13779 results about "Water quality" patented technology

The invention discloses a water quality prediction method based on graph neural network and spatial-temporal feature fusion, and the method comprises the steps: obtaining hydrological data and water quality data of a plurality of monitoring stations in a preset drainage basin in a preset time period, representing the monitoring stations as nodes of a graph, and representing the adjacency relation between the monitoring stations as edges of the graph, a graph topology network mapping multiple monitoring station pollutant data is constructed, a spatial-temporal feature fusion module is adopted to perform feature extraction and fusion on the graph topology network to obtain water pollution spatial-temporal feature data, and the spatial-temporal feature fusion module is obtained by training a graph convolutional network, a gating circulation unit and a self-attention mechanism. And performing fusion processing on the extracted feature data to form a time sequence data set of a graph topology structure, and finally predicting the data set through a graph time sequence prediction model to obtain water quality prediction results of the plurality of monitoring stations in a target time period. The method can effectively deal with the spatial and temporal change process of water pollution, reduce the water quality prediction cost and improve the water quality prediction precision.

The invention relates to a water quality change trend rapid prediction method based on multi-source data fusion and physical constraint, and the method specifically comprises the following steps: 1, synchronously collecting spectral information, DO, COD, temperature, pH and other data at a key monitoring station, constructing a hydrodynamic water quality coupling equation, simulating the spatial-temporal dynamic distribution of water quality parameters, and calculating the water quality change trend; 2, outputting a water quality sensitive area through a hydrodynamic force-water quality model, screening sensor layout point positions in combination with information entropy evaluation and spatial clustering, realizing low-cost water quality sensor network deployment through a multi-objective optimization algorithm, and calculating a water body global water quality distribution diagram by adopting a spatial interpolation method, 3, synchronously collecting spectral information according to key monitoring sites, analyzing main pollution sources, and adopting a principal component analysis and attention mechanism neural network; 4, based on real-time optical characteristic value-DO data, in combination with a spatial topology network, a water quality gradient and a cross-regional covariance, capturing water quality parameter spatial correlation among different sites, and determining the water quality parameter spatial correlation among different sites; an optical characteristic value-DO-COD dynamic prediction model is constructed; a COD predicted value is corrected by combining pollution traceability and spectral characteristics, and multi-source data is assimilated by adopting ensemble Kalman filtering, so that the model precision is improved.

The invention relates to the technical field of marine environment monitoring, in particular to a time-space diagram neural network anomaly detection method and system for marine ecology. The method comprises the following steps: acquiring ocean monitoring data; performing data preprocessing on the acquired marine monitoring data; constructing a space-time diagram structure based on the preprocessed marine monitoring data; performing spatio-temporal feature extraction on the constructed spatio-temporal diagram structure by using an anomaly detection model; carrying out feature fusion on the extracted spatial-temporal features; and carrying out local anomaly and global anomaly scoring on the marine ecosystem based on a multi-scale anomaly scoring mechanism. According to the method, a unified data processing framework is designed, multi-source heterogeneous data such as satellite remote sensing data, water quality monitoring data and biological survey data can be processed at the same time, information contained in various kinds of data is fully utilized, and the comprehensiveness and accuracy of anomaly detection are improved. Through the construction of a space-time diagram structure, the incidence relation between different data sources is effectively captured, and more comprehensive feature expression is provided for anomaly detection.

The invention provides a marine ranch water quality parameter real-time correction and compensation method and system for a multi-source sensor, and relates to the technical field of multi-source sensors, and the method comprises the steps: constructing a double-layer edge computing network, and connecting a sensor through a micro-service architecture to collect water quality data. And carrying out data preprocessing in combination with wavelet transform. And establishing a sensor digital twinborn model, and calculating the real-time credibility. Establishing a multi-dimensional sensor association network, optimizing a weight coefficient by adopting federal learning, and establishing a self-evolution correction parameter matrix; and fusing the sensor data by using a multi-task deep learning model to generate an initial correction value. And calculating a theoretical reference value through a space-time sequence prediction model. A compensation coefficient is adaptively adjusted by adopting a fuzzy decision tree, hierarchical water quality parameter correction is realized, and a closed-loop self-optimization intelligent correction system is formed through verification of a digital twin model. The accuracy and reliability of marine ranch water quality monitoring data are effectively improved.

