Marine environment risk assessment method and system based on multi-source data fusion

Abstract

The invention relates to the technical field of environment management, in particular to a marine environment risk assessment method and system based on multi-source data fusion, and the method comprises the following steps: carrying out the assessment based on the multi-source marine environment monitoring data through the data integrity, consistency and timeliness of a plurality of data sources, and calculating efficiency scores of the plurality of data sources and adjusting the contribution proportion of the target data source to generate a multi-source data fusion model. According to the method, by analyzing the efficiency of multiple data sources, adjustment of data source contribution is achieved, dynamic adaptability and flexibility of data fusion are optimized, the accuracy and timeliness of marine environment risk assessment are improved, and detection and recognition of abnormal data of multiple environment indexes are achieved by combining analysis of water quality related indexes and temperature data; through analysis of marine development activity events, a decision basis is provided for adjustment of marine management policies, and accurate assessment of marine environment risks is realized in combination with prediction of marine biological diversity.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental management, and in particular to a marine environmental risk assessment method and system based on multi-source data fusion. Background Art

[0002] The field of environmental management technology aims to predict, monitor, evaluate and mitigate the impact of environmental problems on ecology and human society through a variety of scientific and technological means, including resource management, pollution control, environmental quality monitoring, ecological protection, disaster prevention and response. It combines big data analysis, environmental simulation prediction and risk assessment methods, and combines information technology with environmental science to improve the efficiency and accuracy of environmental management and promote decision-making support for sustainable development and environmental protection.

[0003] Among them, the marine environmental risk assessment method focuses on identifying and evaluating potential risks in the marine environment, including risk factor identification in the marine ecosystem, risk source assessment, and risk transmission path analysis. By collecting marine environmental data, including water quality, biological community status, and chemical substance concentrations, using data fusion technology, and analyzing the collected environmental sample data, combined with historical data comparison, it determines the severity and possible impact of various risks, assesses the risk level of various risk factors on the marine ecology, quantifies risk factors, and provides a scientific basis for environmental management and policy making.

[0004] In actual operations, traditional marine environmental risk assessment methods are often limited by data fragmentation and processing lags. They rely on a single data source, which limits the depth and breadth of data analysis, resulting in errors or untimely environmental risk assessment results. It is difficult to accurately capture complex environmental change trends and potential risk points, resulting in inaccurate assessment results and affecting the effectiveness of decision-making. The efficiency and accuracy in processing batch and complex data still need to be improved, which is particularly critical in the rapidly changing marine environment, because data timeliness directly affects the timeliness and effectiveness of environmental emergency response. Summary of the invention

[0005] In order to solve the technical problem that it is difficult to fuse multiple data sources in the prior art, the embodiment of the present invention provides a marine environmental risk assessment method and system based on multi-source data fusion. The technical solution is as follows:

[0006] On the one hand, a marine environmental risk assessment method based on multi-source data fusion is provided, which includes:

[0007] S1: Based on multi-source marine environment monitoring data, the data integrity, consistency and timeliness of multiple data sources are evaluated, the effectiveness scores of multiple data sources are calculated, and the contribution ratio of the target data source is adjusted to generate a multi-source data fusion model;

[0008] S2: using the multi-source data fusion model, extracting and analyzing various indicator data related to water quality, performing trend analysis and pattern recognition on the various indicators, detecting and recording abnormal values ​​of the various indicators, and generating ocean water quality status information;

[0009] S3: Based on the ocean water quality status information, extract and analyze the temperature data of the ocean environment, including water temperature and air temperature, analyze the change trend of ocean temperature, detect and identify abnormal fluctuations, and generate temperature abnormality event records;

[0010] S4: Based on the temperature anomaly event records, multiple marine development activity events are analyzed, including oil and gas production and coastline changes, to evaluate the impact of development activities on the marine environment and generate development activity impact information;

[0011] S5: Based on the impact information of the development activities, by analyzing the impact of various influencing factors on marine environmental indicators, predicting the impact of various influencing factors on marine biodiversity, combining the abnormal values ​​of multiple indicators, calculating the marine environmental risk score, and generating real-time risk assessment information.

[0012] As a further scheme of the present invention, the multi-source data fusion model specifically includes a performance score list, a data source contribution ratio adjustment record, and a data fusion result; the ocean water quality status information includes water quality trend analysis results, water quality pattern recognition information, and water quality anomaly value records; the temperature abnormality event record specifically refers to water temperature change trend information, air temperature change trend information, and temperature abnormal fluctuation identification results; the development activity impact information includes development activity type information, development activity impact assessment results, and development activity risk index; the real-time risk assessment information specifically includes an influencing factor relationship diagram, biodiversity impact prediction results, and an environmental risk score.

