Hydropower station operation risk early warning system and method based on digital twin and AI image recognition

Abstract

The invention discloses a hydropower station operation risk early warning system and method based on digital twinborn and AI image recognition. The system comprises a data acquisition module, a digital twinborn model construction module, an AI image recognition module, an early warning management module and an effect evaluation and optimization module. According to the system and the method, comprehensive monitoring of the working environment of the hydropower station is realized, and the efficiency and the accuracy of safety management are improved. Potential safety risks are warned in advance, accidents are effectively prevented, and losses are reduced. Moreover, the management and display of the work ticket are supported, thereby guaranteeing the smooth operation of the maintenance work, and preventing the violation operation. In addition, generation and guidance of an emergency response plan are provided, and the emergency response speed and effect are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of hydropower station safety management technology, and specifically to a hydropower station operation risk early warning system and method based on digital twin and AI image recognition. Background Art

[0002] As an important energy infrastructure, the safe operation of hydropower stations is of great significance to ensuring energy supply and the safety of people's lives and property. However, the operating environment of hydropower stations is complex and changeable, and there are many potential safety risks, such as equipment failure, personnel misoperation, and illegal operations. Traditional safety management models often rely on manual inspections and experience judgments, which makes it difficult to achieve comprehensive monitoring and timely warning of risks. Therefore, it is particularly important to develop a system that can monitor and analyze the operating environment of hydropower stations in real time and warn of potential safety risks in advance. Summary of the invention

[0003] In order to solve the above technical problems, the present invention provides a hydropower station operation risk early warning system and method based on digital twins and AI image recognition.

[0004] The technical solution of the present invention to solve the above technical problems is as follows: A hydropower station operation risk early warning system based on digital twin and AI image recognition, comprising: Data acquisition module, used to collect various data inside the hydropower station; The digital twin model building module is used to build a digital copy of the real scene of the hydropower station. At the same time, the digital twin model is updated in real time based on the real-time collected data to ensure that the model is consistent with the actual operating status of the hydropower station; AI image recognition module, which is used to pre-process the collected video data, extract image features using deep learning algorithms, and perform recognition and classification; The early warning management module generates risk early warning information based on the results of the AI ​​image recognition module; The effect evaluation and optimization module is used to evaluate the effect of the early warning system.

[0005] Furthermore, the digital twin model construction module includes a physical entity module, a virtual entity module, a digital twin engine module and a digital twin service module; The physical entity module is connected to the digital twin engine module through sensors and monitoring equipment to realize real-time data collection and transmission; the digital twin engine module updates and simulates the virtual entity module according to the received data, and transmits the simulation results to the digital twin service module for further analysis and decision-making.

[0006] Furthermore, the AI ​​image recognition module includes a data acquisition layer, a preprocessing layer, a feature extraction layer, a recognition and classification layer, and an application layer; Each layer is connected through data flow. The data acquisition layer transmits the acquired image data to the preprocessing layer for processing. The preprocessing layer transmits the processed data to the feature extraction layer for feature extraction. The feature extraction layer transmits the extracted feature data to the recognition and classification layer for recognition and classification. Finally, the recognition and classification results are transmitted to the application layer for actual application.

[0007] Furthermore, the early warning management module mainly consists of a risk prediction and assessment module, a work order management module, and an emergency response and disposal module; The various modules are connected through data sharing and interaction. The risk prediction and assessment module transmits the prediction and assessment results to the job ticket management module and the emergency response and disposal module. The job ticket management module transmits the job ticket information and electronic fence information to the emergency response and disposal module. The emergency response and disposal module generates an emergency response plan based on the received information and guides relevant personnel to handle it.

[0008] Furthermore, the data acquisition module includes a sensor, a monitoring device and a camera.

[0009] Furthermore, the digital twin model construction module uses high-precision three-dimensional modeling technology to build a digital copy of the real scene of the hydropower station.

