1330 results about "River water" patented technology

The invention provides a river water quality monitoring method based on multi-source remote sensing data, and belongs to the technical field of river water quality monitoring. The method comprises the following steps: establishing an inherent optical characteristic model of a river water body based on a Hybrid water body radiation transmission model, optimizing a calculation path by adopting a shortest path algorithm, and carrying out water body optical component inversion by adopting a quasi-analysis algorithm and a generalized inherent optical characteristic algorithm in combination with a multispectral characteristic enhancement model to obtain absorption coefficients of all components; and constructing a multi-band combination index to realize optical coupling effect decoupling, and applying the fine-tuned water quality parameter inversion basic model to a target river area to output a suspended matter concentration distribution diagram, a chlorophyll concentration distribution diagram and a transparency distribution diagram. The technical problem of low precision of remote sensing inversion of water quality parameters caused by mutual coupling of multiple optical active components in a river water body is solved.

The invention relates to the technical field of hydrological monitoring, in particular to a multi-satellite collaborative hydrological monitoring system. The method comprises the following steps that a water level height measurement module obtains radar pulse recovery time delay data through a height measurement satellite and calculates the water level height, abnormal points are removed to generate river water level data, a water remote sensing module collects river water images through a remote sensing satellite to extract water boundary features, water area change data are deduced, and the water level height measurement module calculates the water level height. The meteorological wind shear analysis module obtains river channel meteorological data through a meteorological satellite, microwave scattering measurement and wind speed inversion are carried out, wind-induced shear stress parameters are generated, and the flow estimation module carries out section dynamic analysis and estimates the water flow in combination with river channel water level and water area data. And the flow correction module corrects the river water flow based on the wind-induced shear stress parameter and carries out real-time monitoring, and data are synchronously uploaded to the control terminal, so that comprehensive dynamic monitoring of the hydrological state of the river is realized. According to the invention, a more efficient multi-satellite collaborative hydrological monitoring system is realized.

The invention provides a river water quality parameter supervision method and system based on deep learning. The method comprises the steps of self-calibration multi-source data acquisition, diffusive water quality prediction, extreme water quality parameter simulation, reverse diffusion pollution positioning and water quality parameter intelligent supervision. The invention relates to the technical field of river water quality supervision, in particular to a river water quality parameter supervision method and system based on deep learning. By introducing a graph convolutional neural network and a physical diffusion constraint model, space-time diffusion trend modeling of pollutants in a river channel is realized; constructing an extreme pollution event simulation and attribution mechanism by combining a generative adversarial network and physical verification; further adopting a multi-modal Bayesian inversion model and a graph deconvolution structure to realize accurate source tracing of the pollution source; the system can dynamically sense hydrological changes, construct an adaptive threshold judgment mechanism, realize prediction, tracking and response to pollution risks, and provide efficient and intelligent technical support for river ecological safety management.

The invention relates to the technical field of water resource scheduling, and provides an urban inland river system intelligent resource scheduling management system and method based on dynamic data collaboration, and the method comprises the following steps: S1, collecting river hydrological data, water quality indexes, urban water demands and water transfer facility state data in real time, and carrying out the time-space alignment and abnormal value filtering; s2, a hydrodynamic force-water quality coupling model of multiple water sources is established, a multi-objective optimization model is established in combination with constraint conditions of ecological base flow and an engineering upper limit, and the multiple water sources include but not limited to river water, underground water, reservoir water, reclaimed water, rainwater and desalinated seawater; and S3, carrying out global dynamic optimization on the water transfer proportion and the water transfer facility parameters by adopting a model algorithm, and minimizing the water transfer cost and the ecological influence. By quantifying the maximum flow upper limit and the ecological base flow threshold value of each water source and optimizing the water transfer proportion and facility parameters, the water resource utilization efficiency is improved, and it is guaranteed that the water transfer cost and the ecological influence are balanced in the urban river water dispatching process.

