Marine oil spill emergency decision-making method based on satellite remote sensing and big data analysis

Abstract

The application discloses a marine oil spill emergency decision-making method based on satellite remote sensing and big data analysis, relates to the field of marine oil spill management, and mainly has the following scheme: calculating an oil spill confidence index through oil spill authenticity data, and judging whether the region is a real oil spill based on the oil spill confidence index; analyzing oil spill hazard data, calculating an oil spill comprehensive hazard index; comprehensively analyzing dynamic diffusion data to obtain a dynamic diffusion risk entropy; comprehensively analyzing dynamic diffusion data to obtain an emergency response efficiency index; comparing the oil spill comprehensive hazard index, the dynamic diffusion risk entropy and the emergency response efficiency index with threshold values in a comprehensive evaluation threshold value set, judging the severity level of the oil spill event, and selecting a corresponding emergency decision-making strategy according to the severity level of the oil spill event; and solving the technical problems of inaccurate oil spill authenticity judgment, incomplete oil spill hazard evaluation and insufficient utilization of dynamic diffusion data.

Description

Technical Field

[0001] This invention relates to the field of marine oil spill management, specifically a marine oil spill emergency decision-making method based on satellite remote sensing and big data analysis. Background Technology

[0002] Inaccurate determination of oil spill authenticity: Existing marine oil spill emergency decision-making methods often struggle to accurately determine the authenticity of an oil spill after acquiring spill data. Traditional methods may rely on single sensor data or on-site observations, making them susceptible to interference from marine environmental factors (such as waves and sea ice), leading to a high rate of misjudgment. For example, some natural oil slicks or optical phenomena caused by plankton aggregation on the sea surface may be mistaken for oil spills, thus wasting emergency resources and reducing the efficiency and accuracy of emergency decision-making.

[0003] Incomplete oil spill hazard assessment: Current technologies, when assessing the hazards of oil spills, mostly consider only basic parameters such as the area and volume of the spill, neglecting the type of oil, toxicity, and sensitivity of the marine environment (such as whether it is located in an ecological protection zone). This one-sided assessment method cannot accurately reflect the actual degree of harm caused by oil spill accidents and is not conducive to developing targeted emergency response strategies.

[0004] Insufficient utilization of dynamic diffusion data: The diffusion of marine oil spills is a complex process influenced by various factors such as meteorology and hydrology. While existing methods can obtain some meteorological and hydrological data, they fail to fully integrate this data with the dynamic changes in oil spill diffusion.

[0005] Inefficient emergency resource allocation: During emergency response, existing technologies lack efficient mechanisms for allocating emergency resources. The allocation of emergency resources often relies on human experience, lacking scientific data analysis and optimization models, leading to problems such as unreasonable resource allocation and long response times, severely impacting the effectiveness of emergency decision-making. Summary of the Invention

[0006] (a) Technical problems to be solved

[0007] To address the shortcomings of existing technologies, this invention provides a marine oil spill emergency decision-making method based on satellite remote sensing and big data analysis, which at least solves one of the technical problems such as inaccurate judgment of the authenticity of oil spills, incomplete assessment of the hazards of oil spills, and insufficient utilization of dynamic diffusion data.

[0008] (II) Technical Solution

[0009] To achieve the above objectives, the present invention provides the following technical solution: a marine oil spill emergency decision-making method based on satellite remote sensing and big data analysis, comprising:

[0010] Step 1: Obtain oil spill authenticity data for the area to be analyzed using satellite remote sensing technology, and calculate the oil spill confidence index by analyzing the oil spill authenticity data. And based on the confidence index of oil spill Determine whether this area represents a genuine oil spill;

[0011] Step Two: When a genuine oil spill is identified, obtain oil spill hazard data for the area to be analyzed, and calculate the comprehensive oil spill hazard index by analyzing the data. ;

[0012] Step 3: Obtain dynamic diffusion data of the oil spill area through meteorological satellite data, and obtain the dynamic diffusion risk entropy through comprehensive analysis of the dynamic diffusion data. ;

[0013] Step 4: Obtain emergency response data through the emergency resource dispatch platform, and obtain the emergency response effectiveness index by comprehensively analyzing the emergency response data. ;

[0014] Step 5: Set a comprehensive assessment threshold set to include the comprehensive hazard index of oil spills. Dynamic diffusion risk entropy and emergency response effectiveness index The severity level of the oil spill event is determined by comparing it with the thresholds in the comprehensive assessment threshold set, and the corresponding emergency decision-making strategy is selected based on the severity level of the oil spill event.

[0015] In the aforementioned marine oil spill emergency decision-making method based on satellite remote sensing and big data analysis, the oil spill authenticity data includes the backscattering coefficient difference. Reflectance of oil film region at different wavelengths Reflectance in standard oil sample library Wavelength difference between two adjacent bands and temperature difference The specific method for obtaining it is as follows:

[0016] The SAR backscattering coefficient of the oil slick area is obtained by SAR satellite, and then compared with the reference value of the oil-free sea surface. The difference was calculated to obtain the difference in backscattering coefficients. ;

[0017] The reflectance of the oil film area at different wavelengths can be obtained using hyperspectral remote sensing equipment carried by satellites or drones. and the wavelength difference between two adjacent bands The reflectance of the standard oil type library was obtained through standard oil type library data. ;

[0018] Temperatures in the oil slick area and the unpolluted sea area were obtained using thermal infrared remote sensing equipment. The temperature difference was then calculated. .

[0019] In the aforementioned marine oil spill emergency decision-making method based on satellite remote sensing and big data analysis, an oil spill confidence index is calculated using oil spill data. The formula used is:

[0020] ;

[0021] in, This represents the reflectivity of the oil film region at the i-th wavelength; This represents the standard reflectance of the oil film region at the i-th wavelength, where i represents the wavelength type number and n represents the wavelength type number. The temperature difference threshold between the oil film-covered area and the surrounding seawater area; express Weighting coefficients; express Weighting coefficients; express Weighting coefficients;

[0022] Set a confidence threshold for oil spill assessment, and use oil spill confidence indicators Compared with the oil spill confidence assessment threshold, when the oil spill confidence index When the oil spill confidence assessment threshold is reached, it is determined that there is a genuine oil spill in this area.

[0023] In the aforementioned marine oil spill emergency decision-making method based on satellite remote sensing and big data analysis, the oil spill hazard data includes the actual oil slick area. Actual oil toxicity equivalent and straight-line distance The specific method for obtaining it is as follows:

[0024] The true oil film area of ​​the oil spill region was obtained using synthetic aperture radar imaging technology. ;

[0025] Oil samples were collected from the area to be analyzed and matched with standard oil data in an oil database. The standard oil with the highest matching degree was selected, and its biotoxicity equivalent was obtained from the oil database as the true oil toxicity equivalent. ;

[0026] Using Geographic Information System (GIS) technology, and combining geographic data of the location of the area to be analyzed with that of ecologically sensitive shorelines, the straight-line distance from the area to be analyzed to the nearest sensitive shoreline is calculated. .

