1384results about "Open water survey" patented technology

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 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 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 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 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.

The invention discloses an automatic monitoring system and method for hydrological data and a storage medium. The automatic monitoring system comprises a multi-modal data acquisition module, an edge intelligent processing module and a cloud platform. The multi-modal data acquisition module acquires water level, water quality and meteorological data in real time through a radar water level gauge, a multi-parameter water quality meter and a rain gauge, and transmits the data to the edge intelligent processing module; the module extracts and aligns spatio-temporal features through a multi-scale spatio-temporal convolution unit, an uncertainty quantization unit is fused with Bayesian to calculate confidence, and complete data or abstract features are selectively transmitted to a cloud according to an abnormal classification judgment result; the cloud platform fuses multi-source features through a space-time attention mechanism, dynamically adjusts an early warning threshold based on a near-end strategy optimization algorithm, aggregates edge node model parameters through a federated learning framework in combination with a homomorphic encryption technology, updates a global model, and then synchronizes the global model to edge nodes to form closed-loop optimization.

The invention discloses a lake algae vertical distribution daily change monitoring method based on a satellite remote sensing image. The invention relates to the technical field of environmental engineering and image processing, and solves the problem that vertical distribution daily variation of algae is difficult to obtain through single observation. The method comprises the following steps: collecting data of algae samples at different depths of a lake, establishing satellite-ground synchronous data, establishing a vertically distributed mathematical model, and parameterizing distribution characteristics of algae in a water depth direction; the remote sensing image is preprocessed, and an inversion model is established based on the relation between the surface chlorophyll concentration in the in-situ data and the remote sensing reflectivity. Utilizing machine learning to optimize model parameters, and combining with a vertical distribution parameterization result to construct an algae vertical distribution remote sensing monitoring model; the method integrates remote sensing data to invert lake algae vertical distribution, performs time sequence analysis on data obtained by inversion, estimates algae vertical distribution and spatial change conditions of a target water area, and provides important scientific basis and technical support for algae bloom prevention and control and environmental protection.

The invention belongs to the technical field of pollution traceability, and provides a pollution traceability analysis method and system based on water quality sampling data. The method comprises the following steps: receiving multiple pieces of returned water quality monitoring data corresponding to a target sampling area, and obtaining geographic information, hydrological information and known pollution source information of the target sampling area; performing evaluation by adopting an analytic hierarchy process to obtain hydrological complexity of the target sampling area, and formulating a simulation strategy of the pollution diffusion simulation model; controlling the pollution diffusion simulation model to perform analogue simulation according to the simulation strategy to obtain a plurality of pollutant traceability path simulation results; and screening the plurality of pollutant traceability path simulation results by using a generative adversarial network to obtain an optimal pollutant traceability path simulation result. According to the invention, accurate and efficient pollution tracing can be realized, a reliable basis is provided for treatment, and the pertinence and effectiveness of pollution treatment can be remarkably improved.

The invention provides a marine meteorological data quality control method based on a cross-parameter correlation network, which belongs to the technical field of marine meteorology, and comprises the following steps: constructing a cross-parameter correlation network model comprising an air temperature and air pressure correlation rule, a humidity and air temperature linear correlation rule and a wind speed and air pressure gradient extraction correlation rule; a sliding window algorithm is adopted to calculate a correlation coefficient in real time and identify correlation abnormity, a multivariate abnormity detection mechanism of four dimensions of parameter threshold overrun, spatio-temporal change rate abnormity, probability density distribution offset and correlation verification failure is established, a fuzzy comprehensive evaluation method is adopted to calculate a comprehensive abnormity index, and data abnormity is judged. And the authenticity of the abnormal data is confirmed in combination with a multi-sensor cross validation mechanism, and finally the abnormal data is restored by adopting a virtual sensor data reconstruction algorithm based on correlation network reverse calculation, so that the technical problem of poor abnormal data detection effect in the prior art is solved.

