技术领域Technical field
本发明属于智能预警技术领域，具体涉及一种基于面场景形变信息的智能预警方法。The invention belongs to the technical field of intelligent early warning, and specifically relates to an intelligent early warning method based on scene deformation information.
背景技术Background technique
智能预警技术是一种利用人工智能和大数据分析等先进技术，通过对大量数据的实时监测和分析，从中发现潜在的风险和异常情况，并及时提供预警信息或建议的技术手段。它可以应用于各个领域，例如自然灾害、公共安全等。智能预警技术的核心是通过智能算法对大量数据进行实时分析和判断，从而识别出异常信号，并提供预警信息，帮助决策者采取相应措施来减轻潜在的风险和损失。Intelligent early warning technology is a technical means that uses advanced technologies such as artificial intelligence and big data analysis to detect potential risks and anomalies through real-time monitoring and analysis of large amounts of data, and provides timely warning information or suggestions. It can be applied in various fields, such as natural disasters, public safety, etc. The core of intelligent early warning technology is to use intelligent algorithms to analyze and judge large amounts of data in real time, thereby identifying abnormal signals and providing early warning information to help decision makers take corresponding measures to mitigate potential risks and losses.
当前，智能预警系统在各个领域得到广泛应用，如交通、安防、环境等领域，然而，传统的智能预警方法主要基于视频图像的分析，对于复杂的场景和目标的变化，预警效果有限。因此，需要一种新的智能预警方法，能够更准确地识别面场景的形变信息，从而提供更可靠的预警效果。Currently, intelligent early warning systems are widely used in various fields, such as transportation, security, environment, etc. However, traditional intelligent early warning methods are mainly based on the analysis of video images, and have limited early warning effects for complex scenes and target changes. Therefore, a new intelligent early warning method is needed that can more accurately identify the deformation information of surface scenes, thereby providing a more reliable early warning effect.
发明内容Contents of the invention
本发明的目的在于提供一种基于面场景形变信息的智能预警方法，在面场景中准确识别和预测潜在的危险情况，该方法利用图像处理和深度学习技术，通过对面场景中的形变信息进行分析和建模，实现对异常情况的检测和预警，该方法具有高精度、实时性和自动化的特点，可广泛应用于安全监控、智能交通等领域，以解决上述背景技术中提出现有技术中的问题。The purpose of the present invention is to provide an intelligent early warning method based on facial scene deformation information to accurately identify and predict potential dangerous situations in facial scenes. This method utilizes image processing and deep learning technology to analyze the deformation information in facial scenes. and modeling to achieve detection and early warning of abnormal situations. This method has the characteristics of high precision, real-time and automation, and can be widely used in security monitoring, intelligent transportation and other fields to solve the problems in the existing technology proposed in the above background technology. question.
为实现上述目的，本发明采用了如下技术方案：In order to achieve the above objects, the present invention adopts the following technical solutions:
一种基于面场景形变信息的智能预警方法，包括以下步骤：An intelligent early warning method based on scene deformation information, including the following steps:
S1、面场景图像获取：通过图像采集设备获取目标面场景的图像；S1. Surface scene image acquisition: obtain the image of the target surface scene through the image acquisition device;
S2、图像处理：对获取的静态图像进行处理；S2. Image processing: process the obtained static images;
S3、图像追踪：对获取的动态图像进处理；S3. Image tracking: process the acquired dynamic images;
S4、建立模型：建立面场景形变信息的模型；S4. Establish model: Establish a model of scene deformation information;
S5、异常情况检测与预警：使用得到的模型进行实时监测和预警。S5. Abnormal situation detection and early warning: Use the obtained model for real-time monitoring and early warning.
优选的，所述S1中，用于图像采集的设备为地基合成孔径雷达或者是GNSS-INBSAR技术的卫星信号接收机，，获取监测场景的反射电磁波信号后，进行SAR成像，获取的目标场景图像包括静态图像和动态图像。Preferably, in S1, the equipment used for image acquisition is a ground-based synthetic aperture radar or a satellite signal receiver with GNSS-INBSAR technology. After acquiring the reflected electromagnetic wave signal of the monitoring scene, SAR imaging is performed to obtain the target scene image. Includes still images and dynamic images.
