Urban disaster prevention and reduction intelligent early warning method based on machine vision

Abstract

The invention provides an urban disaster prevention and reduction intelligent early warning method based on machine vision, and relates to the field of urban disaster prevention early warning systems. According to the urban disaster prevention and reduction intelligent early warning method based on machine vision, control is conducted through a master control center, the master control center comprises a visual monitoring module, a dispatching center module, a data center module and an early warning center module, and the master control center is used as a central processing center for disaster prevention and reduction of a whole city. The visual monitoring center is used for controlling monitoring cameras and processing images required by disaster prevention and reduction of the whole city, and the data center is used for intelligently analyzing and recording data acquired by visual monitoring. According to the invention, the monitoring result is presented in the form of visual images and videos, so that managers and emergency rescue workers can more visually know the situation of a disaster site, including the position, range, severity and the like of a disaster, and more comprehensive and more accurate disaster monitoring and early warning are realized.

Description

Technical Field

[0001] This invention relates to the field of urban disaster prevention and early warning systems, specifically to an intelligent early warning method for urban disaster prevention and mitigation based on machine vision. Background Technology

[0002] With the acceleration of urbanization, urban populations are becoming increasingly dense, and buildings and infrastructure are becoming increasingly complex. Cities face a variety of disaster threats, which often cause huge casualties and property losses, posing a serious challenge to urban safety and stability. Traditional urban disaster prevention and early warning systems mainly rely on manual inspections and sensor monitoring, which have problems such as limited monitoring range, weak real-time performance, low accuracy, and missed or false alarms. In addition, existing early warning systems are also insufficient in terms of information transmission and visualization, failing to transmit disaster information to relevant personnel and departments in a timely and intuitive manner, thus affecting the efficiency and effectiveness of emergency response.

[0003] Therefore, this invention proposes an intelligent early warning method for urban disaster prevention and mitigation based on machine vision, which effectively solves the above-mentioned problems and difficulties through visual monitoring. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a machine vision-based intelligent early warning method for urban disaster prevention and mitigation. Monitoring can be conducted without direct contact with target objects using camera devices, avoiding interference and damage to the monitored objects. Furthermore, it can monitor roads, bridges, tunnels, etc., in real time without affecting normal traffic and urban operations, promptly identifying potential safety hazards. The monitoring range can be quickly adjusted based on the number of monitoring cameras and drones. By installing cameras in key areas and high points throughout the city, large-area coverage monitoring can be achieved. Combined with mobile drone monitoring equipment, comprehensive, blind-spot-free monitoring of key areas within the city can be conducted, thereby improving the accuracy and timeliness of visual monitoring and early warning. Technical solution

[0005] To achieve the above objectives, the present invention provides the following technical solution: a machine vision-based intelligent early warning method for urban disaster prevention and mitigation. This machine vision-based intelligent early warning system for urban disaster prevention and mitigation is controlled by a central control center. The central control center includes a visual monitoring module, a dispatch center module, a data center module, and an early warning center module. The central control center serves as the central processing center for urban disaster prevention and mitigation. The visual monitoring center controls and processes the images from the monitoring cameras required for urban disaster prevention and mitigation. The data center performs intelligent analysis and data recording of the data collected by visual monitoring. The dispatch center provides rapid early warning and processing for urban disaster reduction. The early warning center summarizes the data analyzed by the data center and generates and executes early warning plans. Through the above technical solutions, high-definition cameras are installed in key urban areas, such as high-rise buildings, bridges, subway stations, and train stations, where there is high traffic and disasters are prone to occur. These cameras collect video image data 24 hours a day, providing a basis for subsequent analysis. Drones equipped with high-definition cameras conduct regular or irregular aerial patrols, especially in areas where natural disasters may occur, such as mountainous areas and near rivers, to obtain more comprehensive image information in a timely manner.

