908 results about "River water" patented technology

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 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 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 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 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 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 discloses a polluted river water nitrogen and phosphorus removal efficiency evaluation system and method based on element analysis, and relates to the technical field of sewage treatment evaluation. An algae growth dynamic model is trained, and the future growth dynamic state of algae is obtained according to future water body data and future environment driving data; the method comprises the following steps: analyzing the interference influence of interference ion concentration on sensor measurement according to future water body data, dynamically simulating the attachment rate and biofilm thickness of algae on the surface of a sensor probe according to future growth of the algae, analyzing the attachment influence of the algae on sensor measurement, and correcting the measurement precision of the sensor according to the interference influence and the attachment influence. According to the corrected sensor data, whether the nitrogen and phosphorus removal efficiency of the polluted river water meets the removal standard or not is evaluated, real-time correction of the sensor data is achieved through interference ion concentration analysis and dynamic simulation of biological adhesion influence, and the accuracy of monitoring data and the scientificity of pollution treatment efficiency evaluation are effectively improved.

The application relates to the technical field of sediment sampling, and discloses a river water environment treatment and improvement device, which comprises a fixing frame, the inside of the top end and the bottom end of the fixing frame is provided with a sliding rail, the inside of the sliding rail is provided with a sampling assembly for collecting sediment, the sampling assembly comprises a bottom support column, the bottom support column is in sliding connection with the sliding rail, the top of the bottom support column is fixed with a supporting rod, the top of the supporting rod is fixed with a sampling cylinder, the surfaces on the two sides of the sampling cylinder are fixed with connecting blocks, the river water environment treatment and improvement device can intercept garbage and sundries in the sediment in the water body through a filter screen, the probability of garbage entering the inside of the sampling cylinder is reduced, the water flow is limited through the conical pipe shape, the water body shunted by the filter screen can be recombined, a certain flow rate can be maintained to wrap the sediment and enter the inside of the sampling cylinder, the precision of sampling sediment is improved, and the efficiency of water environment treatment is effectively improved.

The invention discloses a feature point identification method of a river hydrodynamic condition. The method comprises the following steps: S1, hydrological data collection; s2, dividing flow levels and simulating a flow field of each flow level; s3, arranging alternative control sections; s4, determining a target control section; and S5, constructing a correlation function to determine a target feature point. According to the method, historical hydrological and geographic data of a to-be-monitored river reach are utilized, hydraulic engineering simulation software is combined, a target control section is selected and determined by calculating section similarity indexes, relevancy values of all nodes in the target control section are calculated and selected through a constructed relevancy function, and the flow velocity of the nodes is combined; determining alternative feature points, and finally rejecting points influencing navigation, namely target feature points; the identified feature points are optimal monitoring points for arranging the river flow velocity monitoring device, a scientific basis is provided for reasonably arranging flow velocity monitoring points, only the water flow velocity of a limited number of feature points can be monitored, and the hydrodynamic change condition of the whole river can be mastered.

The utility model provides a terrace-imitated multi-pond river bank structure, relates to the technical field of water conservancy and water ecological management, and solves the problems of shortage of land resources and insufficient attractiveness in existing river bank management. The ecological pond comprises a plurality of layers of ecological ponds which are arranged in a step shape from top to bottom, the peripheries of the ecological ponds are defined by retaining walls, the ecological pond on the upper layer is communicated with the ecological pond on the lower layer through a first overflow port, the ecological pond on the top layer is provided with a settling pond, the settling pond is communicated with the ecological pond on the top layer through a filter dam, and the settling pond is communicated with the ecological pond on the bottom layer through a second overflow port. An aquatic purification and soil fixation system is arranged in the ecological pond. The device has the beneficial effects that a wetland effect is formed, the residence time of water entering a river is prolonged, the water quality purification effect is improved, and the effect of beautifying a river bank and the surrounding environment is achieved.