[0013] As a further solution of the present invention, based on multi-source marine environment monitoring data, the data integrity, consistency and timeliness of multiple data sources are evaluated, the performance scores of multiple data sources are calculated and the contribution ratio of the target data source is adjusted, and the steps of generating a multi-source data fusion model are specifically as follows:

[0014] S101: Based on multi-source marine environment monitoring data, conduct integrity analysis on data from multiple data sources, including satellite remote sensing, buoy monitoring, and ship collection, mark missing data and timestamp errors, and generate data inspection results;

[0015] S102: Based on the data inspection results, evaluate the consistency and timeliness of data from multiple sources, calculate performance scores for multiple data sources, and generate a data source performance score table;

[0016] S103: Based on the data source performance rating table, matching contribution ratios of multiple data sources according to performance ratings, and constructing a multi-source data fusion model.

[0017] As a further solution of the present invention, the specific formula for calculating the performance score for multiple data sources is:

[0018] ;

[0019] in, Representative The performance rating of each data source, is the weight coefficient of data integrity, It is The number of data points known for each data source, Theoretically, The total number of data points a data source should have, is the weight coefficient of data consistency, It is The number of data points that are consistent between a data source and other data sources in the same time window, It is The total number of data points for consistency comparison of the data sources, is the weight coefficient of data timeliness, It is The average data delay time of each data source, Represents the index of the data source.

[0020] As a further solution of the present invention, the steps of using the multi-source data fusion model to extract and analyze multiple indicator data related to water quality, perform trend analysis and pattern recognition on multiple indicators, detect and record abnormal values ​​of multiple indicators, and generate ocean water quality status information are specifically as follows:

[0021] S201: Based on the multi-source data fusion model, extract multiple indicator data related to water quality, including microplastic content, salinity, and dissolved oxygen, and generate a water quality information extraction record;

[0022] S202: Based on the water quality information extraction record, combined with time information, identifying the change trends and periodic patterns of multiple water quality indicators, and generating water quality trend analysis results;

[0023] S203: Based on the water quality trend analysis result, abnormal points in multiple indicator data are detected and recorded in real time to generate ocean water quality status information.

[0024] As a further solution of the present invention, based on the ocean water quality status information, extracting and analyzing the temperature data of the ocean environment, including water temperature and air temperature, analyzing the change trend of the ocean temperature, detecting and identifying abnormal fluctuations, and generating the temperature abnormality event record are specifically as follows:

[0025] S301: extracting ocean temperature data, including water temperature and air temperature information, based on the ocean water quality status information, and generating a temperature data set;

[0026] S302: Based on the temperature data set, by analyzing and predicting the change trend of the ocean temperature data, monitoring and recording abnormal data points, and generating temperature anomaly analysis results;

[0027] S303: Based on the temperature anomaly analysis result, by analyzing the amplitude and duration of fluctuations of multiple abnormal data points, calculating the degree of anomaly and marking abnormal temperature events, and generating a temperature anomaly event record.

[0028] As a further solution of the present invention, based on the abnormal temperature event records, multiple marine development activity events are analyzed, including oil and gas production and coastline changes, to evaluate the impact of development activities on the marine environment. The steps of generating development activity impact information are specifically as follows:

[0029] S401: extracting various information related to marine development activity events based on the abnormal temperature event records, including development time and geographic location information, and generating a development activity data set;

[0030] S402: using the development activity dataset, combined with water quality and temperature information of the target location, assessing the impact of multiple development events on the marine environment, identifying and marking the scope of the affected area, including oil and gas exploitation and coastline changes, and generating activity impact analysis results;

[0031] S403: Based on the activity impact analysis results, considering the duration and intensity of the development activities, analyzing the impact of various development activities on the marine environment, marking risky development activities, and generating development activity impact information.

[0032] As a further solution of the present invention, according to the development activity impact information, by analyzing the impact of multiple influencing factors on marine environmental indicators, predicting the impact of multiple influencing factors on marine biodiversity, combining the abnormal values ​​of multiple indicators, calculating the marine environmental risk score, and generating real-time risk assessment information, the specific steps are:

[0033] S501: Analyze the impact of various factors on marine environmental indicators and quantify the impact coefficients based on the development activity impact information, including pollution emissions, human development activities, and meteorological factors, and generate a weight list of influencing factors;

[0034] S502: Based on the weight list of influencing factors, according to the influence of multiple influencing factors on marine environmental indicators, predict the influence of multiple influencing factors on marine biodiversity to form a biodiversity index;

[0035] S503: Based on the biodiversity indicators and in combination with abnormal values ​​of multiple marine environmental indicators, marine environmental risk scores of multiple regions are calculated to generate real-time risk assessment information.

[0036] As a further solution of the present invention, the specific formula for calculating the marine environmental risk scores of multiple regions is:

[0037] ;

[0038] in, represents the risk score of the marine environment, is the number of indicators involved in the calculation, It is The weight coefficient of each indicator is It is The abnormal value of the indicator, It is The anomaly detection threshold of each indicator, Represents the total number of indicators involved in the risk score calculation. is the index of the indicator.

