61 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 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 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 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 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 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 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 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.

In order to solve the technical problem that the diffusion condition of river water quality pollution along with the change of environmental conditions and time cannot be detected, the invention provides an experimental modeling method for monitoring a river water quality diffusion process, which comprises the following steps of: establishing an equivalent river channel structure and a riverbed scaling experimental device of a to-be-researched river water quality pollution diffusion process; a water body equivalent to the actual water quality pollution condition of a river to be researched is injected, a plurality of sealed sampling test ports are formed in the riverway edge, artificial channel ports capable of sampling are established, water quality acquisition data of the river to be researched at different depths at different time and different positions are acquired, and meanwhile, a candidate model of river water quality pollution diffusion is given. Error description is given according to the obtained data, a model is determined through a system identification method and used for prediction and estimation of water quality pollution distribution, and the treatment method of the river to be researched is obtained through repeated experiments.

The invention relates to the technical field of water quality monitoring, in particular to a river water quality monitoring device which comprises a floating plate and a blocking plate, the floating plate is in a hollow-out state, a buoyancy ball is fixedly connected to the periphery of the bottom of the floating plate, a supporting frame is fixedly connected between the buoyancy ball and the floating plate, and the supporting frame is fixedly connected with the blocking plate. A contact opening is fixedly connected to the middle end of the supporting frame, a protective shell is fixedly connected to the top of the floating plate, the blocking plate is arranged on one side of the protective shell, and a transmission assembly is arranged in the protective shell; a blocking plate is driven through the water flow velocity, paying-off and taking-up of a bunching column are achieved through transmission, downstream flowing and recycling of the device are achieved at the same time, water quality monitoring in the river reach is achieved, meanwhile, after a pressing sensor is squeezed, a rolling wheel can be driven to rotate, and a fixing column and a second monitoring head can be driven to move left and right at the same time; and monitoring of different positions of the second monitoring head can be ensured.

The invention belongs to the technical field of river water quality monitoring, and particularly relates to a double-sail consumption reduction type river water quality mobile monitoring system which comprises a power direction unified dispatching mechanism, a water sample collecting mechanism and an anti-rollover plate. The power direction unified scheduling mechanism and the anti-rollover plate are arranged on the water sample collecting mechanism; the power direction unified dispatching mechanism comprises a floating body, a sail angle adjusting mechanism, a mast, a shutter sail adjusting mechanism, a water collecting and releasing wheel set and a swinging mechanism. The floating body is arranged on the water sample collecting mechanism, the sail angle adjusting mechanism is embedded in the floating body, the two ends of the mast are connected with the sail angle adjusting mechanism and the shutter sail adjusting mechanism respectively, the water collecting and releasing wheel set is arranged on the inner side wall of the floating body and connected with the water sample collecting mechanism, and the swinging mechanism is arranged at one end of the floating body. In particular to a double-sail consumption reduction type river water quality mobile monitoring system which can sail on the water surface for a long time and can freely move with extremely low energy consumption.

This invention provides an intelligent river water quality monitoring system based on remote sensing technology, belonging to the field of river monitoring technology. It solves the technical problem that current intelligent river water quality monitoring systems cannot achieve comprehensive, all-weather, and integrated monitoring from large-scale to micro-scale perspectives. The intelligent river water quality monitoring system based on remote sensing technology includes a satellite remote sensing module, a UAV remote sensing module, an upper-level gimbal module, and a ground-based water quality detection module. In this invention, by setting up the satellite remote sensing module, UAV remote sensing module, upper-level gimbal module, and ground-based water quality detection module, multi-source collaborative monitoring of river water quality from space, air, and ground is achieved. Furthermore, with the assistance of the unmanned surface vessel's fixed-ship flow measurement component, mobile and fixed-point monitoring can be performed within the river. Simultaneously, by utilizing satellite remote sensing and UAVs, advanced and mature technologies for agricultural non-point source pollution satellite remote sensing and ground-based automatic monitoring are integrated, enabling water quality monitoring to move from large-scale to micro-scale detection.

1. The name of this design product: River Water Quality Detector (Environmental Protection). 2. Purpose of this design product: used for river water quality testing. 3. The key point of the design of this product lies in its shape. 4. The picture or photo that best illustrates the design points: three-dimensional picture.

The invention relates to a river water quality prediction method based on a decision tree algorithm, which is a prediction model based on an entropy theory and supervised learning, and comprises the following steps of: establishing a relationship between data such as station water level and the like and main water quality categories by applying a CART algorithm in a decision tree model; and the water quality of the corresponding site under the future condition is predicted based on the constructed decision tree model, so that the predicted water quality category is high in accuracy, and the method plays a very important guiding role in mastering the future river water quality condition, solving the river water resource pollution problem and protecting water ecology.

