4070 results about "Buoy" patented technology

The invention discloses a high-temporal-spatial-resolution refined flow field reconstruction method, device and equipment and a medium, and relates to the technical field of ocean current reconstruction, and the method comprises the steps: carrying out the normalization and temporal-spatial alignment of satellite remote sensing, buoy observation and numerical simulation data; based on the alignment data, performing rehearsal on the unstructured nested grid through an FVCOM model, and then dynamically encrypting the grid according to the flow field gradient and generating a background flow field; inputting the background flow field into a PINN-GAN combined framework, and outputting a refined flow field through physical constraint loss and double-discriminator adversarial training; and scheduling a calculation task by adopting a heterogeneous accelerator, verifying the reconstructed refined flow field in real time, and performing feedback optimization. Through generation of the background flow field and refinement reconstruction, the ocean current flow field with high temporal-spatial resolution can be reconstructed efficiently and accurately.

The invention provides a marine reanalysis wind field data correction method based on buoy observation data, and belongs to the technical field of meteorologies.The marine reanalysis wind field data correction method comprises the steps that firstly, abnormal value elimination and quality control are conducted on the buoy observation data, and space-time matching is conducted through a bilinear interpolation method; constructing a dynamic time window, and adaptively adjusting the time precision according to the meteorological event type; calculating wind speed and wind direction errors, and extracting spatio-temporal characteristic parameters; the method comprises the following core steps of: optimizing Gaussian process regression by using an ocean physical constraint neural network model, wherein the model comprises a surface layer physical process coding layer, a boundary layer dynamic embedding layer and a multi-head sparse attention layer; constructing a space-time covariance model to generate an error field; correcting the reanalyzed wind field data and outputting uncertainty evaluation; the technical problem that a significant space-time error exists between reanalysis wind field data and an actual observation value in a complex marine environment is solved.

The invention discloses a ship inclination measurement method and system based on Kalman filtering fusion, and relates to the technical field of ship stability measurement, and the method comprises the steps: multi-sensor synchronous data collection, state space modeling and Kalman filtering fusion, adaptive dynamic adjustment of process noise, drift compensation, and fault tolerance and output. When the method and the system provided by the invention are used for measuring inclination, inclination angle error accumulation caused by gyroscope zero offset integral error, accelerometer dynamic interference, sensor noise and temperature drift, GNSS dependency and the like can be effectively avoided, available precision can still be maintained through multi-sensor depth coupling and an intelligent filtering algorithm, and the accuracy of inclination measurement is improved. The method is simple in operation, independent of satellite data, higher in stability, capable of greatly reducing hardware cost compared with GNSS and IMU schemes, and especially has the advantages of being simple in cost and installation configuration in ship refitting; and the method is also suitable for GNSS rejection or cost sensitive scenes such as inland ships, unmanned ships, offshore buoys and the like.

A drifting buoy trajectory prediction method based on a hybrid neural network prediction model, includes: S1, obtaining marine environmental data and historical trajectory data of a drifting buoy; S2, performing preprocessing on the marine environmental data and the historical trajectory data to obtain input data configured to predict northward and an eastward velocities of the drifting buoy; S3, inputting the input data into the hybrid neural network prediction model to obtain predicted values of the northward and eastward velocities; S4, calculating latitude and longitude coordinates of a trajectory point of the drifting buoy based on the predicted values of the eastward and northward velocities; and S5, predicting, by repeating the step S1-S4, latitude and longitude coordinates of trajectory points of the drifting buoy at multiple time points to obtain a sequence of trajectory point coordinates to thereby achieve trajectory prediction of the drifting buoy over a target future period.

The invention discloses an emergency prediction and early warning method for an overwater oil spill and hazardous chemical substance leakage accident. The method comprises the following steps: establishing an inland river basic database; constructing an environmental ecological effect model of the oil spill event; biological population changes under the influence of the oil spill event are identified; evaluating the influence of the oil spill event on the ecological system by using an eDNA macro stripe code technology; based on a biostatistics method, analyzing the relationship between the microbial community and the environmental elements; multi-dimensional real-time monitoring and early warning are carried out on an oil spill event; and a targeted sewage disposal scheme and ecological restoration measures are provided. According to the method, aerial remote sensing, ship and shore-based radar, video monitoring and buoy data are combined, multi-dimensional real-time monitoring and early warning of oil spill events are achieved through a multi-source data fusion technology, a traditional method usually only depends on a single data source, the monitoring accuracy and timeliness are improved, and the method is suitable for popularization and application. And after an oil spilling event occurs, a response can be made more quickly, and the damage of the accident to the environment and the economy is effectively reduced.