[0039] On the other hand, a marine environment risk assessment system based on multi-source data fusion is provided, the system is applied to a marine environment risk assessment method based on multi-source data fusion, and the system includes:

[0040] The data source processing module is based on multi-source marine environment monitoring data. It evaluates the integrity, consistency and timeliness of data from multiple data sources, adjusts the contribution ratio of data sources, and generates a multi-source data fusion model.

[0041] The water quality index analysis module extracts a variety of key water quality indicators, including microplastic content, salinity and dissolved oxygen, based on the multi-source data fusion model, identifies the change trend patterns of multiple indicators through time series analysis, detects abnormal values ​​in multiple indicators, and generates marine water quality status information;

[0042] The real-time temperature monitoring module extracts water temperature and air temperature data based on the ocean water quality status information and performs trend analysis, detects and identifies abnormal fluctuations in temperature data, and generates temperature abnormality event records;

[0043] The marine activity analysis module analyzes the impact of marine development activities on the marine environment based on the abnormal temperature event records, evaluates the environmental sensitivity of various activities, and generates development activity impact information;

[0044] The environmental risk analysis module predicts the impact of various factors on marine biodiversity based on the impact information of the development activities, calculates the marine environmental risk score in combination with real-time marine environmental monitoring data, and generates real-time risk assessment information.

[0045] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0046] By analyzing the effectiveness of multiple data sources, adjusting the contribution of data sources, optimizing the dynamic adaptability and flexibility of data fusion, and improving the accuracy and timeliness of marine environmental risk assessment, combined with the analysis of water quality-related indicators and temperature data, the detection and identification of abnormal data of multiple environmental indicators can be achieved. Through the analysis of marine development activities, a decision-making basis is provided for the adjustment of marine management policies. Combined with the prediction of marine biodiversity, an accurate assessment of marine environmental risks can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0048] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0049] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0050] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0051] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0052] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0053] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0054] Figure 7 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0055] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0056] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0057] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0058] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0059] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0060] The embodiment of the present invention provides a marine environmental risk assessment method based on multi-source data fusion, such as Figure 1 The flowchart of the marine environmental risk assessment method based on multi-source data fusion is shown in FIG. The processing flow of the method may include the following steps:

[0061] S1: Based on multi-source marine environment monitoring data, the data integrity, consistency and timeliness of multiple data sources are evaluated, the effectiveness scores of multiple data sources are calculated, and the contribution ratio of the target data source is adjusted to generate a multi-source data fusion model;

[0062] S2: Utilize the multi-source data fusion model to extract and analyze various indicator data related to water quality, conduct trend analysis and pattern recognition on various indicators, detect and record abnormal values ​​of various indicators, and generate marine water quality status information;

[0063] S3: Based on the ocean water quality status information, extract and analyze the temperature data of the ocean environment, including water temperature and air temperature, analyze the changing trend of ocean temperature, detect and identify abnormal fluctuations, and generate temperature anomaly event records;

[0064] S4: Based on the records of temperature anomaly events, analyze multiple marine development activities, including oil and gas production and coastline changes, evaluate the impact of development activities on the marine environment, and generate development activity impact information;

[0065] S5: Based on the impact information of development activities, by analyzing the impact of various influencing factors on marine environmental indicators, predicting the impact of various influencing factors on marine biodiversity, combining the abnormal values ​​of multiple indicators, calculating the marine environmental risk score, and generating real-time risk assessment information.

[0066] The multi-source data fusion model specifically includes a performance score list, a data source contribution ratio adjustment record, and data fusion results. The ocean water quality status information includes water quality trend analysis results, water quality pattern recognition information, and water quality anomaly value records. The temperature abnormality event records specifically refer to water temperature change trend information, air temperature change trend information, and temperature abnormal fluctuation identification results. The development activity impact information includes development activity type information, development activity impact assessment results, and development activity risk index. The real-time risk assessment information specifically includes an influencing factor relationship diagram, biodiversity impact prediction results, and environmental risk score.

[0067] See also Figure 2 Based on multi-source marine environment monitoring data, the data integrity, consistency and timeliness of multiple data sources are evaluated, the performance scores of multiple data sources are calculated and the contribution ratio of the target data source is adjusted. The specific steps for generating a multi-source data fusion model are as follows:

[0068] S101: Based on multi-source marine environment monitoring data, conduct integrity analysis on data from multiple data sources, including satellite remote sensing, buoy monitoring, and ship collection, mark missing data and timestamp errors, and generate data inspection results;

[0069] Review the information provided by each data source, verify the timestamp of each data set, identify and mark timestamp anomalies caused by synchronization errors or data transmission delays, check data integrity, determine the specific location and frequency of missing data by setting threshold methods, analyze the coverage and resolution of satellite remote sensing data, focus on checking sensor failures or data discontinuities for buoy monitoring data, and verify the continuity and integrity of data collection for ship-collected data. The process is carried out by using a data quality assessment model that relies on statistical analysis methods such as standard deviation and mean calculation to identify outliers or data quality issues in the data set. The process ensures that each data point is verified and calibrated to improve the accuracy and reliability of data analysis. The generated data inspection results include an integrity report and timestamp correction record for each data source.