The invention discloses a river water surface two-dimensional flow field reconstruction method based on effective tracer detection and tracking, and the method comprises the steps: carrying out the distortion correction of an original video image sequence, and setting an ROI region according to the water level detection river water surface range; the method comprises the following steps: collecting a multi-scene floating object data set, screening floating objects with high relative brightness and clear outlines in different scenes, and training a YOLO model to detect the floating objects on the surface of a river; using an OCSORT matching tracking method to track adjacent frames of targets to obtain a trajectory, and filtering according to the length, angle and variance of the trajectory to obtain a stable trajectory; according to the method, object information obtained through target detection is utilized, according to the area, the area change rate and the length-width ratio of a floating object on an image, the following performance and stability of the floating object are evaluated, a track fusion weight is calculated, and the influence of unreliable floating objects is reduced; and gridding the river surface area after orthographic correction, and for a grid with a plurality of tracks, calculating the average flow velocity of the grid according to the confidence coefficient of each track to obtain a gridding two-dimensional flow velocity vector field. The method can accurately identify the floating object which meets the requirements of visibility, following performance and stability on the river surface as an effective water flow tracer, and is suitable for measuring the flow velocity of the river surface in complex scenes such as night, storm, flare, shadow and the like.

The invention discloses a river water level dynamic monitoring and flood overflow risk prediction method based on deep learning, and the method comprises the following steps: S1, collecting multi-source hydrological data, and constructing a time series data set; s2, performing interpolation, denoising and normalization processing on the data to generate a unified time sequence format; s3, constructing a water level prediction model comprising a bidirectional long short-term memory network and an attention mechanism; s4, inputting the preprocessed data into the water level prediction model, and outputting a multi-time-step predicted water level sequence; s5, a dynamic threshold value is set according to the historical extreme value and the real-time hydrological condition, and the flood overflow risk is judged; s6, generating and caching a risk tag, and recording an error; s7, outputting a prediction result and risk information through a communication interface; and S8, periodically updating the input data in a rolling manner, and repeatedly executing the prediction and monitoring process. According to the invention, depth prediction and a dynamic threshold control mechanism are fused, and water level monitoring and flood overflow risk intelligent early warning are realized.

The invention discloses a boundary detection and positioning method suitable for trans-boundary water pollution, and relates to the technical field of river water quality monitoring. According to data obtained at the longitudinal and transverse positions of a river channel of each monitoring point of a selected drainage basin, a two-dimensional hydrodynamic model of the selected drainage basin is established; then, combining with the obtained target pollutant concentration to construct a diffusion model of the target pollutant, identifying and integrating a to-be-positioned region set in each time period, and screening out the concentration abrupt change regions which are spatially adjacent and consistent in concentration gradient change trend through clustering analysis by utilizing the spatial distribution positions and concentration gradient values of the concentration abrupt change regions; and the boundary contour of the pollutant is generated by fitting in combination with the direction information of the flow velocity field. According to the method, after concentration abrupt change areas identified in each time period are integrated, clustering analysis is carried out according to spatial distribution and gradient change trends of the concentration abrupt change areas, pollution boundary contour fitting is optimized in combination with flow velocity field direction information, and pollution boundary detection requirements in a complex water area environment are met.

The invention discloses a quantitative source tracing method for river and lake water pollution in combination with a knowledge graph and machine learning. The invention relates to the technical field of water quality pollution traceability, and the method comprises the steps: collecting and processing target river section data, constructing a hydrodynamic force-water quality model, and obtaining river downstream pollutant diffusion characteristics and target section concentration time sequence characteristics under different discharge situations of each discharge port based on the discharge port position of a target river section through a unit pulse response test; the method comprises the following steps: constructing a source intensity-time-concentration relation knowledge graph, randomly extracting a sample set from the graph, training the sample set by adopting a machine learning method, learning a nonlinear mapping relation between a downstream section concentration time sequence and multi-outlet source intensity, carrying out dynamic inversion of a river pollutant diffusion process, and realizing rapid positioning and contribution quantification of a pollution source. And finally, generating probability distribution of pollution source positions by adopting a Monte Carlo sampling method to assist in improving the traceability judgment priority, thereby improving the accuracy and response speed of river water quality pollution traceability.