The invention relates to the technical field of hydraulic engineering monitoring control, and discloses an automatic monitoring method and system for ecological hydraulic engineering. The method comprises the steps that water body parameters are collected through a sensor and preprocessed to obtain standard monitoring data; performing noise reduction alignment and correlation analysis on the monitoring data to obtain an optimized data set and a feature library; evaluating the water environment state according to the data set and the feature library to obtain an evaluation index and an early warning signal; analyzing the treatment scheme according to the index and the signal to obtain a regulation and control instruction; adjusting equipment operation parameters according to the instruction to obtain operation state data; and performing cycle analysis and strategy optimization based on the state data to obtain regulation and control parameters. Multi-dimensional correlation analysis of water quality parameters is realized, an accurate water environment regulation and control scheme is formulated on the basis, and the pertinence and efficiency of water quality improvement are improved.

The invention provides a sewage treatment data real-time monitoring method and system based on the Internet of Things, and relates to the technical field of data processing.The method comprises the steps that a sensor network is arranged at key nodes in the sewage treatment process, and multiple kinds of original data including water quality parameters, flow, temperature, pressure and equipment operation states are collected in real time; transmitting the various original data to a cloud data center for preprocessing to generate processed data; according to the processed data, the water quality change trend is monitored in real time, pollution events or equipment faults are recognized, and real-time monitoring and analysis results are generated. Real-time monitoring and intelligent analysis of sewage treatment data are achieved through the Internet of Things, pollution events or equipment faults are effectively warned, a treatment scheme is automatically generated, emergency response is started in time, and the sewage treatment efficiency and safety are improved.

The invention provides an intelligent regulation and control method for a three-stage constructed wetland recirculating aquaculture system, and belongs to the technical field of machine learning. The method comprises the following steps: firstly, continuously collecting water quality data and operation control data of each control unit, and constructing a multi-source heterogeneous data set under a unified time scale; then, constructing a pollution evolution trend prediction model, and capturing a dynamic evolution trend of water quality along with time and control behavior changes; then, under the guidance of a prediction result, analyzing the similarity of historical states and the sensitivity of regulation and control response, automatically identifying key control parameters which influence the water quality change of the system at present, and reasoning the dynamic adjustable boundary of the key control parameters; and finally, constructing a reinforcement learning strategy network fusing state prediction, a parameter boundary and a control target, realizing multi-target tradeoff among pollutant removal efficiency, a water quality standard-reaching rate and operation energy consumption, and outputting an efficient and steady control strategy through continuous interactive training. According to the invention, efficient, accurate and robust operation of the wetland system can be realized.

The invention provides a drainage basin water quality pollution traceability analysis method and system fused with space-time big data, and relates to the technical field of pollution analysis, and the method comprises the steps: collecting water quality, weather, surface runoff and other data, and generating a data set through space-time label association; and on the basis of the data set, constructing a pollutant migration and diffusion model containing a pollutant concentration attenuation and flow direction prediction sub-model, and calculating a pollutant concentration change rate and a migration path. And finally, the pollution sources are divided into point source pollution, non-point source pollution and endogenous pollution through the pollution source identification model, the contribution rate of each pollution source is calculated, and a pollution traceability analysis report is generated. The method can comprehensively consider the influence of various factors on the water quality, realizes the accurate positioning and quantitative analysis of the pollution source, and improves the traceability efficiency and accuracy.

