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

Abstract

The present invention discloses a marine environment monitoring and early warning system based on big data, which belongs to the technical field of marine environment monitoring. The system includes: a data acquisition system, a transmission system, a monitoring system and an early warning system; the data acquisition system is used to collect marine environment data based on acquisition equipment; the transmission system is connected to the data acquisition system, and is used to transmit the marine environment data to the monitoring system; the monitoring system is connected to the transmission system, and is used to analyze the marine environment data to obtain monitoring results; the early warning system is connected to the monitoring system, and is used to issue early warnings based on the monitoring results. Signal transmission in a vast sea area is achieved through a mobile transmission module; at the same time, by setting up monitoring stations and multi-faceted data collection of remote sensing data, all-round detection of the marine environment is achieved, which is conducive to the detection and early warning of common marine environmental disasters.

Description

Technical Field

[0001] The present invention belongs to the technical field of marine environment monitoring, and in particular relates to a marine environment monitoring and early warning system based on big data. Background Art

[0002] The marine environment is a general term for environmental conditions such as the marine air environment, the sea-air boundary layer environment, the underwater environment, and the target environment. The ocean is rich in oil and natural gas resources, so the development and utilization of marine resources are increasingly in demand. With the development of marine resources, the monitoring of the marine environment has become more important. Due to the vast sea area, marine environmental monitoring is difficult and costly. Early marine monitoring used the method of manually collecting seawater samples and bringing them back to the laboratory for analysis. However, it is time-consuming and laborious, and the samples may be contaminated during transportation, resulting in inaccurate results. With the continuous development of technology, the Internet of Things can be used to collect marine data in real time through wireless sensors. However, due to the wide range of the ocean and the harsh communication conditions at sea, it is difficult for the wireless method to ensure high transmission efficiency, and the deployment and maintenance costs of dedicated wireless sensors are high, which cannot meet the requirements of overall marine environmental monitoring. Summary of the invention

[0003] The present invention aims to solve the deficiencies of the prior art and proposes a marine environment monitoring and early warning system based on big data. Marine environment data is collected from multiple aspects through monitoring stations and remote sensing data, and drones are used as signal relay points to achieve signal transmission in a vast sea area. At the same time, by monitoring common red tides and oil spills on marine environment data, marine environment monitoring results are obtained, and early warnings are issued based on the monitoring results.

[0004] To achieve the above-mentioned purpose, the present invention provides the following scheme: A marine environment monitoring and early warning system based on big data, comprising: a data acquisition system, a transmission system, a monitoring system and an early warning system;

[0005] The data acquisition system is used to collect marine environment data based on the acquisition equipment;

[0006] The transmission system is connected to the data acquisition system and is used to transmit the marine environment data to the monitoring system;

[0007] The monitoring system is connected to the transmission system and is used to analyze the marine environment data to obtain monitoring results;

[0008] The early warning system is connected to the monitoring system and is used for issuing early warnings based on the monitoring results.

[0009] Further preferably, the data acquisition system comprises: a first data acquisition module and a second data acquisition module; the marine environment data comprises: marine environment information and remote sensing monitoring data;

[0010] The first data acquisition module is used to collect the marine environment information, and the marine environment information includes: water quality information and temperature and humidity information;

[0011] The second data acquisition module is used to acquire ocean remote sensing image information and pre-process the remote sensing image information to obtain the remote sensing monitoring data.

[0012] Further preferably, the transmission system comprises: a transmission module and a planning module;

[0013] The transmission module includes a fixed transmission module and a mobile transmission module, which are used to transmit the marine environment data;

[0014] The planning module is used to plan the moving path of the mobile transmission module.