[0070] S102: Based on the data inspection results, evaluate the consistency and timeliness of data from multiple sources, calculate performance scores for multiple data sources, and generate a data source performance score table;

[0071] The specific formula for calculating the performance score for multiple data sources is:

[0072] ;

[0073] in, Representative The performance rating of each data source, is the weight coefficient of data integrity, It is The number of data points known for each data source, Theoretically, The total number of data points a data source should have, is the weight coefficient of data consistency, It is The number of data points that are consistent between a data source and other data sources in the same time window, It is The total number of data points for consistency comparison of the data sources, is the weight coefficient of data timeliness, It is The average data delay time of each data source, Represents the index of the data source.

[0074] formula:

[0075] ;

[0076] Detailed explanation of the formula and the process of formula calculation and derivation:

[0077] The formula is used to evaluate the comprehensive effectiveness of data sources, and the effectiveness score is used to adjust the contribution ratio of each data source to data fusion;

[0078] Parameter meaning and setting value:

[0079] , , is the weight coefficient, assuming , , ;

[0080] Is the data source The number of known data points, assumed to be 800;

[0081] is the total number of theoretical data points, assumed to be 1000;

[0082] Is the data source The number of data points that are consistent with other data sources in the same time window is assumed to be 600;

[0083] is the total number of data points for consistency comparison, assumed to be 700;

[0084] Is the data source The average data delay time is assumed to be 2 hours;

[0085] Substitute the parameters into the formula for calculation:

[0086] Calculate the data completeness score:

[0087] ;

[0088] Calculate the data consistency score:

[0089] ;

[0090] Calculate the data timeliness score:

[0091] ;

[0092] Computational performance rating:

[0093] ;

[0094] The result of 0.6336 shows that the comprehensive performance score of the target data source is high, reflecting that the data source has good overall performance in terms of completeness, consistency and timeliness. The score helps to determine the weight and usage priority of multiple data sources in the data fusion process.

[0095] S103: Based on the data source performance rating table, matching contribution ratios of multiple data sources according to the performance ratings, and building a multi-source data fusion model;

[0096] A linear weighted method is applied to adjust the weight coefficient according to the performance score, in which high-efficiency data sources are given higher weights to ensure that the results of data fusion can reflect the most reliable and latest environmental information. The weighted average method is used to integrate various data sources to ensure that high-efficiency data dominates the model and the influence of lower-efficiency data is reduced accordingly. The algorithm combines multiple scoring indicators, including the timeliness of data updates, historical accuracy and coverage. The multi-source data fusion model constructed by this method improves the efficiency of data processing, enhances the accuracy and credibility of model predictions, and provides decision makers with a comprehensive set of risk assessment tools.

[0097] See also Figure 3 , using a multi-source data fusion model, extracting and analyzing multiple indicator data related to water quality, performing trend analysis and pattern recognition on multiple indicators, detecting and recording abnormal values ​​of multiple indicators, and generating ocean water quality status information in the following specific steps:

[0098] S201: Based on the multi-source data fusion model, extract multiple indicator data related to water quality, including microplastic content, salinity, and dissolved oxygen, and generate water quality information extraction records;

[0099] The latest records of multiple indicators are obtained from the integrated data set through the set data interface. Each indicator data undergoes preliminary preprocessing and quality control to ensure its accuracy and reliability. For each indicator, quantitative analysis methods are used for precise extraction to ensure that the acquired data can accurately reflect the current water state. The quantitative analysis methods used in the process include linear interpolation and moving average methods to deal with possible data interruptions or missing conditions, and automatically adjust the data extraction time window according to the real-time data update frequency of environmental changes to adapt to the dynamic nature of environmental monitoring and effectively provide real-time water quality information. The generated water quality information extraction record contains real-time data of multiple indicators, time tags and source tags for data acquisition, providing basic data for subsequent water quality trend analysis and abnormal monitoring.

[0100] S202: Based on the water quality information extraction records, combined with time information, identifying the change trends and periodic patterns of multiple water quality indicators, and generating water quality trend analysis results;

[0101] Use time series analysis methods, such as the autoregressive moving average model, to analyze the changing patterns of each water quality indicator over time, identify long-term trends, seasonal fluctuations and emergencies of indicators such as microplastic content, salinity and dissolved oxygen, and periodically analyze the data of each indicator to determine the main driving factors of its changes, such as weather conditions, seasonal changes or human activities. Abnormal data points are marked and analyzed to track potential environmental problems or equipment failures. The generated water quality trend analysis results provide a scientific basis for environmental management decisions.

