Abnormal plankton migration analysis method based on multi-sensor data fusion and diffusion modeling

Abstract

The present invention relates to the field of marine ecological technology, and more specifically to a method for analyzing abnormal plankton migration based on multi-sensor data fusion and diffusion modeling. The method comprises the following steps: S1: deploying a micro-sensor buoy array in a target sea area; S2: synchronously triggering the micro-sensor buoy array to collect data and obtain an original parameter set; S3: compensating and correcting the collected original parameter set; S4: performing spatial interpolation to generate a plankton density distribution map; S5: calculating the rate of change of density gradients in adjacent time slices and outputting a dynamic diffusion rate field; S6: marking an abnormal biological migration zone when the rate value in the dynamic diffusion rate field exceeds a preset threshold. The present invention, through the combination of multi-source signal acquisition and dynamic diffusion analysis, achieves accurate identification of the spatial distribution and abnormal migration behavior of plankton, thereby effectively achieving high spatial coverage, real-time multi-parameter fusion analysis, and dynamic migration identification capabilities.

Description

Technical Field

[0001] The present invention relates to the field of marine ecological technology, and in particular to a plankton abnormal migration analysis method based on multi-sensor data fusion and diffusion modeling. Background Art

[0002] Marine plankton plays a basic producer role in the marine ecosystem. Its spatial distribution and dynamic migration characteristics not only directly reflect changes in the primary productivity of the ocean, but also serve as sensitive indicators of ecological disturbances, pollution events or climate change. Currently, monitoring of plankton mainly relies on manual sampling and laboratory analysis. Although it has a certain degree of accuracy, it has significant limitations such as low temporal and spatial resolution, limited coverage, and delayed response speed.

[0003] Existing technologies lack a method for analyzing abnormal plankton migration that offers high spatial coverage, real-time multi-parameter fusion analysis, and dynamic migration identification. Specific challenges include: how to deploy a high density of microsensor nodes across a wide ocean area to continuously acquire bioluminescent and luminescent signals; how to construct a continuous density field based on multi-source data and identify rapid plankton migration trends; and how to automatically identify abnormal ecological changes without human intervention. Therefore, a method for analyzing abnormal plankton migration based on multi-sensor data fusion and diffusion modeling is urgently needed to address these challenges. Summary of the Invention

[0004] Based on the above objectives, the present invention provides a method for analyzing abnormal plankton migration based on multi-sensor data fusion and diffusion modeling.

[0005] The method for analyzing abnormal plankton migration based on multi-sensor data fusion and diffusion modeling includes the following steps:

[0006] S1: Deploy an array of micro sensor buoys in the target sea area;

[0007] S2: Synchronously trigger the micro sensor buoy array to collect data and obtain the original parameter set;

[0008] S3: Compensate and correct the collected original parameter set to generate an effective bioluminescent signal reflecting the distribution characteristics of plankton;

[0009] S4: spatially interpolate the compensation correction results and the effective bioluminescence signal according to the geographical location information to generate a plankton density distribution map;

[0010] S5: Based on the plankton density distribution map of the continuous time series, calculate the rate of change of the density gradient of adjacent time slices and output the dynamic diffusion rate field;

[0011] S6: When the rate value in the dynamic diffusion rate field exceeds the preset threshold, it is marked as a biological abnormal migration area.

[0012] Optionally, the micro sensor buoy array is integrated with a chlorophyll fluorescence sensor, a bioluminescence sensor, a depth pressure sensor, and a GPS / Beidou dual-mode positioning module. The collected original parameter set includes geographic location information, depth value, chlorophyll fluorescence intensity, and bioluminescence intensity. S1 specifically includes:

[0013] S11: Determine the monitoring boundary of the target sea area and construct a two-dimensional grid layout plan based on the sea area scope and preset spatial resolution;

[0014] S12: According to the constructed gridding scheme, the geographic coordinates of the center point of each grid cell are generated according to the row and column numbers to form the initial positioning point set for the buoys to be deployed;

[0015] S13: Load the device number and corresponding positioning point of the buoy to be deployed on the shore-based platform, and use the unmanned boat to autonomously navigate to the designated location to perform the fixed-point deployment operation;

[0016] S14: After each buoy is stably positioned in the water, it automatically starts the self-test program to detect the functional status of the chlorophyll fluorescence sensor, bioluminescence sensor, depth pressure sensor and positioning module, and uploads the initial status data to the shore-based control terminal to complete the deployment verification.

