A method and system for marine environment monitoring and early warning based on big data

Abstract

The application discloses a kind of based on big data's marine environment monitoring and early warning method and system, it is related to marine environment monitoring technical field, the method includes: through the sensor in monitoring site real-time collection environmental data and transmission to database;Using positioning technology obtains the position information of each monitoring site, and the distance between randomly selected reference monitoring site and corresponding contrast monitoring station is calculated;The change trend of environmental data is analyzed, and the propagation speed of abnormal environmental data is determined based on the analysis result;Distance and propagation speed are combined, and the arrival time of abnormal environmental data from reference monitoring site to contrast monitoring station is calculated using linear propagation algorithm;According to the alarm starting time set by arrival time, and publish warning information.The application realizes real-time monitoring and data collection to marine environment by deploying monitoring site and integrating sensor technology, provides comprehensive, accurate information basis for subsequent data analysis and early warning.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of marine environment monitoring, in particular to a marine environment monitoring and early warning method and system based on big data. BACKGROUND

[0002] The marine environment, as a vast and interconnected region of seawater on Earth, not only includes seawater itself, but also encompasses various substances dissolved and suspended in it, seabed sediments, and a rich variety of biological communities living in it. In order to ensure the sustainable use of marine resources and protect their ecological diversity, it is particularly important to implement systematic and continuous marine environment monitoring and early warning. This system can help scientists quickly identify any trends or patterns that deviate from the normal range by regularly checking and analyzing key parameters such as water temperature, salinity, water quality conditions (e.g. nutrient salt concentration), oxygen levels, phytoplankton and animal population dynamics, benthic organism status, pollutant distribution, and the impact of climate change. Based on this, predictions can be made to take action before potential problems evolve into serious crises.

[0003] With the advancement of technology, modern marine environment monitoring systems can collect more comprehensive and detailed information. These systems are usually composed of various sensors and data collection devices distributed throughout the vast ocean, which monitor key parameters such as water temperature, salinity, flow rate, and biological activity in real time. The data obtained is not only large in quantity, but also diverse in type, covering everything from basic numerical values to complex images and video information.

[0004] In the face of such a large and complex data set, traditional data processing methods often struggle to provide accurate and reliable analysis results and decision support information within a short period of time, not only leading to delayed analysis response, but also hindering the timely release of early warning systems, thus missing the best opportunity to take timely and effective measures. This will significantly weaken the decision maker's ability to respond quickly in emergency situations, making it difficult to make accurate early warnings and effective responses to dynamic changes in the marine environment.

[0005] To address the problems in the related art, no effective solutions have been proposed so far. SUMMARY

[0006] To address the problems in the related art, the present application proposes a marine environment monitoring and early warning method and system based on big data to overcome the above technical problems existing in the prior art.

[0007] To this end, the specific technical solutions adopted by the present application are as follows:

[0008] According to one aspect of the present application, a marine environment monitoring and early warning method based on big data is provided, which comprises the following steps:

[0009] S1, deploying a plurality of monitoring stations in the marine environment monitoring area, collecting environmental data in real time through sensors in the monitoring stations and transmitting the environmental data to a database;

[0010] S2, obtaining position information of each monitoring station by using positioning technology, and calculating a distance between a randomly selected reference monitoring station and a corresponding comparison monitoring station;

[0011] S3, comparing historical standard environmental data of the reference monitoring station with real-time collected environmental data, analyzing the change trend of the environmental data, and determining the propagation speed of abnormal environmental data based on the analysis result;

[0012] S4, combining the distance and the propagation speed, and using a linear propagation algorithm to calculate the arrival time of the abnormal environmental data from the reference monitoring station to the comparison monitoring station;

[0013] S5, setting an alarm start time according to the arrival time, and issuing an alarm information.