优选的，所述S2中，对静态图像进行处理包括图像去噪、图像增强，以提高后续处理的准确性和稳定性。Preferably, in S2, processing of static images includes image denoising and image enhancement to improve the accuracy and stability of subsequent processing.
优选的，所述S3中，对动态图像进行处理包括对动态图像进行运动目标检测和跟踪，以提取面场景中的运动目标信息。Preferably, in S3, processing the dynamic image includes detecting and tracking moving targets on the dynamic image to extract moving target information in the scene.
优选的，所述S4中，进行模型建立之前，先基于运动目标信息，通过差分干涉处理，获取面场景的形变信息，包括目标的形状、纹理、颜色、形变程度、形变速度，将数据导入深度学习算法，利用深度学习算法对形变信息进行训练和学习，实现对正常和异常形变的区分从而得到所需模型。Preferably, in S4, before establishing the model, first obtain the deformation information of the surface scene through differential interference processing based on the moving target information, including the shape, texture, color, deformation degree, and deformation speed of the target, and import the data into the depth. Learning algorithm uses deep learning algorithms to train and learn deformation information to distinguish between normal and abnormal deformation to obtain the required model.
优选的，所述S5中，使用深度学习模型对三维面场景进行特征提取，该深度学习模型采用卷积神经网络架构，包括多个卷积层、池化层和全连接层，用于从三维面场景中提取形状、纹理和结构等特征，提取的特征将用于后续的形变预测，具体通过智能化形变预警算法(包括形变值、形变速度、加速度等阈值法、斋藤法、切线角法)，给出形变预警预报。Preferably, in S5, a deep learning model is used to extract features of the three-dimensional scene. The deep learning model adopts a convolutional neural network architecture, including multiple convolution layers, pooling layers and fully connected layers, for extracting features from the three-dimensional scene. Features such as shape, texture, and structure are extracted from surface scenes. The extracted features will be used for subsequent deformation prediction, specifically through intelligent deformation early warning algorithms (including deformation value, deformation speed, acceleration and other threshold methods, Saito method, tangent angle method ), giving deformation warning and forecast.
优选的，所述S5中，采用提取到的特征对面场景进行实时监测和分析，根据形变信息模型判断当前场景是否存在异常情况，识别出异常变化或潜在的风险，当检测到异常情况时，触发预警机制，包括声音报警、图像显示、信息发送等方式，以提醒相关人员或系统进行进一步处理。Preferably, in S5, the extracted features are used to conduct real-time monitoring and analysis of the scene, and the deformation information model is used to determine whether there are abnormal situations in the current scene, and abnormal changes or potential risks are identified. When abnormal situations are detected, trigger Early warning mechanism includes sound alarm, image display, information sending, etc. to remind relevant personnel or systems for further processing.
优选的，所述S5中，根据预测结果，使用预设的规则发出相应的预警信号，根据预测结果的严重程度，预设的规则将决定发出的预警信号的类型和级别，例如，当预测结果超过预设阈值时，将发出警告信号；当预测结果低于预设阈值时，将发出提示信号。Preferably, in S5, according to the prediction result, a preset rule is used to issue a corresponding early warning signal. According to the severity of the prediction result, the preset rule will determine the type and level of the early warning signal to be issued. For example, when the prediction result When the preset threshold is exceeded, a warning signal will be issued; when the prediction result is lower than the preset threshold, a prompt signal will be issued.