[0017] Optionally, the S2 specifically includes:

[0018] S21: The shore-based control terminal sends a unified trigger instruction to all buoys, wherein the trigger instruction includes a timestamp, a task number, and a sampling period parameter;

[0019] S22: Each buoy performs standardization on the received timestamp in the local time calibration module;

[0020] S23: At the triggering moment, each buoy simultaneously activates the chlorophyll fluorescence sensor, bioluminescence sensor, and depth pressure sensor to collect the original sensor readings at the current water depth, and obtains the geographic coordinate information corresponding to the sampling moment through the GPS / Beidou dual-mode positioning module;

[0021] S24: After data collection is completed, each buoy will compile a parameter set including the collection time, geographic location information, depth value, chlorophyll fluorescence intensity, and bioluminescence intensity, and transmit it back to the shore-based terminal for unified storage;

[0022] S25: The shore-based terminal verifies and summarizes the data parameter sets sent back by all buoys to form the original monitoring data set corresponding to the sampling time.

[0023] Optionally, S3 includes performing pressure attenuation compensation on the chlorophyll fluorescence intensity based on the depth value to generate a corrected fluorescence intensity; and separating the effective bioluminescence signal based on the spectrum difference between the bioluminescence intensity and the background noise, specifically including:

[0024] S31: Read the raw chlorophyll fluorescence intensity value collected by the buoy and the corresponding water depth value D;

[0025] S32: Calculate the current water pressure value P based on the relationship between water depth and pressure;

[0026] S33: Based on the attenuation law of chlorophyll fluorescence signal under pressure, a fluorescence intensity attenuation model is established to calculate the compensation factor ;

[0027] S34: Perform weighted correction on the original fluorescence intensity value and the compensation factor to obtain the corrected chlorophyll fluorescence intensity value , the calculation formula is: .

[0028] Optionally, the S3 further includes:

[0029] S35: Read the time series raw signal collected by the bioluminescence sensor in the buoy , divide it into sliding window sequences of length 1 second;

[0030] S36: Perform fast Fourier transform on the signal sequence in each sliding window to obtain the corresponding spectrum distribution function ;

[0031] S37: Read the background noise template spectrum ;

[0032] S38: Compare the target spectrum of each window With background template , calculate the spectral energy difference function , whose expression is: , if there are continuous frequency points in the high frequency range of 20-60Hz that meet >T, it is judged that the window contains valid bioluminescent signals, where T is the preset threshold.

[0033] Optionally, the S4 specifically includes:

[0034] S41: Binding the corrected fluorescence intensity values ​​and effective bioluminescence signal values ​​collected by each buoy to their corresponding geographic coordinates to construct a multi-point spatial observation sample set;

[0035] S42: Based on the boundary range of the target sea area and the set spatial resolution, a regularized two-dimensional grid structure is constructed, and the geographic coordinates of each interpolation node are determined as a set of points to be estimated in the density field;

[0036] S43: Using the Kriging interpolation method to perform spatial interpolation calculations on each data point in the sample set, and obtain the estimated values ​​of fluorescence intensity and luminescence intensity at the grid nodes respectively;

[0037] S44: The interpolated fluorescence intensity estimate value and the luminescence intensity estimate value are normalized and superimposed to construct a plankton density estimation index, and the plankton density estimation index is mapped to the corresponding grid node to form a complete plankton density distribution map.

[0038] Optionally, the S43 specifically includes:

[0039] S431: Mark the two-dimensional geographic coordinates of each observation point in the buoy sample set as , and the corresponding corrected fluorescence intensity value is recorded as , the luminous intensity value is recorded as , a total of n sample points;

[0040] S432: Calculate the spatial distance between all pairs of observation points and construct an empirical variation function based on range analysis , used to describe spatial correlation that varies with distance;

[0041] S433: Grid node positions to be estimated , according to the distance between it and all sample points, construct the interpolation weight coefficient equation group and solve the interpolation coefficient ;

[0042] S434: Based on the obtained weight coefficient , interpolate the fluorescence intensity values ​​of the grid nodes to be estimated to obtain the estimated value ;

[0043] S435: uses the same interpolation logic and weight coefficient as S434, and calculates the luminous intensity value. Interpolate to get the estimated value of luminous intensity .