The invention provides a real-time deep sea subsurface buoy monitoring system, which belongs to the technical field of deep sea measurement, and comprises a control chip, a multi-parameter sensor array, a data storage device and a power supply, high-precision monitoring is realized through the following steps: recording ocean data acquired by a multi-parameter sensor; correcting the sound wave propagation model and calculating the relative position of the subsurface buoy; constructing a three-dimensional data matrix and realizing data compression; identifying abnormal data by using a matrix disorder index; processing the time sequence data by adopting a sliding window weighted average method and eliminating jump; carrying out distributed preprocessing on the multi-source heterogeneous data and carrying out parallel processing by applying a matrix partitioning technology; accumulated errors are corrected by combining historical data and applying a Kalman filtering algorithm, and the problems of environment interference and sensor drift are solved through a deep sea environment disturbance compensation network model and a self-adaptive weighted error optimization function.

The invention provides a marine environment real-time monitoring modeling system and method based on multi-modal fusion, and the system comprises a data collection module which is used for collecting marine satellite remote sensing data, buoy sensor data and public crowdsourcing data, and carrying out the preprocessing, and then generating multi-modal data; the multi-modal fusion module is used for registering the multi-modal data in time and space, calculating weights of various data sources in real time, and fusing the registered multi-modal data based on the weights to obtain fused marine environment data; and the real-time three-dimensional modeling module is used for constructing a real-time three-dimensional model of the marine environment by utilizing a three-dimensional modeling technology based on the fused marine environment data, and dynamically updating the real-time three-dimensional model of the marine environment according to the new multi-modal data. By integrating satellite remote sensing, buoy sensor and public crowdsourcing data and utilizing a dynamic weight distribution algorithm, real-time three-dimensional modeling of air-sky-ground-sea multi-source data is realized, so that the marine environment condition is comprehensively and accurately reflected.

The invention provides a synthetic aperture radar sea surface flow velocity inversion method and device based on machine learning. The method comprises the following steps: acquiring an original synthetic aperture radar image, and OCN data, mixed coordinate ocean model data and NDBC buoy data corresponding to the original synthetic aperture radar image; calculating a sea wave spectrum of the synthetic aperture radar image by using a PFSM algorithm, and exploring a relationship between the flow velocity and synthetic aperture radar image parameters; a synthetic aperture radar sea surface flow velocity inversion algorithm based on machine learning is constructed; and verifying the accuracy of the inversion algorithm. The invention provides an effective method for SAR sea surface flow velocity inversion.

The invention relates to the field of ocean conductivity data processing, and discloses an Argo conductivity data correction method based on the influence of thermohaline internal waves, which comprises the following steps: installing a thermohaline depth sensor and an acoustic Doppler flow velocity profiler on an Argo buoy, sampling seawater in the vertical direction, and calculating the depth of the sampled seawater; acquiring temperature, salinity and flow velocity profile data of seawater; determining the upper and lower boundary depths of the thermohalocline and the thickness of the thermohalocline according to the acquired data, calculating the internal wave frequency and the internal wave number, and calculating the temperature internal wave amplitude, the salinity internal wave amplitude, the temperature internal wave phase and the salinity internal wave phase; establishing a conductivity data correction model according to the calculated parameters; and performing dynamic correction on the conductivity data by using the model. According to the method disclosed by the invention, the accuracy and the reliability of conductivity data can be remarkably improved, and support can be provided for research in many fields such as research on physical characteristics of the ocean thermocline and halocline, ocean ecological environment and ocean material circulation.

The invention discloses an on-site monitoring system for the performance of an elastic loose type anchorage system suitable for deep-sea buoys, and particularly relates to the field of deep-sea monitoring. The on-site monitoring system auxiliary comprises a central processing module, wherein the output end of the central processing module is separately connected with an information display module,an information storage module, an auxiliary recovery module and a driving module, and the input end of the central processing module is separately connected with a power supply module, a monitoring module, a correction module and a positioning module. According to the on-site monitoring system for the performance of the elastic loose type anchorage system suitable for the deep-sea buoys, the auxiliary recovery module is arranged, air bags are installed at a plurality of node positions on the anchorage system, a booster pump in a pressurization unit is used, outside air is conveyed into a floating ball unit through a pipeline unit, so that the retractable air bags are enabled to bulge upwards to float, the whole anchor system is directly floated in sections, people conveniently recycle theanchorage system greatly, the workload during the recycling process is reduced, and the practicability is high.

