Fishery sea area monitoring system based on satellite remote sensing

Abstract

The invention discloses a fishery sea area monitoring system based on satellite remote sensing, and belongs to the technical field of fishery monitoring, and the system specifically comprises a remote sensing data acquisition module which obtains and preprocesses sea surface temperature, chlorophyll concentration and sea surface height abnormal data; the buoy data acquisition module synchronously acquires temperature-salinity profile data of the Argo buoy array, and the layout density of the Argo buoy array is dynamically adjusted along with historical fish catch and ocean frontal surface intensity; the space-time alignment module divides a time window through an orbital period, corrects space offset in combination with a buoy drift trajectory, sets a time buffer area based on a georotation flow rate and a satellite image resolution, and generates a space-time unified three-dimensional marine environment data set; the fishery suitability calculation module constructs a biophysical coupling model, correlates surface chlorophyll and subsurface thermohaline structures, and calculates the suitability index of each water layer; the fishery division module intelligently defines a core area and an edge area according to the index three-dimensional distribution characteristics; according to the invention, accurate fishery identification based on multi-source data fusion is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of fishery monitoring, and particularly relates to a fishery sea area monitoring system based on satellite remote sensing. Background Art

[0002] With the continuous decline of global fishery resources, efficient and accurate fishery sea area monitoring technology has become a key means for marine resource management and sustainable development. Traditional fishery monitoring mainly relies on fishing vessel operation reports and local sea area surveys, which have problems such as limited coverage, poor timeliness, and high costs. Due to the advantages of large-scale and periodic observation, satellite remote sensing technology has gradually been applied to marine environment monitoring, and can provide key parameters such as sea surface temperature and chlorophyll concentration, providing data support for fishery prediction. However, satellite remote sensing can only obtain surface information and is difficult to reflect the changes in the subsurface marine environment, while the distribution of fishery resources is often closely related to the vertical structure of the water body.

[0003] Currently, the collaborative observation of satellite remote sensing and Argo floats has become an important development direction for marine environment monitoring. Existing technologies mainly adopt two methods: one is to superimpose the surface data inverted by satellites and the temperature and salinity profiles measured by Argo floats through a spatio-temporal matching algorithm to construct a two-dimensional marine environment field; the other is to establish an empirical relationship model between surface chlorophyll concentration and subsurface temperature and salinity using statistical regression methods. Some advanced solutions also introduce data assimilation technology, combining multi-source observation data with marine numerical models to improve the spatio-temporal resolution of environmental parameters. The international Argo program has achieved operational observation of approximately 4,000 floats globally, providing important vertical profile verification and supplementation for satellite data.

[0004] However, the fusion of satellite and Argo data mostly adopts static spatio-temporal matching, without fully considering the influence of ocean dynamic processes on data alignment, and the environment-fishery correlation model is mostly based on historical statistical relationships, making it difficult to adapt to the rapid changes in the marine environment, which restricts the accuracy and timeliness of fishery monitoring. Summary of the Invention

[0005] The purpose of the present invention is to provide a fishery sea area monitoring system based on satellite remote sensing to solve the following technical problems: The existing fusion of satellite and Argo data mostly adopts static spatio-temporal matching, without fully considering the influence of ocean dynamic processes on data alignment, and is mostly based on historical statistical relationships, making it difficult to adapt to the rapid changes in the marine environment, which restricts the accuracy and timeliness of fishery monitoring.

[0006] The purpose of the present invention can be achieved through the following technical solutions: A fishery sea area monitoring system based on satellite remote sensing, comprising: A remote sensing data acquisition module is used to obtain satellite remote sensing data of the target sea area and perform preprocessing. The satellite remote sensing data includes sea surface temperature distribution maps, chlorophyll concentration distribution maps, and sea surface height anomaly data; A buoy data acquisition module is used to synchronously obtain the temperature and salinity profile data uploaded by the Argo buoy array in the target sea area. The deployment density of the Argo buoy array is positively correlated with the historical catch distribution. When the ocean front intensity exceeds a set threshold, the deployment density increases to a specific multiple of the conventional density, and the specific multiple is 2 - 4 times; A spatio - temporal alignment module is used to fuse the satellite remote sensing data with the Argo buoy data. It divides time windows according to the satellite orbit period, corrects the spatial coordinate offset based on the drift trajectory of the Argo buoy, calculates the geostrophic velocity based on the satellite remote sensing data and the temperature and salinity profile data, sets a time buffer interval based on the geostrophic velocity and the satellite image resolution, and generates a spatio - temporally unified three - dimensional ocean environment dataset; A fishery suitability calculation module is used to construct a biophysical coupling model, and calculate the fishery suitability index at different water depths by correlating the vertical changes of the surface chlorophyll concentration and the subsurface temperature and salinity structure; A fishing ground division module is used to delimit the core area and the edge area of the fishing ground according to the distribution characteristics of the fishery suitability index in three - dimensional space and in combination with the geostrophic flow direction.

[0007] As a further solution of the present invention: The preprocessing of the satellite remote sensing data specifically includes: Select polar - orbiting satellites with the number of daily overpasses of the target sea area exceeding the overpass threshold, and realize multi - spectral data acquisition at different times during the fishing season through orbital parameter adjustment; For filling the cloud - covered areas in the sea surface temperature data, spatial interpolation of cloud - free data within a moving time window is adopted, and the interpolation weight is determined by the reciprocal of the product of the data time difference and the sea current movement speed; Extract the morphological features of the patch edges in the chlorophyll concentration map. When the patch edge curvature exceeds the preset morphological curvature threshold, mark the overlapping area with the temperature isotherm as the candidate fishing ground indication area; Screen the flow velocity and flow direction features according to the geostrophic flow field convergence intensity threshold, and perform a spatial intersection operation on the area meeting the convergence condition and the candidate fishing ground indication area to generate a primary fishing ground identification area.