[0014] Further, deploying a plurality of monitoring stations in the marine environment monitoring area, collecting environmental data in real time through sensors in the monitoring stations and transmitting the environmental data to a database includes the following steps:

[0015] S11, deploying a plurality of monitoring stations in the marine environment monitoring area according to detection requirements, and configuring corresponding sensors in each monitoring station;

[0016] S12, collecting environmental data in real time by using the sensors, and pre-processing the environmental data;

[0017] S13, performing fusion processing on the pre-processed environmental data, and transmitting the fused environmental data to a database by using wireless communication technology.

[0018] Further, performing fusion processing on the pre-processed environmental data, and transmitting the fused environmental data to a database by using wireless communication technology includes the following steps:

[0019] S131, extracting marine environmental parameters and current information from the pre-processed environmental data;

[0020] S132, synchronizing time series of the marine environmental parameters and the current information to obtain marine environmental parameters and current information corresponding to each time point;

[0021] S133, fusing the marine environmental parameters and the current information by using Kalman filtering algorithm, wherein the marine environmental parameters and the current information are used as observation vectors, and the predicted state vectors are gradually corrected through the prediction and update steps of Kalman filtering to obtain the fused environmental data;

[0022] S134, pack the fused environmental data according to a preset format, and transmit the packed environmental data to a database by using wireless communication technology.

[0023] Further, the position information of each monitoring station is obtained by using positioning technology, and the distance between the randomly selected reference monitoring station and the corresponding comparison monitoring station is calculated, including the following steps:

[0024] S21, a positioning device is deployed at each monitoring station by using positioning technology to obtain the position information of each monitoring station, and the position information of the monitoring station includes the longitude and latitude of the monitoring station;

[0025] S22, randomly select a monitoring station as a reference monitoring station from the monitoring stations, and select the monitoring station adjacent to the reference monitoring station as a comparison monitoring station;

[0026] S23, based on the position information of each monitoring station, the distance between the reference monitoring station and the comparison monitoring station is calculated by using a geographic distance calculation formula.

[0027] Further, the geographic distance calculation formula is:

[0028]

[0029] In the formula, D represents the distance between the reference monitoring station and the comparison monitoring station;

[0030] r represents the average radius of the earth;

[0031] x1 represents the latitude of the reference monitoring station;

[0032] x2 represents the latitude of the comparison monitoring station;

[0033] y1 represents the longitude of the reference monitoring station;

[0034] y2 represents the longitude of the comparison monitoring station.

[0035] Further, the historical standard environmental data of the reference monitoring station is compared with the real-time collected environmental data, the change trend of the environmental data is analyzed, and the propagation speed of the abnormal environmental data is determined based on the analysis result, including the following steps:

[0036] S31, extract the historical standard environmental data of the reference monitoring station from the database;

[0037] S32, calculate the difference between the historical standard environmental data of the reference monitoring station and the real-time collected environmental data, and preliminarily judge whether there is abnormal environmental data combined with a preset difference value;

[0038] S33, performing time series analysis on the real-time environmental data to analyze the change trend of the data, and combining the preliminary abnormality judgment result to determine the abnormal environmental data;

[0039] S34, establishing a spatial distribution data set according to the real-time environmental data of the comparison monitoring sites, and applying a spatial analysis algorithm to extract the spatial distribution characteristics of the abnormal environmental data;

[0040] S35, calculating the propagation speed of the abnormal environmental data in the marine environment based on the spatial distribution characteristics of the abnormal environmental data and the time series analysis result.

[0041] Further, according to the real-time environmental data of the comparison monitoring sites, a spatial distribution data set is established, and a spatial analysis algorithm is applied to extract the spatial distribution characteristics of the abnormal environmental data, including the following steps:

[0042] S341, obtaining the environmental data of the comparison monitoring sites, and using positioning technology to obtain the position information of each comparison monitoring site;

[0043] S342, combining the environmental data of the comparison monitoring sites with the position information to establish a spatial distribution data set;

[0044] S343, analyzing the spatial distribution data set using a semi-variance model, and interpolating within the spatial distribution data set to generate a spatial distribution map;

[0045] S344, combining the abnormal environmental data with the spatial distribution map to estimate the estimated value of the abnormal environmental data;

[0046] S345, based on the estimated value of the abnormal environmental data, extracting the spatial distribution characteristics of the abnormal environmental data.