The invention discloses a river water pollution real-time tracing method, which comprises the following steps: obtaining a pollution source position of a river basin, obtaining a pollutant type discharged by a pollution source, and obtaining pollutant particle parameters and pollutant types; obtaining a river connection relation of the river basin, and confirming an input flow and an output flow of pollutants; based on the output flow of the pollutants, the pollutant type of the river basin is obtained, and the input flow of the pollutants is obtained; obtaining a theoretical pollutant level based on the theoretical water flow velocity and pollutant particle parameters of the river basin; based on the actually measured pollutant concentration and the actually measured water flow velocity, acquiring an actually measured pollutant level; and obtaining the pollutant source based on the actually measured pollutant level and the theoretical pollutant level. The problems of difficulty in real-time detection and insufficient traceability precision in a water pollution analysis method based on sampling in the prior art are solved. The traceability speed and precision are fully improved, and various hydrological conditions in rivers can be handled.

The invention relates to the technical field of water quality data analysis, and discloses a river water quality pollution assessment method and system based on big data analysis, and the method comprises the steps: building a water quality parameter set; obtaining a preprocessed water quality data set; obtaining a global statistical analysis result through descriptive statistical analysis; carrying out hierarchical multi-time evaluation analysis to obtain a hierarchical evaluation result; obtaining a deviation value between the current water quality data and standard water quality data of a laboratory; reliable water quality characteristic data are obtained; a difference trend analysis result is obtained; performing relevance verification on the river water quality distribution map and the time change map of the target area according to the difference trend analysis result to form a comprehensive analysis result; and finally generating a comprehensive water quality pollution assessment report containing water quality traceability information and a comprehensive analysis result. According to the invention, a multi-parameter sensing technology and intelligent data processing are organically combined, so that the water quality of the river is efficiently, accurately and carefully analyzed, and the pollution degree is evaluated.

The invention relates to the technical field of point cloud data extraction, in particular to a river water surface extraction method based on SWOT point cloud and center line data. The method comprises the following steps: acquiring PIXC point cloud data; performing data preprocessing on the PIXC point cloud data, and performing spatial index construction to obtain point cloud KD tree index data and center line KD tree index data; based on the point cloud KD tree index data and the center line KD tree index data, carrying out topology shape-preserving cutting to obtain river cutting center line data; performing candidate water surface point set construction on the river cutting center line data; therefore, through multi-level spatial index construction, topological conformal cutting, combined space-elevation modeling and structural processing of geometric and hydrological feature fusion, the problems of difficulty in data association, much space isolated noise and insufficient water surface continuity recognition in traditional river water surface extraction are solved; and the extraction precision and the processing efficiency of the river water surface point cloud data are improved.

The invention discloses a river nitrogen pollutant source analysis method based on drainage basin nitrogen turnover and isotope, and particularly relates to the technical field of water treatment.The river nitrogen pollutant source analysis method comprises the following steps that a drainage basin to be researched is selected, river water and soil samples in the drainage basin are systematically collected, and physical and chemical indexes and isotope indexes # imgabs0 # of the collected samples are tested. Nitrate marked by nitrogen isotope delta 15N / delta 18O and # imgabs1 # are used as key indication marks to perform high-precision identification on nitrogen sources in a river and an environmental system, the contribution proportion of different nitrogen sources to the nitrate concentration in a water body is quantified through a stable isotope analysis model, and the concentration of the nitrate in the water body is calculated. And in combination with data, the rates of denitrification and anaerobic ammonia oxidation in environmental samples, sediments and soil are directly measured through combined application of an isotopic tracer technology and a membrane inlet mass spectrometer technology.