[0044] Optionally, the S44 specifically includes:

[0045] S441: The fluorescence intensity estimate obtained by Kriging interpolation and luminous intensity estimates Normalization was performed separately to obtain the standardized fluorescence intensity value and normalized luminous intensity values ;

[0046] S442: Calculate the estimated plankton density index based on the standardized results , the fluorescence and luminescence components are weightedly fused, and the calculation formula is: ,in, Estimating indices for plankton density; 、 are the weight coefficients of the fluorescence component and the luminescence component, respectively, and their values ​​are =0.6, =0.4;

[0047] S443: All grid nodes The values ​​are mapped to a two-dimensional spatial distribution matrix corresponding to the grid structure of the target sea area, and different density levels are encoded using a color gradient method to form a complete plankton density distribution map.

[0048] Optionally, the S5 specifically includes:

[0049] S51: Read the plankton density distribution map of two consecutive sampling time slices and construct them into density field matrices and ,in is the two-dimensional space coordinate, is the sampling interval;

[0050] S52: Based on the density field matrix and , calculate the spatial gradient field at the corresponding time, and use the central difference method to estimate the density gradient vector of each grid node and ;

[0051] S53: At each spatial node position , calculate the rate of change of the density gradient modulus per unit time, recorded as the diffusion rate value ;

[0052] S54: Diffusion rate value of all spatial nodes Perform grid mapping to generate dynamic diffusion rate field images.

[0053] Optionally, the S6 specifically includes:

[0054] S61: Setting the threshold for determining abnormal biological migration ;

[0055] S62: Traverse all grid nodes in the dynamic diffusion rate field and read the diffusion rate value at the corresponding position , if satisfied , then the corresponding node Mark as abnormal migration point, and record the geographical coordinates and rate value of the abnormal migration point;

[0056] S63: Perform spatial cluster analysis on all abnormal migration points, use the distance-constrained density clustering method to identify continuous distribution areas, and define the cluster areas that meet the minimum number of points and maximum neighborhood radius conditions as biological abnormal migration areas.

[0057] Beneficial effects of the present invention:

[0058] The present invention deploys an array of micro-sensor buoys in the target sea area, integrates multi-source sensor data such as chlorophyll fluorescence, bioluminescence and depth pressure, and combines spatial interpolation modeling with density estimation index construction methods to achieve continuous expression and high-precision estimation of plankton density distribution, thereby effectively achieving high spatial coverage capabilities, as well as real-time multi-parameter fusion analysis capabilities and dynamic migration identification capabilities.

[0059] The present invention, through time-series analysis of density distribution maps, constructs a dynamic diffusion rate field and sets anomaly judgment thresholds, thereby realizing automatic identification of abnormal plankton migration areas, effectively improving the sensitive response capability to sudden ecological disturbances, and providing stable, efficient, and data-driven technical support for marine ecological monitoring and early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0061] Figure 1 Schematic diagram of a method for dynamic monitoring of marine plankton according to an embodiment of the present invention;

[0062] Figure 2 This is a schematic diagram of the process of generating a plankton density distribution map according to an embodiment of the present invention. DETAILED DESCRIPTION

[0063] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It is also noted that, to provide a more detailed description, the following embodiments are best and preferred embodiments, and those skilled in the art may employ alternative methods for implementing certain known technologies. Furthermore, the accompanying drawings are intended only to provide a more detailed description of the embodiments and are not intended to limit the present invention.

[0064] like Figure 1-Figure 2 As shown in FIG, the method for analyzing abnormal plankton migration based on multi-sensor data fusion and diffusion modeling includes the following steps:

[0065] S1: Deploy an array of micro sensor buoys in the target sea area;

[0066] The micro sensor buoy array integrates a chlorophyll fluorescence sensor, a bioluminescence sensor, a depth pressure sensor, and a GPS / Beidou dual-mode positioning module. The collected original parameter set includes geographic location information, depth value, chlorophyll fluorescence intensity, and bioluminescence intensity. S1 specifically includes:

[0067] S11: Determine the monitoring boundary of the target sea area, and construct a two-dimensional grid layout plan based on the sea area scope and preset spatial resolution, with the grid unit spacing set to no more than 500 meters;