The invention relates to the technical field of ecological environment monitoring, and discloses a coastal wetland intelligent monitoring method and system based on artificial intelligence, and the method comprises the steps: collecting unmanned plane data, satellite remote sensing data, Internet of Things sensor data and water quality monitoring buoy data of a coastal wetland; the method comprises the following steps: processing satellite remote sensing data by adopting a wavelet threshold denoising algorithm based on an attention mechanism, calibrating Internet of Things sensor data by adopting an LSTM network, and carrying out data space-time alignment based on a space-time attention fusion model to obtain preprocessed data; inputting the preprocessed data into a Transform-ResNet hybrid model to carry out environmental change evaluation, and outputting an ecological health index; when the predicted ecological health index is lower than a threshold value, a PPO algorithm is adopted to dynamically adjust a monitoring strategy according to the early warning level, and an early warning report is pushed; the whole process is intelligent, manual intervention is greatly reduced, and support is provided for coastal wetland ecological protection.

An environment multi-parameter monitoring base station capable of being used for long-term seafloor work is laid to the seafloor through a mother ship in a cable mode, energy is supplied through a dissolved oxygen seawater battery, long-term seafloor environment in-situ monitoring operation is carried out through carried universal and special instruments and equipment, a shore-based instruction is received to adjust a monitoring strategy, and the monitoring base station is used for monitoring the seafloor environment. Monitoring data is transmitted back to the shore-based laboratory in real time; the monitoring base station comprises a rack system, a monitoring system, a control system, a communication system, an energy supply system and a laying and recycling system; the monitoring system is used for completing in-situ monitoring operation on the influence of a seabed biochemical environment and benthos by adopting a general monitoring instrument and an extended special instrument; and the communication system is used as a signal transmission medium to perform parallel and uplink and downlink data transmission and instruction issuing so as to realize networking between the base station and each node around the base station and interaction and communication between the base station and a water surface buoy or a ship.

The floating type offshore wind turbine based on the tuned mass damper comprises a floating type foundation and a wind generating set, the wind generating set comprises a stand column, and the tuned mass damper is arranged in the stand column; the floating foundation comprises a buoy and a mounting platform, a base plate is slidably connected into the buoy, one end of the base plate is connected with the buoy through a damper spring, and the other end of the base plate is connected with the mounting platform through a supporting column. A submersible pump is arranged on the surface, close to the buoy, of the mounting platform, communicates with seawater through a first pipeline and communicates with the buoy through a second pipeline. And the buoys are distributed in a circumferential array along the mounting platform. The wind-resistant strength is improved, the cost is low, resetting is fast, the resetting effect is good, and offshore wind energy resources can be better utilized; the tuned mass damper is installed, vibration is reduced through compression and reset of the damping spring, and safety is improved; an air speed sensor is arranged, offshore air speed and air volume can be monitored, and the controller can conveniently control whether seawater is extracted or not.

The invention discloses an offshore red tide multi-parameter real-time monitoring method and monitoring system based on a buoy array. The method comprises the following steps: S1, performing array deployment and topology configuration according to a sea area sensitivity degree, tide characteristics and a historical red tide trajectory; according to the invention, through risk differentiation buoy array deployment and redundancy topology, traditional isolated monitoring limitation is solved, and sea area full coverage and red tide diffusion tracking are realized; physical-chemical-biological multi-source sensing and in-situ detection are combined with data fusion, so that the problem of single parameter is solved, and the monitoring accuracy is improved; by means of liftable stratified sampling and in-situ algae species and toxicity analysis, the vertical monitoring blank is filled up, and the complete life cycle process of red tide from occurrence to regression is completely captured; through linkage of edge-cloud multi-model early warning and field equipment, a'monitoring-early warning-disposal 'closed loop is formed, and the red tide monitoring real-time performance and the emergency response capability are greatly improved.