[0047] Further, the formula of the semi-variance model is:

[0048]

[0049] In the formula, T represents the estimated value of the abnormal environmental data at latitude m and longitude n;

[0050] m represents the latitude of the abnormal environmental data;

[0051] n represents the longitude of the abnormal environmental data;

[0052] K represents the spatial distribution data set;

[0053] k represents the real-time environmental data of the kth comparison monitoring site;

[0054] η represents the weight coefficient;

[0055] t represents time.

[0056] Further, in combination with the distance and the propagation speed, the arrival time of the abnormal environmental data propagated from the reference monitoring station to the comparison monitoring station is calculated by using a linear propagation algorithm, including the following steps:

[0057] S41, in combination with the distance between the reference monitoring station and the corresponding comparison monitoring station and the propagation speed of the abnormal environmental data, the propagation time of the abnormal environmental data from the reference monitoring station to the comparison monitoring station is calculated by using a linear propagation algorithm;

[0058] S42, the time of the abnormal environmental data at the reference monitoring station is obtained, and the arrival time of the abnormal environmental data propagated from the reference monitoring station to the comparison monitoring station is calculated by fusing the propagation time.

[0059] According to another aspect of the present application, a marine environment monitoring and early warning system based on big data is also provided, which comprises a data collection module, a position and distance calculation module, a propagation speed determination module, an arrival time calculation module and an alarm issuing module;

[0060] The data collection module is used for deploying a plurality of monitoring stations in the marine environment monitoring area, collecting environmental data in real time through sensors in the monitoring stations and transmitting the environmental data to a database;

[0061] The position and distance calculation module is used for obtaining the position information of each monitoring station by using positioning technology, and calculating the distance between the randomly selected reference monitoring station and the corresponding comparison monitoring station;

[0062] The propagation speed determination module is used for comparing the historical standard environmental data of the reference monitoring station with the real-time collected environmental data, analyzing the change trend of the environmental data, and determining the propagation speed of the abnormal environmental data based on the analysis result;

[0063] The arrival time calculation module is used for calculating the arrival time of the abnormal environmental data propagated from the reference monitoring station to the comparison monitoring station by using a linear propagation algorithm in combination with the distance and the propagation speed;

[0064] The alarm issuing module is used for setting the alarm starting time according to the arrival time, and issuing the alarm information.

[0065] The beneficial effects of the present application are:

[0066] 1, the present application realizes real-time monitoring and data collection of marine environment by deploying monitoring stations and integrating sensor technology, which not only improves the immediacy and integrity of data, but also integrates scattered data sources into a unified data set by using data integration technology, thereby providing a comprehensive and accurate information basis for subsequent data analysis and early warning.

[0067] 2、The application can discover the change trend of environmental data and identify abnormal data in time by comparing the historical standard environmental data of the reference monitoring site with real-time environmental data, which helps to more accurately judge the current environmental state, thereby improving the sensitivity and reliability of the early warning system.

[0068] 3、The application provides scientific basis for setting the alarm starting time by combining the distance between monitoring sites and the propagation speed of abnormal environmental data, using linear propagation algorithm to calculate the predicted arrival time of abnormal data, which can quickly identify and respond to potential environmental risks, effectively shorten the early warning response time, thereby improving the efficiency and effect of emergency handling. BRIEF DESCRIPTION OF DRAWINGS

[0069] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0070] Figure 1 is a flowchart of a marine environmental monitoring and early warning method based on big data according to an embodiment of the present application;

[0071] Figure 2 is a principle block diagram of a marine environmental monitoring and early warning system based on big data according to an embodiment of the present application.