The invention discloses a river water quality prediction method fusing quantum-like features and a graph-time sequence model. The river water quality prediction method comprises the following steps: acquiring multivariable water quality observation data, time sequence meteorological data and static basin attribute data of a plurality of river sites; preprocessing the data; generating a meteorological quantum feature vector and a static basin attribute quantum feature vector; after the meteorological quantum feature vector and the static watershed attribute quantum feature vector are spliced and fused with the spatial dependence features, inputting the spliced and fused data and the preprocessed multivariable water quality observation data into a graph-time sequence model for training; and performing reverse normalization processing on simulation output obtained by calculation of the trained graph-time sequence model to obtain continuous day-by-day river water quality simulation data, and evaluating model performance to predict day-by-day continuous river water quality data. According to the method, the corresponding space-time class quantum feature vectors are generated by executing high-dimensional class quantum feature transformation on the multi-source heterogeneous data, so that the characterization capability of the model on the nonlinear interaction relationship of the space-time features is enhanced.

The present invention discloses a cave-type data center constructed based on an underground river, as well as its construction method and operation method, belonging to the technical field of cave-type data center construction. The data center includes a mountain body, in which an underground river is developed, and the underground river flows through a karst cave in the mountain body. On one side or both sides of the underground river in the mountain body, a number of equipment storage tunnels are provided. The number of the equipment storage tunnels are respectively connected to the karst cave through a slope connecting tunnel. An equipment cabinet and a liquid heat exchanger are arranged in the equipment storage tunnel. A river water conveying assembly is connected to the liquid heat exchanger. One end of the river water conveying assembly far away from the liquid heat exchanger passes through the slope connecting tunnel and extends into the underground river. The liquid cooling method is used to cool and dissipate heat from the equipment cabinet. This direct temperature control method significantly improves the heat dissipation and cooling effect on the IT equipment cabinet. At the same time, the heat exchange efficiency is high, and the operation energy consumption of the data center can be reduced.

The invention provides a river flood season pollution intensity prediction and early warning method and system, and relates to the technical field of hydraulic engineering, and the method comprises the steps: obtaining multi-source data in a drainage basin, and carrying out the preprocessing of the multi-source data; constructing a hydrological water quality model for calculating the hydrological cycle process of the whole drainage basin from the upstream to the downstream; calculating point source and non-point source pollution loads to obtain water quality concentration; constructing a prediction data set according to the water quality concentration, and training the XGBoost machine learning model by using the prediction data set; and utilizing the trained XGBoost machine learning model to predict the pollutant concentration in the flood season under the influence of different meteorological conditions and carry out standard exceeding concentration early warning. The method can predict a short-term river water quality change peak value in advance by fusing weather forecast, a hydrological model and real-time monitoring data, realizes early warning of water quality change in advance, and is finally integrated to a drainage basin management decision-making platform, so that advanced regulation and control of a management department and advanced prevention and control of pollution are realized.

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 discloses an intelligent self-adaptive silt filtering system and method for a pumped storage power station, and belongs to the technical field of pumped storage power stations. The system comprises a multi-parameter water quality monitoring module, an intelligent self-adaptive filtering module, a hydraulic cyclone enhanced separation module, a self-cleaning and backwashing module and a central control and data processing module. According to the method, river sediment characteristics are monitored in real time, filtering parameters are dynamically adjusted through an artificial intelligence algorithm, efficient sediment interception is achieved by combining hydraulic cyclone separation and multi-stage filtering, the problem of filtering medium blockage is solved through a self-cleaning mechanism, and meanwhile the self-adaptive learning capacity is achieved so as to optimize system performance. The system can significantly improve the filtering efficiency, reduce energy consumption and operation and maintenance cost, adapt to complex water quality working conditions, and guarantee safe and stable operation of key equipment of the pumped storage power station.