[0015] Further preferably, the working process of the planning module includes:

[0016] Dividing the area to be monitored, and assigning a mobile area to the mobile transmission module based on the division result;

[0017] Determine the pause points of the mobile transmission module in the moving area, and calculate the distance between the mobile transmission module and each pause point;

[0018] Calculating a pause matrix of a path between two pause points, and sorting the pause matrix;

[0019] Determine whether the two pause points are on the existing path. If the two pause points are not on the existing path and are both adjacent to the starting point of the mobile transmission module, the path where the two pause points are located is retained, otherwise it is discarded;

[0020] The order of the mobile transmission modules at each pause point is determined by the distance between the mobile transmission modules and the pause point, and the moving path of the mobile transmission modules is obtained.

[0021] Further preferably, the monitoring system comprises: an equipment monitoring module and an environment monitoring module;

[0022] The equipment monitoring module is used to collect operating status information of the equipment of the data acquisition system;

[0023] The environmental monitoring module is used to process and analyze the marine environmental data to obtain the monitoring results.

[0024] Further preferably, the environment monitoring module comprises: a first calculation unit and a second calculation unit;

[0025] The first computing unit is used to perform red tide detection based on the marine environment data to obtain a red tide detection result;

[0026] The second computing unit is used to perform oil spill detection based on the marine environment data to obtain an oil spill detection result.

[0027] Further preferably, the first calculation unit includes: a calculation subunit and a prediction subunit;

[0028] The calculation subunit is used to obtain the red tide detection result based on the marine environment data;

[0029] The prediction subunit is used to perform short-term prediction of red tide and obtain prediction results.

[0030] Further preferably, the method by which the prediction subunit obtains the prediction result comprises:

[0031] Collect historical red tide disaster data in the monitoring area and construct an original time series; obtain a first generated sequence based on the original time series;

[0032] Performing differentiation processing on the first generated sequence to obtain a first differential sequence;

[0033] Calculating based on the first differential sequence to obtain a fitting value of the original time series, and obtaining a first residual sequence based on the fitting value of the original time series;

[0034] Obtaining a first generating function based on the first residual sequence, and extending the first generating function to obtain an extended function;

[0035] Obtaining a dominant period of the first generated sequence, and obtaining a second residual sequence based on the dominant period; cyclically performing the dominant period acquisition to obtain a plurality of dominant periods;

[0036] The extension functions of several dominant cycles are superimposed to obtain the extension fitting value;

[0037] The prediction result is obtained based on the fitting value of the original time series and the extended fitting value.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] The marine environment monitoring and early warning system based on big data disclosed in the present invention realizes signal transmission in a wide sea area through a mobile transmission module; at the same time, through the establishment of monitoring stations and multi-faceted data collection of remote sensing data, it realizes comprehensive detection of the marine environment, which is conducive to the detection and early warning of common marine environmental disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in 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 paying creative labor.

[0041] Figure 1 This is a schematic diagram of the structure of a marine environment monitoring and early warning system based on big data according to an embodiment of the present invention;

[0042] Figure 2 This is a schematic diagram of the working process of the planning module in an embodiment of the present invention;

[0043] Figure 3 This is a flow chart of a method for calculating prediction results according to an embodiment of the present invention;

[0044] Figure 4 The figure is a flow chart of a method for calculating oil spill detection results according to an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0047] Embodiment 1:

[0048] like Figure 1 As shown, this embodiment provides a marine environment monitoring and early warning system based on big data, including: a data acquisition system, a transmission system, a monitoring system and an early warning system; the data acquisition system is used to collect marine environment data based on acquisition equipment; the transmission system is connected to the data acquisition system, and is used to transmit the marine environment data to the monitoring system; the monitoring system is connected to the transmission system, and is used to analyze the marine environment data to obtain monitoring results; the early warning system is connected to the monitoring system, and is used to issue early warnings based on the monitoring results.

[0049] Further preferably, the data acquisition system includes: a first data acquisition module and a second data acquisition module; the marine environment data includes: marine environment information and remote sensing monitoring data; wherein the first data acquisition module is used to collect marine environment information, and the marine environment information includes: water quality information and temperature and humidity information; the second data acquisition module is used to collect marine remote sensing image information, and pre-process the remote sensing image information to obtain remote sensing monitoring data.