[0027] In the aforementioned marine oil spill emergency decision-making method based on satellite remote sensing and big data analysis, a comprehensive oil spill hazard index is calculated by analyzing oil spill hazard data. The formula used is:

[0028] ;

[0029] in, Indicates standard toxicity equivalent. Indicates the maximum distance value; This indicates the weight of the oil film area in the overall hazard index; This indicates the weight of the biotoxicity of petroleum products in the comprehensive hazard index.

[0030] In the aforementioned marine oil spill emergency decision-making method based on satellite remote sensing and big data analysis, dynamic diffusion data includes effective transport speed. Critical speed And the energy term Em, the specific method for obtaining it is as follows:

[0031] The velocity components of the ocean current in the oil spill area in the x and y directions were obtained using an ocean dynamics model. and Wind speed in the oil spill area was obtained through weather station data. Based on the velocity components in the x and y directions , and wind speed Calculate effective transport speed The formula used is: ;

[0032] A numerical model of oil spill diffusion was established using fluid dynamics software, and the critical velocity was obtained by simulating the oil spill diffusion process. And the energy term Em.

[0033] In the aforementioned marine oil spill emergency decision-making method based on satellite remote sensing and big data analysis, the dynamic diffusion risk entropy is calculated based on dynamic diffusion data. The formula used is:

[0034] ;

[0035] Where t0 is the initial timestamp. For the assessment time window; is the weighting coefficient for emulsion state k, where k is the index of the emulsion state.

[0036] In the aforementioned marine oil spill emergency response decision-making method based on satellite remote sensing and big data analysis, the emergency response data includes: total resource demand. Emergency resource gap Emergency response time and the actual time required for emergency response The specific calculation method is as follows:

[0037] Obtain the quantities of different types of resources used in emergency response through the emergency resource dispatch platform. and the standardization efficiency of various resources By the quantity of different types of resources Calculate the total demand for different types of resources. The formula used is: ;

[0038] Demand for different types of resources based on oil spill diffusion models And based on the amount of resources and resource demand Calculate emergency resource gaps using real-time data. The formula used is: ;

[0039] The simulation results based on the oil spill diffusion model are used to obtain the model-predicted emergency response time. And obtain the actual emergency response process records to obtain the actual time required for the emergency response. .

[0040] In the aforementioned marine oil spill emergency decision-making method based on satellite remote sensing and big data analysis, an emergency response effectiveness index is calculated using emergency response data. The formula used is:

[0041] ;

[0042] in, This represents the standardization efficiency of the j-th type of resource; This represents the quantity of resource of type j; j is the index of the resource, and J represents the total number of resource types.

[0043] In the above-mentioned marine oil spill emergency decision-making method based on satellite remote sensing and big data analysis, the comprehensive evaluation threshold set includes: oil spill hazard index threshold I, oil spill hazard index threshold II, oil spill hazard index threshold III, diffusion risk threshold I, diffusion risk threshold II, diffusion risk threshold III, response effectiveness threshold I, response effectiveness threshold II, and response effectiveness threshold III.

[0044] The comprehensive hazard index of oil spills The dynamic diffusion risk entropy is compared with the oil spill hazard index thresholds I, II, and III respectively. The emergency response effectiveness index is compared with the diffusion risk thresholds I, II, and III respectively. Compare with response performance threshold I, response performance threshold II, and response performance threshold III respectively;

[0045] When the comprehensive hazard index of oil spill is met simultaneously <Oil Spill Hazard Index Threshold III, Dynamic Diffusion Risk Entropy> <Spread risk threshold III, and emergency response effectiveness index > When the response effectiveness threshold is III, execute the Level IV emergency decision-making plan;

[0046] When the oil spill hazard index threshold Ⅲ ≤ the comprehensive oil spill hazard index is met. <Oil Spill Hazard Index Threshold II and Diffusion Risk Threshold III ≤ Dynamic Diffusion Risk Entropy <Propagation Risk Threshold II, or Response Effectiveness Threshold II <Emergency Response Effectiveness Index When the response performance threshold is ≤ III, execute the Level 3 emergency decision-making plan;

[0047] When the oil spill hazard index threshold II is ≤ the comprehensive oil spill hazard index <Oil Spill Hazard Index Threshold I and Diffusion Risk Threshold II ≤ Dynamic Diffusion Risk Entropy <Propagation risk threshold I, or response effectiveness threshold I <Emergency response effectiveness index When the response performance threshold is ≤ II, execute the Level II emergency decision-making plan;

[0048] When the oil spill hazard index threshold Ⅰ ≤ the comprehensive oil spill hazard index is met Furthermore, the diffusion risk threshold Ⅰ ≤ dynamic diffusion risk entropy or emergency response effectiveness index If the response effectiveness threshold is less than or equal to threshold I, execute the Level 1 emergency decision-making plan.

[0049] (III) Beneficial Effects

[0050] This invention provides a marine oil spill emergency decision-making method based on satellite remote sensing and big data analysis, which has the following beneficial effects:

[0051] (1) By acquiring data on the authenticity of oil spills in the area to be analyzed through satellite remote sensing technology, and further calculating the confidence index of the oil spill to determine whether it is a real oil spill, the accuracy of marine oil spill monitoring can be greatly improved. In traditional marine oil spill monitoring methods, misjudgments are prone to occur, leading to unnecessary waste of emergency resources and false alarms. However, by leveraging the advantages of satellite remote sensing technology in acquiring data over a wide area and quickly, combined with in-depth analysis of the authenticity data of oil spills and calculation of the confidence index, interference information can be effectively filtered out, and the real oil spill area can be accurately located. This enables the emergency decision-making process to avoid a series of ineffective responses caused by misjudgments from the source, accurately focus on the problem in the early stage of an oil spill incident, provide a solid and reliable foundation for subsequent emergency response steps, avoid the needless investment of resources, improve the efficiency and pertinence of the entire emergency decision-making system, and ensure that limited emergency resources can be concentrated on responding to real oil spill accidents.

[0052] (2) By collecting and integrating multi-dimensional data on the hazards of oil spills and using scientific analytical models to calculate a comprehensive hazard index, the potential threat level of oil spill accidents to the ecological environment and other aspects can be clearly presented. This provides emergency decision-makers with a more comprehensive and accurate understanding of risks, which helps them to fully weigh various factors when formulating emergency strategies, prioritize the protection of key areas and important resources, rationally allocate emergency forces, and take targeted measures in advance to mitigate the various losses that oil spill accidents may cause, thereby improving the scientific nature and effectiveness of emergency decision-making and making response measures more targeted.

