73 results about "River quality" 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 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 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 relates to a method for predicting river nitrogen metabolism gene abundance and key influence factors thereof based on automatic machine learning. Comprising the following steps: collecting a river water sample, detecting water quality physicochemical indexes, and performing metagenome sequencing to obtain nitrogen metabolism gene abundance; taking conventional water quality physicochemical indexes as explanatory variables, taking nitrogen metabolism gene abundance as a target variable, adopting automatic machine learning to construct a model, evaluating the performance of the model through a mean square error (MSE) and a fitting coefficient (R), and selecting an optimal algorithm for prediction; the model analyzes the relative contribution of each water quality index through an SHAP value graph, and reveals the relationship between each water quality index and gene abundance by using a partial dependency graph (PDP). The method shows high precision and good generalization performance in the aspect of predicting the abundance of the nitrogen metabolism gene, and is suitable for describing the nitrogen dynamic process of a river water body. The model can explain, analyze and quantify the influence of different water quality indexes on the nitrogen metabolism gene afterwards, and a scientific basis is provided for river water quality management and nitrogen metabolism optimization regulation and control.

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 invention relates to the technical field of water quality monitoring, in particular to an artificial intelligence river water quality pollutant spatial form monitoring system and method, and the system comprises a concentration trend analysis module, a spatial form monitoring module, a spatial trend analysis module and a pollution early warning evaluation module. According to the method, the water quality is monitored at a plurality of river water area nodes, the pollutant concentration data can be accurately captured, the change rate of the pollutant concentration can be predicted, the understanding of the pollution trend is improved, the response speed to the pollution event is enhanced, the image monitoring data is combined, the convolutional neural network is used for analyzing the river water area image, and the accuracy of the analysis is improved. The spatial area of the pollutants is analyzed, so that the spatial distribution state of the pollutants is evaluated in detail, the severity level of the pollutants can be evaluated, managers are notified in real time, meanwhile, dynamic change information of pollutant concentration and spatial distribution is integrated, an early warning mechanism is more agile and accurate, and the early warning efficiency is improved. And the effectiveness and the accuracy of pollution early warning are improved.

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 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 technical field of Internet and environment monitoring, in particular to an Internet-based river water quality remote monitoring system, which comprises a water quality data acquisition module, a pollution detection module, a water quality grading module and a mobile terminal early warning module, the water quality data acquisition module is used for acquiring water quality parameters and water quality pollution parameters of each river section and storing water quality parameter data and water quality pollution parameter data; the pollution detection module is used for calculating a water quality pollution index of each river section; the water quality pollution level of each river section is determined; the early warning module is used for generating river interval water quality early warning signals; the water quality grading module is used for calculating a water quality health index of each river section; the mobile terminal early warning module is used for displaying the segmented linear river graph by using the water quality pollution grade data and the water quality health grade data corresponding to each river interval in different colors; and a corresponding river interval alarm is given.

The invention belongs to the technical field of water conservancy and environmental science, and discloses a drainage basin water environment capacity allocation method based on a control unit, which comprises the following steps: constructing a hydrological hydrodynamic model and a river water quality model according to hydrological information of a target drainage basin; determining hydrological conditions of each control unit in the target drainage basin according to the hydrological hydrodynamic model; determining a water environment capacity calculation standard under the hydrological condition according to the river water quality model; and constructing a multi-objective optimization model according to the water environment capacity evaluation standard and the water environment capacity calculation standard, and determining the water environment capacity distributed to each control unit under the hydrological condition according to the multi-objective optimization model. According to the drainage basin water environment capacity allocation method based on the control units, the control units are divided through the catchment range and the administrative division, the water environment capacity of each control unit is fairly allocated by adopting a multi-objective optimization algorithm, and the pollutant discharge amount of each control unit is further strictly controlled; and a fair and objective treatment basis is provided for accurate treatment of the drainage basin.

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 provides an intelligent river water quality monitoring system based on a remote sensing technology, belongs to the technical field of river monitoring, and solves the technical problem that the current intelligent river water quality monitoring cannot realize full-space-time river water quality monitoring from large scale to microcosmic scale and point-surface combination. A river water quality intelligent monitoring system based on a remote sensing technology comprises a satellite remote sensing module, an unmanned aerial vehicle remote sensing module, a high-altitude holder module and a ground water quality detection module. According to the invention, through the arrangement of the satellite remote sensing module, the unmanned aerial vehicle remote sensing module, the high-altitude holder module and the ground water quality detection module, the air-space-tower-ground multi-source cooperative monitoring of the river water quality is realized, and under the action of the fixed-ship flow measurement assembly of the unmanned ship, moving and fixed-point monitoring can be carried out in the river; and meanwhile, satellite remote sensing and an unmanned aerial vehicle are utilized, and agricultural non-point source pollution satellite remote sensing and ground automatic monitoring advanced mature technologies are integrated, so that water quality monitoring is changed from large-scale monitoring to microscopic detection.