本发明的技术效果和优点：Technical effects and advantages of the present invention:
本发明提供了一种基于面场景形变信息的智能预警方法，用于在面场景中准确识别和预测潜在的危险情况，该方法利用图像处理和深度学习技术，通过对面场景中的形变信息进行分析和建模，实现对异常情况的检测和预警，该方法具有高精度、实时性和自动化的特点，可广泛应用于安全监控、智能交通等领域。The present invention provides an intelligent early warning method based on facial scene deformation information, which is used to accurately identify and predict potential dangerous situations in facial scenes. The method uses image processing and deep learning technology to analyze the deformation information in facial scenes. and modeling to achieve detection and early warning of abnormal situations. This method has the characteristics of high precision, real-time and automation, and can be widely used in security monitoring, intelligent transportation and other fields.
附图说明Description of drawings
图1为本发明一种基于面场景形变信息的智能预警方法的流程示意图。Figure 1 is a schematic flow chart of an intelligent early warning method based on scene deformation information of the present invention.
具体实施方式Detailed ways
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, rather than all the embodiments. The specific embodiments described here are only used to explain the present invention and are not intended to limit the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.
本发明提供了如图1所示的一种基于面场景形变信息的智能预警方法，包括以下步骤：The present invention provides an intelligent early warning method based on scene deformation information as shown in Figure 1, which includes the following steps:
S1、面场景图像获取：通过图像采集设备获取目标面场景的图像；S1. Surface scene image acquisition: obtain the image of the target surface scene through the image acquisition device;
S2、图像处理：对获取的静态图像进行处理；S2. Image processing: process the obtained static images;
S3、图像追踪：对获取的动态图像进处理；S3. Image tracking: process the acquired dynamic images;
S4、建立模型：建立面场景形变信息的模型；S4. Establish model: Establish a model of scene deformation information;
S5、异常情况检测与预警：使用得到的模型进行实时监测和预警。S5. Abnormal situation detection and early warning: Use the obtained model for real-time monitoring and early warning.
本发明提供了一种基于面场景形变信息的智能预警方法，用于在面场景中准确识别和预测潜在的危险情况，该方法利用图像处理和深度学习技术，通过对面场景中的形变信息进行分析和建模，实现对异常情况的检测和预警，该方法具有高精度、实时性和自动化的特点，可广泛应用于安全监控、智能交通等领域。The present invention provides an intelligent early warning method based on facial scene deformation information, which is used to accurately identify and predict potential dangerous situations in facial scenes. The method uses image processing and deep learning technology to analyze the deformation information in facial scenes. and modeling to achieve detection and early warning of abnormal situations. This method has the characteristics of high precision, real-time and automation, and can be widely used in security monitoring, intelligent transportation and other fields.
其中，S1中，用于图像采集的设备为地基合成孔径雷达或者是GNSS-I NBSAR技术的卫星信号接收机，，获取监测场景的反射电磁波信号后，进行SAR成像，获取的目标场景图像包括静态图像和动态图像。S2中，对静态图像进行处理包括图像去噪、图像增强，以提高后续处理的准确性和稳定性。S3中，对动态图像进行处理包括对动态图像进行运动目标检测和跟踪，以提取面场景中的运动目标信息。Among them, in S1, the equipment used for image acquisition is a ground-based synthetic aperture radar or a satellite signal receiver with GNSS-I NBSAR technology. After acquiring the reflected electromagnetic wave signal of the monitoring scene, SAR imaging is performed. The acquired target scene image includes static Images and motion graphics. In S2, static images are processed including image denoising and image enhancement to improve the accuracy and stability of subsequent processing. In S3, processing dynamic images includes detecting and tracking moving targets on dynamic images to extract moving target information in surface scenes.
