Big data platform system for emergency disposal of overwater oil spill and dangerous chemical leakage accidents

Abstract

The invention discloses a big data platform system for emergency disposal of overwater oil spill and hazardous chemical substance leakage accidents, and the system comprises a data integration module which is used for collecting and integrating remote sensing data and environmental data from satellites, unmanned aerial vehicles, oceans and meteorological stations in real time; the monitoring module is used for dynamically displaying real-time diffusion conditions and affected areas of spilled oil and hazardous chemical substance leakage; the analysis and prediction module is used for predicting future diffusion paths of oil spilling and leakage based on the collected data by adopting an artificial intelligence algorithm; and the risk assessment module assesses the event risk and provides an emergency response strategy and a resource allocation suggestion. According to the invention, the multi-source data real-time acquisition and integration capabilities of satellites, unmanned aerial vehicles, ocean sensors, meteorological stations and the like are utilized, the data updating speed and the information integrity are improved, the real-time diffusion conditions of oil spill and hazardous chemical substance leakage are dynamically displayed through a map engine and a real-time data processing algorithm, and rapid decision making and resource allocation are supported.

Description

Technical Field

[0001] The present invention relates to the field of environmental monitoring, and in particular to a big data platform system for emergency disposal of oil spills on water and leakage of hazardous chemicals. Background Art

[0002] Oil spills and hazardous chemical leaks on water are one of the important issues in the current field of environmental protection and public safety. Such accidents may be caused by a variety of reasons such as ship collisions, pipeline ruptures, facility accidents or malicious acts, which seriously threaten the coastal and river ecological environment and have a serious impact on surrounding communities and economic activities. Oil spills and leaks often lead to water pollution, damage to wildlife and plants, loss of fishery resources and human health problems. Therefore, a fast and effective emergency response system is urgently needed to minimize the damage caused by the accident.

[0003] At present, the existing emergency response systems for handling oil spills and hazardous chemical leakage accidents on water often rely on manual collection and processing of environmental data, and the data update speed is slow and information collection is not timely, resulting in insufficient real-time performance and difficulty in supporting rapid response to emergencies. Therefore, it is necessary to design a big data platform system for emergency response to oil spills and hazardous chemical leakage accidents on water. Summary of the invention

[0004] The purpose of the present invention is to provide a big data platform system for emergency disposal of oil spills and hazardous chemical leakage accidents on water, so as to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A big data platform system for emergency response to oil spills and hazardous chemical leakage accidents on water, including

[0007] Data integration module for real-time collection and integration of remote sensing data and environmental data from satellites, drones, ocean and meteorological stations;

[0008] Monitoring module, used to dynamically display the real-time spread and affected areas of oil spills and hazardous chemical leaks;

[0009] An analysis and prediction module that uses artificial intelligence algorithms to predict the future spread path of oil spills and leaks based on the collected data;

[0010] Risk assessment module, which assesses incident risks and provides emergency response strategies and resource allocation recommendations;

[0011] Resource management module, used to manage and dispatch emergency response resources;

[0012] Restoration evaluation module, used to evaluate the restoration effect after accident treatment and conduct long-term ecological monitoring;

[0013] Security management module, used for data security and user authentication and permission management.

[0014] Furthermore, the data integration module includes a satellite data receiver, an unmanned aerial vehicle sensor, an ocean sensor and a weather station data interface, and the satellite data receiver, the unmanned aerial vehicle sensor, the ocean sensor and the weather station data interface are connected to the central server via a wireless or wired network for collecting environmental and meteorological data in real time.

[0015] Furthermore, the monitoring module includes a map engine, a real-time data processing algorithm and a user interface. The monitoring module is connected to the data integration module through a data interface. The map engine displays real-time monitoring data through the user interface, and the real-time data processing algorithm performs instant analysis on the collected data.

