Pollution event quick response and management system based on big data analysis

Abstract

The invention discloses a pollution event quick response and management system based on big data analysis, which belongs to the field of track big data and environmental protection, is used for monitoring, early warning, responding and processing environmental pollution events, and comprises a data acquisition module, a data analysis module, an event simulation and prediction module, an emergency response coordination module and an information issuing module. The data acquisition module receives and processes AIS data, meteorological data, geographic information data and pollutant monitoring data; the data analysis module uses a machine learning algorithm for classification and prediction; the event simulation and prediction module performs simulation; the emergency response coordination module coordinates emergency response activities of all departments and records and tracks the treatment process of pollution events; and the information publishing module publishes real-time information to the public through a public information publishing platform. Compared with the prior art, the response speed and the processing efficiency of the environmental pollution event are greatly improved, and meanwhile, the government and the environmental protection institution can more accurately formulate environmental policies and management measures.

Description

Technical Field

[0001] The invention discloses a pollution incident rapid response and management system based on big data analysis, belonging to the field of trajectory big data and environmental protection. Background Art

[0002] In the field of modern environmental management and monitoring, the comprehensive utilization of ship automatic identification system (AIS) trajectory big data, meteorological data and pollutant monitoring data has become an important development direction. The combination of these technologies provides new possibilities for accurate monitoring and timely response to environmental pollution incidents.

[0003] As an automatic tracking system, AIS can provide real-time location and movement information of ships by exchanging information between ships and coastal stations. The data generated by AIS includes key information such as the ship's identity, location, speed, and heading. The accumulation of these data forms a huge amount of trajectory big data. In the field of environmental protection, AIS trajectory big data can be used to monitor ship emissions, analyze routes, and predict possible pollution incidents, which is particularly important for marine environmental protection.

[0004] Meteorological data includes temperature, humidity, wind speed, wind direction, precipitation and other climate parameters, which are crucial for understanding and predicting the diffusion path of pollutants. Wind direction and wind speed can affect the spread of pollutants in the air, while temperature and humidity affect the dissolution and diffusion of pollutants in water bodies. By analyzing meteorological data, the impact range of pollution incidents can be more accurately predicted and emergency response plans can be optimized.

[0005] The development of pollutant monitoring technology provides real-time and accurate data support for environmental protection. This includes air quality monitoring, water quality monitoring, and soil pollution monitoring. By deploying sensors and monitoring stations, data on pollutant concentrations and distribution can be collected in real time. This data is not only crucial for tracking current pollution levels, but also provides a basis for predicting future pollution trends and formulating prevention and control measures.

[0006] Combining AIS trajectory big data, meteorological data and pollutant monitoring data can form a comprehensive environmental monitoring and management system. For example, AIS data can be used to track possible pollution sources - ships, combined with meteorological data to predict the diffusion path of pollutants, and pollutant monitoring data provides a direct understanding of the pollution conditions on site. The comprehensive application of this data can greatly improve the response speed and processing efficiency of environmental pollution incidents, and also help governments and environmental protection agencies to formulate environmental policies and management measures more accurately. Therefore, the present invention proposes a rapid response and management system for pollution incidents based on big data analysis that combines AIS trajectory big data, meteorological data, and pollutant monitoring data. Summary of the invention

[0007] The purpose of the present invention is to provide a pollution incident rapid response and management system based on big data analysis to solve the problems of low efficiency in monitoring, early warning, response and processing of environmental pollution incidents in the prior art.

[0008] A pollution incident rapid response and management system based on big data analysis, including: a data acquisition module, a data analysis module, an event simulation and prediction module, an emergency response coordination module and an information release module;

[0009] The data acquisition module receives and processes AIS data, meteorological data, geographic information data and pollutant monitoring data;

[0010] The data analysis module uses a big data platform to process and analyze data and applies machine learning algorithms to classify and predict pollution events. The machine learning algorithms include random forests and neural networks.