The invention discloses a rainstorm torrential flood physical constraint prediction method and system based on a graph neural differential equation, and belongs to the technical field of rainstorm torrential flood prediction. Carrying out space-time attention fusion based on a graph; carrying out modeling and dynamic deduction based on a graph neural differential equation of physical constraints; predicting and outputting a multi-task flood hydrograph; and carrying out joint loss function design and end-to-end training. The system comprises a multi-modal hydrological feature obtaining and coding module used for multi-source heterogeneous data feature extraction, a graph-based space-time attention fusion module used for deep fusion of multi-source heterogeneous features, and a physical constraint-based graph neural differential equation dynamic core module used for continuous dynamic process modeling. And the prediction output module is used for outputting the spatial distributed flood hydrograph. According to the method, the problems of low reliability, poor timeliness and poor extrapolation capability during rainstorm torrential flood prediction in the prior art are solved.

The invention relates to an internal isolated wave waveform reconstruction method and device, equipment and a medium. The method comprises the following steps: constructing a data set by using internal isolated wave waveforms of different water depths; constructing an internal isolated wave waveform reconstruction model, and training the internal isolated wave waveform reconstruction model through the data set based on a pre-constructed loss function fusing physical information to obtain a trained internal isolated wave waveform reconstruction model; wherein when the internal isolated wave waveform reconstruction model is processed, firstly, a spatial relationship among different feature values in a data set is extracted, and a first feature is obtained; analyzing time correlation feature information of the first feature to obtain a second feature; and finally, extracting important information of the second feature, and outputting a reconstructed waveform. And performing inner isolated wave waveform reconstruction through the trained inner isolated wave waveform reconstruction model. According to the method, the defects of internal isolated wave waveform observation data can be overcome, the generalization ability of the model is high, and the reconstruction precision is high.

The invention belongs to the technical field of marine environment prediction, and discloses a neural network-based sea wave spectrum intelligent correction method and system. The method comprises the following steps: setting a target sea area and acquiring environmental data; performing numerical calculation on the sea wave spectrum in the target sea area through a sea wave numerical mode; a sea wave spectrum correction model comprising a numerical calculation module, an actual measurement processing module and an intelligent correction module is constructed through a deep learning method; and carrying out precision verification and adaptability evaluation on the constructed sea wave spectrum correction model. According to the model, the problem of errors caused by physical process simplification in the simulation process of a traditional method is solved. Meanwhile, the problem that an existing data assimilation method is limited by observation data is solved. According to the method, the simulation precision of the sea wave mode can be effectively improved, and the method has good sea area adaptability.

The invention belongs to the technical field of data processing, and particularly provides a flood probability forecasting method and device based on artificial intelligence, and the method comprises the steps: obtaining downscaled long-series meteorological observation data through employing a statistical downscaling model based on ground observation data and satellite remote sensing inversion data; constructing a deep learning model considering the hydrological process; reconstructing to obtain a long-series daily runoff process of the scarcity data drainage basin, and extracting a flood event by adopting an over-quantitative sampling method; adopting a plurality of artificial intelligence models suitable for flood magnitude simulation to construct a flood simulation model; based on the simulation result and the actually measured flood magnitude, a Bayesian mode averaging method is adopted to deduce the weight of each model, and an artificial intelligence-Bayesian coupling model is constructed; a Copula function is combined to construct a distribution function of actually measured flow and forecast flow, and an artificial intelligence-Bayesian coupling model is adopted to carry out flood forecasting. The method is used for solving the defect that the uncertainty and the artificial intelligence technology are not fully considered for flood forecasting.