[0102] S203: Based on the water quality trend analysis results, abnormal points in multiple indicator data are detected and recorded in real time to generate ocean water quality status information;

[0103] In sub-step S203, based on the obtained water quality trend analysis results, the system uses an anomaly detection algorithm to monitor and record abnormal points in the data in real time, determines the normal fluctuation range of each water quality indicator through trend analysis, and uses the statistical threshold method through the formula: , identify data points outside the normal range and mark them as anomalies;

[0104] in, Representative The degree to which a data point deviates from the average level. is the index of the data point, It is Observation values ​​represent the specific values ​​of ocean water quality indicators collected in real time. Indicates the average value of the water quality index in historical data. is the standard deviation of the water quality index;

[0105] Detailed explanation of the formula and the process of formula calculation and derivation:

[0106] Assume the historical average of the target indicator is 0.05, standard deviation is 0.01, calculate the degree of deviation:

[0107] ;

[0108] Calculation result 2 shows that the observed value is outside the normal fluctuation range, and the obtained ocean water quality status information accurately reflects the abnormal state of the current water quality condition, which helps to take timely measures to deal with potential water quality problems.

[0109] See also Figure 4 ,Based on the ocean water quality status information, extract and analyze the temperature data of the ocean environment, including water temperature and air temperature, analyze the trend of ocean temperature changes, detect and identify abnormal fluctuations, and generate temperature anomaly event records. The specific steps are:

[0110] S301: extracting ocean temperature data, including water temperature and air temperature information, based on ocean water quality status information, and generating a temperature data set;

[0111] The data are collected from different monitoring stations and sensor equipment such as ocean buoys, satellite remote sensing equipment and meteorological stations. The target temperature data are preliminarily screened and preprocessed to eliminate obvious erroneous data caused by equipment failure or external interference. Data synchronization technology is used to ensure that all temperature data match the correct collection time and location. The data are standardized so that data from different sources can be compared and processed on the same analysis platform. After completing the target steps, the integrated temperature data set includes time series data and geographic location information, which is crucial for further trend analysis and pattern recognition.

[0112] S302: Based on the temperature data set, by analyzing and predicting the change trend of the ocean temperature data, monitoring and recording abnormal data points, and generating temperature anomaly analysis results;

[0113] Through statistical analysis methods and machine learning technology, the changing trend of ocean temperature is analyzed, and moving average and exponential smoothing techniques are applied to smooth time series data to reduce the impact of accidental fluctuations. Time series prediction models, such as the seasonal autoregressive integrated moving average model, are used to predict trends and cyclical changes in temperature data, identify and predict temperature increases or decreases. Combined with statistical process control technology, abnormal temperature data points that exceed the normal range of variation are monitored and marked in real time, including point anomaly detection and identification of abnormal trends. The generated temperature anomaly analysis results record the identified abnormal points and trends, providing an important basis for environmental risk assessment.

[0114] S303: Based on the temperature anomaly analysis result, by analyzing the amplitude and duration of fluctuations of multiple abnormal data points, calculating the degree of anomaly and marking abnormal temperature events, and generating a temperature anomaly event record;

[0115] In the above content, based on the obtained temperature anomaly analysis results, the fluctuation amplitude of the abnormal data points is used and duration , using the weighted average method, according to the formula: , calculate the abnormal degree of the target temperature abnormal event;

[0116] In the formula, Represents the abnormal degree index of each temperature abnormal event, represents the amplitude of temperature fluctuation, represents the duration of the temperature fluctuation, and is the weight coefficient, is the standardized coefficient;

[0117] Detailed explanation of the formula and the process of formula calculation and derivation:

[0118] Assume that the fluctuation range of a certain temperature anomaly is , duration Hour, weight of volatility , the weight of duration , standardized coefficient ,calculate :

[0119] ;

[0120] ;

[0121] ;

[0122] ;

[0123] The calculation results show that the standardized anomaly degree index of this temperature anomaly event is 0.6, reflecting the severity of the anomaly under given fluctuation amplitude and duration conditions. The formula is used to help manage and respond to temperature anomalies in the marine environment.

[0124] See also Figure 5 Based on the records of temperature anomaly events, multiple marine development activities are analyzed, including oil and gas production and coastline changes, to assess the impact of development activities on the marine environment. The specific steps for generating development activity impact information are as follows:

[0125] S401: Based on the temperature anomaly event records, extract various information related to marine development activity events, including the time and geographical location information of the development, and generate a development activity data set;

[0126] Extract key information directly related to marine development activities, including the specific time and exact geographical location of each development activity, such as the coordinates of oil and gas field exploitation areas and coastline construction areas. Data extraction relies on geographic information systems for spatial data analysis to ensure that the location information of each development event is accurate, perform time series correction on development time information, synchronize the time stamps of all events, ensure the consistency of time information, and integrate the target data to form a development activity data set containing multi-dimensional information. Each data item includes timestamp, geographical location, event type and other information, providing basic data for evaluating the environmental impact of development activities.