[0068] S12: According to the constructed gridding scheme, the geographic coordinates of the center point of each grid cell are generated according to the row and column numbers to form the initial positioning point set for the buoys to be deployed;

[0069] S13: Load the device number and corresponding positioning point of the buoy to be deployed on the shore-based platform, and use the unmanned boat to autonomously navigate to the designated location to perform the fixed-point deployment operation;

[0070] S14: After each buoy is stably positioned in the water body, it automatically starts the self-test program to detect the functional status of the chlorophyll fluorescence sensor, bioluminescence sensor, depth pressure sensor and positioning module, and uploads the initial status data to the shore-based control terminal to complete the deployment verification; through the above deployment process, it is ensured that the micro-sensor buoys are evenly distributed in space, the geographic positioning is accurate, and the sensor functions are complete, which effectively improves the representativeness and stability of subsequent plankton monitoring data.

[0071] S2: Synchronously trigger the micro sensor buoy array to collect data and obtain the original parameter set;

[0072] S2 specifically includes:

[0073] S21: The shore-based control terminal sends a unified trigger command to all buoys. The trigger command includes a timestamp, a task number, and a sampling period parameter. After receiving the command, each buoy enters a synchronous preparation state.

[0074] S22: Each buoy standardizes the received timestamps in the local time calibration module to ensure that the acquisition task starts synchronously at the designated time point;

[0075] S23: At the triggering moment, each buoy simultaneously activates the chlorophyll fluorescence sensor, bioluminescence sensor, and depth pressure sensor to collect the original sensor readings at the current water depth, and obtains the geographic coordinate information corresponding to the sampling moment through the GPS / Beidou dual-mode positioning module;

[0076] S24: After data collection is completed, each buoy will compile a parameter set including the collection time, geographic location information, depth value, chlorophyll fluorescence intensity, and bioluminescence intensity, and transmit it back to the shore-based terminal for unified storage;

[0077] S25: The shore-based terminal verifies and summarizes the data parameter sets sent back by all buoys to form the original monitoring data set corresponding to the sampling time, which serves as the input basis for subsequent analysis. Through the above steps, multiple buoys can collaboratively and synchronously collect multi-dimensional marine ecological information, ensuring the consistency of data in time and space dimensions, thereby improving the accuracy and comparability of plankton monitoring results.

[0078] S3: Compensate and correct the collected original parameter set to generate an effective bioluminescent signal reflecting the distribution characteristics of plankton;

[0079] S3 includes pressure attenuation compensation for chlorophyll fluorescence intensity based on depth value to generate corrected fluorescence intensity; at the same time, effective bioluminescence signal is separated according to the spectrum difference between bioluminescence intensity and background noise, specifically including:

[0080] S31: Read the raw chlorophyll fluorescence intensity value collected by the buoy and the corresponding water depth value D;

[0081] S32: Calculate the current water pressure value P based on the relationship between water depth and pressure using the formula: ,in, is the density of seawater, which is 1025; g is the acceleration due to gravity, which is 9.8; D is the water depth;

[0082] S33: Based on the attenuation law of chlorophyll fluorescence signal under pressure, a fluorescence intensity attenuation model is established to calculate the compensation factor , the model expression is: , where e is the base of the natural logarithm, a mathematical constant with a value of approximately 2.71828; k is the empirical attenuation coefficient, which is set through actual measurement and calibration, and has a value range of to ;

[0083] S34: Perform weighted correction on the original fluorescence intensity value and the compensation factor to obtain the corrected chlorophyll fluorescence intensity value , the calculation formula is: ; The corrected fluorescence intensity value The chlorophyll concentration data is used as the basic data reflecting the actual distribution of phytoplankton, and is stored in the corresponding parameter set for subsequent processing. Through the above-mentioned depth-based pressure compensation process, the error caused by water pressure on the chlorophyll fluorescence sensor readings can be effectively eliminated, and the fluorescence signal's ability to respond to the actual phytoplankton concentration can be improved, thereby enhancing the accuracy and ecological representativeness of the monitoring results.