[0050] In this embodiment, the first data acquisition module adopts a monitoring station, which includes a data acquisition part and a data storage and transmission part. The data acquisition part adopts sensors, including: pH, dissolved oxygen, salinity, chlorophyll, turbidity and temperature sensors, etc., which are mainly responsible for obtaining environmental data in seawater. The data acquisition part also includes: a water quality detector and a nutrient salt analyzer; the first data acquisition module takes water through a water intake device according to a set time interval or manual mode. After the water is taken, the above-mentioned water quality detector, nutrient salt analyzer and sensor are used for analysis, and the analysis structure is updated to the data storage and transmission part.

[0051] The second data acquisition module includes: a remote sensing data acquisition unit, a first preprocessing unit and a second preprocessing unit; the first preprocessing unit is connected to the first calculation unit, and the second preprocessing unit is connected to the second calculation unit.

[0052] The first preprocessing unit processes the remote sensing image information by: geometric correction, cropping, etc., to obtain the remote sensing monitoring data of the area to be monitored. The second preprocessing unit processes the remote sensing image information by: grayscale transformation, filtering denoising and frequency domain enhancement, to obtain the remote sensing monitoring data of the area to be monitored.

[0053] Specifically, the transmission system includes: a transmission module and a planning module; the transmission module includes a fixed transmission module and a mobile transmission module, which are used to transmit marine environmental data; the planning module is used to plan the moving path of the mobile transmission module. Among them, the fixed transmission module is used for information exchange within the above-mentioned monitoring station. The mobile transmission module is used to transmit the data stored in the monitoring station to the ground workstation.

[0054] In this embodiment, Figure 2 As shown in the figure, the working process of the planning module includes:

[0055] The area to be monitored is divided, and a mobile area is allocated to the mobile transmission module based on the division result.

[0056] The pause points of the mobile transmission module in the moving area are determined, and the distance between the mobile transmission module and each pause point is calculated.

[0057] Calculate the pause matrix of the path between two pause points and sort the pause matrix.

[0058] It is determined whether the two pause points are on the existing path. If the two pause points are not on the existing path and are both adjacent to the starting point of the mobile transmission module, the path where the two pause points are located is retained, otherwise it is discarded.

[0059] The order of the mobile transmission modules at each pause point is determined by the distance between the mobile transmission modules and the pause point, and the moving path of the mobile transmission modules is obtained.

[0060] Specifically, the monitoring system includes: an equipment monitoring module and an environment monitoring module; the equipment monitoring module is used to collect the operating status information of the equipment of the data acquisition system; the environment monitoring module is used to process and analyze the marine environment data to obtain monitoring results.

[0061] In this embodiment, the device monitoring module is mainly used to monitor the voltage, temperature and operating status of the acquisition equipment of the monitoring station. The system can detect and issue an alarm within 1 second after the device is offline.

[0062] The environmental monitoring module includes: a first calculation unit and a second calculation unit; the first calculation unit is used to perform red tide detection based on marine environmental data to obtain red tide detection results; the second calculation unit is used to perform oil spill detection based on marine environmental data to obtain oil spill detection results.

[0063] Among them, the first calculation unit includes: a calculation subunit and a prediction subunit; the calculation subunit is used to obtain red tide detection results based on marine environmental data; the prediction subunit is used to make short-term predictions on red tides and obtain prediction results.

[0064] The method for the calculation subunit to obtain the red tide detection result includes: extracting characteristic bands from the remote sensing monitoring data of the monitored area obtained by the first preprocessing unit; calculating the correlation coefficient of the extracted characteristic bands to obtain the correlation coefficient detection result; and calculating the mutual information of the extracted characteristic bands to obtain the mutual information detection result; comparing the correlation coefficient detection result and the mutual information detection result, and taking the larger one as the final red tide detection result.