[0053] (3) By utilizing meteorological satellite data to obtain dynamic diffusion data of the oil spill area and calculating the dynamic diffusion risk entropy, the limitations of existing technologies in predicting oil spill diffusion are overcome. This solution continuously acquires meteorological satellite data, updates oil spill diffusion data in real time, and then analyzes and calculates the dynamic diffusion risk entropy, which can dynamically and intuitively display the changes in the risk situation of oil spill diffusion. This allows emergency decision-makers to promptly grasp the latest dynamics of oil spill diffusion, predict the range of potentially affected sea areas in advance, and deploy protective measures in advance, such as guiding ships to avoid dangerous areas and protecting uncontaminated coastlines, effectively reducing the risk of secondary pollution caused by oil spill diffusion, enhancing the foresight and flexibility of emergency decision-making, improving the ability to prevent and control oil spill diffusion risks, and limiting the scope of harm caused by oil spill accidents to the greatest extent.

[0054] (4) By acquiring emergency response data and calculating the emergency response effectiveness index through the emergency resource dispatch platform, quantitative evaluation and optimization of emergency resource allocation and response effectiveness are achieved. In previous marine oil spill emergency responses, the dispatch of emergency resources often relied on manual experience, lacking scientific data support and quantitative evaluation methods, which easily led to problems such as unreasonable resource allocation, untimely response, and low efficiency of coordinated operations among various emergency forces. This solution integrates various emergency response data through the emergency resource dispatch platform and calculates the emergency response effectiveness index through comprehensive analysis, enabling real-time evaluation of the efficiency and effectiveness of emergency response actions. This helps emergency decision-makers to promptly identify problematic aspects.

[0055] (5) By comparing the comprehensive hazard index, dynamic diffusion risk entropy, and emergency response effectiveness index with thresholds, the severity level of an oil spill event can be determined and corresponding emergency decision-making strategies can be matched. This technical point constructs a systematic, scientific, and dynamic marine oil spill emergency decision-making framework. Traditional marine oil spill emergency decisions are mostly based on experience-based judgments or single-indicator assessments, making it difficult to comprehensively consider the impact of multiple factors on the severity of the accident, resulting in insufficient scientificity and accuracy of emergency decisions. However, this solution integrates key indicators of multiple dimensions such as oil spill hazard, diffusion risk, and emergency response effectiveness, and systematically compares them with a pre-set set of comprehensive assessment thresholds. This allows for accurate and objective determination of the severity level of an oil spill event, and then quickly matches a corresponding, scientifically validated emergency decision-making strategy. This decision-making model based on multi-factor comprehensive assessment effectively improves the scientificity and rationality of emergency decisions, ensuring that appropriate and efficient response plans can be quickly formulated in oil spill accidents of different levels. It also makes the emergency decision-making process more standardized and regulated, improves the overall ability and level of responding to marine oil spill accidents, and minimizes the negative impact of oil spill accidents on the marine environment, economy, and society. Attached Figure Description

[0056] Figure 1 This is a schematic diagram illustrating the steps of the marine oil spill emergency decision-making method based on satellite remote sensing and big data analysis according to the present invention. Detailed Implementation

[0057] 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 some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0058] Example 1

[0059] Please see Figure 1This invention provides a marine oil spill emergency decision-making method based on satellite remote sensing and big data analysis, including:

[0060] Step 1: Obtain oil spill authenticity data for the area to be analyzed using satellite remote sensing technology, and calculate the oil spill confidence index by analyzing the oil spill authenticity data. And based on the confidence index of oil spill Determine whether this area is a genuine oil spill.

[0061] It should be noted that in calculating the confidence index for oil spills... During the process, all parameters involved in each calculation step need to be normalized and preprocessed to eliminate the dimensions of different parameters, so as to facilitate subsequent formula calculations.

[0062] It should be noted that the data on the accuracy of the oil spill includes the difference in backscattering coefficients. Reflectance of oil film region at different wavelengths Reflectance in standard oil sample library Wavelength difference between two adjacent bands and temperature difference .

[0063] Step 101: Obtain the SAR backscattering coefficient of the oil slick area using SAR satellites, and then compare it with the reference value of the oil-free sea surface. The difference was calculated to obtain the difference in backscattering coefficients. .

[0064] Step 102: Obtain the reflectance of the oil film area at different wavelengths using hyperspectral remote sensing equipment mounted on a satellite or drone. and the wavelength difference between two adjacent bands ; Obtain reflectance from standard oil sample databases .

[0065] Step 103: Obtain the temperature of the oil slick area and the temperature of the unpolluted sea area using thermal infrared remote sensing equipment, and calculate the temperature difference. .

[0066] By integrating multi-source data from SAR satellites, hyperspectral remote sensing equipment, and thermal infrared remote sensing equipment, a comprehensive analysis of the backscattering characteristics, spectral reflectance characteristics, and temperature characteristics of the oil spill area is achieved. On one hand, the reflectance of different oil types at various wavelengths... With standard oil type storage Comparison can help identify the type of oil film; on the other hand, combined with and It allows for a preliminary assessment of oil film thickness. The comprehensive acquisition of these key parameters provides rich and accurate data support for in-depth assessment of the severity and spread of oil spills, as well as for developing targeted emergency response strategies, thereby enhancing the scientific rigor and effectiveness of emergency decision-making.

[0067] Step 104: Calculate the oil spill confidence index using oil spill data. The formula used is:

[0068] ;

[0069] in, This represents the reflectivity of the oil film region at the i-th wavelength; This represents the standard reflectance of the oil film region at the i-th wavelength, where i represents the wavelength type number and n represents the wavelength type number. The temperature difference threshold between the oil film-covered area and the surrounding seawater area; express Weighting coefficients; express Weighting coefficients; express The weighting coefficients; and It can take the value of , , .

[0070] It should be noted that, This indicates the relative change in the backscattering coefficient. It is the difference in backscattering coefficient between the oil film and the oil-free sea surface. The value is the baseline for oil-free sea surfaces. The greater the change in backscattering characteristics, the higher the relative change value, indicating a greater likelihood of an oil spill. Reflecting differences in spectral reflectance characteristics, It is the reflectance of the oil film at the i-th wavelength. This is the standard reflectance for the corresponding wavelength. It represents the difference between adjacent wavelengths. By summing the reflectance differences of each wavelength to the ratio of the wavelength differences, the greater the difference in reflectance for different oil film types and thicknesses, the higher the sum, indicating a stronger correlation between spectral characteristics and oil spills. Describe temperature characteristics. The temperature difference between the oil film and the surrounding seawater The tanh function normalizes the temperature difference, ensuring that the temperature characteristics are reasonably reflected in the CDI. When the temperature difference exceeds the threshold, it approaches a saturation value, guaranteeing that the temperature factor contributes stably to the characteristic value.

[0071] Traditional marine oil spill monitoring is prone to misjudgment, relying solely on single characteristics or experience-based assessments and lacking comprehensive quantitative evaluation methods. This makes it difficult to accurately distinguish between genuine oil spills and disturbances. By fusing multi-dimensional features to calculate CDI (Clean Oil Discharge Index), we can accurately identify genuine oil spills, reducing false alarms and resource waste. This provides reliable quantitative indicators for emergency decision-making, making decisions more scientific and evidence-based, and improving the efficiency and accuracy of emergency response.