The invention discloses a flood inundation range dynamic simulation method and system based on multi-source data fusion. The method comprises the following steps: S1, collecting multi-source original data of a to-be-simulated target drainage basin; s2, extracting an initial submerging range grid according to the multi-source original data; s3, according to the initial submerging range grid, generating a river channel-beach area coupled section terrain file; s4, obtaining the dynamic water level process of each downstream section according to the section topographic file and the weather forecast rainfall field; and S5, obtaining a dynamic simulation result of the flood inundation range according to the dynamic water level process. According to the invention, through continuous driving of the distributed hydrological model and the one-dimensional hydrodynamic model, full-chain simulation from rainfall forecast to submerging dynamic state is realized, so that the submerging range can be updated in real time along with meteorological conditions and riverway water levels.

Disclosed is a method for regulating confluence of tributaries with a large drop difference and a large angle into a trunk canal. The method includes: banking up a reservoir at an upstream end of a tributary river, and widening a water surface, such that uniform distribution of water flows is completed; then conducting scouring and energy dissipation to reduce a flow velocity and kinetic energy of the water flows; then conducting flow-slowing sedimentation, such that sediment is deposited on one side of the tributary river connected to a downstream side of a trunk stream; and then conducting flow diversion at one side of the tributary river connected to an upstream side of the trunk stream, such that an included angle of confluence of tributary river water into the trunk stream is reduced.

The invention relates to the technical field of water quality monitoring, in particular to an automatic river water quality monitoring and analyzing method and system.The method comprises the following steps that on the basis of real-time and historical water quality monitoring data, multi-source monitoring information is integrated through the data fusion technology, and flow characteristics of a river water body are simulated through a finite element analysis method; and carrying out weighted average on data collected at multiple time points by implementing a Kalman filtering algorithm to generate an integrated hydrodynamics and spatial distribution prediction result. According to the analysis method, the analysis depth and accuracy of complex water body system behaviors such as rivers are improved through the analysis method integrating the water quality monitoring data and the fluid dynamic model, and the relation between the water quality change and the water flow dynamic process is comprehensively analyzed and predicted. The scientificity and effectiveness of a water quality management strategy are enhanced, and the capability of coping with complicated water environment challenges is improved.

The invention relates to the technical field of water quality detection, in particular to a riverway water quality detection analysis method and device for river environment protection, and the method comprises the following steps: pre-treating a collected riverway water sample, equally dividing the pre-treated riverway water sample into two parts, and measuring the turbidity of any one of the riverway water samples, performing water quality absorbance detection on the other part of the river channel water sample; forming a water sample absorbance sequence by the absorbance of all wavelengths of the river channel water sample, and correspondingly, obtaining a pure water absorbance sequence of an equal-volume pure water sample; obtaining a calibration water sample sequence by utilizing the difference between the water sample absorbance sequence and the pure water absorbance sequence; determining an absorbance correction value of each preset water quality influence element; and obtaining the content of each preset water quality influencing element in the river water sample by utilizing the absorbance correction value and combining with the Lambert-Beer law. Therefore, the accuracy of river water quality detection is improved.

The invention discloses a real-time simulation and prediction system for river water flow based on digital twinning, and the system comprises the following modules: a multi-source data synchronous collection module which is used for constructing a core hydrological data flow; the data preprocessing module is used for outputting a core driving data stream; the hydrodynamic digital twinning construction module is used for establishing a water level difference driven twinning model; the real-time simulation and assimilation calibration module is used for performing real-time calculation by taking the core driving data flow as a boundary condition and outputting a real-time simulation twin body; the intelligent rolling prediction module is used for generating a multi-section prediction sequence by utilizing an improved Seq2Seq-Attention model based on the real-time simulated twinborn body and the core driving data stream; and the visualization and early warning output module is used for presenting the prediction result and triggering early warning. According to the method, a physical mechanism and a data model are fused, and through dynamic calibration and self-adaptive prediction, the precision, the stability and the prediction period of river flow prediction are remarkably improved.