[0127] S402: Using the development activity dataset, combined with water quality and temperature information at the target location, evaluate the impact of multiple development events on the marine environment, identify and mark the scope of the affected area, including oil and gas extraction and coastline changes, and generate activity impact analysis results;

[0128] An environmental impact assessment model is used, which takes into account geospatial data analysis techniques, such as buffer zone analysis and overlap analysis, to determine the specific regional scope of the impact of development activities, including the impact of oil and gas extraction on the water quality of surrounding waters, and changes in coastline that change the hydrological conditions and ecosystems of nearby waters. By integrating environmental indicators such as water quality and temperature and applying multivariate statistical analysis methods, such as principal component analysis, the main environmental impacts of development activities are identified and quantified. The environmental impact results for the target area include the degree of impact, duration and ecological consequences, providing a detailed scientific basis for the formulation of response measures and protection strategies.

[0129] S403: Based on the results of the activity impact analysis, the duration and intensity of the development activities are considered to analyze the impact of various development activities on the marine environment, mark risky development activities, and generate development activity impact information;

[0130] In the above content, considering the specific duration, intensity and environmental baseline data of the development activities, according to the formula: , calculate the comprehensive impact score of various development activities on the marine environment;

[0131] In the formula, represents the risk score of the target development activity, represents the duration of the development activity, represents the intensity of development activities, Environmental baseline data representing the development area, , , is the weight coefficient of the corresponding parameter, which is used to adjust the influence of different factors in risk assessment.

[0132] Detailed explanation of the formula and the process of formula calculation and derivation:

[0133] Assumed duration of development activities Months, intensity of development activities , environmental baseline data for development areas , weight coefficient , , , calculate R:

[0134] ;

[0135] ;

[0136] ;

[0137] The results show that the environmental risk score of the target development activity is 101, considering the duration, intensity and current environmental conditions of the development. The calculation process is used to identify high-risk development activities that require attention.

[0138] See also Figure 6 According to the impact information of development activities, by analyzing the impact of various factors on marine environmental indicators, predicting the impact of various factors on marine biodiversity, combining the abnormal values ​​of various indicators, calculating the marine environmental risk score, and generating real-time risk assessment information, the specific steps are as follows:

[0139] S501: Based on the impact information of development activities, analyze the impact of various factors on marine environmental indicators and quantify the impact coefficient, including pollution emissions, human development activities, and meteorological factors, and generate a weight list of influencing factors;

[0140] Statistical analysis methods and environmental assessment models are used to quantify the impact intensity of target factors, including pollution emissions such as industrial wastewater and agricultural runoff, human development activities such as seabed mineral mining, and meteorological factors such as temperature and wind speed changes. By constructing a linear regression model, the impact degree of each factor is linked to existing environmental data to evaluate its specific impact on marine water quality and ecosystems. By analyzing the correlation between pollution emissions and biodiversity in nearby seas in historical data sets, the impact weight of pollution emissions is determined. Through spatial data analysis, the scope and intensity of the impact of human development activities on the ecology of specific sea areas are determined. A list of all assessment factors and corresponding weights is constructed. The weight coefficient of each factor reflects its impact on the marine environment.

[0141] S502: Based on the weight list of influencing factors, according to the impact of various influencing factors on marine environmental indicators, predict the impact of various influencing factors on marine biodiversity and form biodiversity indicators;

[0142] Ecological modeling technology and species sensitivity index are used to predict the impact of changes in different environmental variables on biodiversity. The model integrates the weights of each factor obtained from the weight list of influencing factors, and calculates the comprehensive impact index of each impact through the ecological model. The process includes analysis of the response of different species communities and the application of generalized additive models combined with time series analysis to predict the trend of biodiversity changes caused by changes in environmental factors in the next few years. The biodiversity indicators formed by the analysis results provide a scientific basis for the subsequent formulation of ecological protection and restoration measures, ensuring the long-term maintenance of marine biodiversity.

[0143] S503: Based on biodiversity indicators and combined with abnormal values ​​of multiple marine environmental indicators, calculate marine environmental risk scores for multiple regions and generate real-time risk assessment information;

[0144] The specific formula for calculating the marine environmental risk scores for multiple regions is:

[0145] ;

[0146] in, represents the risk score of the marine environment, is the number of indicators involved in the calculation, It is The weight coefficient of each indicator is It is The abnormal value of the indicator, It is The anomaly detection threshold of each indicator, Represents the total number of indicators involved in the risk score calculation. is the index of the indicator.

[0147] formula:

[0148] ;

[0149] Detailed explanation of the formula and the process of formula calculation and derivation:

[0150] The formula is used to calculate the marine environmental risk score and assess the marine environmental risk of the target area;

[0151] Parameter meaning and setting value:

[0152] It is The weight coefficient of each indicator;

[0153] It is The actual observed value of the indicator;

[0154] It is The threshold of each indicator;

[0155] is the total number of indicators;

[0156] Assume that four environmental indicators in the target area are detected to contain abnormal data points, including heavy metal concentration, dissolved oxygen, temperature, and microplastic content. ppm, threshold ppm, weight , dissolved oxygen mg / L, threshold mg / L, weight ,temperature °C, threshold °C, weight , microplastic content , threshold , weight ;

[0157] Calculate the risk contribution of each indicator:

[0158] ;

[0159] ;

[0160] ;

[0161] ;

[0162] Overall Risk Score:

[0163] ;

[0164] result This indicates that the comprehensive environmental risk score of the area is , reflecting the higher environmental risks in the target area, and the data are used to decide whether further environmental monitoring or management measures are needed.

