15940results about "General water supply conservation" patented technology

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 port ecological construction, and particularly discloses a port ecological construction evaluation method and system, and the method comprises the following steps: collecting dynamic ecological environment data in port construction operation through a multi-source sensor array; performing space-time coupling analysis on the data, calculating a dynamic attenuation rate # imgabs0 # of each dimension environment index by adopting a self-adaptive sliding time window algorithm, adjusting an ecological response time constant theta in combination with hydrodynamic conditions of a sea area around a port, performing synchronous integral calculation on a historical cumulative effect # imgabs1 #, and generating an environment health degree evolution curved surface; constructing a comprehensive evaluation model; and predicting an environmental risk value based on the hybrid prediction model. According to the invention, an intelligent, automatic, safe, reliable, green and low-carbon evaluation solution is provided for ecological construction of the port, and the ecological protection and sustainable development level of the port is remarkably 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 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 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 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 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.

According to the water quality prediction method of the deep learning model GCN-GRU based on the graph neural network, the distribution of the monitoring stations and the adjacency relation between the monitoring stations are regarded as nodes and edges of the graph to construct the graph structure, the original space distribution of the monitoring stations in the drainage basin is reserved by using the graph structure, the transmission process of pollutants can be simulated more accurately, and the water quality prediction accuracy is improved. The explanation capability of the model is obviously improved; spatial information and time information in non-grid structure data are effectively captured by introducing a graph convolutional network and a gating circulation unit, and then long-term spatial-temporal characteristics in water quality time sequence data are effectively fused by adopting a self-attention mechanism and a full-connection network to obtain a water quality time sequence data fusion model; according to the method, the advantages of the GCN in the aspect of space modeling and the capability of the GRU in the aspect of time sequence modeling are combined, comprehensive capture of space correlation and time dependency is achieved, then the reliability of a water quality prediction result is improved, and an important tool is provided for environmental protection and pollution prevention and control.

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 data processing, and discloses a comprehensive dispatching system for a digital intelligent water service pipe network. Comprising a processing module, a layering module, an identification module, a scheduling module, an analysis module and an evaluation module which are connected in sequence. The processing module preprocesses the pipe network data and constructs a dynamic boundary area; the layering module generates operation parameters according to function classification; the identification module analyzes equipment fault features to obtain state data; the scheduling module formulates a preliminary scheme according to a time-sharing rule; the analysis module performs multi-target processing based on the dynamic weight to generate a scheduling instruction; and the evaluation module evaluates pressure, flow and water quality indexes to form a target strategy. The operation efficiency of the water supply network is improved.

The invention relates to the technical field of drainage basin water scheduling, and discloses a drainage basin water regulation and control optimization method based on ecological element changes. The method comprises the following steps: deploying a drainage basin monitoring system, and collecting ecological element real-time data such as a hydrological parameter sequence and a remote sensing image; after the data is cleaned and converted, hydrological trend features and spatial distribution features are extracted by adopting a feature learning model, and the hydrological trend features and the spatial distribution features are fused into unified ecological representation through a cross-modal alignment mechanism; inputting the unified ecological representation into a physically constrained neural network prediction model, and outputting a water regimen dynamic prediction value; and finally, based on the predicted value, a water resource regulation and control instruction is generated and executed by using a multi-objective decision algorithm so as to optimize the watershed water circulation process. According to the method, feature extraction comprehensiveness is improved through multi-source data fusion and cross-modal analysis, prediction reliability is enhanced in combination with physical constraints, reasonable allocation of water resources is achieved by means of multi-target decision, the ecological condition of a drainage basin can be improved, and the water utilization efficiency is improved.

The invention discloses an intelligent underground water quality supervision method and system. The method comprises heterogeneous sensing acquisition, water quality benchmark modeling, sewage traceability enhancement, water quality prediction and early warning and underground water quality supervision. The invention relates to the technical field of water quality supervision, in particular to an intelligent underground water quality supervision method and system, and adopts an underground water quality comprehensive supervision method combining water quality benchmark modeling, pollution traceability enhancement and dynamic threshold water quality prediction and early warning. The perception capability, the identification capability and the response capability of the underground water pollution event are effectively improved, and the supervision conversion from passive response to active pre-judgment is realized; carrying out water quality benchmark modeling by adopting an improved space-time diagram convolutional network method combined with a space-time dynamic baseline algorithm; pollution traceability enhancement is carried out by adopting an improved adversarial generative network method of virtual and real traceability modeling.