[0065] The calculation method of the correlation coefficient detection result includes:

[0066]

[0067] In the formula, δ COR (x) represents the correlation coefficient detection result; COR(M,N) represents the correlation coefficient matrix of M band and N band; x represents the spectral vector; Var(M) and Var(N) represent the variance of M band and N band respectively.

[0068] The calculation method of the mutual information detection result includes:

[0069]

[0070] In the formula, δ MI (x) represents the mutual information detection result; MI(M,N) represents the mutual information matrix of the M band and the N band; P MN (m,n) represents the joint probability density of band M and band N; P M (m) and P N (n) represents the marginal probability density of M band and N band respectively.

[0071] In this embodiment, Figure 3 As shown, the method for the prediction subunit to obtain the prediction result includes:

[0072] Collect historical red tide disaster data in the monitoring area and construct the original time series; the original time series must be accumulated once to the first generated sequence.

[0073] Performing first-order linear differentiation processing on the first generated sequence to obtain a first differential sequence.

[0074] Based on the first differential sequence, cumulative subtraction calculation is performed to obtain the fitting value of the original time series, and the fitting value of the original time series is subtracted from the true value to obtain the first residual sequence.

[0075] A first generating function is obtained based on the first residual sequence, and the first generating function is periodically extended to obtain an extended function.

[0076] The dominant period of the first generated sequence is obtained, and the extension function of the dominant period is subtracted from the original time series to obtain the second residual sequence; the dominant period acquisition is repeated repeatedly to obtain several dominant periods.

[0077] The extension functions of several dominant cycles are superimposed to obtain the extension fitting value.

[0078] Add the fitted values ​​of the original time series and the extended fitted values ​​to get the forecast results.

[0079] like Figure 4 As shown, the calculation method for the second calculation unit to obtain the oil spill detection result includes:

[0080] Gaussian filtering is performed on the remote sensing monitoring data of the area to be monitored obtained after being processed by the second preprocessing unit to obtain a filtered image.

[0081] The gradient amplitude and direction of the filtered image are calculated by finite difference of first-order partial derivatives, and the gradient amplitude is non-maximum suppressed to obtain the gradient amplitude array.

[0082] The gradient amplitude array is double-thresholded to obtain an edge array, and the edge array is traversed until the end of the edge is reached and the traversal is stopped. The above process is repeated continuously to obtain an edge image.

[0083] The contour points of the edge image are extracted to obtain the coordinate points of the contour edge, and the contour size filter is set to obtain the contour coordinate points of the oil spill area.

[0084] The early warning system provides information warnings based on the red tide detection results, red tide prediction results and oil spill detection results.

