Multi-scene intelligent early warning and emergency linkage system

Abstract

The invention discloses a multi-scene intelligent early warning and emergency linkage system, and belongs to the technical field of intelligent early warning and emergency management. The system adopts a layered distributed architecture and comprises a sensing layer, a data transmission layer, a data processing and analyzing layer, an application layer and a user interaction layer. The sensing layer collects multi-source real-time data through various devices and interfaces; the data transmission layer adopts a wired and wireless network combined mode, and data is encrypted and transmitted; the data processing and analyzing layer is used for cleaning, storing, analyzing and mining the data and constructing a knowledge graph; the application layer realizes intelligent early warning, emergency linkage and auxiliary decision making functions; and the user interaction layer provides a multi-channel interaction mode. The system can adapt to various scenes, realizes multi-source data fusion analysis, intelligent and accurate early warning and efficient emergency linkage, effectively solves the defects of the existing system, can be widely applied to the fields of urban management, industrial production and the like, and also has a relatively high practical value.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent early warning and emergency management, in particular to a multi-scene intelligent early warning and emergency linkage system. BACKGROUND

[0002] In the current society, various types of emergencies occur frequently, such as earthquakes, floods, fires, industrial accidents, public health events, etc. These events pose a serious threat to people's life and property safety and social stability. Traditional early warning and emergency handling methods have many drawbacks: information acquisition channels are single and not timely, often relying on manual reporting or limited monitoring equipment, leading to a lag in the perception of emergencies; data analysis capabilities are weak, with simple statistical analysis methods being used, making it difficult to mine potential risk information from massive data; emergency response speed is slow, information is not shared between departments, collaborative linkage is difficult, emergency resource allocation is unreasonable, and effective emergency response force cannot be quickly formed. With the continuous development of technologies such as the Internet of Things, big data, and artificial intelligence, some early warning systems for specific scenarios have emerged, such as forest fire prevention early warning systems and urban traffic monitoring systems. However, these systems are mostly limited to a single field, lack multi-scene adaptability, have a single data source, and fail to achieve deep integration with emergency linkage mechanisms, making it difficult to meet the complex and varied needs of multi-scene emergency management. SUMMARY

[0003] The purpose of the present application is to provide a multi-scene intelligent early warning and emergency linkage system to solve the problems of existing early warning and emergency handling systems, such as untimely information acquisition, limited data analysis capabilities, slow emergency response speed, difficulty in collaborative linkage, and poor multi-scene adaptability.