[0165] See also Figure 7 , A marine environment risk assessment system based on multi-source data fusion, a marine environment risk assessment system based on multi-source data fusion is used to execute the above-mentioned marine environment risk assessment method based on multi-source data fusion, and the system includes:

[0166] The data source processing module is based on multi-source marine environment monitoring data. It evaluates the integrity, consistency and timeliness of data from multiple data sources, adjusts the contribution ratio of data sources, and generates a multi-source data fusion model.

[0167] The water quality index analysis module extracts a variety of key water quality indicators, including microplastic content, salinity and dissolved oxygen, based on a multi-source data fusion model. Through time series analysis, it identifies the change trend patterns of multiple indicators and detects abnormal values ​​in multiple indicators to generate marine water quality status information.

[0168] The real-time temperature monitoring module extracts water temperature and air temperature data based on ocean water quality status information and performs trend analysis, detects and identifies abnormal fluctuations in temperature data, and generates records of abnormal temperature events;

[0169] The marine activity analysis module analyzes the impact of marine development activities on the marine environment based on the records of temperature anomaly events, assesses the environmental sensitivity of various activities, and generates information on the impact of development activities;

[0170] The environmental risk analysis module predicts the impact of various factors on marine biodiversity based on the impact information of development activities, calculates the marine environmental risk score based on real-time marine environmental monitoring data, and generates real-time risk assessment information.

[0171] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function according to the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0172] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0173] In the present invention, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0174] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0175] Those skilled in the art will appreciate that the units and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0176] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0177] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0178] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0179] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0180] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0181] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A marine environmental risk assessment method based on multi-source data fusion, characterized in that: The method comprises: Based on multi-source marine environment monitoring data, the data integrity, consistency and timeliness of multiple data sources are evaluated, the efficiency scores of multiple data sources are calculated, and the contribution ratio of the target data source is adjusted to generate a multi-source data fusion model; Utilizing the multi-source data fusion model, extracting and analyzing various indicator data related to water quality, performing trend analysis and pattern recognition on the various indicators, detecting and recording abnormal values ​​of the various indicators, and generating ocean water quality status information; Based on the ocean water quality status information, extract and analyze the temperature data of the ocean environment, including water temperature and air temperature, analyze the changing trend of ocean temperature, detect and identify abnormal fluctuations, and generate temperature abnormality event records; Based on the temperature anomaly event records, multiple marine development activity events are analyzed, including oil and gas production and coastline changes, to assess the impact of development activities on the marine environment and generate development activity impact information; Based on the impact information of the development activities, by analyzing the impact of various influencing factors on marine environmental indicators, predicting the impact of various influencing factors on marine biodiversity, combining the abnormal values ​​of multiple indicators, calculating the marine environmental risk score, and generating real-time risk assessment information.

2. The marine environmental risk assessment method based on multi-source data fusion according to claim 1 is characterized in that: The multi-source data fusion model specifically includes a performance score list, a data source contribution ratio adjustment record, and a data fusion result. The ocean water quality status information includes water quality trend analysis results, water quality pattern recognition information, and water quality anomaly value records. The temperature anomaly event record specifically refers to water temperature change trend information, air temperature change trend information, and temperature abnormal fluctuation identification results. The development activity impact information includes development activity type information, development activity impact assessment results, and development activity risk index. The real-time risk assessment information specifically includes an influencing factor relationship diagram, biodiversity impact prediction results, and an environmental risk score.

3. The marine environmental risk assessment method based on multi-source data fusion according to claim 1 is characterized in that: Based on multi-source marine environment monitoring data, the data integrity, consistency and timeliness of multiple data sources are evaluated, the efficiency scores of multiple data sources are calculated and the contribution ratio of the target data source is adjusted. The specific steps for generating a multi-source data fusion model are as follows: Based on multi-source marine environment monitoring data, the integrity analysis of multiple data sources, including satellite remote sensing, buoy monitoring, and ship collection, marks missing data and timestamp errors, and generates data inspection results; Based on the data inspection results, the consistency and timeliness of data from multiple sources are evaluated, performance scores are calculated for multiple data sources, and a data source performance score table is generated; Based on the data source performance rating table, contribution ratios of multiple data sources are matched according to the performance ratings, and a multi-source data fusion model is constructed.

4. The marine environmental risk assessment method based on multi-source data fusion according to claim 3 is characterized in that: The specific formula for calculating the performance score for multiple data sources is: ； in, Representative The performance rating of each data source, is the weight coefficient of data integrity, It is The number of data points known to each data source, Theoretically, The total number of data points a data source should have, is the weight coefficient of data consistency, It is The number of data points that are consistent between a data source and other data sources in the same time window. It is The total number of data points for consistency comparison of the data sources, is the weight coefficient of data timeliness, It is The average data delay time of each data source, Represents the index of the data source.