[0072] Step 105: Set the oil spill confidence assessment threshold and set the oil spill confidence index. Compared with the oil spill confidence assessment threshold, when the oil spill confidence index When the oil spill confidence assessment threshold is reached, it is determined that there is a genuine oil spill in this area.

[0073] It should be noted that the method for obtaining the oil spill confidence assessment threshold is as follows: Data on oil spill events from historical records are used as samples. The CDI values ​​for actual oil spill events and non-oil spill events are calculated. Statistical analysis is performed on both types of data to calculate their distribution characteristics. The high-range distribution of the CDI values ​​for actual oil spill events is obtained, and the lower limit of this range is used as the oil spill confidence assessment threshold. Non-oil spill events will have a lower distribution range, making it more likely that the CDI value for actual oil spill events is greater than this threshold, and more likely that the CDI value for non-oil spill events is less than this threshold. Alternatively, receiver operating characteristic (ROC) curves can be plotted to calculate the true positive rate (TPR) and false positive rate (FPR) at different thresholds, and a threshold that results in a high TPR and a low FPR can be selected.

[0074] Step Two: When a genuine oil spill is identified, obtain oil spill hazard data for the area to be analyzed, and calculate the comprehensive oil spill hazard index by analyzing the data. .

[0075] It should be noted that in calculating the comprehensive hazard index of oil spills... During the process, all parameters involved in each calculation step need to be normalized and preprocessed to eliminate the dimensions of different parameters, so as to facilitate subsequent formula calculations.

[0076] It should be noted that the data on the hazards of oil spills includes the actual oil film area. Area threshold of ecologically sensitive areas Actual oil toxicity equivalent and straight-line distance .

[0077] Step 201: Edge detection calculation is performed on the area to be analyzed using synthetic aperture radar (SAR) imagery. SAR imagery can penetrate clouds and darkness, providing high-resolution images of the sea surface. Known image processing techniques are used to identify the oil slick boundaries, and then the true oil slick area of ​​the spill region is calculated. And set ecologically sensitive area thresholds according to the specific protection requirements of ecologically sensitive areas. For example, a mangrove reserve can be set at 10 square kilometers, and the specific value is usually set according to protection regulations.

[0078] Traditional methods for measuring marine oil spill area are easily limited by weather and lighting conditions, failing to function effectively under cloud cover or at night, and their measurement accuracy is low. Furthermore, it is difficult to flexibly set area thresholds according to the protection requirements of ecologically sensitive areas, making it impossible to effectively differentiate oil spill risks in different ecologically sensitive areas. SAR imagery technology can penetrate clouds and darkness, providing high-resolution sea surface images, ensuring accurate identification of oil slick boundaries and calculation of oil slick area (Aoil) under various weather and lighting conditions. By setting an area threshold (Acrit) for ecologically sensitive areas, oil spill risks can be effectively differentiated and warned against based on the protection needs of different ecologically sensitive areas, providing more precise spatial information support for emergency decision-making and improving the targeting and effectiveness of emergency response.

[0079] It should be noted that identifying oil slick boundaries and calculating oil slick area using synthetic aperture radar (SAR) imaging technology is a well-known technique in the field and will not be described in detail here.

[0080] Step 202: Collect oil samples from the area to be analyzed, match them with standard oil data in the oil type database, select the standard oil with the highest matching degree, and obtain the biotoxicity equivalent of this standard oil from the oil type database as the true oil toxicity equivalent. .

[0081] Traditional methods struggle to accurately determine the type of oil involved in marine oil spills and its biotoxicity equivalent. Due to significant differences in composition and properties among different oil types, their toxic effects on marine ecosystems vary, making it difficult for traditional methods to provide accurate toxicity data for toxicity impact assessment and emergency decision-making. By collecting oil samples from the area to be analyzed and matching them with standard oil data in an oil type database, the standard oil with the highest matching degree can be quickly and accurately identified. The biotoxicity equivalent of this standard oil can then be obtained as the true oil toxicity equivalent (Ctox), providing a scientific basis for assessing the toxic impact of oil spills on marine ecosystems. This helps in developing more targeted and effective oil spill emergency decisions, reducing the damage of oil spills to the marine ecological environment, and improving the scientific rigor and rationality of emergency responses.

[0082] It should be noted that a database containing over 4,000 sets of crude oil evaluation data globally, covering information such as distillation composition and molecular structure, can be constructed. Alternatively, a well-known database system, such as the Sinopec Crude Oil Evaluation Database System, can be used as the selection database. Matching methods can include similarity algorithms, such as calculating the matching degree through feature parameter comparison. A commonly used algorithm is the Euclidean distance algorithm, which measures the multidimensional differences between the sample and the database oils, such as the matching degree of viscosity, flash point, and metal element content, to select the standard oil with the highest similarity.

[0083] Step 203: Using Geographic Information System (GIS) technology, combined with geographic data of the location of the area to be analyzed and the ecologically sensitive shoreline, calculate the straight-line distance from the area to be analyzed to the nearest sensitive shoreline. This technique is a standard method in Geographic Information System (GIS) technology.

[0084] Step 204: Calculate the comprehensive oil spill hazard index by analyzing the oil spill hazard data. The formula used is:

[0085] ;

[0086] in, The comprehensive hazard index of oil spills; This indicates the standard toxicity equivalent, used to standardize the toxicity equivalent of different oil products. It can be determined by referring to relevant international, national, or regional standards and regulations, such as the International Maritime Organization (IMO) oil toxicity classification standards, to establish a reasonable benchmark value. This represents the maximum distance value, used to standardize the straight-line distance. A reasonable maximum distance can be determined based on the protection needs of ecologically sensitive areas and the impact range of oil spills. For example, it may be set to 10 kilometers for mangrove reserves. This indicates the weight of the oil film area in the overall hazard index; This indicates the weight of oil biotoxicity in the overall hazard index, and The possible values ​​are: , Furthermore, by conducting statistical and sensitivity analyses on historical data, the impact of different weights on the comprehensive hazard index can be assessed, and the optimal weight combination can be selected.

[0087] It should be noted that, This reflects the relative relationship between the oil spill area and the threshold area of ​​ecologically sensitive areas. The larger the area, the wider the coverage of the ecosystem and the higher the potential harm. It reflects the relative level of the actual toxicity of the oil compared to the standard toxicity; the stronger the toxicity, the greater the harm to living organisms. The OHI (Output Hazard Index) represents the impact of distance between the oil spill area and sensitive areas; the closer the distance, the greater the hazard. Data of different dimensions and magnitudes (such as area, toxicity, and distance) are standardized and inflated to form a dimensionless comprehensive hazard index (OHI). This makes the expression of hazard severity more intuitive and consistent, facilitating horizontal comparisons and decision analysis between different oil spill events. For example, a higher OHI value indicates a greater overall hazard from the oil spill, requiring priority allocation of emergency resources. Calculating the OHI effectively addresses the limitations of traditional methods in marine oil spill hazard assessment, such as their one-sidedness, lack of quantitative considerations, and inadequate weighting. It achieves a comprehensive and accurate quantitative assessment of the overall hazard severity of oil spill accidents, providing a scientific and reliable quantitative basis for marine oil spill emergency decision-making. This helps improve the efficiency and effectiveness of emergency response and reduce the damage of oil spills to the marine ecological environment.