[0011] The event simulation and prediction module simulates the diffusion of pollutants and visualizes the diffusion path and impact area of ​​pollutants in combination with GIS software;

[0012] The emergency response coordination module coordinates the emergency response activities of various departments through the central control center, and records and tracks the handling process of pollution incidents through the emergency response information system;

[0013] The information release module releases real-time information to the public through a public information release platform, which includes a website and a mobile application, and sets up social media channels and SMS services to ensure rapid dissemination of information.

[0014] The data acquisition module receives and processes pollutant monitoring data, which includes sulfur oxide emission data, nitrogen oxide emission data, carbon dioxide emission data and oily wastewater emission data.

[0015] The data analysis module data processing includes data acquisition and data preprocessing.

[0016] Data collection in the data analysis module includes collecting AIS data, meteorological data and pollutant monitoring data from data sources. AIS data includes the ship's position, speed and heading information, meteorological data includes wind speed, wind direction and temperature, and pollutant monitoring data includes the concentration level of pollutants in the environment.

[0017] Data preprocessing in the data analysis module includes:

[0018] S1. After cleaning and formatting the collected data, removing outliers and noise, data fusion is performed to unify the timestamps and formats of different data sources;

[0019] S2. Data analysis is to extract key features based on data characteristics. The extracted key features include the behavior characteristics of ships based on AIS data, environmental conditions based on meteorological data, and pollutant distribution based on pollutant monitoring data. The extracted key features are standardized. The standardized processing includes normalization or coding.

[0020] S3. Applying machine learning algorithms is to select appropriate machine learning models based on the nature of the problem. Machine learning models include decision trees, support vector machines, or neural networks. Use historical data sets to train models to identify and predict different types of pollution events. Evaluate model performance through cross-validation and evaluation indicators, including accuracy and recall. Adjust and optimize the model based on the evaluation results to improve the accuracy of predictions.

[0021] S4. Pollutant emission prediction is to process the collected data in real time, use the trained machine learning model to analyze the current environmental status, identify potential pollution events, classify the current pollution status according to the analysis results, and predict development trends.

[0022] The event simulation prediction module includes pollutant diffusion simulation, which predicts the behavior of pollutants in the air under different conditions based on the particle diffusion model, inputs detailed information of the pollution source, local meteorological data, and key parameters of geographical and topographic data. The detailed information of the pollution source includes type, intensity and emission height, and the local meteorological data includes wind speed, wind direction, temperature and humidity. According to the input data, simulation calculations are performed to predict the diffusion path, concentration distribution and impact range of pollutants under different meteorological and geographical conditions.

[0023] The event simulation prediction module includes GIS integration and visualization. The simulated data is imported into the GIS software for spatial analysis and processing. The spatial analysis and processing include interpolation and overlay analysis. The visualization tools of the GIS software are used to display the graphical results of pollutant diffusion. The pollutant concentration and diffusion area are displayed through color coding, contour lines or heat maps to help users clearly understand the dynamic process of pollutant diffusion.

[0024] The emergency response coordination module includes the coordination process of the central control center, a central control center equipped with communication and data processing equipment, real-time monitoring of pollution incidents and coordination of actions of various departments, the central control center is connected to the environmental protection, public health, transportation and public security departments, when a pollution incident occurs, the connected departments can receive instructions and information in real time, the central control center receives and analyzes information from the data analysis module, external environment monitoring data and pollutant diffusion predictions obtained from the event simulation prediction module in real time, analyzes the results, and the central control center quickly formulates a response strategy and issues emergency response instructions to the connected departments through the communication network.