The invention relates to the technical field of ecological environment protection, in particular to an ecological environment damage intelligent identification management method based on multi-source data fusion, which comprises the steps of data acquisition and preprocessing, data fusion and feature extraction, ecological environment damage evaluation model construction, damage dynamic analysis and trend prediction, restoration suggestion generation and visual display. The method comprises the following steps: collecting water quality, atmosphere, biological and social economic data in real time through a sensor network deployed in an ecological sensitive area, preprocessing the data, extracting key features by adopting a multi-modal data fusion technology and a deep learning model, and constructing an ecological damage assessment model; the change rule of ecological damage and the potential high-risk area are predicted based on a time sequence analysis method, and an optimal ecological restoration scheme is generated in combination with a particle swarm optimization algorithm. According to the method, the evaluation result and the restoration suggestion are displayed through the visual interface, a scientific basis is provided for decision makers, and the efficiency and the effect of ecological environment restoration work can be improved.

A layout optimization method of water quality monitoring points based on a RF-C-SOM clustering algorithm includes: preprocessing collected water quality data to obtain preprocessed water quality data that are used as data, and using water quality categories as labels to train a random forest model to determine feature importance of water quality indicators; selecting important features based on the feature importance and model training accuracy, performing dimensionality reduction on the preprocessed water quality data to obtain dimension-reduced data; performing a fuzzy clustering on the dimension-reduced data to obtain a water quality section classification result, and based on it, determining initial weight values of a self-organizing mapping algorithm; initializing neurons and training a self-organizing mapping network model with the initial weight values; obtaining a point clustering result through the self-organizing mapping network model; and conducting a water quality index evaluation for the point clustering result before and after screening.

The invention discloses an intelligent water quality regulation and control system and method based on multi-parameter real-time monitoring, and belongs to the technical field of water quality monitoring. According to the intelligent water quality regulation and control system, the water quality data of the water body is obtained in real time through the multi-parameter sensor array, and the monitoring regulation and control server can quickly generate an abnormal report and a water quality regulation and control scheme. The data processing module performs feature extraction on the water quality data to obtain target features; the water quality evaluation module is used for accurately evaluating the water quality by using a pre-trained deep neural network model; the abnormity identification module can timely judge whether the water quality has a pollution risk and generate an abnormity report; and the regulation and control module generates a water quality regulation and control scheme by adopting a multi-objective optimization algorithm. The system realizes real-time performance, accuracy and intelligence of water quality monitoring, can quickly respond to water quality changes, effectively reduces pollution risks, and improves the efficiency and effect of water quality regulation and control.

The invention relates to the technical field of data processing, and discloses a hazardous chemical substance leakage and diffusion prediction method and system based on an unmanned ship, and a storage medium. The method comprises the following steps: acquiring water quality, weather and geographic data through an unmanned ship sensor, and processing to obtain an initial data set; calculating a diffusion range according to the data, optimizing a monitoring area, and generating a cooperative monitoring track; performing exception elimination and smoothing processing on the data, and constructing spatial distribution; solving a convection diffusion equation, and establishing a three-dimensional flow field model; analyzing a hazardous chemical substance diffusion path, and predicting pollutant distribution; and establishing a risk assessment system, and generating emergency disposal suggestions and decision-making schemes. According to the application, rapid and accurate monitoring of the polluted area is realized through the multi-parameter sensor array carried by the unmanned ship, and accurate prediction of the hazardous chemical substance pollutant diffusion process is realized in combination with multi-source data fusion processing and a three-dimensional flow field simulation technology.

The invention discloses a modularized full-process high-quality direct drinking water treatment system and a control method, and belongs to the technical field of direct drinking water treatment.The modularized full-process high-quality direct drinking water treatment system is characterized in that an online sensor is deployed at a water inlet of a flocculation basin, raw water turbidity, temperature and flow parameters are collected in real time, and qualified samples are screened to construct a standardized water quality characteristic data set in combination with historical water quality and agent adding records; based on the data set, performing multi-dimensional clustering on historical water quality parameters, generating a characteristic working condition cluster, calculating a three-dimensional efficiency index, generating a dynamic dosage reference interval by using a sliding window confidence interval and a time decay factor, establishing a mapping relation between water quality characteristics and medicament dosage, and generating a graded dosage strategy; the transmembrane pressure difference and the membrane flux change rate are continuously monitored at the water inlet end in the membrane treatment stage, the membrane pollution trend is predicted through time sequence analysis, the prediction result is corrected according to the agent adding deviation, the precipitation effluent turbidity and the membrane inflow COD data, a cleaning early warning signal is generated, and graded response is performed according to the pollution degree.