[0084] Effective bioluminescent signals from S3 include:

[0085] S35: Read the time series raw signal collected by the bioluminescence sensor in the buoy , divide it into a sliding window sequence of 1 second in length, each window contains signal data points with a sampling frequency of 100 Hz;

[0086] S36: Perform fast Fourier transform (FFT) on the signal sequence in each sliding window to obtain the corresponding spectrum distribution function , where f is the frequency variable;

[0087] S37: Read the background noise template spectrum ,The template is obtained through historical unmanned sampling area measurements, which contains stable components such as buoy background noise, seawater disturbance noise, etc., and its spectrum is mainly concentrated in the range of 0-10Hz;

[0088] S38: Compare the target spectrum of each window With background template , calculate the spectral energy difference function , whose expression is: , if there are continuous frequency points in the high frequency range of 20-60Hz that meet >T, it is judged that the window contains valid bioluminescent signals, where T is the preset threshold, and the value range is 2-5; through the above-mentioned spectrum difference analysis method, high-frequency burst bioluminescent signals can be accurately identified based on the frequency domain energy characteristics, and the target signal can be effectively distinguished from the background noise, thereby improving the reliability of the detection of plankton luminescent activity.

[0089] S4: spatially interpolate the compensation correction results and the effective bioluminescence signal according to the geographical location information to generate a plankton density distribution map;

[0090] S4 specifically includes:

[0091] S41: Binding the corrected fluorescence intensity values ​​and effective bioluminescent signal values ​​collected by each buoy to their corresponding geographic coordinates, respectively, to construct a multi-point spatial observation sample set, where each data point in the sample set includes the compensated fluorescence intensity, the luminescence intensity confirmed by spectrum analysis, and its corresponding two-dimensional coordinate position;

[0092] S42: Based on the boundary range of the target sea area and the set spatial resolution, a regularized two-dimensional grid structure is constructed, and the geographic coordinates of each interpolation node are determined as a set of points to be estimated in the density field;

[0093] S43: Kriging interpolation is used to perform spatial interpolation calculations on each data point in the sample set, and estimates of fluorescence intensity and luminescence intensity are obtained at the grid nodes. The interpolation process is based on minimum variance unbiased estimation and is combined with a spatial autocovariance model to correct distance and direction weights.

[0094] S44: The interpolated fluorescence intensity estimate and the luminescence intensity estimate are normalized and superimposed to construct a plankton density estimation index, and the plankton density estimation index is mapped to the corresponding grid node to form a complete plankton density distribution map; through the above spatial interpolation and estimation process, the discrete information of the buoy observation can be expanded into a continuous spatial expression, thereby improving the spatial resolution accuracy and dynamic tracking capability of the plankton distribution status.

[0095] S43 specifically includes:

[0096] S431: Mark the two-dimensional geographic coordinates of each observation point in the buoy sample set as , and the corresponding corrected fluorescence intensity value is recorded as , the luminous intensity value is recorded as , a total of n sample points;

[0097] S432: Calculate the spatial distance between all pairs of observation points and construct an empirical variation function based on range analysis , which is used to describe the spatial correlation that changes with distance. The calculation formula of the empirical variation function is: ,in, is the empirical variation function value when the interval is h, and its unit is consistent with the square of the observed value; is the number of sample points with a distance of h; For location The observed value of fluorescence intensity or luminous intensity ; Represents The observation value of another observation point with a spacing of h; h is the spatial distance between sample points;

[0098] S433: Grid node positions to be estimated , according to the distance between it and all sample points, construct the interpolation weight coefficient equation group and solve the interpolation coefficient , so that it satisfies the following unbiased estimation conditions:

[0099] ; and minimize the interpolation variance. The expression of the interpolation variance is:

[0100] ,in, is the interpolation variance of the estimated point, and its unit is consistent with the square of the observed value; is the interpolation weight coefficient of the i-th sample point to be estimated; For sample points and The variation function value between ; For sample points and the point to be estimated The variation function value between ; :=represent the coordinate positions of the i-th and j-th sample points and the point to be estimated respectively;

[0101] S434: Based on the obtained weight coefficient , interpolate the fluorescence intensity values ​​of the grid nodes to be estimated to obtain the estimated value , and its calculation formula is: ;

[0102] S435: uses the same interpolation logic and weight coefficient as S434, and calculates the luminous intensity value. Interpolate to get the estimated value of luminous intensity , and its calculation formula is: Through the above-mentioned Kriging interpolation process, the fluorescence and luminescence intensity of any grid node can be estimated with high precision based on the spatial autocorrelation structure of each observation point, providing continuous spatial data support for the subsequent construction of plankton density field, and improving monitoring accuracy and image expression quality.