[0085] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A marine environment monitoring and early warning system based on big data, characterized in that: include: Data acquisition system, transmission system, monitoring system and early warning system; The data acquisition system is used to collect marine environment data based on the acquisition equipment; The transmission system is connected to the data acquisition system and is used to transmit the marine environment data to the monitoring system; The transmission system comprises: a transmission module and a planning module; The transmission module includes a fixed transmission module and a mobile transmission module, which are used to transmit the marine environment data; The planning module is used to plan the moving path of the mobile transmission module; The working process of the planning module includes: Dividing the area to be monitored, and assigning a mobile area to the mobile transmission module based on the division result; Determine the pause points of the mobile transmission module in the moving area, and calculate the distance between the mobile transmission module and each pause point; Calculating a pause matrix of a path between two pause points, and sorting the pause matrix; Determine whether the two pause points are on the existing path. If the two pause points are not on the existing path and are both adjacent to the starting point of the mobile transmission module, the path where the two pause points are located is retained, otherwise it is discarded; Determine the order of the mobile transmission modules at each pause point by the distance between the mobile transmission modules and the pause point, and obtain the moving path of the mobile transmission modules; The monitoring system is connected to the transmission system and is used to analyze the marine environment data to obtain monitoring results; The monitoring system comprises: an equipment monitoring module and an environment monitoring module; The equipment monitoring module is used to collect operating status information of the equipment of the data acquisition system; The environmental monitoring module is used to process and analyze the marine environmental data to obtain the monitoring results; The environment monitoring module includes: a first calculation unit and a second calculation unit; The first computing unit is used to perform red tide detection based on the marine environment data to obtain a red tide detection result; The first computing unit includes: a computing subunit and a prediction subunit; The calculation subunit is used to obtain the red tide detection result based on the marine environment data; The method for the calculation subunit to obtain the red tide detection result includes: extracting characteristic bands from the remote sensing monitoring data of the monitored area; calculating the correlation coefficient of the extracted characteristic bands to obtain the correlation coefficient detection result; and calculating the mutual information of the extracted characteristic bands to obtain the mutual information detection result; comparing the correlation coefficient detection result and the mutual information detection result, and taking the larger one as the final red tide detection result; The calculation method of the correlation coefficient detection result includes: In the formula, δ COR (x) represents the correlation coefficient test result; COR(M,N) represents the correlation coefficient matrix of the M band and the N band; x represents the spectral vector; Var(M) and Var(N) represent the variance of the M band and the N band respectively; The calculation method of the mutual information detection result includes: In the formula, δ MI (x) represents the mutual information detection result; MI(M,N) represents the mutual information matrix of the M band and the N band; P MN (m,n) represents the joint probability density of band M and band N; P M (m) and P N (n) represents the marginal probability density of M band and N band respectively; The prediction subunit is used to make short-term predictions of red tides and obtain prediction results; The method for the prediction subunit to obtain the prediction result includes: Collect historical red tide disaster data in the monitoring area and construct an original time series; obtain a first generated sequence based on the original time series; Performing differentiation processing on the first generated sequence to obtain a first differential sequence; Calculating based on the first differential sequence to obtain a fitting value of the original time series, and obtaining a first residual sequence based on the fitting value of the original time series; Obtaining a first generating function based on the first residual sequence, and extending the first generating function to obtain an extended function; Obtaining a dominant period of the first generated sequence, and obtaining a second residual sequence based on the dominant period; cyclically performing the dominant period acquisition to obtain a plurality of dominant periods; The extension functions of several dominant cycles are superimposed to obtain the extension fitting value; Obtaining the prediction result based on the fitting value of the original time series and the extended fitting value; The second computing unit is used to perform oil spill detection based on the marine environment data to obtain an oil spill detection result; The calculation method for obtaining the oil spill detection result by the second calculation unit includes: Performing Gaussian filtering on the remote sensing monitoring data of the area to be monitored obtained after being processed by the second preprocessing unit to obtain a filtered image; The gradient amplitude and direction of the filtered image are calculated by using the finite difference of the first-order partial derivative, and the gradient amplitude is non-maximum suppressed to obtain the gradient amplitude array; Perform double thresholding on the gradient amplitude array to obtain an edge array, traverse the edge array until the end of the edge is reached and the traversal is stopped, and the above process is continuously repeated to obtain an edge image; Extract the contour points of the edge image to obtain the coordinate points of the contour edge, and set the contour size filter to obtain the contour coordinate points of the oil spill area; The early warning system is connected to the monitoring system and is used for issuing early warnings based on the monitoring results.

2. According to claim 1, a marine environment monitoring and early warning system based on big data is characterized in that: The data acquisition system includes: a first data acquisition module and a second data acquisition module; the marine environment data includes: marine environment information and remote sensing monitoring data; The first data acquisition module is used to collect the marine environment information, and the marine environment information includes: water quality information and temperature and humidity information; The second data acquisition module is used to acquire ocean remote sensing image information and pre-process the remote sensing image information to obtain the remote sensing monitoring data.

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