[0006] Preferably, the visual monitoring center includes a data acquisition module and an image preprocessing module. The data acquisition module includes camera deployment, and the image preprocessing module includes image grayscale conversion, image noise reduction, and image geometric correction functions. Through the above technical solutions, grayscale conversion can convert the acquired color image into a grayscale image, reducing the amount of data and speeding up subsequent processing. At the same time, it highlights the contour and texture information of the image. Image denoising uses a median filtering algorithm to remove noise from the image, improve the image clarity and quality, and avoid noise interference with subsequent analysis. Image geometric correction uses a Gaussian algorithm to correct image deformation caused by factors such as camera shooting angle and position, ensuring the accuracy and consistency of the image.

[0007] Preferably, the data analysis module of the data center module includes feature extraction and model optimization functions. The feature extraction function includes target detection, behavior analysis, and change detection. The target detection function uses a neural network to detect and identify specific targets in the image. The behavior analysis function performs motion analysis and prediction on moving objects in the image adaptation to determine whether there is abnormal behavior. The change detection function compares images at different times to detect changed areas in the scene and promptly discover newly emerging disaster hazards. Through the above technical solutions, image recognition can detect and identify specific targets in images, compare them with image data in the database, and thus quickly retrieve disaster images, such as flames and smoke in fires, building collapses and cracks in earthquakes, traffic accidents, etc. Change detection can compare images from different times to detect areas of change in the scene and promptly discover newly emerging disaster hazards, such as new cracks in buildings.

[0008] Preferably, the model optimization function of the data center module includes a dataset construction function, a deep learning function, and a model optimization function. The dataset construction function collects a large amount of image data containing various disaster scenarios and normal scenarios. The deep learning function selects the YOLO deep learning model for intelligent training of the data model. The model optimization function uses model compression and quantization technology to optimize the trained model. Through the above technical solution, the dataset is constructed by collecting and labeling a large amount of image data containing various disaster scenarios and normal scenarios. This data is used to train and validate machine vision models. The YOLO deep learning model is used to continuously adjust the model parameters, improve the model's accuracy and generalization ability, and enable the convolutional neural network to quickly identify disaster features in the images.

[0009] Preferably, the data recording function of the data center module includes local data storage function and blockchain cloud storage function, and the blockchain cloud storage function adopts multiple servers for off-site storage and synchronous backup storage. Through the above technical solutions, two or more data centers or systems can be active simultaneously, synchronizing data with each other in real time. This ensures that if one data storage center fails, it can quickly switch to another storage point, achieving continuous and uninterrupted service through data backup, and improving the reliability and availability of the system.

[0010] Preferably, the early warning center includes early warning analysis function and early warning resource scheduling function. The early warning analysis function generates disaster plans based on the data analysis results generated by the data center, including continuous monitoring of the disaster status, disaster risk assessment, and generation of disaster early warning decision-making plans. The early warning resource scheduling function integrates visual disaster data with sensor monitoring data from other departments through cross-departmental collaboration, realizes information interconnection between departments, and improves the accuracy of disaster prevention and mitigation early warning through multi-dimensional data information. By integrating machine vision data with other sensor data, such as meteorological data, earthquake data, and water level data, the above technical solutions can achieve more comprehensive and accurate disaster monitoring and early warning.

[0011] Preferably, the dispatch center issues and executes the specific implementation plan of the disaster early warning decision scheme generated by the early warning center, and the dispatch center shares data information and decision schemes with the emergency management center, fire center and emergency rescue center in real time to realize information interconnection between departments and provide support for coordinated emergency response; Through the above technical solutions, a collaborative working mechanism can be established with multiple departments in the city, such as fire protection, earthquake, meteorology, and transportation, to achieve information sharing and coordinated response, thereby improving the city's overall disaster prevention and mitigation capabilities.