[0004] To solve the above technical problems, the present application provides a multi-scene intelligent early warning and emergency linkage system, which adopts a layered distributed architecture, including a perception layer, a data transmission layer, a data processing and analysis layer, an application layer, and a user interaction layer. 1. Perception layer The perception layer, as the basis for the system to obtain external information, achieves real-time collection of multi-source data through the deployment of various data collection devices and interfaces. Specifically, it includes: Environmental sensors: high-precision meteorological sensors, geological sensors, air quality sensors, and hydrological sensors, etc. are selected to monitor temperature, humidity, air pressure, seismic waves, mountain displacement, harmful gas concentration, water level, etc. For example, a temperature and humidity pressure integrated sensor with model [specific model] can be used, with a measurement accuracy of ±0.5℃ (temperature), ±2% RH (humidity), ±0.1 hPa (air pressure), which can meet the requirements of accurate monitoring of meteorological conditions. Equipment sensors: Corresponding vibration sensors, temperature sensors, current sensors, etc., are provided for different types of industrial equipment, power facilities, and transportation facilities. For example, installing a [specific model] vibration sensor on a motor can collect the motor's vibration frequency and amplitude in real time, determining whether the motor is in normal operating condition. Video surveillance equipment: Employs high-definition cameras with intelligent analysis capabilities, boasting a resolution of at least 2 megapixels, and supporting automatic identification of abnormal behaviors such as fire smoke and intrusion. For example, selecting a smart camera of [specific model], its built-in intelligent analysis algorithm can identify fire smoke and issue an alarm signal within 10 seconds. IoT devices: Connect to smart meters, water meters, and other terminal devices via IoT gateways, using LoRa communication technology for low-power data transmission. Simultaneously, install positioning modules on emergency rescue equipment such as fire hydrants and first-aid kits to obtain their real-time location information. Third-party data interfaces: Standardized data interface protocols, such as RESTful APIs, are used to periodically obtain data such as weather warnings and traffic flow. 2. Data transmission layer The data transmission layer flexibly selects the transmission method according to the needs of different scenarios: Wired network: In urban centers, industrial parks and other areas, fiber optic Ethernet is used for data transmission, with a transmission rate of over 1000Mbps, ensuring stable transmission of large amounts of real-time data. Wireless Networks: In areas such as wilderness and mountainous regions, 4G / 5G networks, satellite communication, or LoRa wireless self-organizing networks are used. Among them, 4G / 5G networks are suitable for areas with network coverage, satellite communication is used for remote areas without terrestrial network coverage, and LoRa wireless self-organizing networks have a communication range of 3-5 kilometers, suitable for short-range data transmission in localized areas. Data encryption and secure transmission: All data is encrypted using SSL / TLS encryption protocol before transmission. At the same time, the sender and receiver of data are authenticated. Only authenticated devices and systems can exchange data, ensuring the security of data transmission. 3. Data Processing and Analysis Layer Data cleaning and preprocessing: Data denoising algorithms, such as wavelet transform denoising algorithm, are used to remove noise from sensor data; missing values ​​are filled using mean imputation method, interpolation method, etc.; outliers are detected and processed using Z-score method to improve data quality. Data storage: HDFS is used to store massive amounts of unstructured data, such as video surveillance data; NoSQL databases (such as MongoDB) are used to store semi-structured data, such as real-time data streams from sensors; and MySQL databases are used to store structured data, such as basic device information and historical event records. Data Analysis and Mining: Real-time data is processed using the Apache Flink streaming framework, with processing latency controlled within seconds; association rule mining algorithms are used to analyze the relationships between different data, such as the relationship between temperature and equipment failure; a risk prediction model is built by training historical data through a neural network model, with a prediction accuracy of over 85%; a knowledge graph is constructed to visualize entities such as events and locations and their relationships, providing knowledge support for decision-making. 4. Application Layer Intelligent Early Warning Module: Based on preset early warning thresholds and risk prediction models, this module automatically generates early warning information when monitored data exceeds the threshold or the model predicts a risk. Early warning levels are divided into four categories: general, moderate, severe, and extremely severe, indicated by blue, yellow, orange, and red respectively. Notifications are sent to relevant personnel via SMS, app push notifications, and other methods. Emergency Response Module: Upon receiving an early warning, this module automatically activates the corresponding emergency plan and issues rescue mission instructions. It utilizes intelligent algorithms to optimize the allocation of emergency resources such as rescue teams and supplies, shortening rescue time. Decision Support Module: Based on knowledge graphs and data analysis results, this module provides decision-makers with event situation assessment reports. Through simulation technology, it simulates the effects of different emergency response plans, such as simulating evacuation times for different evacuation routes, providing decision-makers with a reference for selecting the optimal solution. 5. User Interaction Layer Web application: Adopting a B / S architecture, users can access the system through a browser to view real-time monitoring data, early warning information, etc., and it supports data query, statistics and export functions. Mobile application: Develop Android and iOS versions of the APP to realize functions such as receiving early warning information and uploading on-site data. The interface is simple and easy to use, suitable for on-site personnel to operate. Large-screen visualization: An LED screen is set up in the emergency command center to display key information of the system in the form of maps, charts and other forms through data visualization technology. It supports multi-screen linkage and data drill-down, making it convenient for commanders to grasp the overall situation.

[0005] In summary, due to the adoption of the above-mentioned technologies, the beneficial effects of this invention are: 1. It has strong adaptability to multiple scenarios and can meet the early warning and emergency needs of various scenarios such as urban management and industrial production. It provides targeted solutions through customized modules and models. 2. It has enabled the fusion and analysis of multi-source data, improving the accuracy and timeliness of early warnings, and enabling the early detection of potential risks, thus buying time for emergency response. 3. The automated emergency response mechanism enables collaborative work among departments and optimized allocation of emergency resources, thereby improving emergency response speed and rescue efficiency. 4. Abundant visualization and decision-making support tools provide decision-makers with comprehensive information support, which helps to formulate scientific and reasonable emergency response plans. Attached Figure Description

[0006] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention, making other features, objects, and advantages of the invention more apparent. The illustrative embodiments of the invention illustrated in the drawings and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a system flowchart of the present invention. Detailed Implementation

[0007] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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 a part of the embodiments of the present invention, not all of them. 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. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to represent selected embodiments of the invention. 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.

[0008] In the description of this invention, it should be understood that the terms indicating orientation or positional relationship are based on the orientation or positional relationship shown in the drawings and are only for the convenience of describing the invention and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention.

[0009] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific context of the specification.