[0072] In the drawings:

[0073] 1, data collection module; 2, position and distance calculation module; 3, propagation speed determination module; 4, arrival time calculation module; 5, alarm issuing module. DETAILED DESCRIPTION

[0074] To further illustrate the embodiments, the present application provides drawings, which are part of the disclosure of the present application, mainly used to illustrate the embodiments, and can explain the operating principle of the embodiments in conjunction with the related description of the specification. With reference to these contents, those skilled in the art should understand other possible embodiments and advantages of the present application.

[0075] According to an embodiment of the present application, a marine environmental monitoring and early warning method and system based on big data are provided.

[0076] The present application will be further described in conjunction with the drawings and specific embodiments, as shown in Figure 1 The marine environmental monitoring and early warning method based on big data according to the embodiment of the present application comprises the following steps:

[0077] S1, deploying a plurality of monitoring sites in the marine environment monitoring area, collecting environmental data in real time through sensors in the monitoring sites and transmitting the environmental data to a database.

[0078] Specifically, deploying a plurality of monitoring sites in the marine environment monitoring area, collecting environmental data in real time through sensors in the monitoring sites and transmitting the environmental data to a database includes the following steps:

[0079] S11, according to the detection requirement, deploying a plurality of monitoring sites in the marine environment monitoring area, and configuring corresponding sensors in each monitoring site.

[0080] S12, collecting environmental data in real time using sensors and preprocessing the environmental data.

[0081] It should be noted that the environmental data includes meteorological data (wind speed, wind direction, etc.) and oceanographic data (sea wave height, etc.).

[0082] The preprocessing of the environmental data includes: checking the collected environmental data, checking whether the data is within a reasonable range, whether there are abnormal values or missing values, and if abnormal values or missing values are found, correcting or marking as invalid data; converting the verified environmental data into a unified format to facilitate subsequent processing and storage.

[0083] S13, fusing the preprocessed environmental data, and transmitting the fused environmental data to a database using wireless communication technology.

[0084] Specifically, fusing the preprocessed environmental data, and transmitting the fused environmental data to a database using wireless communication technology includes the following steps:

[0085] S131, extracting marine environmental parameters and current information from the preprocessed environmental data.

[0086] It should be noted that the marine environmental parameters include wind speed, wind direction, and sea wave height; the current information includes current speed and direction.

[0087] S132, synchronizing the time series of marine environmental parameters and current information to obtain marine environmental parameters and current information corresponding to each time point.

[0088] S133, using Kalman filtering algorithm to fuse marine environmental parameters and current information, wherein marine environmental parameters and current information are used as observation vectors, through the prediction and update steps of Kalman filtering, the predicted state vector is gradually corrected to obtain the fused environmental data.

[0089] S134, packaging the fused environmental data according to a preset format, and transmitting the packaged environmental data to a database using wireless communication technology.

[0090] S2, acquire position information of each monitoring station by using a positioning technology, and calculate distances between randomly selected reference monitoring stations and corresponding comparison monitoring stations.

[0091] Specifically, acquiring position information of each monitoring station by using a positioning technology, and calculating distances between randomly selected reference monitoring stations and corresponding comparison monitoring stations includes the following steps:

[0092] S21, deploy corresponding positioning devices at each monitoring station by using a positioning technology to acquire position information of each monitoring station, and the position information of the monitoring station includes the longitude and latitude of the monitoring station.

[0093] S22, randomly select a monitoring station from the monitoring stations as a reference monitoring station, and select a monitoring station adjacent to the reference monitoring station as a comparison monitoring station.

[0094] S23, calculate the distance between the reference monitoring station and the comparison monitoring station based on the position information of each monitoring station using a geographic distance calculation formula.