The invention discloses a meteorological water conservancy disaster intelligent grading early warning linkage call routing method and a meteorological water conservancy disaster intelligent grading early warning linkage call routing system. A meteorological water conservancy two-factor early warning matrix is constructed, a cross-department data fusion channel is established, an early warning coupling model is developed, and multi-dimensional early warning fusion is achieved. And formulating a water conservancy special response strategy library, wherein the water conservancy special response strategy library comprises river water level grading routing and mountain torrent risk area customization strategies. And developing an intelligent water conservancy enhancement module, such as a flood control project topology analysis engine and a water and rain work condition semantic analyzer. The method is characterized in that a three-element risk rating model is constructed by an initiative dynamic coupling algorithm, and a water conservancy emergency response topology network based on double dimensions and a water conservancy project BIM data and meteorological early warning space superposition analysis module are adopted. The accuracy and comprehensiveness of disaster early warning can be improved, the timeliness and effectiveness of emergency response are ensured, and the intelligent level and disaster response capacity of the system are improved.

The invention discloses a lightweight optical flow neural network method for river velocity measurement, and the method comprises the steps: collecting a dynamic monitoring image of a water area surface flow field, calculating an approximate optical flow field through combining with a DeepFlow optical flow algorithm, and generating an optical flow label; an improved lightweight ConvFFN-Flow optical flow estimation model is built, and the model comprises a feature extraction module, a 4D related structure body module, a GRU optical flow residual error iteration module and an up-sampling module; by introducing a ConvFFN module, the model parameter scale and the calculation amount are reduced, and the optical flow prediction precision is kept; and based on pixel coordinates of front and rear frames of images of the optical flow field data, obtaining corresponding positions in a real three-dimensional space, calculating an actual displacement amount, and deducing to obtain a real flow velocity vector size and a flow direction of the water body surface. The method still has stable and high-precision optical flow estimation capability when being used for processing scenes such as fine river water surface texture and complex flow state change, and meanwhile, the calculation overhead and the storage demand are remarkably reduced.

The invention relates to the technical field of water quality prediction, in particular to a river water quality prediction method and system based on a machine learning coupling hydrological model, and the method comprises the following steps: obtaining a data set, and dividing the data set into a training set and a test set; constructing a limit gradient lifting model, and performing hyper-parameter optimization on the model; performing correlation analysis on the water quality parameter set and the water quality index WQI value based on the trained limit gradient lifting model, and screening to obtain key water quality parameters influencing the water quality index WQI value; constructing an LSTM model and a soil and water evaluation tool model; simulating a future hydrological water quality process based on the soil and water evaluation tool model, and calculating and outputting a future water quality parameter result; and inputting a future water quality parameter result into the trained LSTM-WQI model to predict a future river WQI value so as to obtain a prediction result. According to the invention, the machine learning algorithm is coupled with the hydrological model to construct the water quality evaluation model which is convenient to use and adapts to local conditions, and efficient and accurate prediction of future water quality is realized.

The invention relates to the field of water body sampling, in particular to sampling equipment for water environment governance, which comprises a sampling mechanism, a force storage mechanism is movably connected in the sampling mechanism, a floating mechanism is arranged on the outer wall of the sampling mechanism, and a trigger mechanism and a positioning mechanism are arranged at the top of the sampling mechanism; the sampling mechanism comprises a sampling box, the force storage mechanism comprises a piston arranged in the sampling box in a sliding mode, and the top of the piston is fixedly connected with a sliding rod. The device can achieve rapid sampling at the specified depth, the sampling efficiency is improved, meanwhile, a water body in the central area of river water on a bridge can be sampled, a running water sample can be obtained, and the sampling efficiency is improved. According to the device, water is directly sampled from water below the water surface, floating objects on the water surface are prevented from being brought into the device, the device has the same sampling depth every time, a good water sample can be obtained, the water taking speed of the device can be rapidly increased through the arrangement of the force storage mechanism, a water pumping mode is directly adopted for sampling, and the sampling efficiency is higher compared with water inflow sampling.