本方法基于面场景形变信息的分析：通过提取面场景的形变信息，能够更准确地捕捉到面场景的变化，提高预警的准确性；This method is based on the analysis of surface scene deformation information: by extracting the deformation information of the surface scene, it can more accurately capture the changes in the surface scene and improve the accuracy of early warning;
S4中，进行模型建立之前，先基于运动目标信息，通过差分干涉处理，获取面场景的形变信息，包括目标的形状、纹理、颜色、形变程度、形变速度，将数据导入深度学习算法，利用深度学习算法对形变信息进行训练和学习，实现对正常和异常形变的区分从而得到所需模型。S5中，使用深度学习模型对三维面场景进行特征提取，该深度学习模型采用卷积神经网络架构，包括多个卷积层、池化层和全连接层，用于从三维面场景中提取形状、纹理和结构等特征，提取的特征将用于后续的形变预测，具体通过智能化形变预警算法(包括形变值、形变速度、加速度等阈值法、斋藤法、切线角法)，给出形变预警预报。In S4, before establishing the model, based on the moving target information, the deformation information of the surface scene is obtained through differential interference processing, including the shape, texture, color, deformation degree, and deformation speed of the target, and the data is imported into the deep learning algorithm, using the depth The learning algorithm trains and learns the deformation information to distinguish between normal and abnormal deformation to obtain the required model. In S5, a deep learning model is used to extract features from three-dimensional surface scenes. The deep learning model uses a convolutional neural network architecture, including multiple convolution layers, pooling layers and fully connected layers, to extract shapes from three-dimensional surface scenes. , texture, structure and other features. The extracted features will be used for subsequent deformation prediction. Specifically, through the intelligent deformation early warning algorithm (including deformation value, deformation speed, acceleration and other threshold methods, Saito method, tangent angle method), the deformation is given Early warning forecast.
本方法进行多维度形变信息提取：本发明不仅考虑面场景的形状信息，还包括纹理、颜色等多维度的形变信息，从而更全面地分析面场景的变化；This method extracts multi-dimensional deformation information: This method not only considers the shape information of the surface scene, but also includes multi-dimensional deformation information such as texture and color, thereby more comprehensively analyzing changes in the surface scene;
S5中，采用提取到的特征对面场景进行实时监测和分析，根据形变信息模型判断当前场景是否存在异常情况，识别出异常变化或潜在的风险，当检测到异常情况时，触发预警机制，包括声音报警、图像显示、信息发送等方式，以提醒相关人员或系统进行进一步处理。S5中，根据预测结果，使用预设的规则发出相应的预警信号，根据预测结果的严重程度，预设的规则将决定发出的预警信号的类型和级别，例如，当预测结果超过预设阈值时，将发出警告信号；当预测结果低于预设阈值时，将发出提示信号。In S5, the extracted features are used to monitor and analyze the scene in real time. Based on the deformation information model, it is judged whether there are abnormal situations in the current scene, and abnormal changes or potential risks are identified. When abnormal situations are detected, an early warning mechanism is triggered, including sound. Alarm, image display, information sending, etc. to remind relevant personnel or the system for further processing. In S5, based on the prediction results, preset rules are used to issue corresponding early warning signals. Based on the severity of the prediction results, the preset rules will determine the type and level of the early warning signals. For example, when the prediction results exceed the preset threshold , a warning signal will be issued; when the prediction result is lower than the preset threshold, a prompt signal will be issued.
本方法的预警信号生成与响应：本发明能够根据形变分析结果生成相应的预警信号，并及时触发预警响应措施，增强了系统的实时性和可操作性。Early warning signal generation and response of this method: The present invention can generate corresponding early warning signals based on deformation analysis results, and trigger early warning response measures in a timely manner, thereby enhancing the real-time and operability of the system.
最后应说明的是：以上所述仅为本发明的优选实施例而已，并不用于限制本发明，尽管参照前述实施例对本发明进行了详细的说明，对于本领域的技术人员来说，其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换，凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。Finally, it should be noted that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, it is still The technical solutions described in the foregoing embodiments may be modified, or equivalent substitutions may be made to some of the technical features. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in within the protection scope of the present invention.