[0016] Furthermore, the analysis and prediction module includes a machine learning algorithm, a historical data repository and a prediction model, wherein the historical data repository provides historical oil spill data to the machine learning algorithm through a data interface for establishing and optimizing the prediction model;

[0017] The prediction model function is expressed as:

[0018] [F(x)=\beta_0+\beta_1x_1+\beta_2x_2+\ldots+\beta_nx_n]

[0019] Among them, (F(x)) is the output of the prediction model, that is, the diffusion area or pollution degree of the oil spill, (\beta_0,\beta_1,\ldots,\beta_n) are model parameters, and (x_1,x_2,\ldots,x_n) are input variables, such as wind speed, water flow speed and oil spill volume.

[0020] Further, the risk assessment module includes a risk assessment model, a simulation tool and a decision support system, the risk assessment module is connected to the real-time monitoring data and the prediction model through a data interface, and the risk assessment model and the simulation tool are used to perform risk assessment and emergency response simulation based on real-time and predicted data;

[0021] The risk assessment model function is expressed as:

[0022] [R=\sum_{i=1}^{n}w_i\cdot r_i]

[0023] Where (R) is the total risk value, (w_i) is the weight of the (i)th risk factor, and (r_i) is the risk rating of the (i)th risk factor, which is calculated based on the impact level and probability of occurrence.

[0024] Furthermore, the resource management module includes a resource scheduling system, a GPS tracker and a communication device. The resource scheduling system monitors the location of emergency response resources through the GPS tracker and communicates with the emergency response unit in real time through the communication device.

[0025] Further, the restoration assessment module includes an environmental monitoring sensor, data analysis software and an ecosystem assessment tool, wherein the environmental monitoring sensor regularly collects post-accident environmental data and transmits it to the data analysis software through a data interface for assessment;

[0026] The environmental restoration evaluation function is expressed as:

[0027] [E=\frac{1}{n}\sum_{i=1}^{n}(C_{i0}-C_{it})]

[0028] Among them, (E) is the environmental recovery degree evaluation value, (C_{i0}) is the (i)th environmental indicator before the accident, that is, the initial value of the concentration of a certain pollutant, (C_{it}) is the value of the (i)th environmental indicator at the (t)th time point after the accident, and (n) is the total number of environmental indicators considered.

[0029] Furthermore, the security management module includes an identity authentication system, a data encryption module and an operation audit tool. The identity authentication system ensures user access control, the data encryption module ensures data transmission and storage security, and the operation audit tool records the system operation history for audit purposes.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] 1. The present invention utilizes the real-time data collection and integration capabilities of satellites, drones, ocean sensors, weather stations, and other sources to improve data update speed and information integrity, and dynamically displays the real-time spread of oil spills and hazardous chemical leaks through map engines and real-time data processing algorithms to support rapid decision-making and resource deployment.

[0032] 2. The present invention uses machine learning algorithms to establish accurate prediction models, predict future development trends of accidents based on historical data and real-time monitoring results, provide accurate risk assessments, and monitor and dispatch emergency response resources in real time through GPS trackers and communication equipment to improve resource utilization and the flexibility of emergency response. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is a structural schematic diagram of a big data platform system for emergency disposal of oil spills and hazardous chemical leakage accidents on water according to the present invention;

[0034] Figure 2This is a schematic diagram of the connection of data integration modules of a big data platform system for emergency disposal of oil spills and hazardous chemicals leakage accidents on water according to the present invention;

[0035] Figure 3 This is a schematic diagram of the connection of monitoring modules of a big data platform system for emergency disposal of oil spills and hazardous chemicals leakage accidents on water according to the present invention;

[0036] Figure 4 This is a schematic diagram of the connection of an analysis and prediction module of a big data platform system for emergency disposal of oil spills and hazardous chemicals leakage accidents on water according to the present invention;

[0037] Figure 5 This is a risk assessment connection schematic diagram of a big data platform system for emergency response to oil spills and hazardous chemicals leakage accidents on water according to the present invention;

[0038] Figure 6 This is a schematic diagram of resource management module connection of a big data platform system for emergency disposal of oil spills and hazardous chemicals leakage accidents on water according to the present invention;

[0039] Figure 7 This is a connection diagram of a recovery evaluation module of a big data platform system for emergency disposal of oil spills and hazardous chemicals leakage accidents on water according to the present invention;

[0040] Figure 8 The present invention is a schematic diagram of the connection of a security management module of a big data platform system for emergency disposal of oil spills and hazardous chemical leakage accidents on water. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, 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.