The invention discloses a large-scale ocean data buoy safety monitoring system and method based on multi-source fusion, and relates to the technical field of buoy monitoring. Buoy equipment operation parameters, ocean environment data and surrounding ship dynamic information are synchronously acquired through a buoy body sensor group, an environment monitoring unit and a ship identification module; time-frequency domain feature extraction and anomaly detection are carried out on the collected data, and a collision risk dynamic evaluation model is constructed in combination with ship trajectory prediction; establishing an equipment health, environment interference and ship threat fusion evaluation system, and performing risk grade division in combination with real-time monitoring data and a historical database; and generating early warning information based on the risk level, and automatically triggering an equipment maintenance instruction and a ship collision avoidance alarm. The method adapts to ship variable-speed steering scenes, threshold values are dynamically adjusted, and adaptability and analysis accuracy of the system are improved.

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.

The invention relates to the technical field of environment monitoring, and discloses a radio-based environment monitoring method and system, and the method comprises the steps: deploying an ocean buoy integrated with a plurality of parameter sensors, and detecting the water temperature, salinity, dissolved oxygen, pH value and illumination intensity in real time; transmitting the data detected in real time to a shore-based receiving station by using a radio communication module; performing drift correction and noise filtering on data transmission detected in real time; performing data fusion analysis according to the mutual relation among different data, and generating a marine environment assessment report; and abnormal early warning information is generated according to the marine environment assessment report and is fed back to a monitoring center in real time through radio. According to the invention, through integration of various parameter sensors, comprehensive monitoring of multiple parameters of the marine environment is realized, and the accuracy and reliability of monitoring are improved.

The invention belongs to the cross technical field of artificial intelligence and marine meteorological prediction, and discloses a sea wave probability prediction method and system, and the method comprises the steps: obtaining historical wind field data and sea wave spectrum data of a target sea area, carrying out the preprocessing and organization of the data, and constructing a training data set; constructing a hybrid expert probability model, wherein the model comprises a gating network and a plurality of expert networks; the training data set is used for training the hybrid expert probability model, the trained model receives input wind field data, and hybrid probability distribution is output through the synergistic effect of the gating network and the expert network; and sampling is carried out from the mixed probability distribution to obtain a predicted sea wave spectrum set, and sea wave risk probability prediction is realized. Complete distribution information can be obtained through one-time forward calculation, a numerical mode set is not needed, the computing power and energy consumption expenditure are remarkably reduced, and high-frequency updating and quasi-real-time business application can be conveniently achieved in resource-limited environments such as shipborne, buoys and offshore stations.

The invention provides an ocean red tide early warning method based on remote sensing inversion and a time-space map convolutional network. The ocean red tide early warning method comprises the following steps: collecting hyperspectral data and multispectral data; respectively carrying out data quality inspection on the collected hyperspectral data and multispectral data; preprocessing the hyperspectral data and the multispectral data after the quality inspection is qualified to respectively obtain a hyperspectral result image and a digital orthoimage; analyzing the hyperspectral result image and the digital orthoimage to obtain remote sensing inversion data of water temperature, salinity, total nitrogen, total phosphorus and chlorophyll a, and generating a gradient distribution thematic map of each index; according to water temperature, salinity, total nitrogen, total phosphorus, chlorophyll a and historical monitoring data, in combination with a change trend of key environmental parameters, introducing a space-time diagram convolutional network model, simulating a red tide diffusion situation, setting a risk threshold, and performing risk prediction on red tide occurrence through a red tide occurrence risk prediction model; and updating the red tide occurrence risk prediction model based on buoy monitoring and field monitoring data.

The invention relates to the technical field of earthquake prediction, in particular to a submarine seismic oscillation prediction method and device based on earthquake exploration, and the method comprises the following steps: selecting a monitoring region, collecting the submarine topographic data of the monitoring region, and drawing a topographic map; selecting a plurality of monitoring points according to the topographic map; geomagnetic field information, electric wave information, micro-seismic activity information and seabed deformation information of each monitoring point are collected and recorded; converting the collected information into acoustic signals and transmitting the acoustic signals to a sea surface buoy, and converting the acoustic signals into electric signals by the sea surface buoy and transmitting the electric signals to a ground data center; by optimizing site selection of monitoring points, integrating geomagnetic-electric wave-microseismic-deformation multi-source data, realizing high-efficiency data transmission by using an acoustic-electric staged communication chain, and finally realizing early recognition of earthquake precursor in combination with threshold analysis, the method can significantly improve the coverage range, data precision and early warning timeliness of seafloor earthquake monitoring, and has good application prospects. And a technical support is provided for marine geological disaster prevention and control.

