Public safety near water area risk intelligent monitoring system

Abstract

The invention provides a near-water-area risk intelligent monitoring method for the public safety field to prevent drowning events. The core of the method is to construct a multi-level monitoring system: firstly, deploying a high-definition monitoring camera in a key water area for capturing abnormal dynamic states of people falling into water in real time; secondly, rapidly analyzing the collected data by using a deep learning algorithm, identifying a potential drowning behavior mode, and reducing false alarm and missing alarm; once a suspicious condition is detected, the system immediately triggers an alarm mechanism, including sending accurate position information and warning signals to a city operation team or an emergency department near-water public area management team, so that quick response and rescue are realized, and personnel and property loss is avoided. The invention aims to realize real-time and efficient monitoring of people moving in the public water area by integrating a modern sensing technology, an intelligent analysis algorithm and an instant messaging means, effectively prevent the occurrence of drowning events, and can be widely applied to the field of urban public safety, especially early warning and alarming of near-water-area risk events.

Description

Technical Field

[0001] The present invention belongs to the intersection of public safety and intelligent technology, and specifically relates to a public safety near-water area risk intelligent monitoring system. Background Art

[0002] With the booming tourism industry in recent years, natural and artificial water bodies have attracted a large number of tourists as important tourism resources. However, water activities are accompanied by the risk of frequent drowning accidents, which is not only a major threat to personal safety, but also poses a severe challenge to the management of public areas near water. Although traditional manual lifeguard monitoring can prevent drowning accidents to a certain extent, it is difficult to achieve effective all-weather and all-round monitoring due to factors such as limited manpower coverage and long response time.

[0003] With the rapid development of information technology, especially artificial intelligence technology, intelligent monitoring systems have begun to be explored and applied in the field of drowning prevention. For example, some systems try to use cameras with basic motion detection algorithms to identify abnormal behavior, but such technologies are often limited in their practical application due to low recognition accuracy and prone to false alarms. In addition, most existing technologies lack instant and efficient alarm mechanisms and accurate location services, resulting in insufficient rescue response and missed rescue opportunities.

[0004] Therefore, it is particularly important to develop a method for intelligent monitoring of public safety near-water risks that integrates high-definition video monitoring, intelligent image recognition, real-time data transmission and rapid response. This method needs to be able to accurately and timely identify potential drowning behaviors in complex and changeable water environments, while ensuring the reliability and response speed of the alarm system, thereby greatly reducing the probability of drowning accidents and improving the intelligence and refinement of public water safety management. The present invention came into being in this context, aiming to solve the key technical problems in the current public safety near-water risk monitoring through technological innovation. Specifically, the present invention first installs a surveillance camera with high definition and wide-angle vision in key waters to build an all-round, no-dead-angle monitoring system. Then, low-latency, high-stability wireless transmission technology is used to ensure that the video stream is transmitted to the cloud server in real time, and high-performance computing resources are used to provide a solid foundation for subsequent intelligent analysis. The YOLOv8 deep learning algorithm is integrated, and after a large number of dangerous behavior samples are trained, the model can accurately identify dangerous behaviors such as drowning, which improves the accuracy and timeliness of monitoring. Once the system identifies the risk of drowning, it will immediately trigger a multi-channel alarm mechanism to ensure the rapid start of the rescue operation. Collect and analyze monitoring data, evaluate the distribution of drowning risks, provide scientific prevention measures and resource optimization plans for public areas near water, and continuously improve monitoring efficiency. Summary of the invention

[0005] The purpose of the present invention is to provide a public safety near-water area risk intelligent monitoring system to address the existing problems in drowning prevention in the water environment of public areas near water. By integrating modern sensing technology, intelligent analysis algorithms and instant communication means, it can achieve safe and efficient monitoring of people in public waters and effectively prevent the occurrence of drowning incidents. The purpose of the present invention is achieved through the following technical solutions:

[0006] A public safety near-water risk intelligent monitoring system mainly includes the following contents:

[0007] a. An array of high-definition cameras deployed around public areas near water to continuously capture and transmit video of visitors’ activities in the waters;

[0008] b. Video data transmission module, responsible for transmitting the video stream collected by the camera to the remote server in real time and stably;

[0009] c. A server-side processing unit, configured with high-performance computing resources, receives and processes video streams from each camera;

[0010] d. Intelligent drowning behavior recognition model, specifically a target detection algorithm based on YOLOv8, deployed in the server-side processing unit, used to analyze video data in real time and identify drowning or potentially dangerous behaviors;

[0011] e. Automatic alarm and positioning method, connected to the intelligent drowning behavior recognition model. Once the model confirms the risk of drowning, the alarm mechanism is immediately triggered and information with precise geographic coordinates is sent to designated rescue agencies and managers.