[0010] This invention provides a multi-scenario intelligent early warning and emergency response system, with specific embodiments for different application scenarios as follows: Example 1: Urban Traffic Scenario In urban traffic scenarios, the deployment and operation process of this system is as follows: Perception Layer Deployment: Traffic flow sensors, vehicle speed sensors, and high-definition cameras are installed at major intersections and road sections throughout the city, and are connected to the data interface of the traffic signal control system. The traffic flow sensors use coil sensors to accurately count the number of vehicles passing through per unit time; the vehicle speed sensors can monitor vehicle speed in real time with an accuracy of ±1km / h; the high-definition cameras have license plate recognition capabilities and can identify behaviors such as illegal parking and running red lights. Data transmission and processing: Data collected by the perception layer is transmitted to the data transmission layer via fiber optic Ethernet, and then encrypted before entering the data processing and analysis layer. This layer uses the Apache Flink streaming computing framework to analyze real-time traffic data and combines it with a machine learning model trained on historical traffic congestion data to predict the probability of traffic congestion on each road segment. Intelligent Early Warning and Emergency Response: When the system predicts that the probability of congestion on a certain road segment will exceed 70% in 30 minutes, the intelligent early warning module generates a yellow warning message and pushes it to the web terminal of the traffic management department and the mobile terminal of the management personnel. The emergency response module activates the traffic diversion plan, automatically adjusts the traffic light timings of the road segment and surrounding intersections, and extends the green light duration; at the same time, it issues congestion warnings and detour suggestions to drivers through city traffic radio and navigation apps. Application results: Through the application of this system, the average congestion time on main roads during the morning rush hour in the city has been reduced by more than 20%, and the emergency response time for traffic accidents has been reduced by 30%, which has greatly improved the operational efficiency of urban traffic. Example 2: Natural Disaster Prevention Scenario (Taking Mountain Flood Early Warning as an Example) The systematic implementation method for flood prevention in mountainous areas is as follows: Sensing layer deployment: Water level sensors and water flow velocity sensors are installed along the river channels in the mountainous watershed. Soil moisture sensors and mountain displacement monitoring equipment are deployed on the hillsides. Meanwhile, weather stations are installed in villages surrounding the watershed to monitor meteorological parameters such as rainfall and wind speed. The water level sensors have a measurement range of 0-10m and an accuracy of ±1cm; the mountain displacement monitoring equipment uses GPS positioning technology and has a monitoring accuracy of ±3mm.

[0011] Data transmission and processing: Due to poor network coverage in some mountainous areas, the sensing layer data is transmitted via a combination of LoRa wireless ad hoc network and satellite communication. The data processing and analysis layer compares and analyzes real-time collected data such as water level and rainfall with historical flood data, and uses a constructed flood prediction knowledge graph to assess the probability of flood occurrence and its impact range. Intelligent early warning and emergency response: When the system detects a continuous rise in water levels and rainfall reaching a critical threshold, the intelligent early warning module issues a red alert. The emergency response module immediately activates the personnel evacuation plan, sending evacuation instructions to village officials in nearby villages, specifying evacuation routes and assembly points; simultaneously, it notifies local armed police and rescue teams to prepare for rescue operations, and allocates emergency supplies such as inflatable boats and life jackets to designated locations. Application Results: In mountain flood prevention, the system successfully issued early warnings two hours in advance, helping more than 500 villagers to evacuate safely before the flood arrived, reducing casualties and property losses, and providing strong protection for the prevention of natural disasters in mountainous areas. Example 3: Public Health Emergency Prevention and Control Scenario (Taking Epidemic Prevention and Control in a Large Shopping Mall as an Example) The system was applied in the epidemic prevention and control efforts of large shopping malls as follows: Sensing layer deployment: Install infrared body temperature detectors at the mall entrance, install crowd density sensors in the passageways on each floor, and connect them to the mall's air conditioning system operation data interface. Data transmission and processing: Data from the perception layer is transmitted to the data processing and analysis layer via the mall's internal wired network. Intelligent Early Warning and Emergency Response: The intelligent early warning module generates an orange alert and pushes it to the mobile devices of mall security personnel. The emergency response module activates the emergency plan for handling individuals with fever, notifying security personnel to guide the individual to the isolation and observation area, and automatically retrieving video footage of the individual's movements within the mall. Simultaneously, when the crowd density on a certain floor exceeds 2 people per square meter, the system issues a yellow alert and prompts customers to move to other floors via displays within the mall to avoid crowding. Application Results: Through the application of this system, large shopping malls can quickly identify and promptly handle individuals with fever, effectively reducing the risk of epidemic transmission. At the same time, it can reasonably control the density of people in the mall, ensuring the safety and health of customers.