[0095] Specifically, the geographic distance calculation formula is:

[0096]

[0097] In the formula, D represents the distance between the reference monitoring station and the comparison monitoring station;

[0098] r represents the average radius of the earth;

[0099] x1 represents the latitude of the reference monitoring station;

[0100] x2 represents the latitude of the comparison monitoring station;

[0101] y1 represents the longitude of the reference monitoring station;

[0102] y2 represents the longitude of the comparison monitoring station.

[0103] It should be noted that in specific embodiments, the first monitoring station is selected as the reference monitoring station, and one monitoring station adjacent to the reference monitoring station is obtained, denoted as the comparison monitoring station, and the distance D1 between the reference monitoring station and the comparison monitoring station is calculated by the geographic distance calculation formula; the second monitoring station is selected as the reference monitoring station, and one monitoring station adjacent to the reference monitoring station is obtained, denoted as the comparison monitoring station, and the distance D2 between the reference monitoring station and the comparison monitoring station is calculated by the geographic distance calculation formula, and so on, to obtain the distances Di between all reference monitoring stations and comparison monitoring stations in the marine environment monitoring area.

[0104] S3, compare the historical standard environmental data of the reference monitoring site with the real-time collected environmental data, analyze the trend of the environmental data, and determine the propagation speed of the abnormal environmental data based on the analysis result.

[0105] Specifically, comparing the historical standard environmental data of the reference monitoring site with the real-time collected environmental data, analyzing the trend of the environmental data, and determining the propagation speed of the abnormal environmental data based on the analysis result includes the following steps:

[0106] S31, extract the historical standard environmental data of the reference monitoring site from the database.

[0107] It should be noted that the historical standard environmental data refers to the environmental data recorded and stored in the database by the reference monitoring site in the past period of time, which has been verified and confirmed, and represents the normal or expected state; These data are usually used to compare with real-time collected environmental data to assess whether the current environmental condition deviates from the normal or expected range.

[0108] S32, calculate the difference between the historical standard environmental data of the reference monitoring site and the real-time collected environmental data, and preliminarily judge whether there is abnormal environmental data combined with the preset difference value.

[0109] S33, perform time series analysis on real-time environmental data to analyze the trend of the data, and determine abnormal environmental data combined with the preliminary abnormal judgment result.

[0110] S34, according to the real-time environmental data of the comparison monitoring site, establish a spatial distribution data set, and apply a spatial analysis algorithm to extract the spatial distribution characteristics of the abnormal environmental data.

[0111] Specifically, according to the real-time environmental data of the comparison monitoring site, establish a spatial distribution data set, and apply a spatial analysis algorithm to extract the spatial distribution characteristics of the abnormal environmental data includes the following steps:

[0112] S341, obtain the environmental data of the comparison monitoring site, and use positioning technology to obtain the location information of each comparison monitoring site.

[0113] S342, combine the environmental data of the comparison monitoring site with the location information to establish a spatial distribution data set.

[0114] S343, analyze the spatial distribution data set using a semi-variance model, and interpolate within the spatial distribution data set to generate a spatial distribution map.

[0115] Specifically, the formula of the semi-variance model is:

[0116]

[0117] T represents the estimated value of the abnormal environmental data at latitude m and longitude n;

[0118] m represents the latitude of the abnormal environmental data;

[0119] n represents the longitude of the abnormal environmental data;

[0120] K represents the spatial distribution data set;

[0121] k represents the real-time environmental data of the kth comparative monitoring site;

[0122] η represents the weight coefficient;

[0123] t represents time.

[0124] S344, estimate the estimated value of the abnormal environmental data by combining the abnormal environmental data and the spatial distribution map.

[0125] S345, extract the spatial distribution characteristics of the abnormal environmental data based on the estimated value of the abnormal environmental data.

[0126] It should be noted that the spatial distribution characteristics of the abnormal environmental data include the spatial position of the abnormal environmental data, the density and intensity of the abnormal environmental data, the trend of the abnormal environmental data in space, and the spatial distribution pattern of the abnormal environmental data.