[0088] Step 3: Obtain dynamic diffusion data of the oil spill area through meteorological satellite data or meteorological station data, and obtain the dynamic diffusion risk entropy by comprehensively analyzing the dynamic diffusion data. .

[0089] It should be noted that in calculating the dynamic diffusion risk entropy... During the process, all parameters involved in each calculation step need to be normalized and preprocessed to eliminate the dimensions of different parameters, so as to facilitate subsequent formula calculations.

[0090] Dynamic diffusion data includes effective transport speed Critical speed and the energy term Em;

[0091] Step 301: Obtain the velocity components of the ocean current in the oil spill area in the x and y directions using ocean dynamics models or satellite remote sensing data (such as ocean current velocity fields). and .

[0092] Step 302: Obtain the wind speed in the oil spill area using meteorological satellite data or weather station data. .

[0093] Step 303: Based on the velocity components in the x and y directions , and wind speed Calculate effective transport speed The formula used is: ;

[0094] Traditional methods for assessing oil spill spread rates often neglect the combined effects of ocean currents and wind, or employ simplified assumptions, leading to significant discrepancies between the assessment results and actual conditions. There is a lack of an effective velocity index that comprehensively considers the impact of ocean currents and wind on oil spill spread. This results in insufficient accuracy in oil spill spread predictions, failing to provide a reliable basis for emergency decision-making. The velocity components of ocean currents in the x and y directions should be obtained through ocean dynamics models or satellite remote sensing data. and And wind speed obtained through meteorological satellite data or meteorological station data. It can provide comprehensive, real-time marine dynamics and meteorological data of the oil spill area. This data forms the basis for subsequent calculations of effective transport speeds and simulations of oil spill diffusion processes, ensuring the accuracy and reliability of diffusion assessments.

[0095] The formula takes into account the velocity components of the ocean current in both the x and y directions. and and wind speed The coefficients 0.03 and 0.01 in the formula represent the proportions of the wind's amplifying effect on ocean currents in the x and y directions, respectively. This reflects the actual diffusion rate of the oil spill under the combined action of ocean currents and wind, providing key parameters for subsequent diffusion simulation and risk assessment, and improving the accuracy and scientific rigor of diffusion prediction.

[0096] Step 304: Using fluid dynamics (CFD) software, establish a numerical model of oil spill diffusion, input different ocean currents and wind speeds, simulate the oil spill diffusion process, and determine the critical velocity based on the simulation results. And the value of the energy term Em.

[0097] A numerical model of oil spill diffusion was established using fluid dynamics (CFD) software, which can simulate the diffusion process of oil spills under different ocean currents and wind speeds. This was achieved by inputting actual acquired ocean current velocity components and wind speed data, as well as the calculated effective transport velocity. The model can more accurately predict the diffusion path and extent of oil spills. The critical velocity is determined through simulation results. The values ​​of the energy term Em can be used to assess the risk level and impact range of oil spill spread. For example, when... Exceed When the spill reaches a certain threshold, the oil spill spreads faster and over a wider area; when the spill energy exceeds a certain threshold, the energy of the oil spill increases, exacerbating its environmental impact. These critical parameters provide a quantitative basis for emergency decision-making and help in formulating reasonable emergency response strategies.

[0098] It should be noted that establishing a numerical model of oil spill diffusion using fluid dynamics (CFD) software is an existing technology, which can be implemented by those skilled in the art.

[0099] Step 305: Utilize on-site sampling and remote sensing monitoring equipment to obtain the emulsification state of the oil spill in real time, such as the initial emulsification stage and the stable emulsification stage; and select the corresponding weighting coefficient based on the emulsification state. k is the sequence number of the emulsification state, such as W1 representing the weight coefficient in the early stage of emulsification, with a value of 0.1-0.3; W2 representing the weight coefficient in the early stage of emulsification, with a value of 0.4-0.6.

[0100] The emulsification state of the oil spill, such as the initial emulsification stage and the stable emulsification stage, is obtained in real time using on-site sampling and remote sensing monitoring equipment, and corresponding weighting coefficients are selected according to the emulsification state. The emulsification state affects the diffusion characteristics of oil spills and their environmental impact. Weighting coefficients are used to reflect the importance of different emulsification stages in a comprehensive assessment. For example, in the early stages of emulsification, the weighting coefficient W1 is 0.1-0.3, indicating that the diffusion and harm of the oil spill are relatively low at this time; while in the stable emulsification stage, the weighting coefficient W2 is 0.4-0.6, indicating that the diffusion and harm of the oil spill are more significant at this time. By considering the weighting coefficients of the emulsification state, the risks and hazards of oil spill diffusion can be assessed more comprehensively and accurately, providing a more scientific basis for emergency decision-making.

[0101] Step 306: Calculate the dynamic diffusion risk entropy based on the dynamic diffusion data. The formula used is:

[0102] ;

[0103] Where t0 is the initial timestamp. For the assessment of the time window.

[0104] Traditional methods for assessing marine oil spill spread risk have the following shortcomings: they can only statically assess oil spill spread risk and cannot dynamically reflect changes in risk over time; they ignore the combined impact of the rate of change of oil spill area and the relative relationship between effective transport speed and critical speed; and they do not fully consider the contribution of the rate of change of energy term in the emulsified state to the risk. This makes it impossible to accurately grasp the dynamic changing trend of oil spill spread risk, affecting the timeliness and pertinence of emergency decision-making.

[0105] It should be noted that the integral term middle, The rate of change of the oil spill area over time reflects the speed of oil spill diffusion; This indicates the impact of the relative relationship between effective transport speed and critical speed on risk; the higher the effective transport speed, the greater the relative risk. This is assessed within the evaluation time window. Integrating within the timeframe allows for a dynamic reflection of the changing trend of oil spill spread risk over time, enabling emergency decision-making to adjust strategies promptly based on real-time risk changes. (Summation term) middle This represents the rate of change of the energy term over time, reflecting how fast the energy changes during the oil spill diffusion process; The weighting coefficients representing different emulsification states reflect the relative importance of each emulsification state to the risk. By combining the rate of change of the energy term with the weighting coefficients of the emulsification state, the impact of energy changes on risk under different emulsification states is comprehensively integrated, making risk assessment more comprehensive and accurate. Multiple dynamic factors are integrated into a dimensionless dynamic diffusion risk entropy (DER) through mathematical formulas, making the expression of risk level more intuitive and unified, facilitating horizontal comparisons and decision analysis between different oil spill events. For example, a higher DER value indicates a greater risk of oil spill diffusion, requiring priority allocation of emergency resources for handling, providing a clear and quantifiable target orientation for emergency decision-making. This addresses the shortcomings of traditional marine oil spill diffusion risk assessment methods in terms of dynamism, comprehensiveness, and completeness, achieving dynamic and accurate quantitative assessment of oil spill diffusion risk. It provides a scientific and reliable dynamic quantitative basis for marine oil spill emergency decision-making, helping to improve the timeliness and targeting of emergency response and reduce the risk of damage to the marine ecological environment from oil spill accidents.

