2445results about "Open water survey" patented technology

The invention provides an intelligent runoff pollution monitoring and response method and system, and the method comprises the steps: arranging a plurality of monitoring points in a runoff region, collecting water quality, hydrological and meteorological data in real time through a sensor, transmitting the data to a data processing center through a wireless network, carrying out the preprocessing, storage and analysis, and calculating the comprehensive pollution index of each monitoring point. And comparing with a set index threshold value. When the comprehensive pollution index exceeds a threshold value, the system identifies and evaluates key water quality parameters so as to determine a reasonable parameter threshold value; once data of a monitoring point exceeds a preset pollution threshold value, an alarm mechanism is triggered, then a pipe network water flow model and a geographic information system are combined to reversely track a pollution path, accurately position a pollution source and provide a basis for pollution control, and the process comprises the steps of screening suspected pollution source nodes, calculating a propagation path probability and preferentially checking a high-probability path. The traced pollution source or path probability is lower than a preset value.

The invention discloses a marine environment dynamic monitoring system based on cooperation of artificial intelligence and multi-source remote sensing, relates to the technical field of marine environment monitoring, and realizes multi-dimensional monitoring of a sea surface state by receiving multi-source data of a plurality of monitoring ends. Chlorophyll concentration mutation is detected in real time by setting sliding window difference, a subsequent processing flow is dynamically triggered, and the response speed of sudden events (such as red tide) is increased; aI is utilized to predict clock offset, interpolation reconstruction, weighted fusion and ocean current compensation technologies are combined, and space-time asynchronous errors of multi-source data are remarkably reduced; and generating a data cube fusing the backscattering coefficient, the temperature field and the chlorophyll gradient, and calculating a diffusion velocity field, thereby providing high-precision decision support for marine disaster early warning. The limitation of a traditional single data source is broken through, efficient cooperation of multi-modal data is driven through artificial intelligence, and the real-time performance, the accuracy and the dynamic analysis capability of marine environment monitoring are improved.

The invention provides a high-sand-content water body water depth inversion method based on multispectral remote sensing, and belongs to the technical field of remote sensing water depth inversion. The method comprises the following steps: collecting a plurality of multispectral remote sensing images and actually measured data of a target area; determining spectral reflectivity characteristics by using the preprocessed multispectral remote sensing image, and generating a spectral characteristic parameter matrix in a normalization manner in combination with the suspended sediment concentration; constructing an initial water depth inversion model based on the spectral reflectivity characteristics and the water depth measured value; geographic coordinates and a space weighting mechanism are introduced, and an initial water depth inversion model is optimized; inputting the spectral characteristic parameter matrix into the optimized water depth inversion model to generate an initial water depth inversion result; and positioning a sub-region with an error exceeding an error threshold in the target region, correcting an abnormal value of the sub-region based on a spatial interpolation method, optimizing the spectral characteristic parameter matrix, and generating an optimized water depth inversion result. The method improves the precision and stability of water depth inversion.

The invention relates to the technical field of energy efficiency management, in particular to a water conservancy sensing system based on edge computing. The system comprises the following modules: a riverway edge sensing module, a water conservancy characteristic analysis module, a sediment migration simulation module and a deposition scouring early warning module. Constructing a river channel water conservancy sensing network; performing real-time water conservancy monitoring data acquisition according to the river channel water conservancy sensing network to obtain river channel water conservancy sensing data; performing three-dimensional flow field model processing on the river channel water conservancy sensing data to obtain a three-dimensional sediment flow field network model; carrying out particle flow field simulation on the three-dimensional sediment flow field network model to generate sediment migration space-time distribution data; and according to the sediment migration space-time distribution data, carrying out siltation and scour area identification, and uploading the siltation and scour area data to a cloud platform through an edge computing node to carry out abnormal event alarm so as to obtain river section monitoring data. According to the invention, real-time, accurate and intelligent monitoring and early warning of the sediment deposition and scouring process of the river channel are realized.