Claims

1. A multi-scenario intelligent early warning and emergency response system, characterized in that: Comprising comprise a perception layer, a data transmission layer, a data processing and analysis layer, an application layer and a user interaction layer; The perception layer is deployed with environment sensors, device sensors, video monitoring devices, Internet of Things devices and third-party data interfaces for collecting multi-source real-time data; The data transmission layer adopts a combination of wired networks and wireless networks to transmit the data collected by the perception layer to the data processing and analysis layer, and the transmission data is encrypted by the SSL / TLS encryption protocol; The data processing and analysis layer includes a data cleaning and preprocessing module, a data storage module, a data analysis and mining module, the data analysis and mining module uses stream computing technology for real-time data analysis, combines data mining algorithms and machine learning models to process historical data, and also constructs a knowledge graph; The application layer includes an intelligent early warning module, an emergency linkage module and an auxiliary decision-making module, the intelligent early warning module can generate early warning information of different levels, the emergency linkage module can automatically start the emergency response process and allocate emergency resources, and the auxiliary decision-making module provides decision support based on the knowledge graph and data analysis results; The user interaction layer provides three interactive ways of Web application, mobile application and large-screen visual display. 2.The multi-scene intelligent early warning and emergency linkage system according to claim 1, characterized in that, The environment sensors of the perception layer can monitor meteorological conditions, geological conditions, air quality and hydrological parameters; the device sensors are used to collect the operating state parameters of industrial production equipment, power facilities and transportation facilities; the video monitoring devices can identify abnormal behavior and assist in accident scene evaluation; the Internet of Things devices can obtain energy consumption data, infrastructure operating state information and related information of emergency rescue equipment. 3.The multi-scene intelligent early warning and emergency linkage system according to claim 1, characterized in that, The wired network of the data transmission layer adopts optical fiber Ethernet, which is suitable for urban centers, industrial parks and large building areas; the wireless network includes 4G / 5G mobile communication network, satellite communication network and wireless ad hoc network, which is suitable for field, mountainous area and construction site scenes; and the data transmission layer adopts identity authentication and access control mechanism to verify the data sender and receiver.

4. The multi-scene intelligent early warning and emergency linkage system according to claim 1, characterized in that, The data cleaning and preprocessing module of the data processing and analysis layer uses data denoising algorithm, missing value filling algorithm and outlier detection algorithm to process the original data; the data storage module uses Hadoop distributed file system combined with NoSQL database to store the cleaned data, and introduces a relational database to store the structured data after sorting and aggregation; the machine learning model of the data analysis and mining module includes neural network, decision tree and support vector machine, and the knowledge graph contains entities and relationships between entities in different fields.

5. The multi-scene intelligent early warning and emergency linkage system according to claim 1, characterized in that, The intelligent early warning module of the application layer can generate early warning information according to preset early warning rules and thresholds, in combination with a risk prediction model and an event classification model, the early warning information includes event type, occurrence time, location, severity, possible impact range and suggested countermeasures, and is displayed in a visual manner, and an emergency disposal scheme can be automatically generated and dynamically adjusted; the auxiliary decision-making module can perform situation assessment and risk analysis on the emergency, retrieve a historical case library and assess an emergency disposal scheme through simulation technology. 6.The multi-scene intelligent early warning and emergency linkage system according to claim 1, characterized in that, The Web end application of the user interaction layer allows users to view monitoring data, early warning information and emergency disposal progress and perform management operations; the mobile end application allows users to obtain system information, receive early warning notifications, upload field data and execute task instructions at any time and any place; the large-screen visual display system adopts graphic rendering technology and interactive design, and realizes multi-screen linkage, dynamic switching and data drilling functions.

7. The multi-scene intelligent early warning and emergency linkage system according to claim 1, characterized in that, The intelligent early warning module of the application layer adopts a risk grading early warning mechanism, divides early warning information into four levels of general, relatively heavy, severe and extremely severe, and identifies them by different colors, icons or sounds, and can dynamically adjust the early warning level according to the development of the risk event. 8.The multi-scene intelligent early warning and emergency linkage system according to claim 1, characterized in that, The emergency linkage module of the application layer establishes an emergency resource management database, and realizes real-time control of the quantity, position, state and availability of emergency resources, and dynamically allocates emergency resources in the emergency disposal process by using intelligent algorithms. 9.The multi-scene intelligent early warning and emergency linkage system according to claim 1, characterized in that, The auxiliary decision-making tools provided by the auxiliary decision-making module of the application layer include a risk assessment model, an emergency resource allocation optimization algorithm and an emergency plan intelligent recommendation system; the knowledge base of the auxiliary decision-making module integrates historical cases, laws and regulations, professional knowledge and expert experience.

10. The multi-scene intelligent early warning and emergency linkage system according to claim 1, characterized in that, The data analysis and mining module of the data processing and analysis layer adopts the stream computing technology Apache Flink, and the data mining algorithm includes association rule mining, clustering analysis and classification algorithm.

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