The invention discloses a partition plate type fishway built on a floodplain, and relates to the technical field of fishways, the partition plate type fishway comprises a fishway and a movable cover plate, the fishway is a partition plate vertical seam type fishway, the movable cover plate is arranged at an opening in the upper end of the fishway, and the movable cover plate is detachably connected with the fishway. According to the partition plate type fishway built on the floodplain, fish migration can be achieved for a floodplain type river channel, and silt can be prevented from blocking the fishway when river water overflows the floodplain.

The invention relates to the technical field of intelligent control, and discloses a river flow simulation method and system applied to a full-automatic sediment monitoring system.The method comprises the steps that before sediment conditions are detected, according to multi-dimensional collection information corresponding to a to-be-analyzed river water sample, the to-be-analyzed river water sample is analyzed; further combining with a river flow simulation model which is trained to converge in advance to generate a flow control parameter corresponding to the existing river water sample so as to simulate the river water flow condition when the to-be-analyzed river water sample is collected; after the river water flowing condition is simulated when the river water sample to be analyzed is collected, the sediment is detected through the detection unit. Therefore, the river flow condition can be accurately simulated when the sediment content is tested, and the testing accuracy of the sediment content is improved while the testing efficiency of the sediment content is improved.

The invention relates to the technical field of image processing, in particular to an online calibration method and system for hydrological flow data, and solves the technical problem of low flow calculation precision caused by rough window segmentation and illumination interference in the prior art. The method comprises the following steps: collecting a water surface video in a river water flow process, and setting a velocity measurement line area in the water surface video along a water flow direction; according to the gray level distribution of the velocity measurement line area, carrying out illumination state judgment, and according to a judgment result, carrying out image restoration on an illumination abnormal area in the water surface video to obtain a restored water surface video; constructing a space-time image for the repaired water surface video, performing corner detection, clustering corners to divide a plurality of windows, and constructing a window weight for each window; and weighting the texture angle of each window according to the window weight of each window to obtain a weighted texture angle, and calculating hydrological flow data according to the weighted texture angle.

The invention discloses a low-flow-speed river microorganism and aquatic plant cooperated water environment purification method, and belongs to the technical field of water environment purification. According to the invention, the problems of high cost, easy secondary pollution and the like in the prior art are solved, the adjustment strategies generated in S2 and S3 are comprehensively analyzed and optimized through a fusion algorithm, the strategy weight is dynamically adjusted according to the importance and emergency degree of the water quality index, and the comprehensive adjustment strategy is executed by means of a cooperative control platform; the system can quickly respond to the water quality change, can more accurately adjust the putting amount and putting frequency of a microbial agent, and optimizes the working condition of a micropump and the planting configuration of aquatic plants, so that the water quality purification efficiency is remarkably improved, and the river water quality is quickly improved; by optimizing the planting configuration of the aquatic plants and maintaining the efficient root system-biological membrane electroactive symbiotic purification effect, balance and stability of a water body ecological system are promoted, and continuous optimization of the whole purification process is ensured.

The invention discloses a river water quality prediction method based on decomposition reconstruction and deep learning. The river water quality prediction method comprises the following steps: acquiring water quality data of a research area for preprocessing; the method comprises the following steps of: decomposing water quality data into a plurality of intrinsic mode functions (IMF) with definite physical significance by adopting a variational mode decomposition method, and reconstructing into two types of input data sets of a strong correlation trend component and a weak correlation fluctuation component based on complexity and correlation analysis; constructing an iTransform model based on feature enhancement for the strong correlation trend component, and deeply excavating a nonlinear coupling relationship between water quality variables and long-term evolution features; constructing a TCN and GRU-based hybrid model for the weak correlation fluctuation component, and capturing multi-scale features and a local dynamic mode; and finally, superposing component prediction results to obtain a final prediction value of the target water quality variable, and evaluating the prediction performance through RMSE, MAE and SMAPE. Through example analysis, it is verified that the provided prediction method can effectively improve the capture capability and prediction precision of the complex water quality change mode.