[0103] S44 specifically includes:

[0104] S441: The fluorescence intensity estimate obtained by Kriging interpolation and luminous intensity estimates Normalization was performed separately to obtain the standardized fluorescence intensity value and normalized luminous intensity values , normalization adopts the minimum-maximum standardization method, and the normalization formula is: ; ,in, is the estimated fluorescence intensity of the grid node to be estimated; is the estimated value of the luminous intensity of the grid node to be estimated; Monitor the minimum and maximum values ​​of fluorescence intensity in all grid nodes in the sea area respectively; Monitor the minimum and maximum values ​​of luminous intensity in all grid nodes in the sea area respectively;

[0105] S442: Calculate the estimated plankton density index based on the standardized results , the fluorescence and luminescence components are weightedly fused, and the calculation formula is: ,in, is the plankton density estimation index, which represents the relative density intensity at each grid node; 、 are the weight coefficients of the fluorescence component and the luminescence component, respectively, and their values ​​are =0.6, =0.4;

[0106] S443: All grid nodes The values ​​are mapped to a two-dimensional spatial distribution matrix corresponding to the grid structure of the target sea area, and different density levels are encoded in a color gradient manner to form a complete plankton density distribution map, which is used to express the density change status within the sea area. Through the above-mentioned index construction and image generation steps, the fusion modeling and spatial visualization of multi-source biological signals can be realized, ensuring that the plankton density field has high contrast, continuity and interpretability, which is convenient for subsequent ecological analysis and dynamic trend judgment.

[0107] S5: Based on the plankton density distribution map of the continuous time series, calculate the rate of change of the density gradient of adjacent time slices and output the dynamic diffusion rate field;

[0108] S5 specifically includes:

[0109] S51: Read the plankton density distribution map of two consecutive sampling time slices and construct them into density field matrices and ,in is the two-dimensional space coordinate, is the sampling interval in hours;

[0110] S52: Based on the density field matrix and , calculate the spatial gradient field at the corresponding time, and use the central difference method to estimate the density gradient vector of each grid node and , the gradient direction takes the form of a two-dimensional vector: ;

[0111] S53: At each spatial node position , calculate the rate of change of the density gradient modulus per unit time, recorded as the diffusion rate value , and its calculation formula is: ,in, is the modulus of the density gradient, indicating the intensity of local density change; : The sampling interval between two adjacent time slices; For plankton at point Dynamic diffusion rate at

[0112] S54: Diffusion rate value of all spatial nodes Grid mapping is performed to generate dynamic diffusion rate field images, which are then output to the monitoring system to indicate the spatial-temporal migration trend of plankton density changes in the current sea area.

[0113] S6: When the rate value in the dynamic diffusion rate field exceeds the preset threshold, it is marked as a biological abnormal migration area;

[0114] S6 specifically includes:

[0115] S61: Setting the threshold for determining abnormal biological migration ,The judgment threshold is set based on the statistical results of the stable migration rate of plankton in historical monitoring data, and the unit is the hourly change of the density gradient modulus;

[0116] S62: Traverse all grid nodes in the dynamic diffusion rate field and read the diffusion rate value at the corresponding position , if satisfied , then the corresponding node Mark as abnormal migration point, and record the geographical coordinates and rate value of the abnormal migration point;

[0117] S63: Spatial clustering analysis is performed on all abnormal migration points. The distance-constrained density clustering method (DBSCAN) is used to identify continuous distribution areas. Clustering areas that meet the minimum number of points and maximum neighborhood radius conditions are defined as biological abnormal migration areas. Through the above-mentioned process based on diffusion rate threshold judgment and cluster analysis, abnormal aggregation or migration areas of plankton can be effectively identified from the temporal density changes, providing a high-precision positioning basis for subsequent ecological evaluation and field investigation.

[0118] The present invention encompasses any alternatives, modifications, equivalents, and solutions that fall within the spirit and scope of the present invention. To provide a thorough understanding of the present invention, specific details are described in detail below in connection with the preferred embodiments of the present invention, but those skilled in the art will be able to fully understand the present invention without these detailed descriptions. Furthermore, to avoid unnecessary confusion regarding the essence of the present invention, well-known methods, processes, procedures, components, and circuits have not been described in detail.