The invention discloses a method for improving channel traffic volume based on an underwater map, and belongs to the technical field of water transportation traffic, and the method comprises the following steps: S1, constructing an underwater high-precision map, deploying an unmanned ship and an underwater robot, and collecting underwater topographic data by using a multi-beam sounding technology to construct a high-precision underwater map; s2, establishing a dynamic channel planning model, collecting hydrological data and ship traffic data of different time and places of a channel by using a big data analysis and machine learning algorithm, and generating a dynamic channel use scheme; and S3, integrating a ship navigation and management system, developing ship navigation terminal equipment, and integrating the dynamic channel planning model with a monitoring system of a channel management department. According to the invention, channel resource utilization is optimized, intelligent dynamic planning is realized, ship passing efficiency, navigation autonomy, navigation safety and system stability and reliability are improved, channel management scientificity is enhanced, and cooperation of ships and management departments is enhanced.

The invention discloses a river surface flow velocity measurement method and system based on physical constraint and multi-modal fusion, and relates to the technical field of multi-modal data processing, a target area is determined, and river surface image sequence data, depth information and environment data of the target area are acquired; preprocessing the collected data, embedding redundant codes in data representation, and synthesizing to form a basic data set; and based on the basic data set, performing preliminary optical flow estimation on the river surface image sequence data by using an optical flow method. According to the method, optical flow information, depth information and environment data are fused, comprehensive utilization of multi-source data is achieved, in the data fusion process, redundant codes in data representation are combined for error detection and correction, error points in the data are effectively recognized and corrected, the reliability and accuracy of the data are improved, and the data fusion efficiency is improved. And by utilizing a multi-modal data fusion and error correction mechanism, the robustness of the system is remarkably enhanced, so that the system can stably operate in a complex and changeable river environment.

The invention relates to the technical field of water conservancy projects, and discloses a water conservancy project water quality detection method and system based on artificial intelligence, and the method comprises the steps: collecting first multi-source data for a water conservancy project, carrying out the alignment of water quality parameter data, remote sensing image data and meteorological and hydrological data in the first multi-source data based on a timestamp, and obtaining second multi-source data; performing data preprocessing and feature extraction processing on the second multi-source data to obtain target feature data; designing a multi-task space-time diagram convolutional network, performing parameter optimization by adopting a genetic algorithm, and constructing a water quality detection model; inputting the target characteristic data into a water quality detection model, and outputting a water quality detection result through the water quality detection model; when the water quality detection result determines that the water quality is abnormal, generating a pollution diffusion simulation map based on a space-time diagram structure and a water quality diffusion rule, and providing emergency decision suggestions according to a water conservancy project scheduling scheme; the accuracy and functional integrity of water quality detection are improved.

The invention relates to a river flood flow monitoring method based on remote sensing image and hydrodynamic model fusion, and belongs to the technical field of flood monitoring. The method comprises the following steps: dividing flood levels in a river drainage basin by combining historical rainfall and flood conditions, drawing flood hydrographs of different flood levels, determining 24-hour design rainstorm of each level of flood, establishing a river one-dimensional hydrodynamic model by utilizing section survey data, and constructing a water level-water surface width relationship of a section to be measured; solving the model to obtain a water level-flow relation curve of riverway water rising and water recession under each level of flood, determining the recurrence level of the flood, calculating the current water surface width of the to-be-measured riverway section, obtaining the corresponding water level, and calculating the water level of the to-be-measured riverway section. And selecting a corresponding water level-flow relation curve according to the flood level of the river channel and the stage of the flood flow process, and substituting into the curve to obtain the flood flow of the section to be measured in the current time period. According to the method, through water level inversion of different levels and different stages, the accuracy of inversion of the flood peak flow by the satellite remote sensing image is improved.

The invention relates to the technical field of water regimen forecasting, in particular to a river channel water regimen forecasting method and system based on digital twinning. The method comprises the following steps: arranging sensor nodes along a river channel and carrying out real-time monitoring on the river channel environment water regimen along the river channel to obtain real-time data of the water level along the river channel, real-time data of the flow along the river channel and real-time data of the weather along the river channel; performing digital twinborn construction and risk estimation division along the riverway to generate a riverway water regimen flood risk estimation period and a riverway water regimen drought risk estimation period; the method comprises the following steps: performing flood risk forecast processing in a river water regimen flood risk estimation period to execute corresponding river water regimen flood risk management decision-making work; and performing drought risk forecast processing in the river channel water regimen drought risk estimation time period to execute corresponding river channel water regimen drought risk management decision-making work. According to the invention, efficient and accurate water regimen prediction and decision support can be realized.