The invention discloses a river management-oriented multi-parameter water quality intelligent prediction method and a river management-oriented multi-parameter water quality intelligent prediction system, relates to the technical field of environment monitoring and artificial intelligence, and particularly discloses a water quality intelligent prediction system and a water quality intelligent prediction method. According to the system, original water quality monitoring data is cleaned and repaired through the missing value filling module, so that the data integrity is ensured; performing deep feature extraction and fusion on the multi-parameter water quality data by using a convolutional neural network (CNN) to generate high-dimensional feature mapping; then, a bidirectional long-short-term memory network (Bi-LSTM) model is adopted to carry out forward and backward bidirectional learning on the extracted time sequence features, and a complex dynamic rule and a long-term dependency relationship of water quality parameter changes are accurately captured; and finally, an optimized loss function module is introduced to minimize a training error, so that the prediction accuracy is further improved. According to the method, the problems of insufficient precision, high model calculation complexity and weak multi-parameter collaborative prediction capability caused by data missing in the existing river water quality prediction technology are solved.

The invention discloses a riverway water quality prediction method based on generative data enhancement, and the method comprises the steps: (1) arranging automatic water quality monitoring stations on each monitoring section of a riverway, collecting multi-site and multi-index historical water quality data, combining with meteorological driving factors, sorting according to a time sequence, preprocessing, constructing a standardized multi-dimensional sequence, and obtaining an actual measurement sample; (2) establishing a water quality time sequence generation model based on a conditional variation auto-encoder, and generating a virtual sample with time-space consistency; (3) physical constraint and distribution consistency screening are applied to the virtual samples generated in the step (2), unreasonable data are removed, and high-quality virtual samples are obtained; (4) combining the high-quality virtual sample and the actual measurement sample to form an enhanced data set, and re-dividing the enhanced data set into a training set, a verification set and a test set; and (5) training a water quality prediction model based on the enhanced data set, and outputting a future multi-time-interval water quality index prediction result.

The invention discloses a river water flow measuring device, and relates to the technical field of river water flow measurement, the river water flow measuring device comprises a measuring support frame, the measuring support frame is provided with a folding assembly and a pushing assembly, the folding assembly is provided with a moving assembly and a carrying plate, and the pushing assembly is used for pushing the carrying plate to move towards a riverbed. The measuring support frame is provided with a take-up assembly, the take-up assembly is provided with a measuring device used for measuring river water flow, the moving assembly can drive the take-up assembly to move transversely, and one-key switching from a transportation state to a measuring state is achieved by arranging a foldable folding assembly and an automatic unfolding mechanism. After the controller sends out an instruction, the pushing assembly drives the sliding frame to automatically rotate and unfold, the carrying plate synchronously stretches out and stably makes contact with a riverbed, the whole unfolding process can be completed without manual intervention, the deployment efficiency and operation convenience of the equipment are remarkably improved, and the rapid laying requirements under various topographic conditions are met.

The present disclosure relates to the technical field of model construction, and provides a river water dynamics model construction method based on multi-source remote sensing, which comprises the following steps: acquiring a river remote sensing image to obtain a water edge line, acquiring water level data by means of an altimeter satellite, and giving an elevation value to a water edge line corresponding point according to the water edge line and the water level data; for a straight river section, a value is taken according to a specific rule, and for a curved river section, the value is determined according to linear fitting of multiple elevation measuring point values on a river section; based on the elevation values of the water edge lines of the straight and curved river sections, a river terrain digital raster model is further constructed by interpolation along the water edge line, the river water dynamics model is constructed by using the river terrain digital raster model, the simulation effect of the water dynamics model is determined by comparing the water level consistency or the water surface vector form consistency, and the calibration and verification of the river water dynamics model are realized. The present disclosure realizes accurate simulation of the river water dynamics process. The present disclosure can reduce the cost of model parameter acquisition.