[0008] As a further solution of the present invention: The deployment of the Argo buoy array specifically includes: In the sea area where the historical catch exceeds the catch threshold, deploy a triangular buoy array, and the side lengths of the triangle are dynamically adjusted according to the front intensity inversed in real time. For each unit increase in the front intensity change threshold, the side length is shortened by a fixed ratio; Set the chlorophyll concentration trigger threshold for each Argo float. When the continuous measurement value exceeds the trigger threshold, the shallow sampling interval is shortened to a fixed fraction of the regular interval until the concentration value drops below the threshold; Establish a satellite transit time prediction mechanism. Based on the received satellite orbit parameters, the Argo float completes measurements and uploads data before the transit time, and the time synchronization error is less than the communication delay threshold; Calculate the drift compensation amount through the relative position change of adjacent floats. When the distance change between floats exceeds the position tolerance threshold, use the least squares method to fit the drift trajectory and correct the coordinate data.

[0009] As a further solution of the present invention: The spatio-temporal alignment module specifically includes: Set the data synchronization window according to the satellite transit time. When the Argo float detects that the difference between the current time and the satellite transit time is less than the time synchronization threshold, increase the data upload frequency to a fixed multiple of the regular frequency; Construct a dynamically adjusted grid cell. The initial size of the grid matches the satellite image resolution. When the cloud coverage rate in the satellite image exceeds the cloud coverage threshold, expand the grid range until it includes the measurement points of at least three adjacent Argo floats; Reconstruct the Argo float data in each grid cell according to the standard depth layer, convert the discrete depth data into continuous profile data at equal intervals, and fill in the missing depth data by vertical gradient interpolation of adjacent floats; In the time dimension, set a time buffer interval for the satellite instantaneous observation data. The interval length is determined according to the ratio of the geostrophic velocity to the spatial grid size, and the Argo data within the buffer interval is weighted and fused according to the time proximity.

[0010] As a further solution of the present invention: The construction of the biophysical coupling model specifically includes: Identify the thermocline interface in the subsurface temperature profile. When the temperature gradient value of a certain depth layer exceeds the gradient threshold, define the depth of this depth layer as the thermocline interface; Project the area in the surface chlorophyll concentration map that exceeds the concentration threshold vertically onto the thermocline interface to generate a three-dimensional projection overlap area; For each vertical water column in the overlap area, divide it according to the standard depth layer and calculate the temperature fishing suitability coefficient of each layer. The coefficient is determined by the matching degree between the optimal temperature range of the target fish species and the measured temperature; Multiply the chlorophyll concentration of each layer of each vertical water column by the temperature fishing suitability coefficient and then vertically accumulate. When the accumulated value exceeds the comprehensive index threshold, mark the projection position of the corresponding vertical water column on the horizontal plane as the core area of the fishing ground.

[0011] As a further solution of the present invention: The calculation of the temperature fishing suitability coefficient specifically includes: Set the temperature adaptation range according to the optimal temperature range of the target fish species. When the temperature at a certain depth layer is within this range, the coefficient is set to the maximum value; For temperature data outside the adaptation range, calculate the coefficient attenuation amount according to the temperature difference value from the range boundary. For each unit increase in the temperature difference threshold, the coefficient linearly decreases by a fixed ratio; In the vertical water column, the coefficient weight of each layer is determined by the light attenuation rate of that layer, and the attenuation rate is jointly calculated based on the transparency data retrieved by satellites and the extinction coefficient measured by Argo; The final fishing suitability coefficient is the product of the temperature matching degree and the light weight, and the product value is normalized to a value between 0 and 1.

[0012] As a further solution of the present invention: The delineation of the core area and the edge area of the fishing ground specifically includes: Extract the closed area with an area exceeding the patch area threshold in the surface chlorophyll concentration map as the candidate core area; Perform downstream extension search on each candidate core area along the geostrophic flow direction. When it is detected that the uplift amount of the thermocline interface depth compared to the upstream exceeds the depth uplift threshold and the temperature gradient increase exceeds the gradient increase threshold, it is marked as a valid candidate area; Retain the area where the overlapping area ratio of the candidate core area and the valid candidate area exceeds the overlapping ratio threshold; calculate the spatial variation coefficient of the vertical integral suitability index for the retained area. When the variation coefficient is lower than the homogeneity threshold, it is determined as a single core area, otherwise it is split into sub-core areas according to the index peak; The final core area boundary is determined by the outer envelope of the homogeneous area, and the edge area extends to the area where the index value drops to the edge threshold.

[0013] As a further solution of the present invention: The specific content of the extension search includes: Set the initial search step according to the geostrophic flow velocity, and the product of the step value and the flow velocity is equal to the single-day maximum migration distance threshold; Collect thermocline interface data in real time on the search path. When the data density of the buoy at a certain position is lower than the data density threshold, call the environmental data at the same position in the historical period to complete it; When the interface depth change rate exceeds the depth change rate threshold, expand the search in the direction of interface uplift with this position as the center, and the search density is increased to a fixed multiple of the conventional density; If the distance between the search path and the boundary of the already delineated core area is less than the boundary tolerance threshold, compare the index differences between the two regions. Only when the difference exceeds the competition threshold is boundary overlap allowed; terminate the search when the continuous search times reach the maximum search times threshold or the cumulative search distance exceeds the maximum extension distance threshold.

[0014] As a further solution of the present invention: It also includes a fishing ground dynamic update module, specifically including: When the difference in sea surface temperature between the newly acquired satellite data and the historical data set exceeds the temperature difference threshold, or the difference in chlorophyll concentration exceeds the concentration difference threshold, the fishery area redrawing process is triggered; Start the enhanced observation mode of Argo floats within the changing area, and increase the sampling frequency of the floats to a fixed multiple of the regular frequency until the fluctuation range of environmental parameters is lower than the stability threshold; Re - execute the spatio - temporal alignment and model calculation based on the enhanced observation data to generate the updated fishery boundary; Mark the difference area between the new and old boundaries as the fishery migration area, and determine the migration direction based on the synthetic vector of the geostrophic flow field and the gradient of the suitability index; Incorporate the updated fishery boundary into the historical data set as the comparison benchmark for the next dynamic update.