The invention discloses a multi-parameter in-situ detection data analysis system for a marine ranch, and relates to the technical field of data analysis, the multi-parameter in-situ detection data analysis system comprises a multi-parameter sensor group and a self-adaptive multi-dimensional trigger type sampling control module, the multi-parameter sensor group comprises an environmental parameter sensor and a biological behavior sensor, the environmental parameter sensor is used for measuring real-time environmental parameters of the marine ranch, the biological behavior sensor is used for monitoring biological behavior parameters of the marine ranch in real time, and the self-adaptive multi-dimensional trigger type sampling control module comprises a data preprocessing unit and a dynamic sampling strategy unit. The self-adaptive multi-dimensional trigger type sampling control module is designed, so that sampling modes are dynamically switched according to real-time dual-mode data, environment sudden changes are effectively captured, data redundancy or key change sampling leakage caused by traditional fixed sampling frequency is avoided, monitoring precision is improved, energy optimization is achieved, the service life of equipment is prolonged, and the system is suitable for large-scale popularization and application. And high quality and real-time performance of data are ensured.

The invention relates to the technical field of water conservancy project risk early warning, in particular to a multi-modal time-space fusion water conservancy risk real-time early warning system, which comprises a multi-modal time-space data fusion module for performing time-space alignment and feature extraction calculation based on reservoir group sensor data flow, remote sensing images and weather forecast to obtain a fusion time-space situation value. According to the invention, by fusing the reservoir group sensor data flow, the remote sensing image and the weather forecast data, executing the space-time alignment and feature extraction calculation, and constructing the fused space-time situation value, the collaborative analysis precision and situation awareness of the multi-source heterogeneous data are improved, and the data utilization rate and the risk analysis accuracy are improved; when a potential scheduling operation set is established, gate opening degrees and flood discharge flow adjustment options under different time windows are defined, and the flexibility and the strain capacity of a scheduling scheme are enhanced, so that the scheduling operation better fits actual hydrological condition changes.

The invention relates to the technical field of data processing, and discloses a salinity drift correction method and system for buoy observation data. The method comprises the steps that historical buoy observation data and current environment parameters are collected and preprocessed, and a multi-source heterogeneous data set is obtained; performing multi-dimensional environmental factor analysis on the multi-source heterogeneous data set to obtain a salinity influence factor weight; drifting detection and quantification are carried out on the buoy salinity data through the salinity influence factor weight, and an accumulated deviation integral value is obtained; performing multi-scale correction calculation on the accumulated deviation integral value to obtain preliminary correction data; performing dynamic space-time weight optimization on the preliminary correction data to obtain optimized correction data; and performing real-time performance evaluation and automatic threshold adjustment on the optimized correction data to obtain target correction data and a parameter updating strategy. According to the invention, the accuracy of salinity drift correction for buoy observation data is improved.

The invention relates to the technical field of oceanographic engineering surveying and mapping, and provides an oceanographic engineering surveying and mapping deep sea terrain reconstruction method, which comprises the steps of multi-modal data synchronous acquisition, frequency domain feature cross enhancement, dynamic convolution feature fusion, inverse projection terrain reconstruction and terrain credibility verification. According to the invention, an acousto-optic multi-mode fusion technology is adopted, and the advantages of acoustic penetrability and optical high resolution are combined, so that the complex terrain detail capturing capability is improved; the dynamic convolution network adaptively adjusts convolution kernel parameters through frequency domain energy, the multi-scale feature fusion effect is enhanced, and the accuracy of terrain texture expression is improved; according to the motion distortion compensation method, carrier attitude drift is corrected in real time through inertial navigation data, and the geometric fidelity of a reconstruction model is guaranteed. The method is suitable for a deep sea complex environment, and provides high-precision terrain reconstruction method support for ocean engineering surveying and mapping and ocean resource exploration.