The application discloses a kind of urban river water quality evaluation systems suitable for cold climate, belong to water quality management field, the present application obtains river water quality index, weather data and snowmelt related data, through sub-basin and hydrological response unit division and combine snowmelt and rainfall process, the daily average runoff of hydrological response unit is evaluated, through snowmelt agent input, particulate matter deposition and hydrological response unit runoff process, the surface accumulation of pollutant and river flux are evaluated, through river surface water temperature information identification thermal anomaly area, and comparative analysis thermal anomaly area and the ice condition change characteristics of non-thermal anomaly area, through historical and real-time water quality data, meteorological data, pollutant river flux and thermal anomaly area change characteristics to the evaluation of dissolved oxygen and ion concentration in river, and compared with water quality threshold, to identify water quality anomaly in advance and issue early warning, improve the accuracy and reliability of urban river water quality evaluation of cold climate.

This invention discloses an intelligent data quality review method and system for automatic river water quality monitoring stations, belonging to the field of water quality monitoring technology. The method includes: pre-setting conventional review thresholds and differentiated review thresholds; real-time acquisition of water quality monitoring data and related data; setting fusion weights, and lightweightly fusion of water quality monitoring data and related data according to the fusion weights to obtain a fusion baseline value; reviewing the water quality status based on the fusion baseline value. Specifically, the review method includes: if the fusion baseline value is lower than the conventional review threshold, it is judged as normal; otherwise, the current scenario is judged; in a normal scenario, it is judged as abnormal; in extreme scenarios, if the fusion baseline value exceeds the conventional review threshold but does not exceed the corresponding differentiated review threshold, it is judged as normal; if the fusion baseline value exceeds the differentiated review threshold, it is judged as abnormal; and the review result is output. This invention avoids misjudgments caused by normal natural fluctuations by adapting to extreme scenarios, thus improving the accuracy of the review.

The application discloses a kind of pond multi-objective optimization management methods for agricultural watershed non-point source pollution regulation, it is related to agricultural watershed non-point source pollution control technical field, for the first time, the multi-dimensional attributes such as pond use type, form feature and sediment nutrient state are integrated into unified quantification framework, by constructing generalized additive model, the nonlinearity and interaction of the influence of pond attribute on river water quality can be accurately captured, the accuracy of river nitrogen and phosphorus concentration prediction is improved. Pond management measures are quantified as controllable decision variables, a multi-objective dynamic optimization model is established to balance environmental benefits and economic benefits, the traditional experience decision is replaced by Pareto optimal solution, a scientific and efficient pond management scheme is proposed, which can be quantitatively implemented, avoiding the one-sidedness of single target, maximizing non-point source pollution reduction while minimizing cost, achieving precise allocation and scientific and quantitative decision of pond management resources.

This invention discloses an optimization method, apparatus, equipment, and computer medium for coupling river water quality with mine water replenishment of ecological baseflow, relating to the fields of mine water resource utilization and ecological environmental protection technology. The method includes the following steps: window period monitoring data collection; construction of a river-mine water hybrid coupling model; model parameter identification and verification: inputting the data collected during the window period into the hybrid coupling model, identifying parameters, determining the parameters in the model, and verifying the model using reserved monitoring data; establishing a multi-objective optimization objective function and constraints; solving for the optimal solution using multi-objective programming; dynamic control and feedback correction. This method achieves synergistic optimization of mine water resource utilization and river ecological protection, effectively improving prediction accuracy and demonstrating significant economic, social, and ecological benefits.

The application discloses a river water quality evaluation method based on an improved grey correlation analysis algorithm and a particle swarm optimization multi-classification support vector machine, and comprises the following steps: obtaining water quality data and performing normalization processing; obtaining the correlation coefficient between water quality indexes and water quality categories by using the improved grey correlation analysis algorithm, and performing feature selection; optimizing the multi-classification support vector machine through a particle swarm algorithm, and establishing a river water quality evaluation model; inputting the normalized water quality data into the river water quality evaluation model, and outputting the river water quality categories from the river water quality evaluation model. The subjective and objective weights of the water quality indexes are fully considered, the correlation between the water quality indexes is effectively utilized, and the particle swarm algorithm is used to optimize the multi-classification support vector machine, so that the accuracy of the river water quality evaluation model is greatly improved.