[0012] Furthermore, the high-definition camera array should have night vision and waterproof functions to ensure reliable monitoring under all-weather and all-climate conditions. By deploying a high-definition real-time monitoring camera array in places with large traffic and high safety risks, real-time and continuous monitoring of tourist behavior can be achieved.

[0013] Furthermore, the video data transmission module adopts encryption technology to ensure the timeliness of video data during transmission; the video data transmission module is mainly responsible for transmitting real-time monitoring data to the server for subsequent processing.

[0014] Furthermore, the server-side processing unit is mainly a configuration of the server, which can receive real-time video streams and has strong computing capabilities.

[0015] Furthermore, the intelligent drowning behavior recognition model mainly includes three stages: data labeling preprocessing, training, and deployment application. First, a large amount of video material needs to be collected from actual surveillance videos of public areas near water, tourists playing in the water, and simulated drowning scene videos. These video materials need to cover different ages, genders, and individuals, as well as activities in different time periods and water environments. Secondly, the original video is divided into frame images according to a certain time interval or key frame extraction technology to facilitate subsequent annotation and analysis. This process may remove redundant information and retain key dynamically changing frames. Subsequently, professionals are organized to manually annotate the extracted frame images, clearly mark the key frames of behaviors such as drowning and playing in the water, and mark people in dangerous states. Finally, semantic segmentation technology is used to identify the edge of the water area to facilitate judgment of whether a person is in contact with the water area. At the same time, the YOLOV8 target detection model is used for model training, so that the model can intelligently identify dangerous behaviors such as playing in the water and drowning, and the model is deployed on a high-performance server to identify a steady stream of video streams. After specific training, it can not only identify obvious drowning actions, but also identify early signs of danger such as playing in the water, thereby improving the timeliness of early warning.

[0016] Furthermore, the automatic alarm and positioning system includes but is not limited to security management web notifications, on-site broadcast warnings, and direct contact with the nearest lifeguard or rescue team to ensure multi-channel and rapid response. Once the target detection model identifies dangerous behavior, the server will immediately trigger an alarm, record the alarm time, the type of dangerous behavior and the location of the danger, and promptly notify the management of the public area near the water to take action.

[0017] Secondly, the invention of this application also includes a method for using a public safety near-water risk intelligent monitoring system, comprising the following steps:

[0018] a. Continuously collect video data through a high-definition camera array;

[0019] b. Real-time transmission of video data to the server;

[0020] c. Use the YOLOv8 model to analyze video data and identify drowning or potentially dangerous behaviors;

[0021] d. When danger is detected, the alarm mechanism is automatically activated and the precise location information is sent to the rescue unit;

[0022] e. Record and analyze alarm events, provide feedback to system optimization, and continuously improve monitoring efficiency.

[0023] The beneficial effects of the present invention are:

[0024] Traditional manual observation is difficult to cover all waters and is prone to negligence. Compared with traditional manual observation, the public safety near-water risk intelligent monitoring system provided by the present invention has higher accuracy and efficiency. Before a drowning accident occurs, the intelligent detection system can detect drowning risks in time and prevent drowning accidents in advance; moreover, the system can also work 24 hours a day to ensure the safety of waters and comprehensively improve the accuracy and timeliness of drowning warnings; at the same time, the provided intelligent detection system has a multi-channel information transmission method, thereby constructing a multi-dimensional, multi-channel instant response mechanism, which provides scientific data support and decision-making basis for preventing drowning, and effectively promotes the intelligence and refinement of public water safety management. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 Schematic diagram of the process of the present invention ；

[0026] Figure 2 This is a schematic diagram of edge recognition of water areas achieved by the method of the present invention.

[0027] Figure 3 Schematic diagram of visitor monitoring of the model of the method of the present invention.

[0028] Figure 4 Schematic diagram of drowning prevention monitoring of the method of the present invention. DETAILED DESCRIPTION

[0029] In order to better understand the technical content of the present invention, a specific embodiment is given and described as follows in conjunction with the accompanying drawings. Figure 1 As shown, the present invention provides a public safety near-water risk intelligent monitoring system, comprising:

[0030] a. An array of high-definition cameras deployed around public areas near water to continuously capture and transmit video of visitors’ activities in the waters;

[0031] b. Video data transmission module, responsible for transmitting the video stream collected by the camera to the remote server in real time and stably;

[0032] c. A server-side processing unit, configured with high-performance computing resources, receives and processes video streams from each camera;

[0033] d. Intelligent drowning behavior recognition model, specifically a target detection algorithm based on YOLOv8, deployed in the server-side processing unit, used to analyze video data in real time and identify drowning or potentially dangerous behaviors;

[0034] e. Automatic alarm and positioning method, connected to the intelligent drowning behavior recognition model. Once the model confirms the risk of drowning, the alarm mechanism is immediately triggered and information with precise geographic coordinates is sent to designated rescue agencies and managers.