[0106] Step 4: Obtain emergency response data through the emergency resource dispatch platform or inventory management system, and obtain the emergency response effectiveness index by comprehensively analyzing the emergency response data. .

[0107] It should be noted that in calculating the emergency response effectiveness index... During the process, all parameters involved in each calculation step need to be normalized and preprocessed to eliminate the dimensions of different parameters, so as to facilitate subsequent formula calculations.

[0108] It should be noted that emergency response data includes: total resource demand. Emergency resource gap Emergency response time and the actual time required for emergency response .

[0109] Step 401: Obtain the quantities of different types of resources used in the emergency response through the emergency resource dispatch platform or inventory management system. and the standardization efficiency of various resources By the quantity of different types of resources Calculate the total demand for different types of resources. The formula used is: .

[0110] In emergency response, the diverse and dispersed nature of different types of emergency resources, lacking unified integration, makes it difficult to quickly and accurately understand the overall resource demand, easily leading to blind and inefficient resource allocation. This can be addressed through formulas. By adding up the quantities of various emergency resources, the total resource demand can be quickly integrated and calculated, providing comprehensive and accurate total data for subsequent resource allocation and gap assessment. This ensures that emergency decisions are based on a clear understanding of the overall resource demand and improves the planning and rationality of resource allocation.

[0111] It should be noted that the types of resources can include adsorption bins, dispersants, incineration devices, etc.

[0112] Step 402: Obtain the resource demand for different types based on the oil spill diffusion model. And based on the amount of resources and resource demand Calculate emergency resource gaps using real-time data. The formula used is: .

[0113] Traditional emergency resource management struggles to dynamically and accurately assess emergency resource gaps, often relying on experience or rough estimates that deviate significantly from actual needs, leading to either insufficient or excessive resource allocation during emergency responses. Based on the formula... This allows for precise calculation of emergency resource gaps. It not only clearly shows the specific shortage quantities of various resources but also quantifies the overall degree of resource shortage, helping emergency decision-makers plan resource procurement, allocation, or seeking external support in advance, optimizing resource allocation, and avoiding the adverse effects of resource waste or insufficient supply on emergency response.

[0114] Step 403: Obtain the model-predicted emergency response time based on the simulation results of the oil spill diffusion model or resource scheduling model. And obtain the actual emergency response process records to obtain the actual time required for the emergency response. ;

[0115] The lack of effective integration between predicted and actual emergency response times makes it difficult to verify the accuracy of model predictions and provides no strong time basis for resource scheduling and process optimization, thus hindering the improvement of emergency response efficiency. This is addressed by comparing the emergency response times predicted by the models. and the actual time required for emergency response This data can assess the accuracy of model predictions and identify potential problems and bottlenecks in the emergency response process. Based on this data, emergency response procedures can be optimized and resource allocation strategies adjusted to shorten response time, improve the timeliness and effectiveness of emergency response, and minimize the damage of oil spills to the marine environment and ecosystems.

[0116] Step 404: Calculate the emergency response effectiveness index using emergency response data. The formula used is:

[0117] ;

[0118] in, This represents the standardization efficiency of the j-th type of resource; This represents the quantity of resource of type j; j is the index of the resource, which takes the value of a positive integer, and J represents the total number of resource types.

[0119] It should be noted that, This reflects the efficiency of the utilization of various emergency resources, among which, This represents the standardized efficiency of the j-th type of resource, used to measure the effective utilization rate of this type of resource in emergency response; Let represent the quantity of resource type j. The overall resource utilization efficiency in the entire emergency response is obtained by multiplying the efficiency of each resource type by its quantity and summing the results. This allows for the integration and evaluation of different types and quantities of resources within a unified framework, avoiding the problem of simply considering resource quantity while ignoring resource utilization effectiveness. The larger of the actual emergency response time and the model-predicted time is used as the normalization factor. This ensures that the assessment results, based on the most unfavorable time factor, truly reflect the timeliness of the emergency response. When the actual response time... Greater than the model prediction time At that time, with Using [a specific benchmark] as the baseline, the time delay issue in the actual response process is highlighted; conversely, [a different benchmark] is used. Based on this, the rationality of the model's predictions is evaluated. Through this normalization process, resource utilization efficiency and response time are organically combined, enabling the effectiveness of different emergency response actions to be compared on a unified time scale. This reflects the extent to which the shortage of emergency resources affects effectiveness. This indicates a shortage of emergency resources. This represents the total resource demand. By normalizing the resource gap with the total demand and then exponentially calculating it, the negative impact of the resource gap on emergency response effectiveness is further amplified. This prompts policymakers to pay more attention to the resource gap issue and take timely measures to replenish scarce resources, thereby improving emergency response effectiveness. The ERE calculation integrates multiple complex factors into a dimensionless index, typically ranging from 0 to 1. A higher ERE value indicates better emergency response effectiveness; conversely, a lower value indicates poorer effectiveness. This intuitive quantitative result allows policymakers to quickly understand the overall effectiveness level of emergency response and provides a scientific basis for comparing different emergency response actions or plans. Furthermore, long-term monitoring and analysis of ERE can assess the effectiveness of emergency capacity building, identify weaknesses in emergency response, and provide direction for continuous improvement of emergency management. It effectively addresses the shortcomings of traditional maritime emergency response effectiveness assessment methods in terms of comprehensiveness and quantification, achieving a comprehensive and accurate quantitative assessment of emergency response effectiveness. This provides scientific and effective decision support for optimizing emergency resource allocation, enhancing emergency response capabilities, and improving emergency management, and helps improve the overall effectiveness and scientific rigor of maritime oil spill emergency response.

[0120] Step 5: Set a comprehensive assessment threshold set to include the comprehensive hazard index of oil spills. Dynamic diffusion risk entropy and emergency response effectiveness index The severity level of the oil spill event is determined by comparing it with the thresholds in the comprehensive assessment threshold set, and the corresponding emergency decision-making strategy is selected based on the severity level of the oil spill event.

[0121] Step 501: Set a comprehensive assessment threshold set; the comprehensive assessment threshold set includes: oil spill hazard index threshold I, oil spill hazard index threshold II, oil spill hazard index threshold III, diffusion risk threshold I, diffusion risk threshold II, diffusion risk threshold III, response effectiveness threshold I, response effectiveness threshold II, and response effectiveness threshold III.