[0042] like Figure 1-8 As shown, the present invention provides a technical solution:

[0043] A big data platform system for emergency response to oil spills and hazardous chemical leakage accidents on water, including

[0044] like Figure 2 As shown, the data integration module is used to collect and integrate remote sensing data and environmental data from satellites, drones, ocean and meteorological stations in real time;

[0045] Preferably, the data integration module includes a satellite data receiver, a drone sensor, an ocean sensor and a weather station data interface, and the satellite data receiver, the drone sensor, the ocean sensor and the weather station data interface are connected to the central server via a wireless or wired network for collecting environmental and meteorological data in real time;

[0046] Specifically, the above-mentioned sensors are installed at designated locations by professional technicians and undergo regular maintenance and inspections. Real-time data transmission and processing are automatically performed by the central data server. The sensors continuously monitor the environmental parameters of the target area, including temperature, pressure, and chemical concentration, and transmit the data to the central server via wireless signals.

[0047] like Figure 3 As shown, the monitoring module is used to dynamically display the real-time spread of oil spills and hazardous chemical leaks and the affected areas;

[0048] Preferably, the monitoring module includes a map engine, a real-time data processing algorithm and a user interface, the monitoring module is connected to the data integration module via a data interface, the map engine displays real-time monitoring data via the user interface, and the real-time data processing algorithm performs instant analysis on the collected data;

[0049] Specifically, the system administrator is responsible for installing and configuring the map engine and real-time data processing software on the central data processing server, where the data processing algorithm analyzes the data from the sensors in real time and displays the data to the user through a graphical user interface.

[0050] like Figure 4 As shown, the analysis and prediction module uses artificial intelligence algorithms to predict the future spread path of oil spills and leaks based on the collected data;

[0051] Preferably, the analysis and prediction module includes a machine learning algorithm, a historical data repository and a prediction model, and the historical data repository provides historical oil spill data to the machine learning algorithm through a data interface for establishing and optimizing the prediction model;

[0052] The prediction model function is expressed as:

[0053] [F(x)=\beta_0+\beta_1x_1+\beta_2x_2+\ldots+\beta_nx_n]

[0054] Among them, (F(x)) is the output of the prediction model, that is, the spread area or pollution degree of the oil spill, (\beta_0,\beta_1,\ldots,\beta_n) are model parameters, and (x_1,x_2,\ldots,x_n) are input variables, such as wind speed, water flow speed and oil spill volume;

[0055] Specifically, machine learning software is installed on an analysis server, and data scientists configure and optimize the predictive model, where historical data is regularly updated into the database and the machine learning model uses this data to predict future events and trends.

[0056] like Figure 5 As shown, the risk assessment module assesses the event risk and provides emergency response strategies and resource allocation recommendations;

[0057] Preferably, the risk assessment module includes a risk assessment model, a simulation tool and a decision support system, the risk assessment module is connected to the real-time monitoring data and the prediction model through a data interface, and the risk assessment model and the simulation tool are used to perform risk assessment and emergency response simulation based on real-time and prediction data;

[0058] The risk assessment model function is expressed as:

[0059] [R=\sum_{i=1}^{n}w_i\cdot r_i]

[0060] Where (R) is the total risk value, (w_i) is the weight of the (i)th risk factor, and (r_i) is the risk rating of the (i)th risk factor, which is calculated based on the impact degree and probability of occurrence;

[0061] Specifically, simulation tools calculate possible accident consequences, such as the spread of chemical leaks, based on physical and chemical models and operated by emergency management experts to simulate accident scenarios and develop response strategies.