[0015] Advantages of the present invention: The fishery sea area monitoring method based on satellite remote sensing and Argo floats of the present invention effectively solves key problems such as rough data fusion, inefficient buoy deployment, and insufficient model adaptability in the prior art by innovatively establishing a dynamic spatio - temporal alignment mechanism and a biophysical coupling model. In the spatio - temporal alignment process, calculate the geostrophic velocity based on the sea surface height anomaly data from satellite remote sensing and the temperature - salinity profile data of Argo floats, dynamically determine the length of the time buffer interval according to the ratio of the geostrophic velocity to the spatial grid size, and weighted - fuse the Argo data within the buffer interval according to the time proximity. At the same time, drive the high - frequency sampling of the floats through the satellite transit time, dynamically adjust the grid cells, and correct the coordinates in combination with the drift trajectory of the floats, solving the limitations of static matching, fully considering the influence of ocean dynamic processes such as geostrophic flow on data alignment, and improving the spatio - temporal fusion accuracy; In the calculation of fishery suitability, construct a biophysical coupling model, associate the vertical changes in surface chlorophyll concentration and subsurface temperature - salinity structure, break through the constraints of traditional statistical regression models by identifying the thermocline interface, calculating the temperature - suitable fishing coefficient, and weighting in combination with the light attenuation rate, and realize the real - time dynamic assessment of the fish habitat environment; In the fishery area delineation stage, conduct downstream extension search along the geostrophic flow direction, set the initial step size according to the geostrophic velocity, screen effective candidate areas in combination with the depth change of the thermocline interface and the increase in temperature gradient, and at the same time analyze the regional homogeneity through the spatial coefficient of variation, making the division of the core area and the edge area of the fishery closely combined with ocean dynamic characteristics such as the geostrophic flow direction, effectively adapting to the rapid changes in the ocean environment; In addition, through the dynamic association of the deployment density of the Argo float array with historical catch and front intensity, establishing a satellite transit time prediction mechanism, setting up a fishery dynamic update module, etc., realizing the full - process dynamicization and precision from data collection, fusion to fishery area division, significantly improving the accuracy of fishery monitoring and the response timeliness to environmental changes, and providing scientific support for the efficient management and sustainable utilization of fishery resources. Brief Description of the Drawings

[0016] The present invention will be further described below in conjunction with the accompanying drawings.

[0017] Figure 1 is a schematic diagram of the modules of the present invention; Figure 2 is a schematic flowchart of the space-time alignment module of the present invention; Figure 3 is a schematic flowchart of the fishing ground delineation module of the present invention. Specific Embodiments

[0018] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0019] Please refer to Figures 1 - 3 as shown, the present invention is a fishery sea area monitoring system based on satellite remote sensing, including: Remote sensing data acquisition module: This module is responsible for acquiring satellite remote sensing data of the target sea area, which includes sea surface temperature distribution maps, chlorophyll concentration distribution maps, and sea surface height anomaly data. Sea surface temperature can reflect the thermal condition of the ocean and affect the survival, reproduction, and migration of fish. For example, some tropical fish are suitable to inhabit in specific high-temperature sea areas. The chlorophyll concentration distribution map can indicate the abundance of phytoplankton. As the basis of the marine food chain, the distribution of phytoplankton is closely related to the food source of fish, and areas with high concentration often attract more fish to gather. Sea surface height anomaly data helps to understand ocean circulation and water mass movement, providing a basis for judging the stability of fish habitats. After acquiring the data, the module also preprocesses it, including selecting a suitable satellite, such as a polar orbiting satellite that covers the target sea area more than the coverage frequency threshold per day, and adjusting the orbital parameters during the fishing season to collect multi-spectral data at different times to obtain more comprehensive information. At the same time, a specific interpolation method is used to fill the cloud-covered areas in the sea surface temperature data, and morphological feature extraction is performed on the chlorophyll concentration map, etc., to mine potential fishing ground information for subsequent analysis.

[0020] Buoy data acquisition module: It mainly synchronously acquires the temperature and salinity profile data uploaded by the Argo buoy array in the target sea area. The temperature and salinity structure affects the density stratification of seawater, which in turn affects the vertical distribution of fish. For example, near the thermocline, there are often a large number of fish gathered due to suitable temperature and abundant nutrients. The layout of the Argo buoy array in this module is very sophisticated. Its density is positively correlated with the historical catch distribution. In areas with high historical catch, the fish resources are rich. Increasing the buoy layout density can more accurately obtain the marine environmental data in this area. When the intensity of the ocean front exceeds the set threshold, due to the rich nutrients brought by the seawater convergence at the front, a large number of fish are attracted. At this time, the layout density is increased to a specific multiple of the normal density, so as to more accurately monitor this key area. If the normal density is 1 buoy per 100 square kilometers and the spacing is about 10 kilometers, when the front intensity triggers encryption, the multiple can be set to 2 - 4 times, that is, the spacing is 5 - 2.5 kilometers and the density is 2 - 4 buoys per 100 square kilometers.

[0021] Spatio-temporal alignment module: It undertakes the important task of fusing satellite remote sensing data and Argo buoy data. First, divide the time window according to the satellite orbit period to ensure data processing on a unified time scale. Then, correct the spatial coordinate offset based on the drift trajectory of the Argo buoy. Since the buoy drifts in the ocean affected by factors such as ocean currents, by monitoring its drift trajectory and using methods such as least squares fitting to determine the deviation between its actual position and the initial set position, and then correct the coordinates, so that the buoy data and satellite remote sensing data are accurately corresponding in space. Finally, a spatio-temporally unified three-dimensional marine environmental data set is generated, providing a comprehensive and spatio-temporally consistent data basis for subsequent fishery suitability calculations, etc., enabling users to clearly understand various environmental parameters at different positions in the ocean at a certain moment.