A method for simultaneous localization and mapping underwater. A robot is equipped with an IMU inertial unit and a sonar unit. When the robot submerges underwater, a buoy connected to the robot floats on the water surface, and the buoy moves in coordination with the movement of the robot. The position and observed velocity of the buoy are obtained by a shore-based lidar. During motion estimation in a SLAM algorithm, when an angular velocity is below a preset angular velocity threshold, the observed velocity of the buoy is decomposed into x-axis velocity and y-axis velocity, and updated as the two-dimensional operating velocity of the robot. The polar coordinates of an obstacle in a map under the coordinate system of the robot are associated with the polar coordinates of sonar data of a current frame by using the Mahalanobis distance. The polar coordinates of a new obstacle are converted to the world coordinate system and added to the map. The present invention has the advantages of not relying on underwater visibility, not requiring pre-installed devices, and possessing good generalization and stability.

The invention relates to the field of ocean data buoy data quality control, and discloses a buoy meteorological data restoration method based on a space-time double-attention neural network, and the method comprises the following steps: data collection and preprocessing; establishing a space-time double-attention neural network model, wherein the model comprises a time coding link, a space coding link and a feature fusion layer; model training: inputting the time features containing the target elements to be predicted into a time coding link, inputting the meteorological elements into a space coding link, and outputting the target elements at the moment to be predicted by the model; after model training, evaluating the model by using the test set; and predicting a vacancy value or an abnormal value in the multi-dimensional meteorological element data acquired in real time by using the model which is qualified in evaluation, so as to realize data restoration. According to the method disclosed by the invention, time and space constraints are effectively combined, and abnormal values and vacant values in the meteorological data are predicted and replaced, so that the meteorological data are repaired.

The invention discloses a semi-submersible wave energy-wind energy integrated power generation platform and a damping regulation and control method of a damping regulation and control system of the semi-submersible wave energy-wind energy integrated power generation platform. The platform comprises a semi-submersible wind driven generator platform, a wind driven generator set, a wave energy power generation device, an energy storage system, a sea area detection system and the damping regulation and control system. The wave energy power generation devices are divided into two groups on the inner side and the outer side of the platform, each wave energy power generation device comprises a platform inner side wave energy power generation device and a platform outer side wave energy power generation device, and each wave energy power generation device comprises a swing arm floater, a universal hinge, a connecting arm and a damping adjusting module. The semi-submersible wind power generation device and the wave power generation device are combined, and offshore renewable resources are comprehensively utilized; the six-buoy design is adopted, buoyancy is dispersed, stability and safety are improved, and meanwhile space is provided for a wave power generation device; the floaters are symmetrically arranged in a hexagonal shape and carry a damping regulation and control system, and the whole structure is more easily stabilized by adjusting the damping of the floaters in all directions.

The present invention relates to a wave energy power generation buoy with a variable length and angle damping plate and its control method, belonging to the technical field of wave energy power generation buoys. It includes a main floating body, an air turbine and a damping plate. The main floating body is a hollow rear-bent pipe structure. The air turbine is installed at the air outlet at the top of the rear end of the main floating body. The damping plate is installed at the rear end of the main floating body. The damping plate includes an L-shaped fixed plate, a horizontal telescopic plate, a vertical telescopic plate and an inclined movable plate. The L-shaped fixed plate is fixedly connected to the main floating body. The horizontal telescopic plate is horizontally slidably connected to the horizontal side plate of the L-shaped fixed plate. The vertical telescopic plate is vertically slidably connected to the vertical side plate of the L-shaped fixed plate. The upper and lower ends of the inclined movable plate are respectively movably connected to the vertical telescopic plate and the horizontal telescopic plate. The L-shaped fixed plate is provided with a first telescopic driving mechanism for driving the horizontal telescopic plate to move back and forth and a second telescopic driving mechanism for driving the vertical telescopic plate to move up and down. The present invention can improve the wave energy conversion efficiency.

The invention discloses a multi-field sensing fusion deep sea geological environment dynamic monitoring system and an application method. The system comprises a buoy measuring platform, a multi-beam sounding system, a high-precision marine gravimeter, an external camera, a GPS, a data acquisition and transmission control unit and a power supply system. By designing a modularized and integrated multi-sensor buoy platform, various sensors are fused and integrated. A plurality of sensors are connected to the data acquisition control terminal, the steps of sensor calibration, data synchronization and clock calibration, correction and compensation, data acquisition, real-time processing, fine post-processing, data transmission and the like are completed, and multi-sensor collaborative operation is realized, so that submarine topography and ocean gravity data of a deep sea ditch area are synchronously acquired in real time; and thus, diving erosion information experienced by the area can be accurately monitored. The method can provide important support for submarine topography and landform detection and application and marine geographic information system construction, and has application value in the aspects of geological disaster early warning, resource development and the like.