The invention relates to the technical field of ship navigation, and discloses a ship navigation route optimization method and system. The method comprises a meteorological change sequence, an ocean current fluctuation sequence and channel obstacle distribution data. Then carrying out two-dimensional route topology construction processing to generate route distribution characteristics; calling a pre-trained path optimization model to carry out nonlinear path analysis processing to obtain an optimal route prediction value and a key risk area identifier; performing navigation environment compensation correction processing on the optimal route prediction value based on the association relationship between the meteorological change sequence and the ship control sensitivity to generate a corrected optimal route prediction value; and finally, generating a course maintenance strategy set including a course adjustment path optimization scheme and a speed dynamic regulation and control processing scheme according to the key risk area identifier. According to the method, dynamic environmental factors can be comprehensively processed, the accuracy and adaptability of route planning are improved, risks are effectively identified and avoided, and high efficiency and safety of ship navigation are guaranteed.

The invention relates to the technical field of automatic acquisition, in particular to an automatic riverbed material acquisition control system and method based on Beidou navigation. The method comprises the following steps: acquiring a Beidou satellite multi-dimensional data set; performing original data preprocessing on the Beidou satellite multi-dimensional data set to obtain a Beidou multi-dimensional standard data set; performing multi-source data fusion based on the Beidou multi-dimensional standard data set to obtain riverbed multi-source fusion data; generating a riverbed sampling decision by using the riverbed multi-source fusion data; therefore, through comprehensive application of multi-source data fusion, path optimization and automatic data evaluation, the problems of incomplete data, unreasonable sampling path and low report generation efficiency in the traditional riverbed data sampling process are solved, and the riverbed data acquisition precision, efficiency and intelligent level are improved.

The invention belongs to the technical field of hydrological forecasting, and relates to a reservoir flood peak flow and time prediction method and system considering rainfall center movement. The method comprises the following steps: acquiring topographic data, historical meteorological data and historical runoff data of a target drainage basin; the target drainage basin is divided into a plurality of sub-drainage basins based on topographic data, hydrological models are constructed respectively, and parameters of the hydrological models of the sub-drainage basins are calibrated through historical meteorological data and historical runoff data; acquiring a moving track and rainfall intensity change of a rainfall center of the target drainage basin and real-time meteorological data and real-time runoff data of each sub-drainage basin, and inputting the moving track and the rainfall intensity change and the real-time meteorological data and the real-time runoff data into a hydrological model of the corresponding sub-drainage basin to obtain flood flow hydrograph corresponding to rainfall of a plurality of preset time steps; and superposing the flood flow hydrograph of a plurality of preset time steps to obtain a reservoir flood peak flow and time prediction result. According to the method, the flood prediction precision and reliability are improved, and a scientific basis is provided for flood scheduling and safety management of the hydropower station.

An early warning method for debris flow disaster in a small watershed, and a disaster reduction method for debris flows in a small watershed is disclosed. An early warning method for debris flow disaster in a small watershed is provided, in which parameters of a hydrological model are calibrated by rainfall, evaporation and runoff data of the watershed based on water balance of the watershed, and an early monitoring and warning scheme for debris flows, which takes water storage in the watershed as a core monitoring and evaluation indicator, is established. The GR4J hydrological model is improved by taking into account a watershed area, specific yield of an aquifer and several geological parameters, as well as an evapotranspiration effect of vegetation. A disaster reduction method by means of regulation of a drainage channel can provide a basis for mathematical model simulation studies of disaster reduction in small watersheds.