The invention discloses an uncontrolled multispectral remote sensing water depth inversion method and system, and belongs to the field of shallow sea water depth inversion methods. The method comprises the following steps: firstly, by calculating a depth invariant index of a target area, dividing the target area into a high substrate reflectivity type, a medium substrate reflectivity type and a low substrate reflectivity type; establishing corresponding high, medium and low substrate reflection spectrum sets according to the typical substrate type of the target area; and finally, based on a shallow water radiation transmission model, combining remote sensing reflectivity data of an adjacent optical deep water region, and establishing optimal matching between the simulated remote sensing reflectivity and the actual remote sensing reflectivity by matching a substrate reflection spectrum in a regional pixel-by-pixel manner, so as to obtain a continuous large-area bathymetric map of the target region. The method is suitable for water depth measurement without prior actual measurement of water depth in a multispectral remote sensing scene, solves the problem that a traditional water depth inversion algorithm does not fully consider complex substrate types and spectral variation, and provides a higher-precision method for shallow sea multispectral remote sensing uncontrolled water depth inversion.

The invention relates to the technical field of marine environment monitoring, and discloses an intelligent collection method and system for marine multi-dimensional information and a storage medium. The method comprises the following steps: synchronously acquiring environmental parameters at a plurality of measuring points and a depth layer, and constructing a hydrological characteristic data set; calculating spatial correlation and dividing a high variation region and a low variation region; constructing an equipment spacing model based on an information density classification result, and generating a point distribution scheme by adopting an optimization algorithm; combining real-time monitoring data to judge significant changes and dynamically updating layout parameters; and issuing an instruction to the equipment through the main and standby wireless channels to complete deployment. According to the invention, the adaptability and layout efficiency of marine environment perception are improved, and the method is suitable for intelligent monitoring application of complex and changeable sea areas.

The invention discloses an internal wave characteristic field information rapid prediction method based on deep learning, and belongs to the technical field of ocean internal wave detection. According to the method, rapid prediction is carried out on the basis of traditional numerical simulation data by using deep learning, so that the efficiency of inner solitary wave prediction is remarkably improved; through multi-channel coupling learning and an iterative prediction strategy, the inner wave field prediction achieves the effect of high precision and high efficiency. Meanwhile, in combination with high-resolution numerical mode data, the consistency of a model prediction result and a physical process is ensured, and rapid extension period prediction of a solitary wave characteristic field in the ocean is realized while low error and high relevancy are kept. The method is suitable for application scenes of real-time ocean internal wave monitoring and early warning and the like, and can be widely applied to the technical fields of ocean environment monitoring and early warning, intelligent ocean observation, ocean engineering safety and the like.

The invention discloses an ocean current comprehensive monitoring system based on an ocean observation buoy, and relates to the technical field of ocean observation, the ocean current comprehensive monitoring system comprises a buoy main body module, a net buoyancy adjusting module, a motion state prediction module, a buoy deviation evaluation module, a buoy motion optimization module and a data transmission module, and the modules are in electric signal connection; the buoy main body module carries various sensors to collect physical parameters of the marine environment. Through cooperative work of the motion state prediction module and the buoy motion optimization module, the system can predict and adjust the motion state of the buoy so as to reduce data acquisition errors caused by environmental changes, so that the accuracy of data is improved, a solid data basis is provided for marine scientific research, and through the motion state evaluation module, the accuracy of the buoy is improved. The abnormal motion state of the buoy can be found in time, and the state of the buoy is rapidly adjusted through the motion optimization module, so that the continuity and accuracy of data are ensured.

The invention relates to the technical field of river section measurement, in particular to a river measurement data preprocessing method based on artificial intelligence. The method comprises the following steps: obtaining river section measuring point data, and carrying out measuring point density evaluation on the river section measuring point data based on different scales and section elevation changes so as to obtain high-density measuring point data; performing section thalweg point and turning point identification based on adjacent measuring point slope change on the high-density measuring point data, and performing measuring point thinning processing of retaining thalweg points and turning points so as to obtain a retaining measuring point data set; according to the preset section line and the reserved measuring point data set, the measuring points exceeding the preset measuring point deviation distance threshold value are automatically detected and calibrated, coordinate reconstruction and position correction are conducted on the measuring points deviating from the preset section line, and therefore calibrated measuring point data are obtained. The problems of uneven data, missed measurement and deviation in traditional measurement can be effectively solved, and river channel measurement data are more accurate and complete.