[0127] For example, by using a semi-variance model to analyze the spatial distribution data set and performing interpolation, a spatial distribution map reflecting the spatial distribution characteristics of the abnormal environmental data can be generated, which can intuitively show the distribution range, density, intensity, etc. of the abnormal data; by combining the abnormal environmental data and the spatial distribution map, the estimated value of the abnormal environmental data can be estimated, and the spatial distribution characteristics of the abnormal phenomenon can be further quantified; based on the estimated value of the abnormal environmental data, more specific spatial distribution characteristics can be extracted, such as the clustering center of the abnormal data, the diffusion boundary, the influence range, etc., which can provide a basis for subsequent environmental monitoring and early warning.

[0128] S35, calculate the propagation speed of the abnormal environmental data in the marine environment based on the spatial distribution characteristics of the abnormal environmental data and the time series analysis results.

[0129] It should be noted that the formula for the propagation speed is:

[0130]

[0131] In the formula, v represents the propagation speed of the abnormal environmental data; d represents the distance between the reference monitoring site and the comparative monitoring site; and Δt represents the time difference.

[0132] S4, combining the distance and the propagation speed, the arrival time of the abnormal environmental data from the reference monitoring site to the comparative monitoring site is calculated by using the linear propagation algorithm.

[0133] Specifically, combining the distance and the propagation speed, the arrival time of the abnormal environmental data from the reference monitoring site to the comparative monitoring site is calculated by using the linear propagation algorithm, including the following steps:

[0134] S41, combining the distance between the reference monitoring site and the corresponding comparative monitoring site and the propagation speed of the abnormal environmental data, the propagation time of the abnormal environmental data from the reference monitoring site to the comparative monitoring site is calculated by using the linear propagation algorithm.

[0135] It should be noted that the formula of the linear propagation algorithm is:

[0136]

[0137] In the formula, t represents the propagation time; d represents the distance between the reference monitoring site and the comparative monitoring site; v represents the propagation speed of the abnormal environmental data.

[0138] S42, the time of the abnormal environmental data at the reference monitoring site is obtained, and the arrival time of the abnormal environmental data from the reference monitoring site to the comparative monitoring site is calculated by fusing the propagation time.

[0139] It should be noted that the arrival time = abnormal data occurrence time + propagation time t.

[0140] S5, setting the alarm starting time according to the arrival time, and publishing the alarm information.

[0141] It should be noted that a reasonable alarm advance (for example, 30 minutes, 1 hour, etc.) is set according to the arrival time, and this advance can be determined according to the actual situation and the time required for emergency response; the expected arrival time is reduced by the preset alarm advance to obtain the alarm starting time. For example, if the expected arrival time is 14:00, and the preset alarm advance is 1 hour, then the alarm starting time is 13:00.

[0142] In addition, the alarm information includes the alarm level (such as low, medium, high), the specific situation of the abnormal environmental data (such as sea current speed, sea wave height, etc.), the expected impact area, and the recommended response measures; the alarm information is transmitted through short message, email, telephone call, mobile application push notification, etc.

[0143] For example, Figure 2As shown, according to another embodiment of the present application, a big data-based marine environment monitoring and early warning system is also provided, which comprises a data collection module 1, a position and distance calculation module 2, a propagation speed determination module 3, an arrival time calculation module 4 and an alarm issuing module 5;

[0144] The data collection module 1 is used to deploy a plurality of monitoring sites in the marine environment monitoring area, and real-time environmental data is collected by sensors in the monitoring sites and transmitted to a database;

[0145] The position and distance calculation module 2 is used to obtain the position information of each monitoring site by using positioning technology, and to calculate the distance between a randomly selected reference monitoring site and a corresponding comparison monitoring site;

[0146] The propagation speed determination module 3 is used to compare the historical standard environmental data of the reference monitoring site with the real-time collected environmental data, analyze the change trend of the environmental data, and determine the propagation speed of the abnormal environmental data based on the analysis result;

[0147] The arrival time calculation module 4 is used to calculate the arrival time of the abnormal environmental data from the reference monitoring site to the comparison monitoring site by using a linear propagation algorithm in combination with the distance and the propagation speed;

[0148] The alarm issuing module 5 is used to set the alarm start time according to the arrival time, and issue the alarm information.

