction of remote sensing images, identification of basic morphology and changes of the surface, and generation of preliminary records of geological features; The change analysis submodule uses digital image correlation analysis based on the preliminary record of geological features to conduct comparative analysis on the changes in surface morphology, identify key geological change points by tracking the time series changes of geological features, and generate geological change trend analysis results; The geological assessment submodule conducts an in-depth assessment of the captured geological changes based on the geological change trend analysis results and combines geological analysis methods to determine their impact on the surrounding areas of the barrier lake and generate geological change detection results.

6. The real-time video monitoring system for emergency disposal of barrier lake according to claim 5 is characterized by: The risk assessment and response module includes a risk analysis submodule, a strategy planning submodule, and a response coordination submodule. The risk analysis submodule uses a multi-factor risk assessment model to integrate and analyze data based on control operation strategies, structural safety assessment records, water flow behavior predictions, and geological change detection results, assess the impact and interaction of multiple risk factors, and generate a comprehensive risk analysis record; The strategy planning submodule applies the emergency response planning algorithm based on the comprehensive risk analysis records to formulate response strategies for various risk situations, including preventive measures and emergency response plans, and generates risk response strategy plans; The response coordination submodule optimizes and coordinates the predetermined emergency response measures based on the risk response strategy plan, adopts decision tree analysis and resource optimization allocation algorithm, evaluates the effects and results of differential response strategies, configures and utilizes key resources and personnel, and generates a comprehensive risk management plan.

7. A real-time video monitoring method for emergency response to a barrier lake, characterized in that: According to the real-time video monitoring system for emergency disposal of a barrier lake according to any one of claims 1 to 6, the method comprises the following steps: Based on the video data collected by the camera, the target detection algorithm is used to analyze the video frames, and the moving objects are located and tracked through feature extraction and pattern recognition technology. The long short-term memory network is used to process time series data, analyze the behavior trends and event development, and the statistical characteristics and trends of pixel changes are analyzed through statistical methods. Dynamic changes and abnormal behaviors are identified to generate video dynamic analysis results; Based on the video dynamic analysis results, the dam surface is analyzed using image texture analysis and Sobel algorithm, texture features are extracted through gray-level co-occurrence matrix analysis, changes are identified, digital image correlation analysis and three-dimensional reconstruction technology are applied to construct a three-dimensional model of the dam surface, surface deformation is analyzed, crack change trends are tracked through crack detection algorithms, and dam stability indicators are generated; Based on the dam stability index and historical hydrological data, a multi-objective optimization algorithm is used in combination with an energy demand forecasting model to match the power station energy output with demand, a hydraulic simulation model is used to simulate changes in reservoir water level, flow rate and pressure, and a real-time feedback control strategy is used to adjust operating parameters and generate a control operation strategy; Based on the dynamic analysis results of the video, mechanical modeling and stress-strain state analysis of the hydraulic structure are carried out using elastic mechanics theory and finite element analysis method, and water flow behavior, including flow velocity and direction, is analyzed using fluid dynamics theory and computational fluid dynamics model to evaluate the impact on the structure, generate structural safety assessment records and water flow characteristics analysis conclusions; Based on topographic and historical geological data, remote sensing image processing technology is used to capture geological features, and digital image correlation analysis is used to identify geological change trends. Combined with geological analysis methods, the captured geological changes are deeply evaluated to determine their impact on the surrounding areas of the barrier lake and generate geological change detection results; Based on the control operation strategy, structural safety assessment records, water flow characteristics analysis conclusions and geological change detection results, a multi-factor risk assessment model and data-driven decision analysis method are used to conduct risk assessment and emergency strategy formulation to generate a comprehensive risk management plan.

Images