[0010] The present invention also provides an early warning method based on the early warning system, comprising the following steps: S1: Data collection and preprocessing: First, the system starts the data collection function. Through the sensors, monitoring equipment and camera equipment deployed inside the hydropower station, various data of the hydropower station operation site are collected in real time, including but not limited to equipment status data, environmental data, and video data, and the collected data is transmitted to the system's data processing center via wired or wireless means; In the data processing center, the system pre-processes the collected data, including data cleaning, data format conversion and data standardization steps to ensure the accuracy and reliability of subsequent analysis. For video data, the system also performs grayscale, denoising and binarization pre-processing operations to improve the efficiency of image recognition. S2: Digital twin model construction and update While collecting and preprocessing data, the system uses high-precision 3D modeling technology to build a digital copy of the real scene of the hydropower station, namely the digital twin model. As real-time data is continuously input, the system dynamically updates the digital twin model. Step 3: AI Image Recognition and Anomaly Detection The pre-processed video data is fed into the AI ​​image recognition module, which uses deep learning algorithms to extract features, identify and classify video frames. By comparing with preset safety standards and specifications, the system can identify abnormal behaviors and equipment failures. The system marks and records the identified anomalies and calculates the probability and severity of the anomalies, providing a basis for subsequent early warning and disposal; S4: Risk warning and emergency response When the AI ​​image recognition module detects abnormal behavior or equipment failure, the system immediately triggers an early warning mechanism. The early warning information includes the type, location, severity and recommended emergency measures of the abnormality, and the relevant personnel are notified in real time through display screens, sound prompts, text messages or emails. At the same time, the system predicts the risks and consequences that may be caused by anomalies based on the digital twin model and real-time data, and provides detailed emergency response plans for relevant personnel; S5: Work ticket management and electronic fence monitoring In addition to early warning and emergency response, the system is also responsible for the management of work tickets and the monitoring of electronic fences. The system generates and manages work tickets based on the actual operation needs of the hydropower station, including information on the operation area, operation content, and operation time. At the same time, the system uses electronic fence technology to monitor the entry and exit of personnel in the operation area in real time to prevent unauthorized personnel from entering dangerous areas. Step 6: Effect evaluation and optimization The system evaluates the effectiveness of the early warning method. The evaluation indicators include the accuracy of the early warning, response time, and the effectiveness of the emergency response. Based on the evaluation results, the system optimizes and improves the early warning method.

[0011] The present invention has the following beneficial effects: The hydropower station operation risk early warning system and method based on digital twin and AI image recognition provided by the present invention realizes comprehensive monitoring of the hydropower station operation environment and improves the efficiency and accuracy of safety management. Early warning of potential safety risks can effectively prevent accidents and reduce losses. It also supports the management and display of operation tickets to ensure the smooth progress of maintenance work and prevent the occurrence of illegal operations. In addition, it provides the generation and guidance of emergency response plans to improve the speed and effectiveness of emergency response. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 This is a flow chart of the early warning method provided by the present invention; DETAILED DESCRIPTION

[0013] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0014] like Figure 1 As shown, a hydropower station operation risk early warning system based on digital twin and AI image recognition includes: a data acquisition module for collecting various types of data inside the hydropower station; The digital twin model construction module is used to construct a digital copy of the real scene of the hydropower station. At the same time, the digital twin model is updated in real time according to the real-time collected data to ensure that the model is consistent with the actual operating status of the hydropower station; the data acquisition module includes sensors, monitoring equipment and cameras.

[0015] AI image recognition module, which is used to pre-process the collected video data, extract image features using deep learning algorithms, and perform recognition and classification; The early warning management module generates risk early warning information based on the results of the AI ​​image recognition module; The effect evaluation and optimization module is used to evaluate the effect of the early warning system.

[0016] The digital twin model construction module includes a physical entity module, a virtual entity module, a digital twin engine module and a digital twin service module; the digital twin model construction module uses high-precision three-dimensional modeling technology to build a digital copy of the real scene of the hydropower station.

[0017] The physical entity module is connected to the digital twin engine module through sensors and monitoring equipment to achieve real-time data collection and transmission; the digital twin engine module updates and simulates the virtual entity module based on the received data, and transmits the simulation results to the digital twin service module for further analysis and decision-making. Physical entity module: includes various types of equipment, pipelines, building structures and other actual physical objects inside the hydropower station. These physical objects are connected to the digital twin system through sensors, monitoring equipment, etc. to achieve real-time data collection and transmission.