The invention discloses a multi-modal data fusion-based high dam underwater defect measurement method, which comprises the following steps of: obtaining water flow velocity data, water pressure data and water quality turbidity data, performing preprocessing by adopting a self-adaptive median filtering algorithm, and constructing a hydrodynamic field feature model; then generating an initial trajectory based on the hydrodynamic field feature model and detection target area data, and performing trajectory optimization and stability evaluation in combination with dynamic constraint conditions; adjusting sonar parameters according to the turbidity influence and the trajectory parameters to perform data acquisition; carrying out space-time alignment through extended Kalman filtering, and carrying out feature fusion and optimization by adopting an attention mechanism; and finally, performing multi-scale consistency verification and accurate positioning mapping to obtain a final defect detection result. According to the invention, the problems of defect detection precision and reliability in a complex underwater environment are solved.

The invention provides a sewage treatment data optimization method and system based on cloud computing, and relates to the technical field of data processing.The method comprises the steps that real-time key information, including water quality indexes, equipment operation parameters and energy consumption data, collected by monitoring equipment in real time is received, the key information is preprocessed, and a processed data set is obtained; constructing a cloud database according to the processed data set, and performing data analysis including trend prediction and anomaly detection by using the cloud database to obtain an analysis result; and according to the analysis result, constructing a sewage treatment data optimization model, and inputting the water quality standard, the equipment operation efficiency and the energy consumption cost parameters into the sewage treatment data optimization model to generate an optimization scheme. By collecting and analyzing sewage treatment key information in real time and constructing and optimizing the data model, intelligent adjustment and optimization of the sewage treatment process are realized, the water quality is improved, the energy consumption is reduced, and the equipment operation efficiency and the overall treatment effect are improved.

The invention provides an underground water pollution source tracing and evaluation method for a riverside water source in a karst area, and the method comprises the following steps: collecting and preprocessing multi-source information, and obtaining a multi-source data set; a water quality sample is collected and analyzed, the surface water-underground water interaction process and pollution characteristics are comprehensively analyzed, and a pollution source type is obtained; performing space-time analysis on the multi-source data set to obtain a pollution activity time period and a potential pollution source area; constructing a karst area hydrological model, simulating the hydrological characteristics of the karst area, constructing a pollutant migration model, and simulating the migration process of pollutants; and constructing a traceability algorithm, tracking pollutants, confirming a pollution source, carrying out risk partitioning and risk assessment, and carrying out visual display of a result. According to the invention, interaction between surface water and underground water can be effectively assessed, accurate traceability of pollutants and multi-angle accurate identification of pollution sources can be realized, and management efficiency can be optimized through reliable risk assessment prediction.

The invention relates to the technical field of environment management, in particular to a marine environment risk assessment method and system based on multi-source data fusion, and the method comprises the following steps: carrying out the assessment based on the multi-source marine environment monitoring data through the data integrity, consistency and timeliness of a plurality of data sources, and calculating efficiency scores of the plurality of data sources and adjusting the contribution proportion of the target data source to generate a multi-source data fusion model. According to the method, by analyzing the efficiency of multiple data sources, adjustment of data source contribution is achieved, dynamic adaptability and flexibility of data fusion are optimized, the accuracy and timeliness of marine environment risk assessment are improved, and detection and recognition of abnormal data of multiple environment indexes are achieved by combining analysis of water quality related indexes and temperature data; through analysis of marine development activity events, a decision basis is provided for adjustment of marine management policies, and accurate assessment of marine environment risks is realized in combination with prediction of marine biological diversity.