The invention discloses a hydrological monitoring point dynamic planning method and system, and relates to the technical field of hydrological monitoring, and the method comprises the steps: collecting hydrological monitoring point parameters of a target region, and making an initial point planning scheme; and performing hydrological dynamic prediction on the region based on prediction of a future time zone to obtain a hydrological prediction result. And according to the result, performing redundancy adjustment on the initial point location scheme to generate a first adjustment scheme. And based on the monitoring demand and the prediction result, correcting the point location blind area to form a second adjustment scheme. And in combination with the geographic feature data and the damage risk threshold, compensation adjustment of the damaged point location is carried out, and a third adjustment scheme is generated. And integrating the above adjustment schemes, carrying out global optimization, and generating an optimized hydrological monitoring point layout. The technical problems of poor planning flexibility and low precision of the hydrological monitoring point positions in the prior art are solved, and the technical effects of improving the planning flexibility and precision of the hydrological monitoring point positions and improving the hydrological monitoring quality are achieved.

The invention belongs to the technical field of ocean monitoring, and discloses a heaving reciprocating observation buoy with a controllable depth range, a control method and a system. The control method comprises the following steps: in a diving stage, the depth range controllable heaving reciprocating observation buoy receives a satellite signal, and a control system executes a diving action; the air bag system controls an air bag to contract through an air path adjusting device, net buoyancy is reduced, and diving is achieved by executing a diving action instruction sent by the control system. In the depth-keeping stage, the air bag volume is not changed; and a floating stage, wherein the marine physical data observed in the floating stage are uniformly sent to a satellite. The multi-stage adjustable mode of the electromagnetic valve is adopted, the opening degrees of the electromagnetic valve are different, and compared with the traditional opening degree of 0-1, the multi-stage adjustable mode is low in energy consumption consumed by opening and closing each time, the energy consumption is reduced, the service life of the buoy is prolonged, and the maintenance frequency is reduced.

The invention discloses a marine water environment abnormal state secondary and derivation probability modeling and estimating method, which belongs to the field of state modeling estimation, and comprises the following steps: acquiring real-time data flow of marine environment parameters, performing segmentation processing by adopting a sliding window mechanism, and reducing data dimension by utilizing principal component analysis. Then, constructing a graph structure model based on a time sequence, and capturing correlation and a space-time dependency relationship among parameters; and performing nonlinear mapping on the graph structure model by adopting a radial basis function core, and capturing complex nonlinear characteristics among the marine environment parameters. And finally, classifying the abnormal states by using a multi-class support vector machine algorithm, and outputting secondary and derivative probability estimation results of the abnormal states in real time. According to the method, abnormal events in the marine environment can be found in time, dynamic modeling support can be provided for marine environment monitoring, and the accuracy and real-time performance of marine environment monitoring are remarkably improved.

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 discloses an ocean multi-source observation data fusion assimilation method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining multi-source ocean observation data; uniformly converting the timestamps and the space grids of the multi-source ocean observation data into a data format of an FVCOM numerical model; constructing a data fusion assimilation model; the data fusion assimilation model comprises an FVCOM physical model and an AC-PKAN neural network; training the data fusion assimilation model, and adjusting the weight parameter of the data fusion assimilation model according to the dynamic observation data currently acquired by the edge device; and fusing and assimilating the multi-source marine observation data by using the data fusion assimilation model to obtain multi-source fusion marine observation data. According to the method, the timestamps and the space grids of the multi-source marine observation data are synchronized, and then the data fusion assimilation model is constructed for fusion assimilation, so that the fusion accuracy of the multi-source marine observation data can be improved.