[0018] The virtual entity module is a digital mapping of the physical entity in the information space, including geometric models, mechanism models, and data models. These models can reflect the detailed information of the structure, function, performance parameters, etc. of the physical entity. The digital twin engine module is the driving engine that realizes the real-time connection and synchronization between the physical system and the virtual system, and is also the core of the intelligent algorithm and intelligent computing engine of the digital twin system. It is responsible for transmitting the data of the physical entity to the virtual entity in real time, and performing intelligent analysis and decision-making based on the simulation results of the virtual entity. The digital twin service module includes simulation services, monitoring services, analysis services, and prediction services, providing users with advanced intelligent services. The digital twin system can realize accurate mapping and real-time monitoring of the operating environment of the hydropower station, providing strong guarantee for the safe operation of the hydropower station. By constructing a high-precision three-dimensional model, the system can intuitively display the internal structure and equipment status of the hydropower station, helping users to better understand the operation of the power station.

[0019] The AI ​​image recognition module includes a data acquisition layer, a preprocessing layer, a feature extraction layer, a recognition and classification layer, and an application layer; The data acquisition layer is responsible for acquiring image data from various sources, such as cameras, image databases, etc.

[0020] The preprocessing layer preprocesses the collected image data, such as denoising, enhancement, and normalization, to improve the accuracy and efficiency of image recognition. The feature extraction layer uses computer vision and deep learning technology to extract features from the preprocessed image data. The recognition and classification layer automatically recognizes and classifies the image content based on the extracted features using pattern recognition technology. The application layer applies the recognition and classification results to actual scenarios, such as safety monitoring of hydropower stations and personnel behavior recognition. Each layer is connected through data flow. The data acquisition layer transmits the acquired image data to the preprocessing layer for processing, the preprocessing layer transmits the processed data to the feature extraction layer for feature extraction, the feature extraction layer transmits the extracted feature data to the recognition and classification layer for recognition and classification, and finally transmits the recognition and classification results to the application layer for actual application. The AI ​​image recognition system can realize intelligent perception and recognition of video data at the hydropower station operation site, and monitor and analyze key information such as personnel behavior and equipment status in real time. Through deep learning algorithms and computer vision technology, the system can automatically identify abnormal behaviors and equipment failures in images, providing strong guarantees for the safe operation of hydropower stations.

[0021] The early warning management module is mainly composed of a risk prediction and assessment module, a job ticket management module, and an emergency response and disposal module; Risk prediction and assessment module: through the analysis of historical data and real-time data, using digital twin models and AI image recognition algorithms to predict potential safety risks and conduct risk assessments. The job ticket management module supports the display of real-time job ticket location, identification, and attribute information for each layer and each area, as well as the display of electronic fence information and early warning information for the corresponding area. Ensure the smooth progress of maintenance work and prevent the occurrence of illegal operations. In the emergency response and disposal module, when a warning or alarm occurs, the system can automatically generate an emergency response plan and guide relevant personnel to quickly deal with it. The modules are connected through data sharing and interaction. The risk prediction and assessment module transmits the prediction and assessment results to the job ticket management module and the emergency response and disposal module. The job ticket management module transmits the job ticket information and electronic fence information to the emergency response and disposal module. The emergency response and disposal module generates an emergency response plan based on the received information and guides relevant personnel to handle it. The early warning management system can realize comprehensive monitoring and risk warning of the operating environment of the hydropower station, and provide strong support for the safety management of the hydropower station. Through the risk prediction and assessment module, the system can discover potential safety hazards in advance and formulate corresponding preventive measures. Through the job ticket management module, the system can ensure the smooth progress of maintenance work and prevent the occurrence of illegal operations. Through the emergency response and disposal module, the system can quickly generate an emergency response plan when an early warning or alarm occurs, and guide relevant personnel to quickly deal with it.

[0022] like Figure 1 As shown, a hydropower station operation risk early warning method based on digital twin and AI image recognition includes the following steps: S1: Data collection and preprocessing First, the system starts the data collection function, and through the sensors, monitoring equipment, cameras and other equipment deployed inside the hydropower station, various data on the hydropower station operation site are collected in real time, including but not limited to equipment status data (such as motor temperature and vibration frequency), environmental data (such as water level, water flow speed, temperature and humidity), and video data. The collected data is transmitted to the system's data processing center via wired or wireless means.