The invention relates to an image processing and data processing technology, in particular to an intelligent coagulant dosing control method and system based on image recognition and multi-parameter modeling, the floc state is accurately quantified through image recognition and deep learning modeling, and a dosing prediction model with self-adaptive capacity is established in combination with raw water feed-forward information. And accurate control of coagulant addition is realized. The method comprises the following steps: collecting a floc image, carrying out image processing and floc feature extraction, and constructing a floc image description vector; time sequence input structure data fusing the floc image and the water quality data is constructed, and floc image sampling at each moment is defined as a time frame; performing enhancement and reconstruction processing on the training data of the dosing amount prediction model by adopting a data enhancement and sample equalization strategy to obtain continuously distributed synthetic samples; and constructing a hierarchical feature fusion enhanced dosing amount prediction model, fusing the previous water quality parameters, the current water quality parameters and the floc image joint feature vectors, and optimizing the dosing amount prediction precision layer by layer.

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 present invention relates to the technical field of bioenvironmental science. Provided are an auxiliary chlorophyll content prediction method and system for a few-shot water body. The method comprises: acquiring related data of a water body; preprocessing the data to obtain an ideal data set; calculating an average impact value, and performing dimension reduction operation processing on the ideal data set; setting a hybrid training set and a test set, and setting a weight and a sample update coefficient for each sample; combining the data set and a weight vector, training a basic weak regression model, and continuously updating sample weights; continuously and repeatedly establishing models, storing and recording each weak regression model, and finally selecting a regression model having the best effect as a strong regression model; and inputting a test sample into the strong regression model, so as to obtain a predicted chlorophyll value of the test sample. In the present invention, the correlation between a water environment impact factor and chlorophyll and the correlation between different water environments are used to establish, for a chlorophyll prediction process, a chlorophyll prediction model having a low cost and a high generalized performance.

The invention discloses a water quality prediction deep learning model and prediction method based on spatio-temporal feature fusion and LSTM, and the model comprises one or more time convolution networks which comprise a plurality of TemporalBlock modules, and one or more classic convolution networks which comprise one or more convolution layers, a pooling layer, and a full connection layer. A Xavier initialization method is adopted to initialize parameters, a feature fusion mechanism is provided, and each convolution layer comprises a convolution operation, a ReLU activation function, a Dropout layer and a pooling layer; at least one additional temporal convolutional network; and a long-short-term memory network, a feature fusion module and an output module which flexibly adjust the output into single-step prediction and multi-step prediction and are used for generating a final prediction result. According to the method, the time sequence dependence and the nonlinear relation in the water quality monitoring data can be effectively processed, and the deep learning model of the multi-source information can be flexibly integrated, so that the accurate prediction of the water quality change is realized.

The invention belongs to the technical field of water pollutant traceability, and particularly discloses a water quality fingerprint database analysis method based on feature recognition, which relates to the field of water pollutant traceability, and comprises the following steps: cross-comparing feature curved surface information of different upstream and downstream under feature peak whole database data and sorting to obtain pollutant features under different intensities; the pollution source type is identified according to the pollutant difference; sorting the characteristic curved surface information of the water body samples reflecting different nodes, calling a chemical characteristic analysis result, outputting a chemical characteristic sequence, and selecting a first sequence sampling point position of the chemical characteristic sequence corresponding to the spectral characteristics of the identified pollution source type as a pollution source; according to the method, whether the identification result is met or not is analyzed according to the biological characteristics of the pollution source, efficient source tracing is carried out on water pollutants, an accurate pollutant transmission path is output, meanwhile, the corresponding pollutant type is evaluated, the corresponding pollutant diffusion trend is given, and powerful data is provided for follow-up pollutant treatment.