[0119] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A plankton abnormal migration analysis method based on multi-sensor data fusion and diffusion modeling, characterized by: The following steps are involved: S1: Deploy a micro sensor buoy array in the target sea area. The micro sensor buoy array is integrated with a chlorophyll fluorescence sensor, a bioluminescence sensor, a depth pressure sensor, and a GPS / Beidou dual-mode positioning module. The original parameter set collected includes geographic location information, depth value, chlorophyll fluorescence intensity, and bioluminescence intensity; S2: Synchronously trigger the micro sensor buoy array to collect data and obtain the original parameter set; S3: Compensate and correct the collected original parameter set to generate an effective bioluminescent signal reflecting the distribution characteristics of plankton; S4: Perform spatial interpolation of the compensation correction results and the effective bioluminescent signal according to the geographic location information to generate a plankton density distribution map; specifically including: S41: Binding the corrected fluorescence intensity values ​​and effective bioluminescence signal values ​​collected by each buoy to their corresponding geographic coordinates to construct a multi-point spatial observation sample set; S42: Based on the boundary range of the target sea area and the set spatial resolution, a regularized two-dimensional grid structure is constructed, and the geographic coordinates of each interpolation node are determined as a set of points to be estimated in the density field; S43: Using the Kriging interpolation method to perform spatial interpolation calculations on each data point in the sample set, and obtain the estimated values ​​of fluorescence intensity and luminescence intensity at the grid nodes respectively; S44: performing normalized superposition processing on the interpolated fluorescence intensity estimate value and the luminescence intensity estimate value to construct a plankton density estimation index, and mapping the plankton density estimation index to the corresponding grid node to form a complete plankton density distribution map; The S44 specifically includes: S441: The fluorescence intensity estimate obtained by Kriging interpolation and luminous intensity estimates Normalization was performed separately to obtain the standardized fluorescence intensity value and normalized luminous intensity values ; S442: Calculate the estimated plankton density index based on the standardized results , the fluorescence and luminescence components are weightedly fused, and the calculation formula is: ,in, Estimating indices for plankton density; 、 are the weight coefficients of the fluorescence component and the luminescence component, respectively, and their values ​​are =0.6, =0.4; S443: All grid nodes The values ​​are mapped to a two-dimensional spatial distribution matrix corresponding to the grid structure of the target sea area, and different density levels are encoded using a color gradient method to form a complete plankton density distribution map; S5: Based on the plankton density distribution map of the continuous time series, calculate the rate of change of the density gradient of adjacent time slices and output the dynamic diffusion rate field; specifically including: S51: Read the plankton density distribution map of two consecutive sampling time slices and construct them into density field matrices and ,in is the two-dimensional space coordinate, is the sampling interval; S52: Based on the density field matrix and , calculate the spatial gradient field at the corresponding time, and use the central difference method to estimate the density gradient vector of each grid node and ; S53: At each spatial node position , calculate the rate of change of the density gradient modulus per unit time, recorded as the diffusion rate value ; S54: Diffusion rate value of all spatial nodes Perform grid mapping to generate dynamic diffusion rate field images; S6: When the rate value in the dynamic diffusion rate field exceeds the preset threshold, it is marked as a biological abnormal migration area.

2. The method for analyzing abnormal plankton migration based on multi-sensor data fusion and diffusion modeling according to claim 1 is characterized in that: Said S1 specifically includes: S11: Determine the monitoring boundary of the target sea area and construct a two-dimensional grid layout plan based on the sea area scope and preset spatial resolution; S12: According to the constructed gridding scheme, the geographic coordinates of the center point of each grid cell are generated according to the row and column numbers to form the initial positioning point set for the buoys to be deployed; S13: Load the device number and corresponding positioning point of the buoy to be deployed on the shore-based platform, and use the unmanned boat to autonomously navigate to the designated location to perform the fixed-point deployment operation; S14: After each buoy is stably positioned in the water, it automatically starts the self-test program to detect the functional status of the chlorophyll fluorescence sensor, bioluminescence sensor, depth pressure sensor and positioning module, and uploads the initial status data to the shore-based control terminal to complete the deployment verification.