[0062] like Figure 6 As shown, the resource management module is used to manage and dispatch emergency response resources;

[0063] Preferably, the resource management module includes a resource scheduling system, a GPS tracker and a communication device, wherein the resource scheduling system monitors the location of emergency response resources through the GPS tracker and communicates with the emergency response unit in real time through the communication device;

[0064] Specifically, the resource dispatch system automatically tracks the location of emergency resources and dispatches them through communication equipment, which is monitored around the clock by the emergency response center.

[0065] like Figure 7 As shown, the restoration evaluation module is used to evaluate the restoration effect after accident treatment and conduct long-term ecological monitoring;

[0066] Preferably, the restoration assessment module includes an environmental monitoring sensor, data analysis software and an ecosystem assessment tool, wherein the environmental monitoring sensor regularly collects post-accident environmental data and transmits it to the data analysis software through a data interface for assessment;

[0067] The environmental restoration evaluation function is expressed as:

[0068] [E=\frac{1}{n}\sum_{i=1}^{n}(C_{i0}-C_{it})]

[0069] Among them, (E) is the environmental restoration evaluation value, (C_{i0}) is the (i)th environmental indicator before the accident, that is, the initial value of the concentration of a certain pollutant, (C_{it}) is the value of the (i)th environmental indicator at the (t)th time point after the accident, and (n) is the total number of environmental indicators considered;

[0070] Specifically, monitoring sensors regularly collect post-event environmental data and transmit them to the server for analysis to evaluate the environmental recovery process.

[0071] like Figure 8 As shown, the security management module is used for data security and user identity authentication and authority management;

[0072] Preferably, the security management module includes an identity authentication system, a data encryption module and an operation audit tool, wherein the identity authentication system ensures user access control, the data encryption module ensures data transmission and storage security, and the operation audit tool records system operation history for audit purposes;

[0073] Specifically, it is installed and configured by the IT security team to ensure that all data transmission and storage processes comply with security standards and that users can access the system only after verification.

[0074] Suppose an oil spill occurs in a certain sea area. It is necessary to assess the impact of the oil spill on the marine ecology and formulate corresponding response measures.

[0075] When the system is running, it collects oil spill information through satellite images, sensor networks and field observations, including the location, area, type and relevant data of the surrounding marine environment, such as water temperature and flow rate, and uses the prediction model function (F(x)) of the event analysis and prediction module to input the collected data to predict the speed and direction of the oil spill spread;

[0076] (F(x)=\beta_0+\beta_1x_1+\beta_2x_2+\ldots+\beta_nx_n)

[0077] In this scenario, (x_1,x_2,\ldots,x_n) represent variables such as wind speed, water flow speed, and oil spill volume;

[0078] According to the risk assessment and decision support module, the potential harm of oil spill to the environment is assessed and the total risk value (R) is calculated;

[0079] (R=\sum_{i=1}^{n}w_i\cdot r_i)

[0080] In this scenario, (w_i) is the weight of different risk factors, including biological impact and economic losses, and (r_i) is the corresponding risk rating.

[0081] Based on the risk assessment results, the system automatically generates response recommendations, including physical isolation, chemical dispersion or biodegradation cleanup plans. The command center dispatches cleaning ships and emergency teams for on-site treatment based on the optimal plan provided by the system, and continuously monitors environmental indicators, including pollutant concentrations and biodiversity indicators.

[0082] At the same time, the environmental restoration evaluation function (E) of the subsequent restoration and evaluation module is used to evaluate the effectiveness of the cleanup work;

[0083] (E=\frac{1}{n}\sum_{i=1}^{n}(C_{i0}-C_{it}))

[0084] In this scenario, (C_{i0}) is the baseline value of each environmental indicator before the accident, and (C_{it}) is the value of the environmental indicator at different time points after the accident. Then analyze the effectiveness of the accident response, adjust the prediction model and risk assessment parameters according to the feedback, and optimize the system's response capability and accuracy.