The invention relates to the field of unmanned ship path planning, in particular to an autonomous water quality monitoring unmanned ship navigation control method and system based on multimode communication. The method comprises the following steps: collecting regional water quality monitoring parameters based on a shipborne water quality monitoring sensor, carrying out space pollution distribution excavation, and constructing a regional pollution concentration distribution diagram; obtaining a regional satellite remote sensing map, carrying out water flow distribution dynamic evolution, and constructing a water flow path distribution network; carrying out pollutant migration logic evolution and multi-region gradient diffusion prediction on the water flow path distribution network according to the regional pollution concentration distribution map, and constructing a water quality pollution diffusion prediction situation map; and according to the water quality pollution diffusion prediction situation map, carrying out highest diffusion progressive gradient identification, carrying out optimal navigation monitoring sequence analysis, and constructing an optimal navigation monitoring sequence. According to the invention, through dynamic and real-time unmanned ship water quality cruise path adjustment, the accuracy and efficiency of water quality monitoring are improved.

The invention discloses a sewage plant total nitrogen concentration real-time prediction and process regulation and control method based on an optimization integration algorithm, and belongs to the technical field of environment monitoring and treatment. According to the method, sewage plant data are monitored and collected, a sliding window and a time sequence are combined to analyze and clean the data and reconstruct features, total nitrogen concentration strong correlation variables are screened, data quality is standardized and optimized, a plurality of machine learning algorithms are adopted to construct a prediction model, and an optimal model is optimized through cross validation and performance evaluation. The robustness is improved by global parameter adjustment in combination with optimization algorithms such as a particle swarm, process schemes such as aeration intensity and carbon source adding are generated through multi-objective optimization after containerization deployment, and a whole-process intelligent management and control system is constructed by integrating virtual verification, graded early warning and a self-adaptive feedback mechanism. According to the method, the problems of detection lag, insufficient model generalization ability, regulation response delay and the like of a traditional method are solved, and the operation energy consumption and the medicament cost are remarkably reduced while it is guaranteed that the effluent quality stably reaches the standard.

The invention relates to the technical field of data processing, and discloses a water quality and water quantity accurate simulation method and system based on an SWAT model and a storage medium. The method comprises the following steps: analyzing rainfall and evapotranspiration data through a two-parameter trend test, and generating standard hydrological data by adopting a triple configuration algorithm; dividing sub-basins by combining topographic information, and calculating a hydrological response unit; performing water balance operation and water conservation evaluation; pollutant diffusion is analyzed, and a diffusion equation is solved; running state monitoring is implemented, and an early warning system is designed; and comparing and correcting measured data, and constructing a hierarchical monitoring mechanism. According to the invention, the whole-process precise management from hydrological data acquisition, water quality evaluation to early warning monitoring is realized, and the accuracy and reliability of water quality and water quantity monitoring are remarkably improved.

The invention relates to the technical field of environment monitoring, in particular to an underground water pollution real-time monitoring method and system, and the method comprises the following steps: analyzing the underground water flow direction and original pollution records in geographic information, combining the geologic structure and hydrological characteristics of a monitoring area, and through the water flow propagation characteristics; and marking the position of the pollution source and matching the pollution range to generate a matching result of the pollution source and the range. According to the invention, the underground water flow direction, the pollution record and the geological and hydrological characteristics are analyzed, the position and the range of a pollution source are accurately marked, the identification precision and the monitoring pertinence are improved, monitoring points are deployed at key positions, diffusion information is extracted in real time, an early warning threshold value is dynamically generated, early warning is issued in time, and emergency response is started, so that the pollution control efficiency is improved; rapid development of isolation and water quality treatment is ensured, closed-loop adjustment and optimization of monitoring parameters are realized, so that the method dynamically adapts to environmental changes, and comprehensive control of groundwater pollution is realized.

The present invention provides an integrated hyperspectral water quality analysis method, which belongs to the field of hyperspectral water quality analysis. First, data preprocessing is conducted by water quality data collection and water quality image collection in early stage; second, three dimensionality reduction methods are adopted to conduct dimensionality reduction processing, and fused dimensionality reduction is conducted by parameter trade-off selection; third, machine learning algorithms are adopted to train and test hyperspectral water quality inversion models on spectral data after dimensionality reduction; finally, the hyperspectral water quality inversion models are selected and optimized. The present invention adopts an innovative fusion strategy in the aspect of data dimensionality reduction processing, which can achieve a better data dimensionality reduction effect, effectively remove noise and redundant information, and provide a more accurate and reliable data basis.