[0025] The emergency response coordination module includes the emergency response information system, including event registration, progress monitoring, resource scheduling and communication and coordination functions;

[0026] The emergency response information system can exchange data with the central control center and various emergency response departments in real time. When a pollution incident is identified, the emergency response information system automatically generates a detailed incident file, recording the specific information of the incident, including the time, location, type of pollution, scope of impact, and initial response measures;

[0027] The emergency response information system has a real-time progress tracking module to monitor the status of pollution incidents. The connected departments need to regularly update the activity status, including the progress of rescue operations and the completion of pollution cleanup work;

[0028] The emergency response information system contains a resource library that records various emergency resources, including personnel, rescue vehicles, and cleaning equipment. The emergency response information system intelligently matches and dispatches according to event needs and resource availability;

[0029] The Emergency Response Information System provides a built-in communications platform that supports text, voice and video communications.

[0030] The information release module includes a public information release platform, social media channels and SMS services, and a rapid information dissemination mechanism;

[0031] The public information release platform has established a special content management team, which is responsible for collecting, organizing and publishing the latest information on pollution incidents. The latest information includes the basic situation of the pollution incidents, the scope of impact, preventive measures and treatment progress, and integrates information from the data analysis module and the event simulation and prediction module to display the spread of pollutants and the affected areas in real time. Visual data is used to help the public understand complex environmental data, and visual data includes maps and charts.

[0032] Setting up social media channels and SMS services means establishing accounts on social media platforms, publishing information related to pollution incidents, updating them synchronously with websites and mobile applications, formulating social media content strategies, using tags and information push functions to increase the visibility and speed of information dissemination, and sending information to the public through SMS services for important warnings and updates.

[0033] The rapid information dissemination mechanism is to simultaneously release important information through websites, mobile applications, social media and SMS services to ensure wide coverage and rapid dissemination of information. A priority processing mechanism is implemented for emergency notifications, which include immediate evacuation instructions or safety alerts.

[0034] Compared with the prior art, the present invention has the following beneficial effects: it greatly improves the response speed and processing efficiency to environmental pollution incidents, and also helps the government and environmental protection agencies to formulate environmental policies and management measures more accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a diagram of the overall architecture of the system of the embodiment of the present invention;

[0036] Figure 2 It is the data collection flow chart mentioned in the embodiment;

[0037] Figure 3 This is a schematic diagram of data analysis and early warning generation mentioned in an embodiment of the present invention;

[0038] Figure 4 This is a diagram of the emergency response coordination mechanism mentioned in an embodiment of the present invention;

[0039] Figure 5 This is a framework diagram of information release and social participation mentioned in an embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention is described clearly and completely below. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] A pollution incident rapid response and management system based on big data analysis, including: a data acquisition module, a data analysis module, an event simulation and prediction module, an emergency response coordination module and an information release module;

[0042] The data acquisition module receives and processes AIS data, meteorological data, geographic information data and pollutant monitoring data;

[0043] The data analysis module uses a big data platform to process and analyze data and applies machine learning algorithms to classify and predict pollution events. The machine learning algorithms include random forests and neural networks.

[0044] The event simulation and prediction module simulates the diffusion of pollutants and visualizes the diffusion path and impact area of ​​pollutants in combination with GIS software;

[0045] The emergency response coordination module coordinates the emergency response activities of various departments through the central control center, and records and tracks the handling process of pollution incidents through the emergency response information system;

[0046] The information release module releases real-time information to the public through a public information release platform, which includes a website and a mobile application, and sets up social media channels and SMS services to ensure rapid dissemination of information.

[0047] The data acquisition module receives and processes pollutant monitoring data, which includes sulfur oxide emission data, nitrogen oxide emission data, carbon dioxide emission data and oily wastewater emission data.

[0048] The data analysis module data processing includes data acquisition and data preprocessing.

[0049] Data collection in the data analysis module includes collecting AIS data, meteorological data and pollutant monitoring data from data sources. AIS data includes the ship's position, speed and heading information, meteorological data includes wind speed, wind direction and temperature, and pollutant monitoring data includes the concentration level of pollutants in the environment.