[0023] At the data processing center, the system preprocesses the collected data, including data cleaning (removing noise, outliers and missing values), data format conversion and data standardization to ensure the accuracy and reliability of subsequent analysis. For video data, the system also performs preprocessing operations such as grayscale, denoising and binarization to improve the efficiency of image recognition.

[0024] S2: Digital twin model construction and update While collecting and preprocessing data, the system uses high-precision 3D modeling technology to build a digital copy of the real scene of the hydropower station, namely the digital twin model. The model includes accurate representations of all key equipment, pipelines, building structures and other elements inside the hydropower station, as well as the spatial relationships and interactions between them.

[0025] As real-time data is continuously input, the system dynamically updates the digital twin model to reflect changes in the actual operating status of the hydropower station, including updates to equipment status, adjustments to environmental parameters, and real-time changes at the work site.

[0026] S3: AI Image Recognition and Anomaly Detection The pre-processed video data is fed into the AI ​​image recognition module. This module uses deep learning algorithms to extract features, identify and classify video frames. By comparing with preset safety standards and specifications, the system can identify abnormal behaviors (such as personnel not wearing safety equipment, illegal operations, etc.) and equipment failures (such as equipment overheating, leakage, etc.).

[0027] The system marks and records the identified anomalies and calculates the probability and severity of the anomalies, providing a basis for subsequent early warning and disposal.

[0028] S4: Risk warning and emergency response When the AI ​​image recognition module detects abnormal behavior or equipment failure, the system immediately triggers the early warning mechanism. The early warning information includes the type, location, severity and recommended emergency measures of the abnormality, and is notified to relevant personnel in real time through display screens, sound prompts, text messages or emails.

[0029] At the same time, the system predicts the risks and consequences of abnormalities based on the digital twin model and real-time data, and provides relevant personnel with a detailed emergency response plan. The plan includes information such as the steps of emergency measures, required resources, and responsible persons to ensure a rapid and effective emergency response.

[0030] S5: Work ticket management and electronic fence monitoring In addition to early warning and emergency response, the system is also responsible for the management of work tickets and the monitoring of electronic fences. The system generates and manages work tickets based on the actual operation needs of the hydropower station, including information such as the operation area, operation content, and operation time. At the same time, the system uses electronic fence technology to monitor the entry and exit of personnel in the operation area in real time to prevent unauthorized personnel from entering dangerous areas.

[0031] S6: Effect evaluation and optimization Finally, the system evaluates the effectiveness of the early warning method. The evaluation indicators include the accuracy of the early warning, the response time, the effectiveness of the emergency response, etc. Based on the evaluation results, the system optimizes and improves the early warning method to improve the accuracy and reliability of the early warning. This includes adjusting the parameters of the AI ​​image recognition algorithm, optimizing the update strategy of the digital twin model, and improving the emergency response plan.

[0032] Through the above steps, the hydropower station operation risk warning method based on digital twins and AI image recognition can realize comprehensive monitoring and risk assessment of the hydropower station operating environment, provide early warning of potential safety risks, and provide strong guarantees for the safe operation of the hydropower station.

[0033] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A hydropower station operation risk early warning system based on digital twins and AI image recognition, characterized in that: include: Data acquisition module, used to collect various data inside the hydropower station; The digital twin model building module is used to build a digital copy of the real scene of the hydropower station. At the same time, the digital twin model is updated in real time based on the real-time collected data to ensure that the model is consistent with the actual operating status of the hydropower station; AI image recognition module, which is used to pre-process the collected video data, extract image features using deep learning algorithms, and perform recognition and classification; The early warning management module generates risk early warning information based on the results of the AI ​​image recognition module; The effect evaluation and optimization module is used to evaluate the effect of the early warning system.

2. The hydropower station operation risk early warning system based on digital twin and AI image recognition according to claim 1 is characterized in that: The digital twin model construction module includes a physical entity module, a virtual entity module, a digital twin engine module and a digital twin service module; The physical entity module is connected to the digital twin engine module through sensors and monitoring equipment to achieve real-time data collection and transmission; The digital twin engine module updates and simulates the virtual entity module based on the received data, and transmits the simulation results to the digital twin service module for further analysis and decision-making.