The invention discloses a three-dimensional fluorescence dynamic monitoring and automatic processing system for water quality. The system comprises a water sample collection module, a three-dimensional fluorescence spectrum detection module, a pollution fingerprint database, a spectral analysis unit, a diffusion path simulation module based on a distributed sensor network and an automatic control execution module. The diffusion path simulation module is used for acquiring spatio-temporal data through a water quality spectrum sensor, a hydrological sensor and a meteorological sensor, and dynamically predicting a pollutant migration track by combining a water body flow field model constructed by a finite volume method and a first-order dynamic fluorescence attenuation model; the automatic control execution module is based on the pollution recognition result and track prediction, emergency operations such as gate opening and closing and medicament adding are linked through a fuzzy logic algorithm, and full-process automation from monitoring to disposal is achieved. The system completes pollution identification within 10 minutes through a spectral angle mapping algorithm, and the defects that a traditional method lags in response and lacks dynamic prediction and automatic disposal are overcome.

The invention discloses a hydrological flow monitoring method and a hydrological flow monitoring system based on a dynamic topology adaptive technology. The hydrological flow monitoring method comprises the following steps: performing hydrological flow monitoring in real time by adopting a trained online flow measurement model; the method comprises the following steps: firstly, acquiring actually measured hydrological element data in the same period of an online flow measurement data sequence, and establishing a river section hydrological element information base; then based on edge computing gateway equipment, flow velocity analysis is carried out by adopting a dynamic topology adaptive technology, parameter calibration is carried out on an online flow measurement model, and the average flow of a test section is calculated; performing comparative measurement analysis and precision evaluation on the flow online monitoring result based on the edge computing gateway equipment; and finally, outputting the finally trained online flow measurement model. According to the method, the applicability of the online flow measurement equipment under different hydraulic conditions and water flow characteristics can be further explored, and a key technical support is provided for application of flood control early warning and other aging sensitive scenes and construction of a cloud-side-end collaborative distributed hydrological monitoring network.

The invention relates to the technical field of hydrological data processing, in particular to a water conservancy intelligent internet-of-things sensing method and system, and the method comprises the following steps: combining statistical data into a feature set based on hydrological monitoring data including flow, water level and rainfall information, judging whether the data accords with normal distribution, and generating a data distribution feature vector set. According to the method, the change trend of the data is captured through multi-level data distribution characteristic analysis, and the monitoring precision of the data exception is enhanced. In the prediction process, new data and weight adjustment are gradually fused, so that the model can adapt to environmental changes in real time, and the hydrological state in a future short period is accurately predicted. Besides, abnormal data can be identified in real time based on analysis of quantiles, potential problems can be quickly found, and state division of different levels can be carried out according to abnormal degrees. In sampling scheduling, the priority of data acquisition is adjusted according to a probability model of state transition, so that monitoring resources are efficiently configured.

The invention relates to an ocean internal temperature inversion method fusing PIES and sea surface multi-source parameters, and belongs to the technical field of ocean environment parameter inversion and ocean dynamics observation. Comprising the following steps: constructing a nonlinear empirical model (Multi-RF) of seawater temperature inversion by using a random forest algorithm and fusing data such as a historical observation temperature-salinity profile observed by a temperature-salinity-depth meter (CTD) and sea surface multi-source parameters; and inverting the seawater temperature through a Multi-RF model by using the sound propagation time and multi-source sea surface data calculated by the PIES. Compared with a traditional empirical mode mapping (GEM) model, the Mull-RF model provided by the invention has the advantages that on one hand, the Mull-RF model utilizes fewer samples to establish a mapping relation between the seawater temperature and multiple parameters, and the problem of sample shortage in construction of a seawater temperature inversion model fusing sea surface multiple parameters and PIES observation sound propagation time is solved; on the other hand, compared with polynomial fitting in a GEM model, the random forest adopted by the Multi-RF model can achieve nonlinear fitting from multiple parameters to the seawater temperature, and therefore the temperature inversion precision is remarkably improved.