[0050] Data preprocessing in the data analysis module includes:

[0051] S1. After cleaning and formatting the collected data, removing outliers and noise, data fusion is performed to unify the timestamps and formats of different data sources;

[0052] S2. Data analysis is to extract key features based on data characteristics. The extracted key features include the behavior characteristics of ships based on AIS data, environmental conditions based on meteorological data, and pollutant distribution based on pollutant monitoring data. The extracted key features are standardized. The standardized processing includes normalization or coding.

[0053] S3. Applying machine learning algorithms is to select appropriate machine learning models based on the nature of the problem. Machine learning models include decision trees, support vector machines, or neural networks. Use historical data sets to train models to identify and predict different types of pollution events. Evaluate model performance through cross-validation and evaluation indicators, including accuracy and recall. Adjust and optimize the model based on the evaluation results to improve the accuracy of predictions.

[0054] S4. Pollutant emission prediction is to process the collected data in real time, use the trained machine learning model to analyze the current environmental status, identify potential pollution events, classify the current pollution status according to the analysis results, and predict development trends.

[0055] The event simulation prediction module includes pollutant diffusion simulation, which predicts the behavior of pollutants in the air under different conditions based on the particle diffusion model, inputs detailed information of the pollution source, local meteorological data, and key parameters of geographical and topographic data. The detailed information of the pollution source includes type, intensity and emission height, and the local meteorological data includes wind speed, wind direction, temperature and humidity. According to the input data, simulation calculations are performed to predict the diffusion path, concentration distribution and impact range of pollutants under different meteorological and geographical conditions.

[0056] The event simulation prediction module includes GIS integration and visualization. The simulated data is imported into the GIS software for spatial analysis and processing. The spatial analysis and processing include interpolation and overlay analysis. The visualization tools of the GIS software are used to display the graphical results of pollutant diffusion. The pollutant concentration and diffusion area are displayed through color coding, contour lines or heat maps to help users clearly understand the dynamic process of pollutant diffusion.

[0057] The emergency response coordination module includes the coordination process of the central control center, a central control center equipped with communication and data processing equipment, real-time monitoring of pollution incidents and coordination of actions of various departments, the central control center is connected to the environmental protection, public health, transportation and public security departments, when a pollution incident occurs, the connected departments can receive instructions and information in real time, the central control center receives and analyzes information from the data analysis module, external environment monitoring data and pollutant diffusion predictions obtained from the event simulation prediction module in real time, analyzes the results, and the central control center quickly formulates a response strategy and issues emergency response instructions to the connected departments through the communication network.

[0058] The emergency response coordination module includes the emergency response information system, including event registration, progress monitoring, resource scheduling and communication and coordination functions;

[0059] The emergency response information system can exchange data with the central control center and various emergency response departments in real time. When a pollution incident is identified, the emergency response information system automatically generates a detailed incident file, recording the specific information of the incident, including the time, location, type of pollution, scope of impact, and initial response measures;

[0060] The emergency response information system has a real-time progress tracking module to monitor the status of pollution incidents. The connected departments need to regularly update the activity status, including the progress of rescue operations and the completion of pollution cleanup work;

[0061] The emergency response information system contains a resource library that records various emergency resources, including personnel, rescue vehicles, and cleaning equipment. The emergency response information system intelligently matches and dispatches according to event needs and resource availability;

[0062] The Emergency Response Information System provides a built-in communications platform that supports text, voice and video communications.

[0063] The information release module includes a public information release platform, social media channels and SMS services, and a rapid information dissemination mechanism;

[0064] The public information release platform has established a special content management team, which is responsible for collecting, organizing and publishing the latest information on pollution incidents. The latest information includes the basic situation of the pollution incidents, the scope of impact, preventive measures and treatment progress, and integrates information from the data analysis module and the event simulation and prediction module to display the spread of pollutants and the affected areas in real time. Visual data is used to help the public understand complex environmental data, and visual data includes maps and charts.