[0022] Fishery suitability calculation module: Build a biophysical coupling model to calculate the fishery suitability index at different water depths. By correlating the vertical changes in the surface chlorophyll concentration and the subsurface temperature and salinity structure, comprehensively consider various factors affecting the survival and reproduction of fish. For example, identify the thermocline interface in the subsurface temperature profile, project the area where the surface chlorophyll concentration exceeds the threshold vertically onto the thermocline interface to generate a three-dimensional projection overlap area. Divide each vertical water column in the overlap area according to the standard depth layer and calculate the temperature fishing suitability coefficient for each layer. This coefficient is determined by the matching degree between the optimal temperature range of the target fish species and the measured temperature. Then multiply the chlorophyll concentration of each layer by the temperature fishing suitability coefficient and accumulate vertically. When the accumulated value exceeds the comprehensive index threshold, mark this horizontal position as the core area of the fishing ground. In this way, comprehensively and scientifically evaluate the suitability of different water layers for fishery.

[0023] Fishery area division module: The core area and marginal area of the fishing ground are delimited according to the distribution characteristics of the fishery suitability index in three-dimensional space. In the surface chlorophyll concentration map, closed areas with an area exceeding the patch area threshold are extracted as candidate core areas. Downstream extension search is carried out on the candidate core areas along the direction of geostrophic flow. When it is detected that the uplift amount of the thermocline interface depth is more than the depth uplift threshold and the temperature gradient increase exceeds the gradient increase threshold compared with the upstream, it is marked as a valid candidate area. Areas where the overlapping area ratio of the candidate core area and the valid candidate area exceeds the overlapping ratio threshold are retained. The spatial variation coefficient of the vertical integral suitability index is calculated for the retained areas. When the variation coefficient is lower than the homogeneity threshold, it is determined as a single core area; otherwise, it is split into sub-core areas according to the index peak. Finally, the boundary of the core area is determined by the outer envelope line of the homogeneous area, and the marginal area extends to the area where the index value drops to the marginal threshold. Through such division, different areas of the fishing ground are clarified, providing a scientific basis for the rational development and utilization of fishery resources.

[0024] In a preferred embodiment of the present invention, the preprocessing of the satellite remote sensing data specifically includes: The system first screens the optimal constellation combination of polar-orbiting satellites with the best coverage ability through an orbit prediction algorithm, comprehensively considering factors such as satellite orbit parameters, sensor characteristics, and weather conditions, to ensure that at least three or more effective observation data are obtained daily during the critical period of the fishing season, and the observations at different times can cover the morning and evening change characteristics of the target sea area. For the common problem of cloud occlusion, the system innovatively develops a four-dimensional spatio-temporal filling algorithm based on the characteristics of ocean current movement. This algorithm not only considers cloud-free data with similar times but also combines real-time ocean current field information to construct a three-dimensional spatial correlation model. By solving the optimal interpolation weight matrix, it ensures that the filled temperature field not only maintains physical consistency but also conforms to statistical laws. In the chlorophyll data processing link, the system uses a multi-scale image morphological analysis method. By calculating the curvature spectrum characteristics and texture characteristics of the patch edges, it automatically identifies the chlorophyll aggregation areas with ecological significance and conducts multi-level spatial correlation analysis with the position of the high-precision temperature front, significantly improving the accuracy and reliability of the primary fishing ground identification. In addition, the system also integrates an intelligent quality control module to automatically detect and correct abnormal observation values to ensure that the data quality meets the requirements of subsequent analysis.

[0025] In another preferred embodiment of the present invention, the layout of the Argo buoy array specifically includes: Based on years of fishery statistical data and marine environment databases, the system uses machine learning algorithms to intelligently divide key monitoring areas and deploy a triangular topological buoy array with optimal spatial coverage in these areas. The geometric parameters of the array are designed with full consideration of marine dynamic characteristics. Its side length setting establishes a dynamic correlation model with the real-time marine front intensity to ensure higher spatio-temporal resolution profile observations in areas with dense fishery resources and active marine dynamics. Each buoy is equipped with an advanced intelligent trigger sampling system, which integrates multi-parameter environmental sensors. When detecting an abnormal increase in chlorophyll concentration or a drastic change in temperature, it can automatically enter the intensive observation mode. This intelligent sampling strategy driven by environmental events significantly improves the ability to capture and the response speed to sudden changes in the fishery environment. At the same time, the system constructs a complete spatio-temporal quality control system through a precise satellite-ground time synchronization mechanism and a drift trajectory correction algorithm based on Kalman filtering, ensuring a high degree of consistency between buoy data and satellite observations, laying a solid foundation for subsequent multi-source data fusion. The buoy array also has the function of self-organizing network and can automatically optimize the observation strategy according to environmental changes.

[0026] In another preferred embodiment of the present invention, the spatio-temporal alignment module specifically includes: The spatio-temporal alignment module is a key link in realizing multi-source data fusion in a fishery sea area monitoring system based on satellite remote sensing. Its core is to eliminate the spatio-temporal differences between satellite remote sensing data and Argo buoy data through collaborative processing in the time and space dimensions and generate a unified three-dimensional marine environment dataset. In terms of time synchronization, the system first obtains the precise transit time through satellite orbit prediction and sets a time synchronization window centered on this. When the Argo buoy detects that the current time is close to the satellite transit time (such as the difference is less than the set threshold), it automatically increases the data upload frequency to ensure obtaining high-density buoy data before and after satellite observations, so as to capture the marine environmental state closest to the satellite instantaneous observation moment. For example, if the satellite transits at 10:00 am, the buoy will encrypt the sampling during the period from 9:30 to 10:30 and increase the data upload frequency from the regular once per hour to once every 15 minutes to more accurately match the time point of satellite observation.