The invention discloses a multi-sensor data synchronous acquisition and fusion processing system and method, and belongs to the technical field of marine monitoring. Unified connection between different sensors and a sensor communication module and unified transmission of multi-sensor data to an upper computer are realized by designing a unified multi-sensor communication protocol conversion interface; and then data timestamps of different sensors are aligned through a clock synchronization mechanism and a time offset correction model, and real-time fusion processing is performed on the data of the multiple sensors by adopting a dynamic fusion algorithm based on a time sequence and frequency, so that real-time fusion of different multi-sensor data is realized, the precision of multi-sensor data fusion is improved, and the accuracy of multi-sensor data fusion is improved. And the marine underwater environment monitoring effect is improved.

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 a two-dimensional wave spectrum intelligent forecasting method based on a physical information neural network, and belongs to the technical field of ocean information prediction.The two-dimensional wave spectrum intelligent forecasting method comprises the steps that basic data are collected, and the basic data comprise high-temporal-spatial-resolution sea surface wind field data and sea wave height data within the research range; historical sea wave data are obtained, ocean and meteorological data closely related to sea wave changes are integrated and processed, and a sea wave forecasting database is constructed; physical constraints are determined according to the sea wave forecasting database; an ANN neural network model is adopted as a basic model, physical constraints are added, the neural network meets the control equation of the mode, and a PINN physical information neural network is constructed; and carrying out interpretability analysis on the model forecasting process and result. According to the method, the sea wave dynamic process and the deep learning method are fused, interpretability is added for deep learning, and the black box problem of the deep learning method is solved.

The invention relates to a saline intrusion measurement method, system and equipment based on dynamic ship motion conditions and a medium, and aims to solve the technical problem that the salinity inversion precision is reduced due to spatial-temporal dislocation between particle image velocity measurement flow field data and plane laser-induced fluorescence concentration field data caused by ship six-degree-of-freedom motion. According to the method, future poses are predicted by collecting ship motion parameters and environment disturbance parameters, and space-time compensation parameters are generated in combination with a static calibration matrix; performing three-dimensional coordinate transformation and time synchronization on synchronously acquired original flow field and concentration field data by applying compensation parameters, and eliminating space-time deviation caused by ship movement; a three-dimensional salinity distribution field is calculated based on registration data fusion flow field diffusion characteristics and concentration field distribution; and meanwhile, the pose of the measuring equipment is dynamically adjusted through the closed-loop controller so as to optimize the data acquisition quality. According to the method, ship motion interference is effectively inhibited, and accurate characterization of saline invasion frontal surface spatial characteristics is guaranteed.

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 provides a hydro meteorology early warning method for an offshore oil and gas platform, and belongs to the technical field of offshore hydro meteorology. Extreme weather events are identified by adopting minimum probability abnormal event identification vectors to match abnormal characteristic parameters, and abnormal signal characteristic parameters are input into an ocean dynamics prediction model to calculate real-time sea condition parameters; calling a multi-temporal-spatial-scale early warning fusion matrix to combine with a wavelet decomposition technology and a recurrent neural network to realize multi-scale information integration, analyzing an environmental parameter change trend through a sea condition jump identification model and triggering an emergency response, dynamically adjusting system parameters according to a stability evaluation index vector, and optimizing prediction precision by adopting an early warning residual value compensation matrix. And finally, multi-level early warning information is generated and a real-time early warning notification is sent to an operator, so that the technical problem of insufficient early warning precision of an offshore oil and gas platform hydro meteorology early warning system in multi-spatio-temporal scale data fusion processing is solved.

The invention provides an in-ocean solitary wave amplitude inversion method, an electronic device and a readable storage medium, SWOT satellite observation data is combined with other remote sensing means observation data of close time to form a double-time-phase remote sensing image, and then the observed in-ocean solitary wave amplitude of a target sea area is obtained. According to the method, large-area two-dimensional sea surface height observation data with high resolution and high precision can be provided based on radar interference measurement, and under the condition that sea surface height abnormity caused by the marine internal solitary waves on the sea surface can be captured, the amplitude of the internal solitary waves is obtained in combination with other remote sensing observation data, so that the calculation process is simplified, and the calculation efficiency is improved. And the inversion precision is improved.