5. The marine environmental risk assessment method based on multi-source data fusion according to claim 1 is characterized in that: The steps of using the multi-source data fusion model to extract and analyze various indicator data related to water quality, perform trend analysis and pattern recognition on various indicators, detect and record abnormal values ​​of various indicators, and generate ocean water quality status information are as follows: Based on the multi-source data fusion model, a variety of indicator data related to water quality are extracted, including microplastic content, salinity, and dissolved oxygen, and a water quality information extraction record is generated; Based on the water quality information extraction record, combined with time information, identifying the change trends and periodic patterns of multiple water quality indicators, and generating water quality trend analysis results; Based on the water quality trend analysis results, abnormal points in multiple indicator data are detected and recorded in real time to generate ocean water quality status information.

6. The marine environmental risk assessment method based on multi-source data fusion according to claim 1 is characterized in that: Based on the ocean water quality status information, extract and analyze the temperature data of the ocean environment, including water temperature and air temperature, analyze the change trend of ocean temperature, detect and identify abnormal fluctuations, and generate temperature abnormality event records in the following steps: Based on the ocean water quality status information, extract ocean temperature data, including water temperature and air temperature information, to generate a temperature data set; Based on the temperature data set, by analyzing and predicting the change trend of the ocean temperature data, monitoring and recording abnormal data points, and generating temperature anomaly analysis results; Based on the temperature anomaly analysis result, by analyzing the amplitude and duration of fluctuations of multiple abnormal data points, the degree of anomaly is calculated and abnormal temperature events are marked to generate a temperature anomaly event record.

7. The marine environmental risk assessment method based on multi-source data fusion according to claim 1 is characterized in that: Based on the abnormal temperature event records, multiple marine development activity events are analyzed, including oil and gas production and coastline changes, to evaluate the impact of development activities on the marine environment. The specific steps for generating development activity impact information are as follows: Based on the abnormal temperature event records, extract various information related to marine development activity events, including development time and geographic location information, to generate a development activity data set; Using the development activity dataset, combined with water quality and temperature information at the target location, the impact of multiple development events on the marine environment is evaluated, the scope of the affected area is identified and marked, including oil and gas extraction and coastline changes, and the activity impact analysis results are generated; Based on the results of the activity impact analysis, the duration and intensity of the development activities are taken into consideration, the impact of various development activities on the marine environment is analyzed, risky development activities are marked, and development activity impact information is generated.

8. The marine environmental risk assessment method based on multi-source data fusion according to claim 1 is characterized in that: According to the impact information of the development activities, by analyzing the impact of various factors on marine environmental indicators, predicting the impact of various factors on marine biodiversity, combining the abnormal values ​​of various indicators, calculating the marine environmental risk score, and generating real-time risk assessment information, the specific steps are as follows: Based on the impact information of the development activities, analyze the impact of various factors on marine environmental indicators and quantify the impact coefficients, including pollution emissions, human development activities, and meteorological factors, and generate a weight list of influencing factors; Based on the weight list of influencing factors, according to the impact of various influencing factors on marine environmental indicators, the impact of various influencing factors on marine biodiversity is predicted to form a biodiversity index; Based on the biodiversity indicators and combined with the abnormal values ​​of multiple marine environmental indicators, the marine environmental risk scores of multiple regions are calculated to generate real-time risk assessment information.

9. The marine environmental risk assessment method based on multi-source data fusion according to claim 8 is characterized in that: The specific formula for calculating the marine environmental risk scores of multiple regions is: ； in, represents the risk score of the marine environment, is the number of indicators involved in the calculation, It is The weight coefficient of each indicator is It is The abnormal value of the indicator, It is The anomaly detection threshold of each indicator, Represents the total number of indicators involved in the risk score calculation. is the index of the indicator.

10. The marine environmental risk assessment system based on multi-source data fusion is characterized by: According to any one of claims 1 to 9, the marine environmental risk assessment method based on multi-source data fusion comprises: The data source processing module is based on multi-source marine environment monitoring data. It evaluates the integrity, consistency and timeliness of data from multiple data sources, adjusts the contribution ratio of data sources, and generates a multi-source data fusion model. The water quality index analysis module extracts a variety of key water quality indicators, including microplastic content, salinity and dissolved oxygen, based on the multi-source data fusion model, identifies the change trend patterns of multiple indicators through time series analysis, detects abnormal values ​​in multiple indicators, and generates marine water quality status information; The real-time temperature monitoring module extracts water temperature and air temperature data based on the ocean water quality status information and performs trend analysis, detects and identifies abnormal fluctuations in temperature data, and generates temperature abnormality event records; The marine activity analysis module analyzes the impact of marine development activities on the marine environment based on the abnormal temperature event records, evaluates the environmental sensitivity of various activities, and generates development activity impact information; The environmental risk analysis module predicts the impact of various factors on marine biodiversity based on the impact information of the development activities, calculates the marine environmental risk score in combination with real-time marine environmental monitoring data, and generates real-time risk assessment information.

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