The invention discloses a pond multi-objective optimization management method oriented to agricultural watershed non-point source pollution regulation and control, and relates to the technical field of agricultural watershed non-point source pollution abatement. Multi-dimensional attributes such as pond application types, morphological characteristics and sediment nutrient states are integrated into a unified quantitative framework for the first time, and a generalized additive model is constructed to obtain a multi-objective optimization model; nonlinearity and interaction of pond attributes on river water quality influence can be accurately captured, and the accuracy of river nitrogen and phosphorus concentration prediction is improved. According to the method, pond management measures are quantified into controllable decision variables, a multi-target dynamic optimization model for balancing environmental benefits and economic benefits is established, a Pareto optimal solution is used for replacing a traditional experience decision, a scientific, efficient and quantitatively-implemented pond management scheme is provided, the one-sidedness of a single target is avoided, and the method is suitable for large-scale popularization and application. The cost minimization is realized while the non-point source pollution reduction is maximized, and the accurate configuration and scientific quantitative decision of pond management resources are realized.

The invention discloses a river water level prediction method and system based on a TCN-LSTM-Attention model, and the method comprises the following steps: obtaining the monitoring data of a plurality of sensors related to the water level, carrying out the sorting according to the collection time, obtaining the multi-feature variable time series data, carrying out the data preprocessing of the multi-feature variable time series data, and filling the missing data; dividing the preprocessed data into a training set and a test set, extracting input features and output features by using a sliding window, and performing standardization processing on the data to obtain a feature data set; a TCN-LSTM-Attention model is constructed, and model training is carried out; and performing river water level real-time prediction based on the trained TCN-LSTM-Attention model, and outputting a river water level prediction result. According to the method, the river water level can be accurately and effectively predicted by using the historical water level and rainfall data of the river, and high-accuracy multi-step water level prediction is realized.

The utility model discloses a multi-parameter water quality monitoring device which comprises a circular bottom plate, a plurality of transverse rods are fixedly connected to the side face of the circular bottom plate, one end of each transverse rod is fixedly connected with a floating ring, a circular shell is fixedly installed on the upper surface of the circular bottom plate through screws, and a supporting plate is fixedly connected to the inner wall of the circular shell. And one side of the support plate is fixedly connected with a detection cylinder. A floating ring and a water suction pump are arranged, the floating ring provides buoyancy to enable the device to float on the water surface, a sampling pipe is inserted into a river and moves along with water flow, in the moving process, an extraction pipe extracts river water for analysis and detection at set intervals, the device floats all around, monitoring sites are different, and the purpose of random monitoring is achieved. Meanwhile, river water in different environment sites can be detected, the detection result is more accurate, workers do not need to carry out routine sampling on different river basins of the river, and more time and labor are saved.

The invention discloses a river health evaluation method suitable for northern water-deficient cities, and relates to the technical field of environmental science and engineering.The system comprises the following steps that the pH value, the dissolved oxygen concentration and the total electrolyte amount are monitored through a pH sensor, a dissolved oxygen sensor and a conductivity sensor, the river water surface, the river channel and the surrounding environment are monitored through a GIS, and the river health evaluation result is obtained. Comprising a land utilization type, a vegetation coverage rate and a river bank erosion condition, collecting environmental problems uploaded by the public through a mobile phone APP, and investigating the satisfaction degree of the public to the environment; a river health evaluation system is constructed, the trend of river health is predicted, and potential factors affecting river health are identified; and according to a prediction result, configuring a decision support system to assist in making a management strategy, and regularly reviewing and evaluating system effectiveness. The river health evaluation method and management system are continuously optimized by dynamically adjusting river management and protection work according to a dynamic adjustment result, and the river ecological system of northern water-deficient cities is effectively protected for a long time.

The invention relates to a river water level prediction and early warning model based on multiple data sources, which comprises the following steps: firstly, collecting meteorological data, hydrological data and hydrological station observation data, integrating multiple data sources, and establishing a data set; preprocessing the collected data to ensure the quality and integrity of the data; according to domain knowledge and data analysis requirements, extracting various characteristic indexes as input characteristics of the water level prediction model; appropriate machine learning and statistical models are selected, and the integrated multi-data-source information is used for training and modeling. The water level prediction scheme based on the multiple data sources can be combined with information of multiple data sources, such as meteorological data, hydrological data and hydrological station observation data, so that the accuracy and reliability of water level prediction are improved, the method is better applied to hydrological monitoring, and the accuracy is improved.