[0149] In summary, by means of the above technical solutions of the present application, through the deployment of monitoring sites and the integration of sensor technology, real-time monitoring and data collection of the marine environment are realized, which not only improves the immediacy and integrity of the data, but also uses data integration technology to integrate scattered data sources into a unified data set, thereby providing a comprehensive and accurate information basis for subsequent data analysis and early warning. By comparing the historical standard environmental data of the reference monitoring site with the real-time environmental data, the change trend of the environmental data can be discovered in time and the abnormal data can be identified, which helps to more accurately judge the current environmental state, thereby improving the sensitivity and reliability of the early warning system. By combining the distance between the monitoring sites and the propagation speed of the abnormal environmental data, the expected arrival time of the abnormal data is calculated by using a linear propagation algorithm, which provides a scientific basis for setting the alarm start time, can quickly identify and respond to potential environmental risks, effectively shorten the warning response time, and thereby improve the efficiency and effectiveness of emergency handling.

[0150] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for marine environment monitoring and early warning based on big data, characterized in that, The method comprises the following steps: S1, deploying a plurality of monitoring stations in the marine environment monitoring area, collecting environmental data in real time through sensors in the monitoring stations and transmitting the data to a database; S2, obtaining position information of each monitoring station using positioning technology, and calculating the distance between a randomly selected reference monitoring station and a corresponding comparison monitoring station, the comparison monitoring station being a monitoring station adjacent to the reference monitoring station; S3, comparing the historical standard environmental data of the reference monitoring station with the real-time collected environmental data, analyzing the trend of the environmental data, and determining the propagation speed of the abnormal environmental data based on the analysis result, comprising: extracting the historical standard environmental data of the reference monitoring station from the database; calculating the difference between the historical standard environmental data of the reference monitoring station and the real-time collected environmental data, and combining the preset difference to preliminarily judge whether there is abnormal environmental data; performing time series analysis on real-time environmental data to analyze the trend of the data, and combining the preliminary abnormal judgment result to determine abnormal environmental data; obtaining environmental data of the comparison monitoring station, and obtaining position information of each comparison monitoring station using positioning technology; combining the environmental data and position information of the comparison monitoring station to establish a spatial distribution data set; analyzing the spatial distribution data set using a semi-variance model, and interpolating in the spatial distribution data set to generate a spatial distribution map; combining the abnormal environmental data and the spatial distribution map to estimate the estimated value of the abnormal environmental data; based on the estimated value of the abnormal environmental data, extracting the spatial distribution characteristics of the abnormal environmental data; wherein the formula of the semi-variance model is: wherein, T represents an estimated value of abnormal environmental data at a latitude of m and a longitude of n ; m represents a latitude of abnormal environmental data; n represents a longitude of abnormal environmental data; K represents a spatial distribution data set; k represents real-time environmental data of an k th comparative monitoring site; η represents a weight coefficient; t represents time; based on the spatial distribution characteristics of the abnormal environmental data and the time series analysis result, calculating the propagation speed of the abnormal environmental data in the marine environment; S4, combining the distance and the propagation speed, and using a linear propagation algorithm to calculate the arrival time of the abnormal environmental data from the reference monitoring station to the comparison monitoring station; S5, setting an alarm start time according to the arrival time, and issuing an alarm information. 2.The method of claim 1, wherein, The deployment of a plurality of monitoring stations in the marine environment monitoring area, collecting environmental data in real time through sensors in the monitoring stations and transmitting the data to a database comprises the following steps: S11, according to the detection requirement, deploying a plurality of monitoring stations in the marine environment monitoring area, and configuring corresponding sensors in each monitoring station; S12, collecting environmental data in real time using sensors, and preprocessing the environmental data; S13, fusing the preprocessed environmental data, and transmitting the fused environmental data to the database using wireless communication technology. 3.The method of claim 2, wherein, The fusion processing of the preprocessed environmental data and the transmission of the fused environmental data to the database using wireless communication technology comprises the following steps: S131, extracting marine environmental parameters and sea current information from the preprocessed environmental data; S132, synchronizing the time series of marine environmental parameters and sea current information to obtain marine environmental parameters and sea current information corresponding to each time point; S133, the Kalman filtering algorithm is used for fusing the marine environment parameters and the current information, wherein the marine environment parameters and the current information are used as observation vectors, the predicted state vectors are gradually corrected through the prediction and updating steps of the Kalman filtering, and the fused environment data is obtained; S134, the fused environment data is packaged according to a preset format, and the packaged environment data is transmitted to a database by using a wireless communication technology. 4.The method of claim 1, wherein, The position information of each monitoring station is obtained by using the positioning technology, and the distance between the randomly selected reference monitoring station and the corresponding comparison monitoring station is calculated, including the following steps: S21, the position information of each monitoring station is obtained by using the positioning technology to deploy corresponding positioning devices at each monitoring station, and the position information of the monitoring station includes the longitude and latitude of the monitoring station; S22, a monitoring station is randomly selected from the monitoring stations as a reference monitoring station, and a monitoring station adjacent to the reference monitoring station is selected as a comparison monitoring station; S23, based on the position information of each monitoring station, the distance between the reference monitoring station and the comparison monitoring station is calculated by using a geographic distance calculation formula.