[0027] The initial grid size matches the satellite image resolution. The satellite image resolution determines the smallest ground details that can be distinguished. Matching the initial grid size with it can ensure that each grid cell corresponds to a relatively uniform area on the satellite image, so that the information provided by the satellite image can be better utilized during data processing and analysis. For example, if the satellite image resolution is 100 meters × 100 meters, then the initial grid size can also be set to 100 meters × 100 meters. In this way, each grid cell has a corresponding clear area on the satellite image, which is convenient for subsequent data processing and analysis. When the cloud coverage rate in the satellite image exceeds the cloud coverage threshold, since the clouds will block the ocean surface information, resulting in inaccurate data acquisition in some areas. In order to obtain sufficient ocean environment information, at this time, it is necessary to expand the grid range until it includes at least three measurement points of adjacent Argo floats. The purpose of doing this is to include more unobstructed areas by the clouds into the grid cells by expanding the grid range, and use the data of the Argo float measurement points in these areas to supplement and improve the information of the grid cell, so as to make up for the missing data caused by cloud occlusion to a certain extent. For example, if the initial grid size is 100 meters × 100 meters, after the cloud coverage rate exceeds the threshold, the grid range may be expanded to 300 meters × 300 meters to ensure that at least three measurement points of adjacent Argo floats can be included, so as to obtain more comprehensive ocean environment data.

[0028] At the same time, for the discrete depth data collected by Argo floats, the system reconstructs them according to the preset standard depth layers (such as 0 meters, 10 meters, 20 meters, etc.), and interpolates the missing depth layer information through the vertical gradient of adjacent float data to ensure the continuity and standardization of the temperature and salinity data in the vertical direction. For example, if a certain float does not measure the temperature at a depth of 10 meters, the system will calculate the temperature value at a depth of 10 meters through linear interpolation based on the temperature data at depths of 5 meters and 20 meters, and verify whether it conforms to the oceanographic gradient law.

[0029] In the spatio-temporal joint fusion stage, the module determines the length of the time buffer interval according to the ratio of the geostrophic velocity to the spatial grid size. The satellite remote sensing data is obtained at a certain moment, while the Argo float data is continuously collected over a period of time. Since the ocean environment is constantly changing, if only the time of the satellite observation moment is used to match the Argo float data, it may not be able to comprehensively and accurately reflect the ocean conditions at that moment. Therefore, setting a time buffer interval can include the Argo float data within a period of time before and after the satellite observation moment, so as to more comprehensively and accurately reflect the ocean environment information at that time and improve the accuracy of data fusion.

[0030] The length of the buffer interval is determined by the ratio of the geostrophic velocity to the spatial grid size. The geostrophic velocity represents the flow velocity of seawater under the action of the Coriolis force, and the spatial grid size is the size of each grid when dividing the monitoring sea area into grids. When the geostrophic velocity is fast and the spatial grid size is large, seawater will flow over a large area in a short time, and the marine environment will change more rapidly. In this case, to ensure that representative marine environment data can be obtained, the calculated time buffer interval will be relatively long, for example, assumed to be 3 hours. Conversely, if the geostrophic velocity is slow and the spatial grid size is small, the marine environment is relatively stable, and the buffer interval length is shorter.

[0031] Within the set 3-hour time buffer interval, the data collected by the Argo float are weighted and fused according to the time proximity. The data closer to the satellite observation time are considered to be more similar to the marine environment state at the time of satellite observation, and the value for reflecting the real situation at that time is higher, so the assigned weight is also higher. For example, assume that the satellite observes at 10 o'clock. The weight of the data collected by the Argo float at 9:50 may be set to 0.8, the weight of the data collected at 9:30 may be set to 0.5, and the weight of the data collected at 9:00 may be set to 0.2. Through this weighting method, the Argo data collected at different times within the buffer interval are fused and calculated, which can more reasonably align these data with the satellite remote sensing data and effectively improve the quality of data fusion.

[0032] In another preferred embodiment of the present invention, the construction of the biophysical coupling model specifically includes: The system first refines the subsurface temperature profile, calculates the temperature gradient change rate of each depth layer through the sliding window algorithm. When it detects that the temperature gradient value of a certain depth layer exceeds the preset gradient threshold, usually set to 0.05 °C / m, the system automatically marks the depth of this depth layer as the position of the thermocline interface and records the characteristics of its upper and lower boundaries. At the same time, the system intelligently analyzes the surface chlorophyll concentration distribution map, and uses the adaptive threshold algorithm to identify the chlorophyll high-value areas that are significantly higher than the background value. These areas usually indicate the vigorous growth of phytoplankton. By establishing a three-dimensional spatial mapping relationship, the system projects these surface high-value areas vertically onto the identified thermocline interface to form a three-dimensional projection overlapping area with ecological significance, which reflects the complete biophysical coupling characteristics from the surface to the bottom layer.

[0033] During the analysis of the overlapping area, the system adopts a standardized deep stratification scheme, usually divided into layers such as 0 - 50m, 50 - 100m, 100 - 150m, etc., and conducts layer-by-layer analysis on each vertical water column. For each depth layer, the system first calculates the temperature fishing suitability coefficient, which is determined by comparing the measured temperature with the optimal temperature range of the target fish species. Specifically, the system has a built-in database of temperature preferences for various economic fish species, and sets differentiated optimal temperature intervals according to different life cycle stages of different fish species (such as spawning period, feeding period, etc.). When the measured temperature at a certain depth layer completely falls within the optimal interval, the highest fishing suitability coefficient (1.0) is assigned to this layer; when the temperature deviates from the optimal interval, the coefficient decreases linearly according to the degree of deviation, and the decreasing rate takes into account the tolerance of the fish species to temperature changes.