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 provides a freezing circle watershed hydrological prediction method and system based on multi-source data fusion, and relates to the technical field of hydrological monitoring and sediment monitoring, and the method comprises the steps: obtaining a vibration signal of the interaction of a water flow and a riverbed and meteorological data in a region, and carrying out the preprocessing; the preprocessed meteorological data and vibration signals are input into a multi-source data fusion model, sequential feature extraction is conducted on the meteorological data and the vibration signals through a multi-layer perceptron in the multi-source data fusion model, the extracted sequential features are fused through a gating circulation unit, and multi-source data fusion features are obtained; and carrying out empirical mode decomposition and Hilbert spectrum analysis on the preprocessed vibration signal to obtain a plurality of signal characteristics such as instantaneous frequency and instantaneous amplitude of the vibration signal, inputting the signal characteristics and the multi-source data fusion characteristics into a hydrological parameter prediction model, and outputting corresponding predicted values of the water flow velocity, the flow and the sediment transport capacity.

The invention discloses a near-shore sea ice remote sensing extraction method based on FY-3 / MERSI satellite data, and the method comprises the following steps: S1, carrying out the preprocessing of an obtained MERSI image, and obtaining an MERSI image only containing sea ice and seawater; s2, establishing a coastal sea ice remote sensing recognition algorithm according to the difference of the spectral characteristics of sea ice and seawater in each channel of the MERSI image, and evaluating the precision and accuracy of the coastal sea ice remote sensing recognition algorithm; s3, according to a coastal sea ice remote sensing recognition algorithm, extracting coastal sea ice information from the MERSI image of each day; and carrying out 10-day averaging and monthly averaging on the daily sea ice data extracted in each month to generate a sea ice ten-day product and a monthly product. According to the invention, automatic identification of high-spatial-resolution sea ice information of a high-latitude offshore area in winter can be realized.

The invention provides a marine environment-oriented integrated monitoring and early warning method, medium and system, and belongs to the technical field of electrical digital data processing.The marine environment-oriented integrated monitoring and early warning method comprises the steps that firstly, remote sensing monitoring data such as seawater temperature, salinity, pollutant concentration and flow velocity are collected, and environmental stability and variation components are obtained through multi-element mixed decomposition; a diffusion kinetic model of N time scales is established based on the flow velocity data, and a diffusion matrix group is obtained through calculation. And carrying out cascade operation and time sequence weighted fusion on the environment change component and the diffusion matrix to obtain a comprehensive environment change trend matrix, and constructing a risk vector containing temperature, salinity and pollutant components according to the comprehensive environment change trend matrix. And finally, performing matching operation with an early warning threshold matrix set established by historical data to obtain an early warning index and outputting an early warning level. According to the method, mathematical and physical models are utilized to overcome the defects of strong empirical and subjectivity in a traditional method, and the technical problem that effective conversion from static remote sensing data to dynamic environment risk early warning is difficult to realize in the prior art is solved.

The invention relates to the technical field of exploration, and discloses a water depth inversion method and system based on a multispectral remote sensing image, which utilizes an independent verification sample set to carry out hierarchical precision evaluation, and analyzes model performance differences according to dimensions such as a water depth range and a substrate type. Error propagation of links such as atmospheric correction and water level correction is quantified through full-chain uncertainty analysis, an uncertainty quantification model of ensemble learning is constructed, and a pixel-level precision distribution diagram is generated. Therefore, according to the technical scheme, a closed-loop dynamic adaptation framework is formed by systematically integrating multi-temporal data dynamic modeling and an uncertainty quantification mechanism, a spatio-temporal variation compensation mechanism is embedded in the whole process from data acquisition to result verification, the interference of spatial-temporal heterogeneity of water optical characteristics on water depth inversion is effectively dealt with, and the accuracy of water depth inversion is improved. The problems of parameter mismatch and precision reduction of the water depth inversion model caused by dynamic change of optical characteristics of a coastal water body in seasonal and tidal scales are solved.