[0012] Working principle: This machine vision-based intelligent early warning method for urban disaster prevention and mitigation is implemented through a machine vision-based intelligent early warning system for urban disaster prevention and mitigation. The method steps are as follows: S1: Data collection involves installing machine vision equipment such as high-definition cameras and infrared thermal imagers in key urban areas and disaster-prone locations to ensure comprehensive coverage of key areas, and using drones equipped with cameras to conduct regular or irregular patrols. S2: Image data processing, which performs image denoising, geometric correction and image grayscale conversion, and greatly compresses the image to achieve the effect of high-speed transmission of massive image data; S3: Target detection and recognition. It uses convolutional neural network deep learning algorithms to quickly identify features in the processed image, determine the category of target objects that may cause disasters, and accurately determine the location and range of the target in the image through target localization, and label the disaster targets. S4: After receiving and analyzing the data, the early warning center continuously monitors the abnormal data and conducts risk assessments. Based on the results of disaster analysis and assessment, when the disaster risk reaches a preset threshold, it promptly issues an early warning signal and generates disaster prevention and mitigation decisions. S5: The dispatch center conducts emergency response and shares disaster information acquired by the machine vision system in real time with relevant units such as the city's disaster prevention and mitigation command center, fire department, and traffic management department, so as to realize information interconnection and interoperability between departments and provide support for coordinated emergency response. Beneficial effects

[0013] This invention provides a machine vision-based intelligent early warning method for urban disaster prevention and mitigation. It has the following beneficial effects: This invention provides a machine vision-based intelligent early warning method for urban disaster prevention and mitigation, achieving non-contact monitoring. Monitoring can be conducted without direct contact with target objects using camera devices, avoiding interference and damage to the monitored objects. Furthermore, it can monitor roads, bridges, tunnels, etc., in real time without affecting normal traffic and urban operations, promptly identifying potential safety hazards. Its monitoring range can be quickly adjusted according to the number of monitoring cameras and drones. By installing cameras in key areas and high points throughout the city, large-area coverage monitoring can be achieved. Combined with mobile drone monitoring equipment, comprehensive, blind-spot-free monitoring of key areas within the city can be conducted, thereby improving the accuracy and timeliness of visual monitoring and early warning.

[0014] This invention provides a machine vision-based intelligent early warning method for urban disaster prevention and mitigation. It presents monitoring results in the form of intuitive images and videos, enabling managers and emergency rescue personnel to understand the situation at the disaster site more intuitively, including the location, scope, and severity of the disaster. Furthermore, it can be integrated with sensor data such as meteorological, geological, and hydrological data to achieve more comprehensive and accurate disaster monitoring and early warning. At the same time, it can be easily integrated and connected with other urban management systems and emergency rescue systems to achieve information sharing and collaborative work, thereby improving the overall effectiveness of urban disaster prevention and mitigation. Attached Figure Description

[0015] Figure 1This is the overall system diagram of the machine vision-based intelligent early warning system for urban disaster prevention and mitigation of the present invention; Figure 2 This is a system diagram of the visual monitoring module of the machine vision-based intelligent early warning system for urban disaster prevention and mitigation according to the present invention. Figure 3 This is a system diagram of the data center module of the machine vision-based intelligent early warning system for urban disaster prevention and mitigation according to the present invention. Figure 4 This is a system diagram of the early warning center module of the machine vision-based intelligent early warning system for urban disaster prevention and mitigation according to the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. In the description of this application, it should be noted that the terminology used herein is only for describing specific implementations and is not intended to limit the exemplary implementations according to this application. For ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale. Techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters in the following drawings indicate similar items, and therefore, once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings. Example

[0017] like Figure 1-4As shown, this embodiment of the invention provides a machine vision-based intelligent early warning method for urban disaster prevention and mitigation. This machine vision-based intelligent early warning system for urban disaster prevention and mitigation is controlled by a central control center. The central control center includes a visual monitoring module, a dispatch center module, a data center module, and an early warning center module. The central control center serves as the central processing center for urban disaster prevention and mitigation. The visual monitoring center controls and processes the monitoring cameras needed for urban disaster prevention and mitigation. The data center performs intelligent analysis and records the data collected by visual monitoring. The dispatch center provides rapid early warning and processing for urban disaster mitigation. The early warning center summarizes the data analyzed by the data center and generates and executes early warning plans. High-definition cameras are installed in key urban areas, such as high-rise buildings, bridges, subway stations, and train stations—places with high traffic and prone to disasters—to continuously collect video image data 24 hours a day, providing a foundation for subsequent analysis. Drones equipped with high-definition cameras conduct regular or irregular aerial patrols, especially in areas where natural disasters may occur, such as mountainous areas and near rivers, to obtain more comprehensive image information in a timely manner.