3. The hydropower station operation risk early warning system based on digital twin and AI image recognition according to claim 1 is characterized in that: The AI ​​image recognition module includes a data acquisition layer, a preprocessing layer, a feature extraction layer, a recognition and classification layer, and an application layer; Each layer is connected through data flow. The data acquisition layer transmits the acquired image data to the preprocessing layer for processing. The preprocessing layer transmits the processed data to the feature extraction layer for feature extraction. The feature extraction layer transmits the extracted feature data to the recognition and classification layer for recognition and classification. Finally, the recognition and classification results are transmitted to the application layer for actual application.

4. The hydropower station operation risk early warning system based on digital twin and AI image recognition according to claim 1 is characterized in that: The early warning management module mainly consists of a risk prediction and assessment module, a work order management module, and an emergency response and disposal module; The various modules are connected through data sharing and interaction. The risk prediction and assessment module transmits the prediction and assessment results to the job ticket management module and the emergency response and disposal module. The job ticket management module transmits the job ticket information and electronic fence information to the emergency response and disposal module. The emergency response and disposal module generates an emergency response plan based on the received information and guides relevant personnel to handle it.

5. The hydropower station operation risk early warning system based on digital twin and AI image recognition according to claim 1 is characterized in that: The data acquisition module includes a sensor, a monitoring device and a camera.

6. The hydropower station operation risk early warning system based on digital twin and AI image recognition according to claim 1 is characterized in that: The digital twin model building module uses high-precision three-dimensional modeling technology to build a digital copy of the real scene of the hydropower station.

7. A hydropower station operation risk early warning method based on digital twins and AI image recognition, characterized in that: The early warning system according to any one of claims 1 to 6 is used for early warning, comprising the following steps: S1: Data collection and preprocessing: First, the system starts the data collection function. Through the sensors, monitoring equipment and camera equipment deployed inside the hydropower station, various data of the hydropower station operation site are collected in real time, including but not limited to equipment status data, environmental data, and video data, and the collected data is transmitted to the system's data processing center via wired or wireless means; In the data processing center, the system pre-processes the collected data, including data cleaning, data format conversion and data standardization steps to ensure the accuracy and reliability of subsequent analysis. For video data, the system also performs grayscale, denoising and binarization pre-processing operations to improve the efficiency of image recognition. S2: Digital twin model construction and update While collecting and preprocessing data, the system uses high-precision 3D modeling technology to build a digital copy of the real scene of the hydropower station, namely the digital twin model. As real-time data is continuously input, the system dynamically updates the digital twin model. Step 3: AI Image Recognition and Anomaly Detection The pre-processed video data is fed into the AI ​​image recognition module, which uses deep learning algorithms to extract features, identify and classify video frames. By comparing with preset safety standards and specifications, the system can identify abnormal behaviors and equipment failures. The system marks and records the identified anomalies and calculates the probability and severity of the anomalies, providing a basis for subsequent early warning and disposal; S4: Risk warning and emergency response When the AI ​​image recognition module detects abnormal behavior or equipment failure, the system immediately triggers an early warning mechanism. The early warning information includes the type, location, severity and recommended emergency measures of the abnormality, and the relevant personnel are notified in real time through display screens, sound prompts, text messages or emails. At the same time, the system predicts the risks and consequences that may be caused by anomalies based on the digital twin model and real-time data, and provides detailed emergency response plans for relevant personnel; S5: Work ticket management and electronic fence monitoring In addition to early warning and emergency response, the system is also responsible for the management of work tickets and the monitoring of electronic fences. The system generates and manages work tickets based on the actual operation needs of the hydropower station, including information on the operation area, operation content, and operation time. At the same time, the system uses electronic fence technology to monitor the entry and exit of personnel in the operation area in real time to prevent unauthorized personnel from entering dangerous areas. Step 6: Effect evaluation and optimization The system evaluates the effectiveness of the early warning method. The evaluation indicators include the accuracy of the early warning, response time, and the effectiveness of the emergency response. Based on the evaluation results, the system optimizes and improves the early warning method.

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