The invention relates to the technical field of Internet of Things monitoring, and discloses a water environment monitoring method and system based on Internet of Things, and a storage medium. The method comprises the following steps: deploying a plurality of sensor nodes at different water area monitoring points for collecting monitoring signals of a water environment, obtaining attribute information of the sensor nodes, establishing a transmission mode based on the attribute information, and transmitting the monitoring signals to a cloud platform based on the transmission mode; the cloud platform checks whether errors exist in the monitoring signals, if yes, the monitoring signals are corrected, standard data are generated, and if not, the monitoring signals are set as the standard data; summarizing all the standard data to generate a sample set based on the collection categories of the sensor nodes, establishing a water quality prediction model, outputting a water quality dynamic evaluation result by the water quality prediction model based on the sample set, and outputting pollution early warning information based on the water quality dynamic evaluation result; and the cloud platform sends the pollution early warning information to the management end to complete monitoring of the water environment. The water environment monitoring efficiency and accuracy are improved.

The invention relates to the technical field of water environment risk monitoring, in particular to a method for evaluating the algal bloom risk of a water body. The method comprises the following steps: collecting historical monitoring data of a to-be-evaluated water body, wherein the historical monitoring data comprises blue-green algae abundance data, water quality data and hydrological data; analyzing the correlation between the cyanobacteria abundance or chlorophyll a concentration and the water quality and hydrological data of the to-be-evaluated water body; hydrological and water quality parameters with the highest correlation with the cyanobacteria abundance or chlorophyll a concentration are screened out; hydrology and water quality parameters of a water body to be evaluated are taken as predictive variables, and cyanobacteria abundance or chlorophyll a concentration is taken as a response variable to construct a Bayesian network model; the weight of each parameter in the Bayesian network model is calculated, and the algal bloom risk probability that the cyanobacteria abundance exceeds a specific threshold value under the given parameter condition is calculated according to the weights. According to the invention, the scene-based probability deduction of the stable period and the dynamic period is realized through the double-branch Bayesian network model, so that the accuracy and timeliness of algal bloom risk assessment are improved.

The invention relates to the technical field of environmental pollution monitoring, and discloses an intelligent monitoring and tracing method and system for groundwater pollution, and the method comprises the steps: arranging a first monitoring well at the periphery of a target pollution source, obtaining data, calculating a comprehensive pollution index, dividing a risk area, and obtaining second monitoring data based on the optimized point distribution of the risk area, a four-dimensional space-time distribution model and a three-dimensional dynamic migration diffusion model are constructed, and pollution traceability analysis is carried out by combining the two models; according to the method, accurate monitoring of groundwater pollution is realized through a risk classification-driven differentiated point distribution strategy, and the temporal-spatial resolution and traceability precision of pollution plume migration simulation are remarkably improved through fusion of monitoring data and dynamic traceability analysis of a geologic model.

The invention discloses a time-space diagram neural network water quality prediction method based on DEM composition, and the method comprises the steps: obtaining and processing DEM data of a target region, extracting a river topological structure in the target region, and constructing a graph structure reflecting the spatial relation of water quality monitoring stations; acquiring water quality data, preprocessing the data, and eliminating the influence of missing values and abnormal values; a water quality prediction model is constructed based on an STGCN model, the STGCN model comprises a plurality of space-time convolution blocks, and each space-time convolution block comprises a time gating convolution layer, a space graph convolution layer and a time gating convolution layer which are arranged in sequence; and detecting the water quality to be detected based on the constructed water quality prediction model. According to the method, the accuracy of water quality prediction is improved by capturing the spatial dependency relationship between the water quality monitoring stations. A river network topological structure is extracted by using DEM data, a graph structure capable of truly reflecting hydrological relation between monitoring stations is constructed, and the accuracy of water quality prediction is effectively improved by combining effective capture of the STGCN model on spatio-temporal characteristics.