[0018] The visual monitoring center includes a data acquisition module and an image preprocessing module. The data acquisition module includes camera deployment, and the image preprocessing module includes image grayscale conversion, image noise reduction, and image geometric correction functions. Grayscale conversion converts the acquired color image into a grayscale image, reducing the amount of data and speeding up subsequent processing, while highlighting the image's contour and texture information. Image noise reduction uses a median filtering algorithm to remove noise from the image, improving image clarity and quality and avoiding noise interference with subsequent analysis. Image geometric correction uses a Gaussian algorithm to correct image distortion caused by factors such as camera shooting angle and position, ensuring image accuracy and consistency. The data analysis module of the data center module includes feature extraction and model optimization functions. The feature extraction function includes target detection, behavior analysis, and change detection functions. The target detection function uses neural networks to detect and identify specific targets in images. The behavior analysis function performs motion analysis and prediction on moving objects in image adaptation to determine whether there is abnormal behavior. The change detection function compares images from different times to detect changed areas in the scene and promptly discover new potential disaster hazards. Image recognition can detect and identify specific targets in images and compare them with image data in the database to quickly retrieve disaster images, such as flames and smoke in fires, building collapses and cracks in earthquakes, traffic accidents, etc. Change detection can compare images from different times to detect changed areas in the scene and promptly discover new potential disaster hazards, such as new cracks in buildings. The model optimization function of the data center module includes dataset construction, deep learning, and model optimization. The dataset construction function collects a large amount of image data containing various disaster and normal scenes. The deep learning function selects the YOLO deep learning model for intelligent training of the data model. The model optimization function uses model compression and quantization technology to optimize the trained model. The dataset construction collects a large amount of image data containing various disaster and normal scenes and annotates it for training and validating machine vision models. The YOLO deep learning model is used to continuously adjust the model parameters to improve the model's accuracy and generalization ability, enabling the convolutional neural network to quickly identify disaster features in the images. The data recording function of the data center module includes local data storage and blockchain cloud storage. The blockchain cloud storage function uses multiple servers for off-site synchronous backup storage. Two or more data centers or systems are simultaneously active and synchronize data with each other in real time. This ensures that if one data storage center fails, it can quickly switch to another storage point, realizing the continuity and uninterrupted service of the system through data backup, and improving the reliability and availability of the system. The early warning center includes early warning analysis and early warning resource scheduling functions. The early warning analysis function generates disaster response plans based on data analysis results generated by the data center, including continuous disaster status monitoring, disaster risk assessment, and disaster early warning decision-making plan generation. The early warning resource scheduling function, through cross-departmental collaboration, integrates visual disaster data with sensor monitoring data from other departments, achieving information interconnection and interoperability among departments. It improves the accuracy of disaster prevention and mitigation early warnings through multi-dimensional data information, integrating machine vision data with other sensor data, such as meteorological data, earthquake data, and water level data, to achieve more comprehensive and accurate disaster monitoring and early warning. The scheduling center issues and executes specific implementation plans for the disaster early warning decision-making plans generated by the early warning center. The scheduling center shares data information and decision-making plans in real time with the emergency management center, fire center, and emergency medical center, achieving information interconnection and interoperability among departments, supporting coordinated emergency response, and establishing collaborative working mechanisms with multiple departments in the city, such as fire, earthquake, meteorology, and transportation, to achieve information sharing and coordinated response, thereby improving the city's overall disaster prevention and mitigation capabilities.

[0019] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A machine vision-based intelligent early warning system for urban disaster prevention and mitigation, characterized in that: This machine vision-based intelligent early warning system for urban disaster prevention and mitigation is controlled by a central control center. The central control center includes a visual monitoring module, a dispatch center module, a data center module, and an early warning center module. The central control center serves as the central processing center for urban disaster prevention and mitigation. The visual monitoring center controls and processes images from monitoring cameras required for urban disaster prevention and mitigation. The data center performs intelligent analysis and records data collected by visual monitoring. The dispatch center provides rapid early warning and processing for urban disaster reduction. The early warning center summarizes the data analyzed by the data center and generates and executes early warning plans.