[0085] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0086] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A big data platform system for emergency response to oil spills and hazardous chemical leakage accidents on water, characterized by: include Data integration module for real-time collection and integration of remote sensing data and environmental data from satellites, drones, ocean and meteorological stations; Monitoring module, used to dynamically display the real-time spread and affected areas of oil spills and hazardous chemical leaks; An analysis and prediction module that uses artificial intelligence algorithms to predict the future spread path of oil spills and leaks based on the collected data; Risk assessment module, which assesses incident risks and provides emergency response strategies and resource allocation recommendations; Resource management module, used to manage and dispatch emergency response resources; Restoration evaluation module, used to evaluate the restoration effect after accident treatment and conduct long-term ecological monitoring; Security management module, used for data security and user authentication and permission management.

2. A big data platform system for emergency response to oil spills and hazardous chemicals leakage accidents on water according to claim 1, characterized in that: The data integration module includes a satellite data receiver, an unmanned aerial vehicle sensor, an ocean sensor and a weather station data interface, which are connected to a central server via a wireless or wired network for real-time collection of environmental and meteorological data.

3. A big data platform system for emergency response to oil spills and hazardous chemicals leakage accidents on water according to claim 1, characterized in that: The monitoring module includes a map engine, a real-time data processing algorithm and a user interface. The monitoring module is connected to the data integration module via a data interface. The map engine displays real-time monitoring data via the user interface, and the real-time data processing algorithm performs instant analysis on the collected data.

4. A big data platform system for emergency response to oil spills and hazardous chemicals leakage accidents on water according to claim 1, characterized in that: The analysis and prediction module includes a machine learning algorithm, a historical data repository and a prediction model, wherein the historical data repository provides historical oil spill data to the machine learning algorithm through a data interface for establishing and optimizing the prediction model; The prediction model function is expressed as: [F(x)=\beta_0+\beta_1x_1+\beta_2x_2+\ldots+\beta_nx_n] Among them, (F(x)) is the output of the prediction model, that is, the diffusion area or pollution degree of the oil spill, (\beta_0,\beta_1,\ldots,\beta_n) are model parameters, and (x_1,x_2,\ldots,x_n) are input variables, such as wind speed, water flow speed and oil spill volume.

5. A big data platform system for emergency response to oil spills and hazardous chemicals leakage accidents on water according to claim 1, characterized in that: The risk assessment module includes a risk assessment model, a simulation tool and a decision support system. The risk assessment module is connected to the real-time monitoring data and the prediction model through a data interface. The risk assessment model and the simulation tool are used to perform risk assessment and emergency response simulation based on real-time and prediction data; The risk assessment model function is expressed as: [R=\sum_{i=1}^{n}w_i\cdot r_i] Where (R) is the total risk value, (w_i) is the weight of the (i)th risk factor, and (r_i) is the risk rating of the (i)th risk factor, which is calculated based on the impact level and probability of occurrence.

6. A big data platform system for emergency response to oil spills and hazardous chemicals leakage accidents on water according to claim 1, characterized in that: The resource management module includes a resource scheduling system, a GPS tracker and a communication device. The resource scheduling system monitors the location of emergency response resources through the GPS tracker and communicates with the emergency response unit in real time through the communication device.

7. A big data platform system for emergency response to oil spills and hazardous chemicals leakage accidents on water according to claim 1, characterized in that: The restoration assessment module includes environmental monitoring sensors, data analysis software and ecosystem assessment tools. The environmental monitoring sensors regularly collect post-accident environmental data and transmit them to the data analysis software for assessment via a data interface. The environmental restoration evaluation function is expressed as: [E=\frac{1}{n}\sum_{i=1}^{n}(C_{i0}-C_{it})] Among them, (E) is the environmental recovery degree evaluation value, (C_{i0}) is the (i)th environmental indicator before the accident, that is, the initial value of the concentration of a certain pollutant, (C_{it}) is the value of the (i)th environmental indicator at the (t)th time point after the accident, and (n) is the total number of environmental indicators considered.

8. A big data platform system for emergency response to oil spills and hazardous chemicals leakage accidents on water according to claim 1, characterized in that: The security management module includes an identity authentication system, a data encryption module and an operation audit tool. The identity authentication system ensures user access control, the data encryption module ensures data transmission and storage security, and the operation audit tool records the system operation history for audit purposes.

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