[0034] In a preferred case of this embodiment, the calculation of the temperature fishing suitability coefficient specifically includes: The system first establishes a temperature adaptability curve based on the physiological characteristics of the target fish species, which takes into account the sensitivity differences of the fish species to temperature at different life stages. For the measured data outside the optimal temperature interval, the system uses a piecewise function for processing: within the tolerable interval, the coefficient decreases linearly with the increase of the temperature difference; when the temperature exceeds the tolerance range, the coefficient rapidly decays to a value close to zero. This processing method not only conforms to the laws of fish physiology but also can accurately reflect the impact of environmental stress on fishery resources.

[0035] In the comprehensive assessment of the vertical water column, the system innovatively introduces the light attenuation factor as a weight coefficient. By fusing the transparency data retrieved by satellites and the extinction coefficient measured by Argo floats, the system can accurately calculate the photosynthetically active radiation (PAR) received at each depth layer. This calculation takes into account multiple factors such as the optical properties of the water body, the solar altitude angle, and cloud cover to ensure the accuracy of the light attenuation model. Finally, the system multiplies the temperature fishing suitability coefficient of each layer by the light weight to obtain a comprehensive score reflecting the fishery potential of this layer, and then obtains the fishery suitability index of the entire water column through vertical integration. This index not only considers the traditional temperature factor but also integrates the impact of light conditions on the distribution of plankton, and can more comprehensively evaluate the potential for the formation of fishing grounds.

[0036] The system automatically identifies the sea surface horizontal area corresponding to the vertical water column with an index exceeding the threshold as the core fishing ground area by setting a dynamic comprehensive index threshold (this threshold can be adjusted according to the target fish species and seasonal characteristics). These core areas usually have the following characteristics: significant surface chlorophyll concentration, appropriate thermocline depth, stable water temperature and salinity structure, and good light conditions. The system also conducts spatial clustering analysis on these core areas, eliminates isolated small patches, and retains high-quality fishing ground areas with ecological continuity. In addition, the system can establish a fishing ground evolution model based on historical data to predict the possible movement trends of the core areas, providing forward-looking guidance for fishery production.

[0037] In another preferred embodiment of the present invention, the delineation of the core area and the marginal area of the fishing ground specifically includes: The delineation of the core area and the marginal area of the fishing ground is a refined spatial division process based on the fishery suitability index generated after the fusion of satellite remote sensing data and Argo buoy data, combined with ocean dynamic characteristics. First, in the surface chlorophyll concentration map, the system will automatically extract closed areas with an area exceeding a preset patch area threshold (such as 10 square kilometers) as candidate core areas. This is because high chlorophyll concentration areas usually correspond to waters rich in plankton, which is the basis of the fish food chain and has the potential conditions to form a fishing ground. However, the effectiveness of the candidate core areas needs to be further verified in combination with the dynamic characteristics of the marine environment. Therefore, the system will conduct a downstream extension search for each candidate core area along the geostrophic flow direction - the geostrophic flow direction reflects the overall movement trend of seawater, and fish usually migrate with the flow, and the downstream area may be the main direction of fish dispersion in the candidate core areas. During the extension search process, the system monitors the change in the depth of the thermocline interface in real time. When it is detected that the uplift amount of the thermocline interface depth at a certain position exceeds the depth uplift threshold (such as 5 meters) compared with the upstream and the temperature gradient increase exceeds the gradient increase threshold (such as 0.5 °C / m), it indicates that a more suitable thermohaline structure for fish habitat may be formed in this area due to water convergence. At this time, it is marked as an effective candidate area. This is because the thermocline is a water layer with a significant vertical temperature gradient in the ocean, and the change in its position and intensity directly affects the vertical distribution of fish. The uplifted thermocline interface is usually related to upwelling or frontal activities, which will bring rich nutrients and dissolved oxygen.

[0038] Next, the system will spatially overlay the candidate core areas and the effective candidate areas, and retain the areas where the overlapping area ratio exceeds the overlapping ratio threshold (such as 60%) to ensure that the delineated core areas have both high productivity (supported by chlorophyll concentration) and meet the environmental conditions for fish habitat (suitable thermocline structure). For the retained areas, the system further calculates the spatial coefficient of variation of the vertically integrated suitability index, which reflects the environmental homogeneity within the area: when the coefficient of variation is lower than the homogeneity threshold, it indicates that the distribution of the suitability index in the vertical and horizontal directions in this area is uniform, and it can be determined as a single core area; if the coefficient of variation is relatively high, it indicates that there are multiple suitability peaks within the area, and it needs to be split into sub-core areas according to the index peaks. Finally, the core area boundary is determined by the outer envelope of the homogeneous area, and the marginal area extends outward from the core area boundary until the fishery suitability index drops to the marginal threshold (such as 50% of the core area index). This area is the transition zone of the core area, and the fish distribution density decreases as the index decreases.

[0039] In a preferred case of this embodiment, the specific content of the extension search includes: Extended search, as a key step in delineating the core area, its specific implementation logic closely depends on geostrophic flow velocity and ocean environment data. First, the system sets an initial search step size based on the geostrophic flow velocity. The product of the step size value and the flow velocity equals the threshold of the maximum migration distance per day (for example, if the maximum migration distance per day is set at 20 kilometers and the geostrophic flow velocity is 1 kilometer per hour, then the step size is 20 kilometers). This design ensures that the search range covers the maximum distance that fish may migrate with the water flow in a day, avoiding omission of potential fishing grounds. Along the search path, if the buoy data density at a certain location is lower than the data density threshold (such as less than 1 buoy observation point per 50 square kilometers), the system will call the environmental data at this location during the same period in history (such as the average temperature and salinity in the same month of the past 3 years) for supplementation to address data loss problems caused by remote sea areas or buoy drift. When it is monitored that the change rate of the thermocline interface depth exceeds the depth change rate threshold (such as 10 meters per 10 kilometers), it indicates that there may be strong ocean frontal or upwelling activities in this area. At this time, the system expands the search range centered on this location in the direction of interface uplift (usually the direction of water flow convergence) and increases the search density to 2 times the normal density to capture the impact of subtle environmental changes on fish distribution. In addition, if the search path is close to the boundary of the already delineated core area and the distance is less than the boundary tolerance threshold (such as 5 kilometers), the system will compare the difference in suitability indices between the two areas. Only when the difference exceeds the competition threshold (such as 20% of the core area index) is boundary overlap allowed to avoid repeated delineation due to unclear environmental gradients. When the number of consecutive searches reaches the maximum search number threshold (such as 5 times) or the cumulative search distance exceeds the maximum extension distance threshold (such as 100 kilometers), the search process terminates to ensure efficiency and avoid meaningless range expansion.