The utility model discloses a multi-filtering-form multifunctional sewage blocking treatment system for a riverway, which comprises a group of soft enclosures which are sequentially arranged along the riverway to form a plurality of independent water purification areas, floating plants and ecological plant floating beds are arranged in the soft enclosures, and a vertical bank slope automatic liftable river water body filtering device is arranged between the soft enclosures. According to the system disclosed by the utility model, through the innovative design of the filter cloth and the application of a plurality of soft enclosures, step-by-step filtration and regional division of a water body are realized, and the filtration efficiency is improved. Meanwhile, the system adopts a hydrophilic high polymer material and a super-hydrophobic high polymer material, and combines biological filtration and physical filtration, so that the water quality is effectively purified. The system is further provided with an in-situ functional treatment area, and the water treatment efficiency and effect are improved by planting aquatic plants and adding functional filler.

The invention discloses a device for acoustically monitoring suspended sediment concentration and section particle size mean value of an ocean and a river, and relates to the technical field of ocean and river water environment monitoring. The four groups of piezoelectric ceramic transducers at the transmitting end are used for transmitting sound pulses with multiple frequencies into the same water body, and the broadband hydrophone array at the receiving end is used for synchronously receiving acoustic backward scattering signals which are scattered back. Wavelet threshold denoising and frequency domain filtering are combined, acoustic back scattering signals are preprocessed, turbulence and biological noise are eliminated, nonlinear errors caused by the particle size-concentration coupling effect are solved through an inversion algorithm fusing attenuation and scattering signals and in combination with a dynamic hierarchical data processing technology, and the accuracy of the acoustic back scattering signals is improved. The device realizes high-precision real-time measurement of suspended sediment parameters in ocean and river environments, has the capability of adaptive correction of environmental parameters, and is suitable for scientific research and engineering monitoring scenes.

The invention discloses a Yellow River water conservancy project real-time monitoring system based on edge computing, and particularly relates to the technical field of cloud computing. The system constructs a flow state characteristic interval by collecting water and sediment data; loading an algorithm component based on flow state characteristics, and changing a master control pointer by using atomization operation to realize calculation power logic hot switching; analyzing a difference value between the instantaneous torque of the motor and the no-load reference to obtain a sediment deposition resistance torque; mapping the resistance moment to a digital shadow table to extract a gain compensation parameter; and resetting controller parameters and modulating the pulse width signal according to the compensation coefficient, and outputting a driving voltage to control the gate. Through the edge end algorithm hot switching technology, the problems that a traditional system is difficult to adapt to the variable water and sediment working conditions of the Yellow River and logic updating shutdown are solved, and monitoring continuity is guaranteed. The defect of power mismatching of a single driving mode is overcome, motor overload and mechanical jamming are prevented, gate control accuracy and operation stability are greatly improved, and the service life of equipment is greatly prolonged.

The invention belongs to the technical field of co-scheduling of a river water path hydrogen chain and a watershed power system thereof, and particularly relates to a medium and long-term scheduling method of a power system fused with a flexible water path hydrogen chain. Comprising the following steps: establishing THPE operation constraints considering upstream river water level influence; the influence of the seasonal water level of the river channel on HV navigation conditions is considered, and HV space-time energy transfer constraints are constructed; taking the lowest operating cost of the waterway hydrogen chain and the watershed power system thereof as an objective function, and proposing a medium-and-long-term scheduling model of the waterway hydrogen chain watershed power system; the influence of the hydrogen carrying capacity of the hydrogen transport ship on the real-time hydrogen charging and discharging rate is considered, and a precision self-adaptive piecewise linearization method is designed to improve the solving efficiency; processing the nonlinear constraint of the model by adopting a linearization method, and solving the scheduling model to obtain an optimization strategy; according to the optimization strategy, medium-and-long-term cooperative operation of the waterway hydrogen chain and the watershed power system thereof is guided. According to the method, the cooperative scheduling capability and economical efficiency of the hydrogen-electricity system can be remarkably improved.