5. The method according to claim 4, wherein, The geographic distance calculation formula is: wherein D denotes the distance between the reference monitoring site and the comparison monitoring site; r R represents the average radius of the Earth; x 1 latitude representing the latitude of the reference monitoring site; x 2 represents the latitude of the contrast monitoring site; y 1 longitude representing the reference monitoring site; y 2 represents the longitude of the contrast monitoring site. 6.The method of claim 1, wherein, The arrival time of the abnormal environment data from the reference monitoring station to the comparison monitoring station is calculated by using the linear propagation algorithm combined with the distance and the propagation speed, including the following steps: S41, the propagation time of the abnormal environment data from the reference monitoring station to the comparison monitoring station is calculated by using the linear propagation algorithm combined with the distance between the reference monitoring station and the corresponding comparison monitoring station and the propagation speed of the abnormal environment data; S42, the time of the abnormal environment data at the reference monitoring station is obtained, the propagation time is fused, and the arrival time of the abnormal environment data from the reference monitoring station to the comparison monitoring station is calculated.

7. A big data-based marine environment monitoring and early warning system for implementing the big data-based marine environment monitoring and early warning method of any one of claims 1-6, characterized in that, The system includes a data collection module, a position and distance calculation module, a propagation speed determination module, an arrival time calculation module and an alarm issuing module; The data collection module is used for deploying a plurality of monitoring stations in the marine environment monitoring area, collecting environment data in real time through sensors in the monitoring stations and transmitting the environment data to a database; The position and distance calculation module is used for obtaining the position information of each monitoring station by using the positioning technology, and calculating the distance between the randomly selected reference monitoring station and the corresponding comparison monitoring station; The propagation speed determination module is used for comparing the historical standard environment data of the reference monitoring station with the real-time collected environment data, analyzing the change trend of the environment data, and determining the propagation speed of the abnormal environment data based on the analysis result; The arrival time calculation module is used for calculating the arrival time of the abnormal environment data from the reference monitoring station to the comparison monitoring station by using the linear propagation algorithm combined with the distance and the propagation speed; The alarm issuing module is used for setting the alarm starting time according to the arrival time, and issuing the alarm information.

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