2. The intelligent early warning system for urban disaster prevention and mitigation based on machine vision according to claim 1, characterized in that: The visual monitoring center includes a data acquisition module and an image preprocessing module. The data acquisition module includes camera deployment, and the image preprocessing module includes image grayscale conversion, image noise reduction, and image geometric correction functions.

3. The intelligent early warning system for urban disaster prevention and mitigation based on machine vision according to claim 1, characterized in that: The data analysis module of the data center module includes feature extraction and model optimization functions. The feature extraction function includes target detection, behavior analysis, and change detection functions. The target detection function uses a neural network to detect and identify specific targets in the image. The behavior analysis function performs motion analysis and prediction on moving objects in the image adaptation to determine whether there is abnormal behavior. The change detection function compares images at different times to detect changed areas in the scene and promptly discover newly emerging disaster hazards.

4. The intelligent early warning system for urban disaster prevention and mitigation based on machine vision according to claim 1, characterized in that: The model optimization function of the data center module includes dataset construction, deep learning, and model optimization. The dataset construction function collects a large amount of image data containing various disaster scenarios and normal scenarios. The deep learning function selects the YOLO deep learning model for intelligent training of the data model. The model optimization function uses model compression and quantization technology to optimize the trained model.

5. The intelligent early warning system for urban disaster prevention and mitigation based on machine vision according to claim 1, characterized in that: The data recording function of the data center module includes local data storage and blockchain cloud storage functions. The blockchain cloud storage function uses multiple servers for off-site storage and synchronous backup.

6. The intelligent early warning system for urban disaster prevention and mitigation based on machine vision according to claim 1, characterized in that: The early warning center includes early warning analysis and early warning resource scheduling functions. The early warning analysis function generates disaster plans based on the data analysis results generated by the data center, including continuous monitoring of disaster status, disaster risk assessment, and generation of disaster early warning decision-making plans. The early warning resource scheduling function integrates visual disaster data with sensor monitoring data from other departments through cross-departmental collaboration, realizing information interconnection and interoperability between departments, and improving the accuracy of disaster prevention and mitigation early warning through multi-dimensional data information.

7. The intelligent early warning system for urban disaster prevention and mitigation based on machine vision according to claim 1, characterized in that: The dispatch center issues and executes the specific implementation plan of the disaster early warning decision scheme generated by the early warning center. The dispatch center shares data information and decision schemes with the emergency management center, fire center and emergency rescue center in real time to realize information interconnection and interoperability between departments and provide support for coordinated emergency response.

8. A machine vision-based intelligent early warning method for urban disaster prevention and mitigation, characterized in that: This machine vision-based intelligent early warning method for urban disaster prevention and mitigation is implemented through a machine vision-based intelligent early warning system for urban disaster prevention and mitigation. The method steps are as follows: S1: Data collection involves installing machine vision equipment such as high-definition cameras and infrared thermal imagers in key urban areas and disaster-prone locations to ensure comprehensive coverage of key areas, and using drones equipped with cameras to conduct regular or irregular patrols. S2: Image data processing, which performs image denoising, geometric correction and image grayscale conversion, and greatly compresses the image to achieve the effect of high-speed transmission of massive image data; S3: Target detection and recognition. It uses convolutional neural network deep learning algorithms to quickly identify features in the processed image, determine the category of target objects that may cause disasters, and accurately determine the location and range of the target in the image through target localization, and label the disaster targets. S4: After receiving and analyzing the data, the early warning center continuously monitors the abnormal data and conducts risk assessments. Based on the results of disaster analysis and assessment, when the disaster risk reaches a preset threshold, it promptly issues an early warning signal and generates disaster prevention and mitigation decisions. S5: The dispatch center conducts emergency response and shares disaster information acquired by the machine vision system in real time with relevant units such as the city's disaster prevention and mitigation command center, fire department, and traffic management department, so as to realize information interconnection and interoperability between departments and provide support for coordinated emergency response.

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