The invention relates to the technical field of water quality detection, and provides river water quality detection equipment for hydraulic engineering, which comprises a floating raft, a mounting seat is mounted on the floating raft, a feeding mechanism and a multi-stage telescopic rod are arranged at the top of the mounting seat, a plurality of sampling barrels are mounted in the feeding mechanism, a plurality of sampling pipes are mounted in the sampling barrels, and the sampling pipes are connected with the multi-stage telescopic rod. A guide block is fixedly connected to the output end of the multi-stage telescopic rod, a sliding block is arranged in the guide block, a connector and a trigger head are fixedly connected to the top of the sliding block, a sampling mechanism is arranged on the mounting base and the guide block, and a transmission mechanism is arranged on the top of the mounting base; the sampling pipes are fed into water areas of different depths through the multi-stage telescopic rod to sample water of different depths, river water of different depths is stored through the multiple sampling pipes, mixing of water of different depths is avoided, sampling of the next time can be conducted after sampling of one time of depth is completed, sampling continuity is improved, and sampling efficiency is improved. And the sampling efficiency is greatly improved.

The invention provides a remote sensing estimation method for the chlorophyll a concentration of a river, and belongs to the technical field of hydrological remote sensing, and the method comprises the following steps: during a satellite transit period, sampling at a plurality of point locations of the river to obtain measured data, obtaining satellite data, carrying out Rayleigh scattering correction on the satellite data to obtain reflectivity after Rayleigh correction, and calculating the chlorophyll a concentration of the river; the method comprises the following steps: screening measured data, establishing an XGBoost model, training the model data, and calculating the chlorophyll a concentration of the river by using the trained XGBoost model. The invention relates to a river chlorophyll a concentration remote sensing estimation method by using Landsat and machine learning. According to the method, based on actually measured chlorophyll a concentration data of the Pearl River basin, a machine learning model capable of estimating the river chlorophyll a concentration is established by using Landsat data after Rayleigh scattering correction, a new thought is provided for monitoring river water quality, and powerful technical support is provided for river eutrophication monitoring and water environment protection and treatment.

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 river water quality inversion and health evaluation method and system based on satellite remote sensing, terminal equipment and a medium, and relates to the technical field of environment remote sensing monitoring. The method comprises the following steps: acquiring a time-space matched multispectral remote sensing image and ground actually measured water quality parameter data set of a research area; preprocessing the image and extracting a water body area image; constructing a data sample set based on the water body area image and the water quality parameter data set; establishing a plurality of inversion models for each water quality parameter, and selecting an optimal inversion model through performance verification; and calculating a comprehensive health index and a health level of the river in the water body area through objective weight determination. According to the method, the problems of low monitoring efficiency, insufficient inversion precision and high evaluation subjectivity in the prior art are solved, efficient and accurate inversion and objective health evaluation of large-range river water quality are realized, and decision support is provided for environment management.

The present invention relates to a river ecological environment monitoring system, comprising: a river water quality evaluation module, a soil quality evaluation module, a river purification capacity evaluation module, and a comprehensive evaluation module; the river water quality evaluation module is used to collect water quality data of the river to be monitored, and based on the water quality data, determine a water quality monitoring score value; the soil quality evaluation module is used to collect soil data of the river to be monitored, and based on the soil data, determine a soil monitoring score value; the river purification capacity evaluation module is used to obtain the water quality distribution of the river, and through the water quality distribution, determine a river purification capacity monitoring score value; the comprehensive evaluation module is used to comprehensively evaluate the water quality monitoring score value, the soil monitoring score value, and the river purification capacity monitoring score value to obtain a monitoring result. The present invention monitors through water quality and soil, and considers the river purification capacity, making the detection result more accurate and closer to the actual situation.