The invention relates to the technical field of sewage treatment, and particularly discloses a sewage and wastewater treatment control system and method based on an intelligent optimization algorithm. Water quality data are collected through a data acquisition module, and a water quality characteristic matrix is generated through preprocessing. And the prediction module analyzes the feature matrix by using the trained water quality dynamic prediction model to obtain a water quality prediction result. And processing the prediction result by using a multi-objective optimization algorithm to obtain an initial control parameter. And the parameter optimization module calculates a water load fluctuation ratio, a model confidence coefficient and an equipment state according to the sewage and wastewater treatment data, inputs the water load fluctuation ratio, the model confidence coefficient and the equipment state into the adaptive fuzzy network and generates a multi-target parameter optimization suggestion. And the dynamic optimization module adjusts the multi-objective optimization algorithm parameters according to the parameters, and processes the prediction result again to obtain optimization control parameters. And the control module regulates and controls sewage and wastewater treatment according to the optimized parameters. The system realizes closed-loop management from data acquisition, prediction and optimization to control, can dynamically adapt to water quality change, and operates stably and efficiently.

The invention relates to the technical field of sewage treatment, and discloses a coagulant addition control method and system in sewage treatment, and the method comprises the steps: obtaining a floc image in a flocculation basin in real time, and inputting the floc image into an alumen ustum segmentation recognition model pre-trained based on a U-Net model after generative adversarial network optimization to obtain an alumen ustum recognition result; extracting floc characteristic parameters based on the alumen ustum identification result; acquiring water quality parameters of the sewage treatment plant in real time; inputting the floc characteristic parameters and the water quality parameters into a coagulant prediction model trained based on ensemble learning to obtain predicted coagulant adding data; and controlling the operation of the coagulant dosing pump based on the predicted coagulant dosing data. According to the invention, a proper coagulant addition amount can be accurately given according to real-time water quality data in combination with floc characteristics, and the method can be combined with an operation mechanism of a coagulation process, has very strong adaptability, is helpful to realize reasonable and accurate addition of the coagulant, optimizes resource utilization, and improves the cost performance of sewage treatment.

The invention discloses a spatial-temporal joint modeling system and method for watershed water quality prediction. The system comprises a data acquisition module, a feature extraction module, a feature fusion module, a model training module and a prediction output module. In the watershed water quality prediction process, the combined influence of time and space is considered, a space-time position coding combined embedded layer is designed, an attention mechanism guided by hydrological characteristics is combined, a door control network dynamically fused with space-time characteristics is constructed, and the space-time coupling influence of a watershed topological structure on pollutant diffusion is considered; the attention weight is dynamically adjusted by quantifying the topological importance of the monitoring points in the network, a feature channel which is most effective for a current prediction task is highlighted, and noise or redundant information is suppressed; in the model training process, a simplified gating mechanism is adopted, the gradient dispersion problem is reduced, the nonlinearity of a layer is kept, convergence is accelerated, a dynamic graph learning device is used, physical rules are respected, data changes are self-adapted, and more accurate water quality modeling is achieved.

The invention relates to the field of water quality pollution traceability, in particular to a water quality pollution traceability analysis method and system based on an intelligent unmanned ship. The method comprises the following steps: acquiring a multi-mode network monitoring parameter of a real-time position based on a multi-mode communication module, performing a dynamic multi-mode network intelligent switching decision, and constructing an intelligent multi-mode network switching strategy; real-time water area multi-dimensional water quality monitoring parameters of the intelligent unmanned ship are obtained according to the multi-dimensional water quality sensor, abnormal pollution parameter change detection is carried out, and abnormal change pollution parameters are marked; performing reverse water quality spatial change analysis and gradient descent iterative traceability calculation on the abnormal change pollution parameters to generate a potential pollution diffusion path; and performing intelligent traceability cruise according to the potential pollution diffusion path, and tracking and positioning the polluted water source to obtain a polluted water source point. According to the invention, efficient and accurate water quality pollution traceability is realized.