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311474405.4ACN117636601A (en) | 2023-11-07 | 2023-11-07 | Intelligent early warning method based on surface scene deformation information |
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311474405.4ACN117636601A (en) | 2023-11-07 | 2023-11-07 | Intelligent early warning method based on surface scene deformation information |
| Publication Number | Publication Date |
|---|---|
| CN117636601Atrue CN117636601A (en) | 2024-03-01 |
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311474405.4APendingCN117636601A (en) | 2023-11-07 | 2023-11-07 | Intelligent early warning method based on surface scene deformation information |
| Country | Link |
|---|---|
| CN (1) | CN117636601A (en) |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN120071147A (en)* | 2025-02-11 | 2025-05-30 | 安徽广安电子科技有限公司 | Deformation monitoring method based on GNSS and Ai video intelligent analysis |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN120071147A (en)* | 2025-02-11 | 2025-05-30 | 安徽广安电子科技有限公司 | Deformation monitoring method based on GNSS and Ai video intelligent analysis |
| Publication | Publication Date | Title |
|---|---|---|
| Wang et al. | A real-time bridge crack detection method based on an improved inception-resnet-v2 structure | |
| CN120014378B (en) | Ice landslide monitoring and early warning method and system based on AI image recognition | |
| CN111862065B (en) | Power transmission line diagnosis method and system based on multitask deep convolutional neural network | |
| CN111539325A (en) | Forest fire detection method based on deep learning | |
| CN120164206B (en) | Intelligent identification method and system for quality defects of power infrastructure hidden engineering | |
| CN116665016B (en) | Single-frame infrared dim target detection method based on improved YOLOv5 | |
| CN119672613B (en) | A surveillance video information intelligent processing system based on cloud computing | |
| CN117636601A (en) | Intelligent early warning method based on surface scene deformation information | |
| CN117854215A (en) | Fire alarm method and device based on time sequence image | |
| Ma et al. | Scene understanding method utilizing global visual and spatial interaction features for safety production | |
| CN119299332B (en) | A safety monitoring system and monitoring method based on machine vision | |
| CN119721134A (en) | Training method of acid-base leakage monitoring model, acid-base leakage monitoring method and device | |
| CN119296302A (en) | A safety risk analysis method for petrochemical port areas based on deep learning | |
| CN115343573A (en) | Voiceprint model evolution equipment fault fusion judgment method facing power grid monitoring | |
| CN119625572A (en) | A safety inspection and positioning method for oil and gas pipelines | |
| CN118549923A (en) | Video radar monitoring method and related equipment | |
| CN112183191A (en) | Method for detecting bird nest in power transmission line based on countermeasure training | |
| CN106791647A (en) | A state monitoring system and method for hydropower plants based on video intelligent recognition | |
| CN117152689A (en) | River channel target detection method and system based on vision | |
| CN105759605A (en) | Nonlinear system defect detection and positioning algorithm based on adaptive parameter model particle filter (PF) | |
| CN116129275A (en) | Mountain fire smoke identification method based on interference suppression threshold voting strategy | |
| Park et al. | Displacement detection of wooden cultural properties using unsupervised learning | |
| CN115100546A (en) | A mobilenet-based small target defect identification method and system for power equipment | |
| Pan et al. | Improving High-Voltage Line Obstacle Detection with Multi-Scale Feature Fusion in YOLO Algorithm | |
| Li et al. | [Retracted] The Key Technologies of Marine Multiobjective Ship Monitoring and Tracking Based on Computer Vision |
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right | Effective date of registration:20241127 Address after:No.1, shijichengzhong Road, Guandu District, Kunming City, Yunnan Province Applicant after:HUANENG LANCANG RIVER HYDROPOWER Inc. Country or region after:China Applicant after:SUZHOU SCIENCE AND TECHNOLOGY LEIKE SENSING TECHNOLOGY Co.,Ltd. Applicant after:Huaneng Group R&D Center Co., Ltd. Applicant after:BEIJING INSTITUTE OF TECHNOLOGY Address before:No.1, shijichengzhong Road, Guandu District, Kunming City, Yunnan Province Applicant before:HUANENG LANCANG RIVER HYDROPOWER Inc. Country or region before:China Applicant before:SUZHOU SCIENCE AND TECHNOLOGY LEIKE SENSING TECHNOLOGY Co.,Ltd. Applicant before:Huaneng Group R&D Center Co., Ltd. |