[0065] Setting up social media channels and SMS services means establishing accounts on social media platforms, publishing information related to pollution incidents, updating them synchronously with websites and mobile applications, formulating social media content strategies, using tags and information push functions to increase the visibility and speed of information dissemination, and sending information to the public through SMS services for important warnings and updates.

[0066] The rapid information dissemination mechanism is to simultaneously release important information through websites, mobile applications, social media and SMS services to ensure wide coverage and rapid dissemination of information. A priority processing mechanism is implemented for emergency notifications, which include immediate evacuation instructions or safety alerts.

[0067] The overall system architecture is shown in the figure below: Figure 1 The data collection flow chart is shown in Figure 2 As shown in the figure, SO X stands for sulfur oxide, NO X represents nitrogen oxides, CO2 represents carbon dioxide, and the schematic diagram of data analysis and early warning generation is as follows Figure 3 As shown in the figure, the emergency response coordination mechanism is as follows Figure 4 As shown in the figure, the information release and social participation framework is as follows Figure 5 shown.

[0068] The data acquisition module is equipped with necessary hardware and software to receive and process AIS data, meteorological data, geographic information data and pollutant monitoring data, and uses high-performance servers and network equipment to ensure the stability and speed of data transmission.

[0069] Setting up social media channels and text messaging services means setting up accounts on social media platforms to publish information related to pollution incidents. Social media platforms include Facebook, Twitter and Weibo.

[0070] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some or all of the technical features may be replaced by equivalents, and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A pollution incident rapid response and management system based on big data analysis, characterized in that: include: Data collection module, data analysis module, event simulation and prediction module, emergency response coordination module and information release module; The data acquisition module receives and processes AIS data, meteorological data, geographic information data and pollutant monitoring data; The data analysis module uses a big data platform to process and analyze data and applies machine learning algorithms to classify and predict pollution events. The machine learning algorithms include random forests and neural networks. The event simulation and prediction module simulates the diffusion of pollutants and visualizes the diffusion path and impact area of ​​pollutants in combination with GIS software; The emergency response coordination module coordinates the emergency response activities of various departments through the central control center, and records and tracks the handling process of pollution incidents through the emergency response information system; The information release module releases real-time information to the public through a public information release platform, which includes a website and a mobile application, and sets up social media channels and SMS services to ensure rapid dissemination of information.

2. According to claim 1, a pollution incident rapid response and management system based on big data analysis is characterized in that: The data acquisition module receives and processes pollutant monitoring data, which includes sulfur oxide emission data, nitrogen oxide emission data, carbon dioxide emission data and oily wastewater emission data.

3. A pollution incident rapid response and management system based on big data analysis according to claim 2, characterized in that: The data analysis module data processing includes data acquisition and data preprocessing.

4. A pollution incident rapid response and management system based on big data analysis according to claim 3, characterized in that: Data collection in the data analysis module includes collecting AIS data, meteorological data and pollutant monitoring data from data sources. AIS data includes the ship's position, speed and heading information, meteorological data includes wind speed, wind direction and temperature, and pollutant monitoring data includes the concentration level of pollutants in the environment.

5. A pollution incident rapid response and management system based on big data analysis according to claim 4, characterized in that: Data preprocessing in the data analysis module includes: S1. After cleaning and formatting the collected data, removing outliers and noise, data fusion is performed to unify the timestamps and formats of different data sources; S2. Data analysis is to extract key features based on data characteristics. The extracted key features include the behavior characteristics of ships based on AIS data, environmental conditions based on meteorological data, and pollutant distribution based on pollutant monitoring data. The extracted key features are standardized. The standardized processing includes normalization or coding. S3. Applying machine learning algorithms is to select appropriate machine learning models based on the nature of the problem. Machine learning models include decision trees, support vector machines, or neural networks. Use historical data sets to train models to identify and predict different types of pollution events. Evaluate model performance through cross-validation and evaluation indicators, including accuracy and recall. Adjust and optimize the model based on the evaluation results to improve the accuracy of predictions. S4. Pollutant emission prediction is to process the collected data in real time, use the trained machine learning model to analyze the current environmental status, identify potential pollution events, classify the current pollution status according to the analysis results, and predict development trends.