[0122] It should be noted that historical oil spill data, such as the IMO Global Oil Spill Database and the NOAA Accident Database, are collected to obtain the hazard threshold value for natural degradation as the Oil Spill Hazard Index Threshold III. Specifically, this can be used for oil spill incident samples where the oil slick degradation rate in open water within 72 hours is >95%, and the corresponding threshold value is calculated. The range of values ​​is determined, with the lower limit of the range used as the threshold III for the oil spill hazard index. Based on data from Level II oil spill events, the threshold values ​​for historical oil spill events are calculated. Value distribution, selecting samples from Level II oil spill events. The range of values ​​for the 80th percentile of the value is used, and the lower limit of the range is selected as the threshold II for the oil spill hazard index; based on the data of Level I oil spill event samples, the historical oil spill event samples are calculated. Value distribution, selecting samples from Level I oil spill events. The range of values ​​for the 85th percentile of the value is used, and the lower limit of the range is selected as the threshold I of the oil spill hazard index.

[0123] Collect oil spill event samples from areas with static ocean currents (velocity < 0.2 m / s) and calculate the corresponding dynamic diffusion risk entropy. The range of values ​​was determined, and the lower limit of the range was selected as the diffusion risk threshold III. An ROC curve was plotted for optimization, and the equilibrium point corresponding to TPR = 80% (detection of high-risk diffusion) and FPR < 20% was identified. The value is used as the diffusion risk threshold II; a numerical model of oil spill diffusion is established using fluid dynamics (CFD) software, inputting ocean current and wind speed conditions from a Level I oil spill event sample, such as wind speed > 10 m / s + current velocity > 1.0 knot. The simulation results are then used to calculate... The range of values ​​is selected, and the lower limit of the range is chosen as the diffusion risk threshold I.

[0124] Based on simulation results of oil spill diffusion models or resource scheduling models from historical oil spill event data for different resource gap states, an emergency response effectiveness index can be calculated for historical events when the resource gap is ≥50%. The range of values ​​is defined, with the lower limit of the range used as the response performance threshold I; for responses with a duration ≤ 6 hours and a resource deficit ≤ 30%, the threshold is defined as follows. The lower limit of the value range is used as the response performance threshold II; for resource gaps ≤ 10%, The lower limit of the value range, the response performance threshold III.

[0125] Step 502: Calculate the comprehensive hazard index of the oil spill. The dynamic diffusion risk entropy is compared with the oil spill hazard index thresholds I, II, and III respectively. The emergency response effectiveness index is compared with the diffusion risk thresholds I, II, and III respectively. Compare with response performance threshold I, response performance threshold II, and response performance threshold III respectively;

[0126] When the comprehensive hazard index of oil spill is met simultaneously <Oil Spill Hazard Index Threshold III, Dynamic Diffusion Risk Entropy> <Spread risk threshold III, and emergency response effectiveness index > When the response effectiveness threshold is III, execute the Level IV emergency decision-making plan;

[0127] When the oil spill hazard index threshold Ⅲ ≤ the comprehensive oil spill hazard index is met. <Oil Spill Hazard Index Threshold II and Diffusion Risk Threshold III ≤ Dynamic Diffusion Risk Entropy <Propagation Risk Threshold II, or Response Effectiveness Threshold II <Emergency Response Effectiveness Index When the response performance threshold is ≤ III, execute the Level 3 emergency decision-making plan;

[0128] When the oil spill hazard index threshold II is ≤ the comprehensive oil spill hazard index <Oil Spill Hazard Index Threshold I and Diffusion Risk Threshold II ≤ Dynamic Diffusion Risk Entropy <Propagation risk threshold I, or response effectiveness threshold I <Emergency response effectiveness index When the response performance threshold is ≤ II, execute the Level II emergency decision-making plan;

[0129] When the oil spill hazard index threshold Ⅰ ≤ the comprehensive oil spill hazard index is met Furthermore, the diffusion risk threshold Ⅰ ≤ dynamic diffusion risk entropy or emergency response effectiveness index If the response effectiveness threshold is less than or equal to threshold I, execute the Level 1 emergency decision-making plan.

[0130] Step 503: The Level 4 emergency decision-making plan is a general response plan. The specific plan includes manual cleanup of oil spills on the shore, portable oil suction machines to treat nearshore oil slicks; daily verification of the cleanup effect using hyperspectral remote sensing; local emergency vessels responding within 24 hours; and resource gaps being supported by adjacent ports.

[0131] The Level 3 emergency response plan is a larger response plan. The specific plan includes single-layer oil booms to protect key tourist areas / water intakes, small oil skimmer operations, spraying of low-toxicity emulsifying dispersants (compliant with IMO standards) to accelerate natural degradation, and mobilizing resources within 8 hours by relying on provincial emergency reserves.

[0132] The Level 2 emergency response plan is a major response plan. The specific plan may include a double-layer oil boom layout: an outer rigid boom to protect against wind and waves, and an inner absorbent boom to intercept residual oil; a combination of skimmers and absorbent pads as the main equipment, supplemented by high-pressure flushing vessels to clean the reef coastline; a GIS resource scheduling platform to match the nearest cleanup vessel (response radius ≤ 50 nautical miles) to dynamically fill gaps; and drones to patrol three times a day and update the diffusion model parameters.

[0133] The Level 1 emergency response plan is a particularly serious response plan. Specific measures include: immediately initiating vessel towing / transfer and sealing damaged parts of the vessel; cutting off oil pipelines from land-based oil spills and transferring remaining oil; deploying large oil booms (≥2000 meters) to seal off the perimeter of sensitive areas, prioritizing the use of high-efficiency skimmers (recovery rate >90%), placing pre-positioned adsorption booms in mangroves and aquaculture areas, and deploying biodegradable agents (such as petroleum degradation bacteria); cross-regional coordination: calling upon national-level emergency reserves (such as marine oil spill emergency equipment depots), coordinating helicopter airdrops of supplies; establishing an on-site command center within 2 hours, and completing the first round of resource deployment within 4 hours.