[0040] In another preferred embodiment of the present invention, it further includes a fishing ground dynamic update module, specifically including: The system continuously monitors the changes in environmental parameters. When the change amplitude of sea surface temperature or chlorophyll concentration exceeds 2 standard deviations of the historical fluctuation range, the fishing ground re-evaluation process is automatically triggered. Within the changing area, the system increases the sampling frequency of Argo buoys from the normal once every 10 days to once a day through remote commands. This enhanced observation mode continues until the environmental parameters return to a stable state, such as the change amplitude being <10% for 3 consecutive days. During the update process, the system preferentially uses the newly acquired high spatio-temporal resolution data, re-runs the spatio-temporal alignment and biophysical coupling models, and generates an updated fishing ground distribution map.

[0041] The system is specially designed with a fishery migration analysis function. By comparing the change characteristics of the old and new boundaries, it automatically identifies the migration areas and directions. The determination of the migration direction comprehensively considers the dynamic transport effect of the geostrophic flow field and the spatial gradient characteristics of the suitability index, and uses the vector synthesis method to calculate the most likely migration path. All the updated results will be entered into the system knowledge base for optimizing subsequent prediction models. In addition, the system has established a version control mechanism to retain the fishery distribution data of each historical period, supporting the analysis of spatio-temporal change trends and fishery management decisions. This dynamic update system ensures the timeliness and accuracy of fishery information, providing reliable navigation services for fishery production.

[0042] The above has described in detail an embodiment of the present invention, but the content described is only a preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. All equivalent changes and improvements made in accordance with the scope of the application of the present invention should still fall within the scope covered by the patent of the present invention.

Claims

1. A fishery sea area monitoring system based on satellite remote sensing, characterized in that, Including: A remote sensing data acquisition module, which is used to obtain satellite remote sensing data of the target sea area and perform preprocessing. The satellite remote sensing data includes sea surface temperature distribution maps, chlorophyll concentration distribution maps, and sea surface height anomaly data; A buoy data acquisition module, which is used to synchronously obtain the temperature and salinity profile data uploaded by the Argo buoy array in the target sea area. The layout density of the Argo buoy array is positively correlated with the historical catch distribution. When the intensity of the ocean front exceeds the set threshold, the layout density increases to a specific multiple of the conventional density, and the specific multiple is 2-4 times; A spatio-temporal alignment module, which is used to fuse the satellite remote sensing data and the Argo buoy data, divide the time window according to the satellite orbit period, correct the spatial coordinate offset based on the drift trajectory of the Argo buoy, calculate the geostrophic velocity based on the satellite remote sensing data and the temperature and salinity profile data, set the time buffer interval based on the geostrophic velocity and the satellite image resolution, and generate a three-dimensional ocean environment dataset with unified spatio-temporal coordinates; A fishery suitability calculation module, which is used to construct a biophysical coupling model and calculate the fishery suitability index at different water depths by correlating the vertical changes of the surface chlorophyll concentration and the subsurface temperature and salinity structure; A fishing ground division module, which is used to delimit the core area and the edge area of the fishing ground according to the distribution characteristics of the fishery suitability index in three-dimensional space and in combination with the geostrophic flow direction; The construction of the biophysical coupling model specifically includes: Identifying the thermocline interface in the subsurface temperature profile. When the temperature gradient value of a certain depth layer exceeds the gradient threshold, the depth of this depth layer is defined as the thermocline interface; Projecting the area in the surface chlorophyll concentration map that exceeds the concentration threshold vertically onto the thermocline interface to generate a three-dimensional projection overlapping area; Dividing each vertical water column in the overlapping area according to the standard depth layer and calculating the temperature fishing suitability coefficient of each layer. The coefficient is determined by the matching degree between the optimal temperature range of the target fish species and the measured temperature; Multiplying the chlorophyll concentration of each layer of each vertical water column by the temperature fishing suitability coefficient and then vertically accumulating. When the accumulated value exceeds the comprehensive index threshold, mark the projection position of the corresponding vertical water column on the horizontal plane as the core area of the fishing ground.

2. The fishery sea area monitoring system based on satellite remote sensing according to claim 1, characterized in that, The preprocessing of the satellite remote sensing data specifically includes: Selecting a polar-orbiting satellite whose daily coverage times of the target sea area exceed the coverage times threshold, and realizing multi-spectral data acquisition at different times through orbit parameter adjustment during the fishing season; Filling the cloud-covered area in the sea surface temperature data, using spatial interpolation of cloud-free data within a moving time window, and the interpolation weight is determined by the reciprocal of the product of the data time difference and the sea current movement speed; Extracting the morphological characteristics of the patch edge in the chlorophyll concentration map. When the patch edge curvature exceeds the preset morphological curvature threshold, mark the overlapping area with the temperature isotherm as the candidate fishing ground indication area; Screening the flow velocity and flow direction characteristics according to the geostrophic flow field convergence intensity threshold, and performing a spatial intersection operation on the area that meets the convergence condition and the candidate fishing ground indication area to generate a primary fishing ground identification area.