6. A pollution incident rapid response and management system based on big data analysis according to claim 5, characterized in that: The event simulation prediction module includes pollutant diffusion simulation, which predicts the behavior of pollutants in the air under different conditions based on the particle diffusion model, inputs detailed information of the pollution source, local meteorological data, and key parameters of geographical and topographic data. The detailed information of the pollution source includes type, intensity and emission height, and the local meteorological data includes wind speed, wind direction, temperature and humidity. According to the input data, simulation calculations are performed to predict the diffusion path, concentration distribution and impact range of pollutants under different meteorological and geographical conditions.

7. A pollution incident rapid response and management system based on big data analysis according to claim 6, characterized in that: The event simulation prediction module includes GIS integration and visualization. The simulated data is imported into the GIS software for spatial analysis and processing. The spatial analysis and processing include interpolation and overlay analysis. The visualization tools of the GIS software are used to display the graphical results of pollutant diffusion. The pollutant concentration and diffusion area are displayed through color coding, contour lines or heat maps to help users clearly understand the dynamic process of pollutant diffusion.

8. The pollution incident rapid response and management system based on big data analysis according to claim 7 is characterized in that: The emergency response coordination module includes the coordination process of the central control center, a central control center equipped with communication and data processing equipment, real-time monitoring of pollution incidents and coordination of actions of various departments, the central control center is connected to the environmental protection, public health, transportation and public security departments, when a pollution incident occurs, the connected departments can receive instructions and information in real time, the central control center receives and analyzes information from the data analysis module, external environment monitoring data and pollutant diffusion predictions obtained from the event simulation prediction module in real time, analyzes the results, and the central control center quickly formulates a response strategy and issues emergency response instructions to the connected departments through the communication network.

9. A pollution incident rapid response and management system based on big data analysis according to claim 8, characterized in that: The emergency response coordination module includes the emergency response information system, including event registration, progress monitoring, resource scheduling and communication and coordination functions; The emergency response information system can exchange data with the central control center and various emergency response departments in real time. When a pollution incident is identified, the emergency response information system automatically generates a detailed incident file, recording the specific information of the incident, including the time, location, type of pollution, scope of impact, and initial response measures; The emergency response information system has a real-time progress tracking module to monitor the status of pollution incidents. The connected departments need to regularly update the activity status, including the progress of rescue operations and the completion of pollution cleanup work; The emergency response information system contains a resource library that records various emergency resources, including personnel, rescue vehicles, and cleaning equipment. The emergency response information system intelligently matches and dispatches according to event needs and resource availability; The Emergency Response Information System provides a built-in communications platform that supports text, voice and video communications.

10. A pollution incident rapid response and management system based on big data analysis according to claim 9, characterized in that: The information release module includes a public information release platform, social media channels and SMS services, and a rapid information dissemination mechanism; The public information release platform has established a special content management team, which is responsible for collecting, organizing and publishing the latest information on pollution incidents. The latest information includes the basic situation of the pollution incidents, the scope of impact, preventive measures and treatment progress, and integrates information from the data analysis module and the event simulation and prediction module to display the spread of pollutants and the affected areas in real time. Visual data is used to help the public understand complex environmental data, and visual data includes maps and charts. Setting up social media channels and SMS services means establishing accounts on social media platforms, publishing information related to pollution incidents, updating them synchronously with websites and mobile applications, formulating social media content strategies, using tags and information push functions to increase the visibility and speed of information dissemination, and sending information to the public through SMS services for important warnings and updates. The rapid information dissemination mechanism is to simultaneously release important information through websites, mobile applications, social media and SMS services to ensure wide coverage and rapid dissemination of information. A priority processing mechanism is implemented for emergency notifications, which include immediate evacuation instructions or safety alerts.

Images