This invention relates to the field of water quality data monitoring methods, specifically to a remote real-time monitoring system and method for water quality chemical parameters. The method includes: deploying multiple distributed monitoring nodes at a river monitoring section; the distributed monitoring nodes collect water quality chemical parameter data in real time at a set frequency, preprocess the data locally, and store it in a cache; the distributed monitoring nodes analyze the real-time collected data to determine whether abnormal fluctuations occur; when abnormal fluctuations in water quality chemical parameters are detected and preset trigger conditions are met, the distributed monitoring nodes upload raw high-precision data packets for a period of time before and after the trigger time to a cloud server; the cloud server receives the abnormal data packets, stores them, performs secondary verification, and triggers an early warning; this invention achieves low-power, low-cost, real-time, and accurate monitoring of river water quality chemical parameters, and can improve the real-time performance of emergency response during water quality monitoring.

The utility model discloses an urban landscape river water quality supervision device, which comprises a floating plate main body, a water quality monitor, a power supply control box and a monitoring box structure, a Z-shaped monitoring cavity and an equipment cavity are arranged on two sides in the monitoring box structure, the water quality monitor is arranged in the middle of the Z-shaped monitoring cavity, and a water filtering cavity is arranged on one side of the equipment cavity of the monitoring box structure. A water suction pump is arranged in the equipment cavity, the water suction pump is provided with a water suction pipe extending into the water filtering cavity and a water outlet pipe extending into the Z-shaped monitoring cavity, a second electric control valve is arranged on the water suction pipe, the Z-shaped monitoring cavity comprises a top water inlet channel and a bottom water outlet channel, first electric control valves are arranged in the water inlet channel and the water outlet channel, and the water outlet pipe is communicated with the water inlet channel. Compared with the prior art, the water quality monitoring device has the advantages that excessive impurities can be prevented from staying in the cavity through the Z-shaped monitoring cavity, and meanwhile, excessive impurities can be prevented from being attached to the water quality monitor to influence the monitoring precision through clean water flushing.

The invention discloses an intelligent hydrological monitoring and analysis system, and relates to the technical field of hydrological monitoring and analys.The intelligent hydrological monitoring and analysis system comprises a monitoring module, an analysis module, an adjustment module, a secondary monitoring and analysis module and an early warning terminal.The intelligent hydrological monitoring and analysis system is characterized in that a sensor is used for monitoring river water quality parameters in real time and judging whether river water quality is qualified or not; the method comprises the following steps: collecting a panoramic image of each monitoring area, obtaining the position of each garbage on the river bank and the river biomass quantity from the panoramic image, clustering the garbage positions, calculating the pollution coefficient of each clustering area, formulating an adjustment strategy, detecting the adjustment effect, and modifying the strategy if the effect is not good. According to the method, the problem that data analysis is single is solved, multi-dimensional data analysis is achieved, the real-time performance and accuracy of data monitoring are guaranteed, and the reliability of an adjustment strategy and the timeliness of river ecological protection are also guaranteed.

The invention discloses a precipitation-driven river pollution source analysis method based on multi-scale analysis and application. The analysis method comprises the following steps: acquiring precipitation data, river water quality data and hydrological data of a to-be-researched region for many years; determining a pollution type according to the river water quality data sudden change point; based on the pollution type, positioning pollution time and a pollution area by adopting a space-time heterogeneity analysis method; aiming at the pollution time and the pollution area, establishing a relation between rainfall and river water quality pollution. According to the method, the analysis limitation of a traditional single spatial-temporal scale is broken through, and point-line-plane multi-dimensional response analysis is achieved; through coupling modeling of water quality mutation recognition and rainfall driving analysis, the superimposed influence of human activities and meteorological hydrology on river water quality is systematically revealed, and comprehensive analysis of the relationship between rainfall and river water quality pollution is realized in combination with year, season and day multi-scale collaborative analysis.

The present invention discloses a quantitative source tracing method for river and lake water pollution that combines knowledge graphs and machine learning. The present invention relates to the technical field of water pollution source tracing. The present invention collects and processes data from target river sections, constructs a hydrodynamic-water quality model, and based on the outlet location of the target river section, obtains the pollutant diffusion characteristics of the downstream river channel and the concentration time series characteristics of the target section under different discharge scenarios of each outlet through unit pulse response testing, constructs a "source strength-time-concentration" relationship knowledge graph, randomly extracts a sample set from the graph, uses a machine learning method to train the sample set, learns the nonlinear mapping relationship between the downstream section concentration time series and the source strength of multiple outlets, performs dynamic inversion of the river pollutant diffusion process, realizes the rapid positioning of pollution sources and quantification of their contributions, and finally uses the Monte Carlo sampling method to generate a probability distribution of the pollution source location, which helps to improve the priority of source tracing judgment, thereby improving the accuracy and response speed of river water pollution source tracing.