[0035] The further description of the specific implementation is as follows:

[0036] First of all, the high-definition camera array should use equipment with high resolution (such as 1080P or above) and wide-angle lens to ensure that the water conditions can be clearly captured under different lighting conditions. The cameras should be evenly distributed along the perimeter of the public area near the water, covering all swimming and playing areas, while considering the overlap of viewing angles to reduce blind spots. Each camera is equipped with a waterproof housing and a stable bracket to ensure normal operation in bad weather. In addition, the camera should have an intelligent dynamic tracking function, automatically focusing on abnormal behavior to enhance monitoring accuracy.

[0037] Then, video data transmission uses high-speed optical fiber or wireless communication technology to ensure low latency and high stability of video stream transmission. The server side uses a high-performance architecture, including a high-performance GPU server cluster, to support the parallel operation of the YOLOv8 model and accelerate video processing. The server operating system is Linux, and a professional video stream processing software stack, such as OpenCV, is configured for video decoding, preprocessing, and format conversion.

[0038] Then, based on the YOLOv8 object detection algorithm, we first trained it on a large-scale labeled dataset of dangerous behaviors such as playing in water and drowning. The dataset contains multiple scenes of public areas near water. We regularly iterate the model with newly collected data to maintain its recognition accuracy. In addition, we use semantic segmentation to identify the contours of dangerous areas in the water, such as Figure 2 shown.

[0039] Once the intelligent drowning behavior recognition model detects a drowning risk, it immediately triggers the alarm system through the API interface, combining the camera GPS information with the video analysis results to accurately locate the location of the drowning incident. The alarm information includes the real-time location coordinates of the drowning person, camera ID, timestamp and brief description, and quickly notifies rescue personnel and management personnel through multiple channels such as the Web and applet push. At the same time, the alarm location is highlighted on the monitoring center interface to facilitate rapid response and command and dispatch.

[0040] In summary, the present invention realizes all-round, real-time monitoring of public areas near water and intelligent drowning risk warning through highly integrated hardware and software systems, greatly improves safety management efficiency and emergency response speed, and provides strong protection for tourist safety.

[0041] Although the present invention has been disclosed as above with preferred embodiments, it is not intended to limit the present invention. A person with ordinary knowledge in the technical field to which the present invention belongs may make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention shall be determined by the definition of the claims.

Claims

1. A public safety near-water risk intelligent monitoring system, characterized in that: include: a. An array of high-definition cameras deployed around public areas near water to continuously capture and transmit video of visitors’ activities in the waters; b. Video data transmission module, responsible for transmitting the video stream collected by the camera to the remote server in real time and stably; c. A server-side processing unit, configured with high-performance computing resources, receives and processes video streams from each camera; d. Intelligent drowning behavior recognition model, specifically a target detection algorithm based on YOLOv8, deployed in the server-side processing unit, used to analyze video data in real time and identify drowning or potentially dangerous behaviors; e. Automatic alarm and positioning system, connected to the intelligent drowning behavior recognition model. Once the model confirms the risk of drowning, it will immediately trigger the alarm mechanism and send information with precise geographic coordinates to designated rescue agencies and managers.

2. According to claim 1, a public safety near-water risk intelligent monitoring system is characterized in that: The high-definition camera array should have night vision and waterproof functions to ensure reliable monitoring under all-weather and all-climate conditions.

3. According to claim 1, a public safety near-water risk intelligent monitoring system is characterized in that: The video data transmission module adopts encryption technology to ensure the timeliness of video data during transmission.

4. The public safety near-water area risk intelligent monitoring system according to claim 1 is characterized in that: The intelligent drowning behavior recognition model has undergone specific training and can not only recognize obvious drowning movements, but also recognize early danger signs such as playing in the water, thereby improving the timeliness of early warning.

5. The public safety near-water area risk intelligent monitoring system according to claim 1 is characterized in that: The automatic alarm and positioning system includes but is not limited to security management web notifications, on-site broadcast warnings, and direct contact with the nearest lifeguard or rescue team to ensure multi-channel and rapid response.

6. A public safety near-water area risk intelligent monitoring system according to any one of claims 1 to 5, characterized in that: It further includes a data analysis and optimization module to train and optimize model recognition accuracy, analyze high-risk areas and time periods, and guide the adjustment of safety management strategies in public areas near water.

7. A method for using the public safety near-water area risk intelligent monitoring system according to claims 1 to 6, characterized in that: The following steps are involved: a. Continuously collect video data through a high-definition camera array; b. Real-time transmission of video data to the server; c. Use the YOLOv8 model to analyze video data and identify drowning or potentially dangerous behaviors; d. When danger is detected, the alarm mechanism is automatically activated and the precise location information is sent to the rescue unit; e. Record and analyze alarm events, provide feedback to system optimization, and continuously improve monitoring efficiency.

Images