3. The method for analyzing abnormal plankton migration based on multi-sensor data fusion and diffusion modeling according to claim 2 is characterized in that: The S2 specifically includes: S21: The shore-based control terminal sends a unified trigger instruction to all buoys, wherein the trigger instruction includes a timestamp, a task number, and a sampling period parameter; S22: Each buoy performs standardization on the received timestamp in the local time calibration module; S23: At the triggering moment, each buoy simultaneously activates the chlorophyll fluorescence sensor, bioluminescence sensor, and depth pressure sensor to collect the original sensor readings at the current water depth, and obtains the geographic coordinate information corresponding to the sampling moment through the GPS / Beidou dual-mode positioning module; S24: After data collection is completed, each buoy will compile a parameter set including the collection time, geographic location information, depth value, chlorophyll fluorescence intensity, and bioluminescence intensity, and transmit it back to the shore-based terminal for unified storage; S25: The shore-based terminal verifies and summarizes the data parameter sets sent back by all buoys to form the original monitoring data set corresponding to the sampling time.

4. The method for analyzing abnormal plankton migration based on multi-sensor data fusion and diffusion modeling according to claim 3 is characterized in that: S3 includes performing pressure attenuation compensation on the chlorophyll fluorescence intensity based on the depth value to generate a corrected fluorescence intensity; and separating the effective bioluminescence signal based on the spectrum difference between the bioluminescence intensity and the background noise, specifically including: S31: Read the raw chlorophyll fluorescence intensity value collected by the buoy and the corresponding water depth value D; S32: Calculate the current water pressure value P based on the relationship between water depth and pressure; S33: Based on the attenuation law of chlorophyll fluorescence signal under pressure, a fluorescence intensity attenuation model is established to calculate the compensation factor ; S34: Perform weighted correction on the original fluorescence intensity value and the compensation factor to obtain the corrected chlorophyll fluorescence intensity value , the calculation formula is: .

5. The method for analyzing abnormal plankton migration based on multi-sensor data fusion and diffusion modeling according to claim 4 is characterized in that: Said S3 further comprises: S35: Read the time series raw signal collected by the bioluminescence sensor in the buoy , divide it into sliding window sequences of length 1 second; S36: Perform fast Fourier transform on the signal sequence in each sliding window to obtain the corresponding spectrum distribution function ; S37: Read the background noise template spectrum ; S38: Compare the target spectrum of each window With background template , calculate the spectral energy difference function , whose expression is: , if there are continuous frequency points in the high frequency range of 20-60Hz that meet When , it is determined that the window contains valid bioluminescent signals, where T is the preset threshold.

6. The method for analyzing abnormal plankton migration based on multi-sensor data fusion and diffusion modeling according to claim 1 is characterized in that: The S43 specifically includes: S431: Mark the two-dimensional geographic coordinates of each observation point in the buoy sample set as , and the corresponding corrected fluorescence intensity value is recorded as , the luminous intensity value is recorded as , a total of n sample points; S432: Calculate the spatial distance between all pairs of observation points and construct an empirical variation function based on range analysis , used to describe spatial correlation that varies with distance; S433: Grid node positions to be estimated , according to the distance between it and all sample points, construct the interpolation weight coefficient equation group and solve the interpolation coefficient ; S434: Based on the obtained weight coefficient , interpolate the fluorescence intensity values ​​of the grid nodes to be estimated to obtain the estimated value ; S435: uses the same interpolation logic and weight coefficient as S434, and calculates the luminous intensity value. Interpolate to get the estimated value of luminous intensity .

7. The method for analyzing abnormal plankton migration based on multi-sensor data fusion and diffusion modeling according to claim 1 is characterized in that: The S6 specifically includes: S61: Setting the threshold for determining abnormal biological migration ; S62: Traverse all grid nodes in the dynamic diffusion rate field and read the diffusion rate value at the corresponding position , if satisfied , then the corresponding node Mark as abnormal migration point, and record the geographical coordinates and rate value of the abnormal migration point; S63: Perform spatial cluster analysis on all abnormal migration points, use the distance-constrained density clustering method to identify continuous distribution areas, and define the cluster areas that meet the minimum number of points and maximum neighborhood radius conditions as biological abnormal migration areas.

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