3. The fishery sea area monitoring system based on satellite remote sensing according to claim 1, characterized in that, The layout of the Argo buoy array specifically includes: In the sea areas where the historical catch exceeds the catch threshold, a triangular buoy array is deployed. The side lengths of the triangle are dynamically adjusted according to the front intensity inversed in real time. For each unit increase in the front intensity by the intensity change threshold, the side length is shortened by a fixed ratio. A chlorophyll concentration trigger threshold is set for each Argo buoy. When the continuous measurement value exceeds the trigger threshold, the shallow layer sampling interval is shortened to a fixed fraction of the regular interval until the concentration value drops below the threshold. A satellite transit time prediction mechanism is established. Based on the received satellite orbit parameters, the Argo buoy completes the measurement and uploads the data before the transit time, and the time synchronization error is less than the communication delay threshold. The drift compensation amount is calculated through the relative position change of adjacent buoys. When the distance change between buoys exceeds the position tolerance threshold, the least squares method is used to fit the drift trajectory and correct the coordinate data.

4. A fishery sea area monitoring system based on satellite remote sensing according to claim 1, characterized in that, The spatio-temporal alignment module specifically includes: Set a data synchronization window according to the satellite transit time. When the Argo buoy detects that the difference between the current time and the satellite transit time is less than the time synchronization threshold, the data upload frequency is increased to a fixed multiple of the regular frequency. Construct a dynamically adjusted grid cell. The initial size of the grid matches the satellite image resolution. When the cloud coverage rate in the satellite image exceeds the cloud coverage threshold, the grid range is expanded until it includes the measurement points of at least three adjacent Argo buoys. The Argo buoy data within each grid cell is reconstructed according to the standard depth layer, converting the discrete depth data into continuous profile data with equally spaced depths. The missing depth data is filled by interpolation using the vertical gradient of adjacent buoys. In the time dimension, set a time buffer interval for the satellite instantaneous observation data. The interval length is determined according to the ratio of the geostrophic flow velocity to the spatial grid size. The Argo data within the buffer interval is weighted and fused according to the time proximity.

5. A fishery sea area monitoring system based on satellite remote sensing according to claim 1, characterized in that, The calculation of the temperature fishing suitability coefficient specifically includes: Set a temperature adaptation interval according to the optimal temperature range of the target fish species. When the temperature at a certain depth layer is within this interval, the coefficient is set to the maximum value. For the temperature data outside the adaptation interval, calculate the coefficient attenuation amount according to the temperature difference from the interval boundary. For each unit increase in the temperature difference by the temperature difference threshold, the coefficient linearly decreases by a fixed ratio. In the vertical water column, the coefficient weights of each layer are determined by the light attenuation rate of that layer. The attenuation rate is jointly calculated based on the transparency data inversed by the satellite and the extinction coefficient measured by Argo. The final fishing suitability coefficient is the product of the temperature matching degree and the light weight, and the product value is normalized to a value between 0 and 1.

6. The fishery sea area monitoring system based on satellite remote sensing according to claim 1, characterized in that The delineation of the core area and the edge area of the fishing ground specifically includes: Extract the closed areas with an area exceeding the patch area threshold in the surface chlorophyll concentration map as candidate core areas. Conduct a downstream extension search for each candidate core area along the geostrophic flow direction. When it is detected that the uplift amount of the thermocline interface depth is more than the depth uplift threshold and the temperature gradient increase amplitude exceeds the gradient increase amplitude threshold compared with the upstream, it is marked as a valid candidate area. Retain the areas where the overlapping area ratio of the candidate core area and the valid candidate area exceeds the overlapping ratio threshold. Calculate the spatial variation coefficient of the vertical integral suitability index for the retained areas. When the variation coefficient is lower than the homogeneity threshold, it is determined as a single core area; otherwise, it is split into sub-core areas according to the index peak. The boundary of the final core area is determined by the outer envelope of the homogeneous area, and the edge area extends to the area where the index value drops to the edge threshold.

7. The fishery sea area monitoring system based on satellite remote sensing according to claim 6, characterized in that, The specific content of the extended search includes: Set the initial search step according to the geostrophic velocity, and the product of the step value and the velocity is equal to the single-day maximum migration distance threshold; Collect the thermocline interface data in real time on the search path. When the data density of the buoy at a certain position is lower than the data density threshold, call the environmental data at the same position in the historical period to complete it; When the interface depth change rate exceeds the depth change rate threshold, expand the search in the direction of interface uplift with this position as the center, and the search density is increased to a fixed multiple of the normal density; If the distance between the search path and the boundary of the already demarcated core area is less than the boundary tolerance threshold, compare the index difference between the two areas. Only when the difference exceeds the competition threshold is boundary overlap allowed; when the continuous search times reach the maximum search times threshold or the cumulative search distance exceeds the maximum extension distance threshold, the search is terminated.

8. The fishery sea area monitoring system based on satellite remote sensing according to claim 1, characterized in that It also includes a fishing ground dynamic update module, which specifically includes: When the difference in sea surface temperature between the latest obtained satellite data and the historical data set exceeds the temperature difference threshold, or the difference in chlorophyll concentration exceeds the concentration difference threshold, trigger the fishing ground redemarcation process; Start the enhanced observation mode of Argo buoys in the changing area, and the sampling frequency of the buoys is increased to a fixed multiple of the normal frequency until the fluctuation range of the environmental parameters is lower than the stability threshold; Based on the enhanced observation data, re-execute the spatio-temporal alignment and model calculation to generate the updated fishing ground boundary; Mark the difference area between the new and old boundaries as the fishing ground migration area, and the migration direction is determined by the synthetic vector of the geostrophic flow field and the suitability index gradient; the updated fishing ground boundary is incorporated into the historical data set as the comparison benchmark for the next dynamic update.

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