The invention relates to the technical field of water quality monitoring, in particular to a mining area groundwater environment monitoring system and method, and the system comprises a mining area groundwater partition recognition module, a groundwater pollution risk assessment module, a groundwater monitoring point optimization module, a groundwater pollution path analysis module and a groundwater dynamic monitoring planning module. According to the invention, through data-driven zoning and risk assessment processes, fine and dynamic management is realized in underground water environment monitoring, functional zone zoning boundaries are accurately calibrated, understanding of an underground water flowing mode is enhanced, pollution source control is more targeted, diffusion risk intensity is assessed in combination with flow velocity and pollutant release intensity, and the risk assessment accuracy is improved. The risk management strategy is optimized, the pollution diffusion range is predicted and identified more efficiently, the emergency response accuracy is improved, monitoring point arrangement is optimized through risk data, reasonable monitoring resource allocation is ensured, the sampling task weight and the time interval are dynamically adjusted, and resource optimization and monitoring efficiency improvement are achieved.

The invention discloses a water quality heavy metal pollution detection method and system based on Raman spectrum.The water quality heavy metal pollution detection method comprises the steps that water body Raman scattering light is collected in situ through a miniature optical fiber probe, and a continuous time sequence spectrum signal flow is generated; wavelet transform is combined with self-adaptive threshold setting, and high-frequency noise and effective spectral signals are separated; dynamically strengthening the characteristic peak of the target heavy metal through a frequency domain characteristic screening module, and inhibiting a water molecule interference peak at the same time; generating a cross-domain fusion feature vector; processing the fusion feature vector through a pre-trained heavy metal concentration prediction model, and outputting concentration prediction values of various heavy metals; a confidence score is dynamically calculated based on a deviation between a current predicted value and historical data distribution, and model parameter update and system calibration are automatically triggered. The method has the advantages that through acousto-optic signal cross-domain fusion and closed-loop self-calibration, the target peak is dynamically strengthened while water molecule interference is inhibited, and the real-time performance, the anti-interference performance and the prediction precision of heavy metal detection are improved.

The invention relates to the technical field of marine biological diversity assessment, and particularly discloses a marine biological diversity geographic information system and an ecological diversity risk assessment method.The marine biological diversity geographic information system comprises a water quality diversity risk assessment module, a diversity and water quality assessment comprehensive model module and an RDA analysis method screening module; according to the system and the method, marine organism data and water quality data of a sea area for many years are integrated, an interactive visual system is developed, data interconnection and intercommunication are realized, and the sea area can be evaluated through diversity investigation of multiple organisms in the sea area, water environment physical and chemical factor analysis, correlation mining and evaluation model construction. The model can assess the sea water environment condition, the water body environment quality and the biodiversity risk, provides basic data and methods for related research, powerfully promotes ocean monitoring to be converted from manual work to intelligence, promotes biodiversity data integration and ecological environment monitoring in a bay area, and tracks the biological activity and survival state.

The invention relates to the field of water quality evaluation, in particular to a rain source type river water quality evaluation method and system.The method comprises the steps that a sensor of a water quality monitoring system monitors micro-rain detection data of a micro-rain event, and the water quality monitoring system adjusts the sampling frequency of the sensor through an adaptive algorithm; obtaining water quality monitoring data through a micro-pollution mutation detection method; constructing a water quality recovery model of the micro-rain event through nonlinear kinetic analysis, and predicting a peak value and recovery time of water quality change of the micro-rain event; identifying a significant sudden change point of water quality change through a chaos theory algorithm, and adjusting the sampling frequency of the sensor through fuzzy logic control; key pollutants are obtained according to the peak value of the water quality change and the recovery time, and the key pollutants and pollution source data are subjected to data correlation analysis to judge the pollution source. The problems that the impact and the influence of a micro-rain event on the river water quality are not fully considered, so that the water quality evaluation is not timely and accurate enough, and the water quality change cannot be effectively captured and a pollution source cannot be analyzed are solved.