[0134] This can be achieved through a combination of methods. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0135] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0136] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A marine oil spill emergency decision-making method based on satellite remote sensing and big data analysis, characterized in that, include: Step 1: Obtain oil spill authenticity data for the area to be analyzed using satellite remote sensing technology. This data includes the difference in backscattering coefficients. Reflectance of oil film region at different wavelengths Reflectance in standard oil sample library Wavelength difference between two adjacent bands and temperature difference The specific method for obtaining oil spill authenticity data is as follows: SAR backscattering coefficients of the oil slick area are obtained via SAR satellites, and then compared with a baseline value from an oil-free sea surface. The difference was calculated to obtain the difference in backscattering coefficients. The reflectance of the oil film region at different wavelengths is obtained using hyperspectral remote sensing equipment carried by satellites or drones. and the wavelength difference between two adjacent bands The reflectance of the standard oil type library was obtained through standard oil type library data. The temperature of the oil slick area and the temperature of the unpolluted sea area are obtained using thermal infrared remote sensing equipment, and the temperature difference is calculated. ; and by analyzing the authenticity data of the oil spill, an oil spill confidence index was calculated. And based on the confidence index of oil spill Determine whether this area represents a genuine oil spill; Step Two: When it is determined that a real oil spill exists, obtain the oil spill hazard data for the area to be analyzed. The oil spill hazard data includes the actual oil film area. Actual oil toxicity equivalent and straight-line distance Furthermore, by analyzing the data on the hazards of oil spills, a comprehensive oil spill hazard index was calculated. The specific method for obtaining data on the hazards of oil spills is as follows: The true oil film area of ​​the oil spill region was obtained using synthetic aperture radar imaging technology. ; Oil samples were collected from the area to be analyzed and matched with standard oil data in an oil database. The standard oil with the highest matching degree was selected, and its biotoxicity equivalent was obtained from the oil database as the true oil toxicity equivalent. ; Using Geographic Information System (GIS) technology, and combining geographic data of the location of the area to be analyzed with that of ecologically sensitive shorelines, the straight-line distance from the area to be analyzed to the nearest sensitive shoreline is calculated. ; Step 3: Obtain dynamic diffusion data of the oil spill area using meteorological satellite data. Dynamic diffusion data includes: effective transport speed. Critical speed And the energy term Em, and by comprehensively analyzing the dynamic diffusion data, the dynamic diffusion risk entropy is obtained. ; Step 4: Obtain emergency response data through the emergency resource dispatch platform. The emergency response data includes the total resource demand. Emergency resource gap Emergency response time and the actual time required for emergency response Furthermore, by comprehensively analyzing emergency response data, an emergency response effectiveness index is obtained. ; Step 5: Set a comprehensive assessment threshold set to include the comprehensive hazard index of oil spills. Dynamic diffusion risk entropy and emergency response effectiveness index The severity level of the oil spill event is determined by comparing it with the thresholds in the comprehensive assessment threshold set, and the corresponding emergency decision-making strategy is selected based on the severity level of the oil spill event. Calculate the confidence index of oil spill based on the authenticity data of the oil spill. The formula used is: ； in, This represents the reflectivity of the oil film region at the i-th wavelength; i represents the sequence number of the wavelength type, and n represents the sequence number of the wavelength type. The temperature difference threshold between the oil film-covered area and the surrounding seawater area; express Weighting coefficients; express Weighting coefficients; express Weighting coefficients; This is the baseline value for oil-free sea surface; Set a confidence threshold for oil spill assessment, and use oil spill confidence indicators Compared with the oil spill confidence assessment threshold, when the oil spill confidence index If the oil spill confidence assessment threshold is reached, it is determined that there is a genuine oil spill in this area; By analyzing the data on the hazards of oil spills, a comprehensive oil spill hazard index was calculated. The formula used is: ； in, Indicates standard toxicity equivalent. Indicates the maximum distance value; Indicates the area threshold of ecologically sensitive areas; This indicates the weight of the oil film area in the overall hazard index; This indicates the weight of petroleum product biotoxicity in the overall hazard index; Calculate the dynamic diffusion risk entropy based on dynamic diffusion data. The formula used is: ； Where t0 is the initial timestamp. For the assessment time window; Here, k represents the weighting coefficient for emulsion state k, where k is the index of the emulsion state. This indicates the rate of change of the oil spill area over time; This represents the rate of change of the energy term over time; Calculate the emergency response effectiveness index using emergency response data. The formula used is: ； in, This represents the standardization efficiency of the j-th type of resource; This represents the quantity of resource of type j; j is the index of the resource, and J represents the total number of resource types.

2. The marine oil spill emergency decision-making method based on satellite remote sensing and big data analysis according to claim 1, characterized in that, The specific method for obtaining dynamic diffusion data is as follows: The velocity components of the ocean current in the oil spill area in the x and y directions were obtained using an ocean dynamics model. and Wind speed in the oil spill area was obtained through weather station data. Based on the velocity components in the x and y directions , and wind speed Calculate effective transport speed The formula used is: ; A numerical model of oil spill diffusion was established using fluid dynamics software, and the critical velocity was obtained by simulating the oil spill diffusion process. And the energy term Em.

3. The marine oil spill emergency decision-making method based on satellite remote sensing and big data analysis according to claim 2, characterized in that, The specific calculation method for emergency response data is as follows: Obtain the quantities of different types of resources used in emergency response through the emergency resource dispatch platform. and the standardization efficiency of various resources By the quantity of different types of resources Calculate the total demand for different types of resources. The formula used is: ; Demand for different types of resources based on oil spill diffusion models And based on the amount of resources and resource demand Calculate emergency resource gaps using real-time data. The formula used is: ; The simulation results based on the oil spill diffusion model are used to obtain the model-predicted emergency response time. And obtain the actual emergency response process records to obtain the actual time required for the emergency response. .

4. The marine oil spill emergency decision-making method based on satellite remote sensing and big data analysis according to claim 3, characterized in that, The comprehensive assessment threshold set includes: oil spill hazard index threshold I, oil spill hazard index threshold II, oil spill hazard index threshold III, diffusion risk threshold I, diffusion risk threshold II, diffusion risk threshold III, response effectiveness threshold I, response effectiveness threshold II, and response effectiveness threshold III; The comprehensive hazard index of oil spills The dynamic diffusion risk entropy is compared with the oil spill hazard index thresholds I, II, and III respectively. The emergency response effectiveness index is compared with the diffusion risk thresholds I, II, and III respectively. Compare with response performance threshold I, response performance threshold II, and response performance threshold III respectively; When the comprehensive hazard index of oil spill is met simultaneously <Oil Spill Hazard Index Threshold III, Dynamic Diffusion Risk Entropy> <Spread risk threshold III, and emergency response effectiveness index > When the response effectiveness threshold is III, execute the Level IV emergency decision-making plan; When the oil spill hazard index threshold Ⅲ ≤ the comprehensive oil spill hazard index is met. <Oil Spill Hazard Index Threshold II and Diffusion Risk Threshold III ≤ Dynamic Diffusion Risk Entropy <Propagation Risk Threshold II, or Response Effectiveness Threshold II <Emergency Response Effectiveness Index When the response performance threshold is ≤ III, execute the Level 3 emergency decision-making plan; When the oil spill hazard index threshold II is ≤ the comprehensive oil spill hazard index <Oil Spill Hazard Index Threshold I and Diffusion Risk Threshold II ≤ Dynamic Diffusion Risk Entropy <Propagation risk threshold I, or response effectiveness threshold I <Emergency response effectiveness index When the response performance threshold is ≤ II, execute the Level II emergency decision-making plan; When the oil spill hazard index threshold Ⅰ ≤ the comprehensive oil spill hazard index is met Furthermore, the diffusion risk threshold Ⅰ ≤ dynamic diffusion risk entropy or emergency response effectiveness index If the response effectiveness threshold is less than or equal to threshold I, execute the Level 1 emergency decision-making plan.

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