The invention provides an ocean buoy data real-time acquisition method based on low earth orbit satellite communication, and belongs to the technical field of electrical digital data processing.The ocean buoy data real-time acquisition method comprises the steps that firstly, a low earth orbit satellite network communication link is built, data characteristics are analyzed through a compression conversion gain index model, and a low earth orbit satellite network communication link is established; and establishing a multi-stage compression mechanism. The system adopts an optimization equation set to dynamically adjust compression parameters, the optimization equation set comprises a resource consumption equation, a compression quality equation and a transmission efficiency equation, and self-adaptive compression of data is achieved. And meanwhile, through a hierarchical algorithm selection mechanism and a parameter optimization strategy and in combination with real-time feedback control, continuous optimization of the compression efficiency is ensured. According to the compression scheme based on multi-level optimization, the technical problem of low compression efficiency caused by poor algorithm adaptability in the ocean buoy data compression process in the prior art is effectively solved.

The invention discloses a deep-sea self-expanding horizontal array acoustic subsurface buoy, which relates to the field of deep-sea underwater acoustic detection and comprises an expanding platform arranged at the top of the acoustic subsurface buoy, a subsurface buoy main body positioned below the expanding platform and an anchoring structure connected below the subsurface buoy main body, the subsurface buoy body comprises a buoyancy platform arranged below the unfolding platform and an equipment platform arranged below the buoyancy platform. The problem that a hydrophone linear array composed of a plurality of large-scale acoustic linear arrays is folded and automatically unfolded into a cross horizontal array is solved, and integration of a deep-sea acoustic emission system, an acoustic receiving system, an underwater acoustic communication system and an equipment supply system is achieved. The whole subsurface buoy can be quickly and conveniently released and recycled, and high-reliability anchor mooring is achieved.

The invention belongs to the technical field of ocean water color and ocean ecology, and relates to a near-field actual measurement data set construction method and system for ocean net primary productivity, and the method comprises the steps: collecting profile data observed by a biogeochemical buoy BGC-Argo; s2, respectively performing data quality control processing on the Chl-a profile data and the bbp (700) profile data to obtain profile data after quality control; s3, based on the profile data after quality control, matching remote sensing parameters of a medium-resolution imaging spectrometer (MODIS); wherein the remote sensing parameters comprise photosynthetically active radiation (PAR) and a diffusion attenuation coefficient Kd (490) at a 490nm wave band; s4, on the basis of the profile data after quality control, PAR and Kd (490), utilizing a CbPM model to calculate marine net primary productivity NPP profile data; and S5, carrying out integration on the NPP profile data in a true light layer depth range to obtain NPP data of water column integration. The method can economically and efficiently obtain a large number of NPP samples actually measured in the near field, and makes up for the deficiency of existing actually measured data.

The invention discloses an integrated buoy for ocean monitoring and a buoy testing method.The buoy comprises a floating body, a solar panel support, a sensor support and a data acquisition communication box are fixedly installed on the top of the floating body, and a plurality of solar panels with the bottoms inclined outwards are installed on the solar panel support; a plurality of sensors are installed on the sensor support, and communication equipment and a data collector are installed in the data collection communication box. According to the invention, the laboratory integration test, the coastal water surface test and the offshore laying test are combined, three times of data comparison and verification are carried out, the consistency and precision of the data can be effectively ensured, and in addition, the environmental simulation equipment is added during the offshore laying test to simulate different environments in the marine environment, so that the test efficiency is improved. The floating property and the stability of the buoy under different extreme conditions are tested, remote monitoring and self-diagnosis technologies are introduced into the buoy in a matched mode, personnel can conveniently and visually know the condition of the buoy in the testing process, and the use requirement is met.

The invention discloses a wave in-situ calibration method based on a GNSS buoy, and belongs to the field of wave in-situ calibration, and the method comprises the following steps: obtaining buoy deviation data based on the position and attitude data of the GNSS buoy under different sea conditions; constructing a deviation compensation model and a deviation sliding model based on the buoy deviation data; a deviation compensation result is obtained based on the deviation compensation model and the deviation sliding model, and the deviation compensation result comprises first deviation compensation and second deviation compensation; and fusing the deviation compensation result with the GNSS positioning data to obtain a wave parameter estimation result.