The invention relates to the technical field of industrial water system safety monitoring, in particular to an intelligent source tracing method for a process medium leaked in circulating water, which comprises the following steps of: acquiring multi-dimensional operating parameters such as conductivity, pH value, turbidity, dissolved oxygen, temperature, pressure and characteristic ion concentration; a standardized water quality parameter matrix is generated after space-time alignment and wavelet noise reduction; the method comprises the following steps: extracting an abnormal fluctuation signal by using a leakage feature recognition model based on transfer learning, simulating a diffusion process through a three-dimensional leakage diffusion model, realizing leakage source positioning by combining reverse particle tracking and kernel density estimation, associating a high-probability leakage region with upstream process equipment, extracting backtracking path features, and matching a process medium feature library, thereby realizing leakage source positioning. The leakage medium type is judged; and finally generating a structured traceability report. According to the method, high-precision identification, positioning and medium analysis of process leakage in a complex circulating water system can be realized, and the method has relatively high practicability and popularization value.

The invention discloses an urban water body pollution traceability system based on multi-source sensing data fusion, and the system comprises a data acquisition module which is used for deploying a multi-source water quality sensor to collect initial multi-source water body data, and carrying out the time-space unified alignment processing, and obtaining the time-space aligned multi-source time-space water body data; the pollution factor tracing module is used for constructing a pollution event deconstructor and a factor tracing reasoning engine based on a water network topological graph neural network on the basis of multi-source space-time water body data, and outputting pollution component vectors through pollution component decomposition driven by the pollution event deconstructor; inputting the pollution component vector into a tracing reason inference engine to carry out tracing reason space-time correlation to obtain a tracing reason pollution fusion map; and the traceability decision module is used for performing inversion through a reverse traceability algorithm based on the traceability pollution fusion map, calculating the probability that each upstream area is a pollution source, mapping the probability that each upstream area is the pollution source to a GIS platform, obtaining a pollution traceability confidence distribution map, and realizing accurate traceability of the urban water pollution source.

The invention relates to the technical field of environmental impact assessment, in particular to an intelligent environmental impact assessment method and system, and the method comprises the following steps: collecting environmental data including pollutant discharge, water resource consumption, air quality change and biodiversity, building an impact factor data set, and segmenting the data set to obtain an impact factor data set; and calling a kernel function type to carry out numerical prediction on pollutant emission. According to the method, by integrating data processing and machine learning technologies, the accuracy and efficiency of environmental impact assessment are improved, numerical prediction is performed on environmental data by using a kernel function and a support vector regression model, the adaptability and precision of a prediction model are enhanced, model parameters are optimized through automatic population generation and crossover mutation operation, and the accuracy and efficiency of environmental impact assessment are improved. The model can better learn and adapt in a complex data environment, sensitivity and response speed to environmental changes are further improved through dynamic clustering analysis and time sequence adjustment, and scientific support is provided for formulating timely and effective environmental protection measures.

The present invention discloses a method for detecting and applying C1-C2-alkyl-substituted phenol isomers, which relates to the technical field of environmental detection and includes: selecting a corresponding sample pretreatment process to prepare a test sample according to the petroleum content range in the sample; under a mobile phase of methanol-5.0-6.5 mmol / L ammonia water, using LC-MS to detect the test sample to obtain the concentration of the first component and the standardized concentrations of co-eluted 3,5-dimethylphenol and 2,6-dimethylphenol; under a mobile phase of acetonitrile-4.0-10.0 mmol / L ammonia water, using LC-MS to detect the test sample to obtain the concentration of 3,5-dimethylphenol, or the standardized concentrations of co-eluted 3,5-methylphenol and 3-ethylphenol, or the standardized concentrations of co-eluted 2,6-dimethylphenol and 2,4-dimethylphenol; under a mobile phase of methanol-5.0-6.5 mmol / L ammonia water or acetonitrile-4.0-10.0 mmol / L ammonia water, using LC-MS to separately detect the calibration samples of the co-eluted components, and calculate the conversion factor of the co-eluted components and the concentration of the other component. The present invention realizes the accurate quantification of phenol and 12 C1-C2-alkyl-substituted phenol isomers.

A water filter system includes an upper chamber removably coupled with a lower chamber. In implementations the lower chamber has a sidewall and a rim wider than the sidewall. A filter medium filters water from the upper to the lower chamber. A seal couples between the chambers and has a circular cross-section and a gap receiving a portion of the sidewall. The seal may also have a cavity receiving the rim. The seal includes one or more arced flaps contacting the upper chamber, a tip of each arced flap defining a circular inner opening of the seal. The seal includes one or more first barbs contacting the upper chamber, each first barb forming a circle. The seal includes one or more second barbs contacting the lower chamber, each second barb forming a circle.