The invention discloses a water environment pollution monitoring method and system based on data analysis, and belongs to the technical field of pollution monitoring. The system comprises an environment monitoring module, an influence analysis module, an abnormity identification module and a visualization and storage module. The environment monitoring module collects a GIS map and historical records and collects monitoring indexes and hydrological indexes of a river water body through monitoring equipment. The influence analysis module marks the position of each monitoring device on the GIS map, analyzes the upstream and downstream relationship and sets a relative time difference; establishing an influence relation chain for each monitoring index, and calculating an influence coefficient between the monitoring devices; the anomaly recognition module takes the monitoring indexes currently collected by the monitoring devices as measured values, calculates predicted values according to the influence relation chain, calculates anomaly indexes of the monitoring devices in combination with influence coefficients and divides anomaly regions; and the visualization and storage module displays the position of each abnormal area, prompts a worker to check each abnormal area one by one according to an abnormal index in an inverted order, and stores the collected index data into a historical record.

The invention belongs to the field of river levee slope protection, and particularly relates to a river levee slope protection structure and a construction technology, the protection structure comprises a river levee slope body, a protection plate is arranged on the outer wall of the river levee slope body, a plurality of planting cavities are formed in the protection plate at equal intervals, and a top plate is arranged at the top of the river levee slope body; according to the river levee slope, the river levee slope body is conveniently protected through the arranged protection plates, direct scouring of river water and rainwater to the river levee slope body is avoided, the situation that landslide occurs after the river levee slope body is soaked in water is effectively prevented, the road holding force of the protection plates is increased through the arranged insertion plates, the protection plates are installed more firmly, and through flow guiding of the flow guiding plates, the river levee slope body is protected more firmly. According to the river bank slope planting device, the situation that water flow directly impacts the turf during drainage on the river side is effectively prevented, the firmness of turf planting is improved, through the arranged mounting plate and the formed empty grooves, turf rhizomes can be conveniently grabbed and attached, and the turf can be more firmly attached to the river bank slope body after being planted.

The invention belongs to the technical field of monitoring, and particularly provides a multi-objective optimization simulation water conservancy flood prevention monitoring method, which comprises the following steps: collecting real-time water flow data through a high-density sensor network, calibrating original signals collected by different monitoring points by adopting a time synchronization mechanism, and obtaining a water flow sequence under the same timestamp; according to the water flow sequence under the same timestamp, noise and deviation are processed through a Kalman filtering algorithm, heterogeneous data of the upstream monitoring point and heterogeneous data of the downstream monitoring point are fused, and a smoothed multi-source fusion water flow value is obtained; if the smoothed multi-source fusion water flow value exceeds a preset threshold value, triggering spatial interpolation calculation, and correcting a time-space difference in combination with a terrain model to obtain an overall water flow distribution diagram of the river reach; extracting peak region features from the overall water flow distribution diagram of the river reach; the invention aims to solve the problem that river water flow monitoring and flood risk prediction cannot be performed in the prior art.

The invention discloses chemical dosing equipment for river water pollution treatment, and relates to the technical field of river water pollution treatment. Comprising a connecting pipe, a middle pipe is fixedly connected to the end, away from a fixed pipe, of the connecting pipe, a middle cylinder is fixedly connected to the end, away from the connecting pipe, of the middle pipe, a rotating plate is rotatably connected to the outer side of a rotating rod fixedly connected to the middle of the interior of the middle cylinder, and an extension pipe is fixedly connected to the outer side of the middle cylinder; the ends, away from the middle cylinder, of the extension pipes are fixedly connected with annular pipes, and through holes are formed in the tops of the annular pipes. According to the chemical dosing equipment for river water pollution control, the rotating plate rotates around the rotating rod under the flowing of airflow, chemical powder particles introduced from the bottom can be further cut and dispersed, the original possible local agglomeration phenomenon is broken through, cross disturbance is formed by centrifugal force generated by rotation and rising force of the airflow, and the chemical dosing effect is improved. Powder particles are in full contact with airflow in a three-dimensional space, and the mixing efficiency of the airflow and the powder is improved.