The invention relates to the field of water quality monitoring, and provides a river water quality pollution early warning method and system and electronic equipment, and the method comprises the steps: obtaining water quality parameters of a to-be-treated river; based on the water quality parameter of the to-be-treated river and a set threshold value, determining the content of pollutants to be eliminated in the to-be-treated river, wherein the set threshold value represents a target value of the water quality parameter of the to-be-treated river; and performing early warning on the pollution degree of the to-be-treated river based on the content of the to-be-discharged pollutants in the to-be-treated river. According to the scheme, the defect that the monitoring and treatment effects of river water quality pollution are poor is overcome, the treatment scheme can be determined on the basis of parameters of a river, the early warning precision can be improved through the early warning mode according to local conditions, and then the better pollution treatment effect is achieved.

The invention relates to a river water quality influence evaluation method based on strontium isotope analysis and a radon model. Precise traceability is realized through a strontium isotope model technical method, and meanwhile, the influence of underground water of an irrigation area along a river on river water quality is quantified by applying a radon mass balance model. Specifically, by taking water quality analysis of the Yellow River irrigation area as an example, the method can accurately locate sources of complex pollutants such as carbonate rock weathering, soil mineral decomposition, chemical fertilizer loss and sewage leakage in underground water along the Yellow River irrigation area, solves the problem of confusion of natural and artificial pollution contributions, and fills the blank of underground water-surface water interaction influence quantification.

The utility model relates to the technical field of river purification, in particular to a hanging net type river water quality purification device which comprises a bidirectional screw rod, a third driving motor, a first moving plate and a second moving plate. The first scraping plate and the second scraping plate can be used for cleaning garbage in a river through the first filter screen and the second filter screen, then a worker only needs to conduct salvage on the two sides of the river bank, garbage cleaning can be completed, and compared with the prior art, the salvage efficiency can be improved, and manpower and material resources can be saved.

The application discloses a river nitric oxide emission calculation method based on a land-river-air simulation, and belongs to the field of environmental engineering. The existing method for measuring river N2O has poor accuracy. A RF regression model is trained by using a nitrogen emission prediction set, a geographical variable prediction set and a climate variable prediction set, and a trained RF regression model is obtained. The nitrogen emission test set, the geographical variable test set and the climate variable test set are input into the trained RF regression model, and the river water concentration of each sub-basin in each region is output. The river hydrological parameters of each sub-basin in each region and the river water concentration of each sub-basin are input into an air-water interface gas exchange model, and the total river nitric oxide emission of each sub-basin in each region is obtained. The application is used for obtaining the total river nitric oxide emission.

The invention relates to an oxygenable ecological medium box for improving river water quality, comprising a box body, a floating component fixed at an opening on the top of the box body and capable of adjusting a fixed height up and down, a bottom layer medium arranged on the bottom surface of the box body for simulating a riverbed environment, a plurality of freshwater clams cultured in the bottom layer medium, a middle layer medium laid on the surface of the bottom layer medium for simulating a particle environment of freshwater clams filtering food, and a surface layer medium arranged on the top of the middle layer medium, the water-facing surface, the water-receiving surface and the bottom surface of the box body are all provided with water holes, the floating component comprises a U-shaped floating box, a floating frame fixed on the inner side of the U-shaped floating box, a partition vertically fixed inside the U-shaped floating box and located above the water-facing surface of the box body, and a non-powered oxygenation mechanism arranged inside the U-shaped floating box; the invention can significantly prolong the survival time of freshwater clams, and can also effectively improve the environment and conditions required for the survival of freshwater clams to greatly reduce the mortality rate of freshwater clams.

The present application relates to the technical field of data processing, in particular to a river water quality prediction method and system based on big data, which solves the technical problem of large deviation of interpolation value from actual value and reduced prediction accuracy in the water quality prediction process caused by ignoring the space-time coupling of water quality data when interpolating missing values in the prior art. The method comprises: obtaining water quality data at multiple time points; the water quality data obtained at each time point is from multiple sampling positions arranged along the flow direction of the river; analyzing the spatial distribution characteristics and time sequence characteristics of the water quality data in the historical water quality data set, and interpolating the missing values in the historical water quality data set in space-time coupling to obtain a complete data set; the historical water quality data set comprises water quality data obtained at multiple historical time points; training a sequence prediction model according to the complete data set, and obtaining a water quality prediction model after training; inputting the water quality data at the current time into the water quality prediction model to output the water quality data prediction value at the future time.