The invention discloses a fusion traceability algorithm based on water environment multi-source monitoring data, which comprises the steps of collecting multi-source data, collecting water environment data from a plurality of monitoring sources including a ground monitoring station, a remote sensing satellite, unmanned aerial vehicle patrol and a ground sensor network, and analyzing water quality spatial-temporal characteristics. Analyzing a change trend and a periodic rule of the water quality monitoring data under a long-time sequence; and environment influence analysis: based on water quality monitoring data, rainfall data and hydro meteorological data of the pollution time period, analyzing correlation between pollutant concentration and rainfall, flow and temperature through a Pearson's correlation coefficient and rank phase relation calculation method, and obtaining point source pollution research and judgment, non-point source research and judgment and comprehensive research and judgment conclusions. Through the multi-source data acquisition and progressive analysis method, the range of the pollution source can be quickly narrowed, timely and accurate decision support is provided for the environmental protection department, and the efficiency and accuracy of tracing the pollution source are greatly improved.

The invention discloses a water quality time sequence prediction method of an SSA-VMD-LSTM-XGBoost hybrid model, and belongs to the technical field of water quality monitoring and prediction. Comprising the following steps: (1) data preparation and preprocessing; (2) optimizing the water quality time sequence decomposition of the VMD based on SSA: optimizing a penalty factor and a modal number of the VMD by adopting a sparrow search algorithm (SSA), and decomposing the water quality time sequence into a plurality of sub-components with high stability and low complexity by utilizing the optimized VMD; (3) construction and training of an LSTM-XGBoost hybrid prediction model: constructing a hybrid prediction model fusing long-short term memory (LSTM) and extreme gradient boost (XGBoost), inputting a high-frequency component into the LSTM model, inputting a low-frequency component into the XGBoost model, and finally performing superposition and integration on prediction results of the models; and (4) multi-component prediction result integration and performance verification. According to the method, adaptive optimization of VMD parameters is realized through SSA, the feature extraction and time sequence modeling capability is improved by combining the advantages of LSTM and XGBoost, and the prediction precision and stability of the water quality time sequence are effectively improved.

The invention provides a water supply pipe network optimization scheduling method and system based on multi-model coupling, and the method comprises the steps: collecting the basic data of a regional pipe network, and building a water supply pipe network hydraulic model; obtaining a water supply network operation scheduling target, and establishing a water supply network scheduling scene model; historical monitoring data of a water supply network are acquired, a node water demand prediction model and a node water demand change curve prediction model are constructed by using a neural network algorithm, and node predicted water demand and a node water demand change curve during prediction are respectively output; constructing a water supply pipe network optimization scheduling model based on multi-model coupling, performing optimization by using an improved particle swarm algorithm, outputting a water supply pipe network optimization scheduling scheme, and introducing an optimization scheduling evaluation index to perform scheduling scheme evaluation; and constructing a water supply pipe network optimization scheduling scheme screening model, and obtaining an optimal water supply pipe network optimization scheduling scheme. According to the method, a multi-model coupling mode is adopted, the prediction error is effectively reduced, the optimization efficiency is improved, and intelligent and precise scheduling of the water supply pipe network is realized.

The invention discloses a sewage plant accurate dosing method based on multi-mode automatic machine learning, and belongs to the technical field of sewage treatment. The method comprises the following steps: acquiring historical sensing data and image data of a sewage plant; extracting key features in the image by using a YOLO visual model, fusing the key features with sensor data, and constructing a multi-modal sample data set; carrying out modeling training on the fused data by adopting an automatic machine learning framework, and constructing a'water inlet-dosing-water outlet 'prediction model; generating a reference sample data set according to an existing dosing rule; inputting the sample data into the trained prediction model to obtain predicted effluent quality data, and adjusting the dosage until the predicted effluent quality of all samples meets a water quality standard condition; and finally outputting an optimized dosing sample data set. According to the invention, precision and real-time dosing of the sewage plant can be realized, chemical waste can be effectively reduced, the treatment efficiency is improved, the cost is saved, and the water quality is ensured to stably reach the standard.