1275 results about "Water pollution" patented technology

The invention relates to a groundwater safety assessment method under an extreme climate event, which comprises the following steps: collecting multi-source heterogeneous data such as meteorological data, geological data, hydrological data and remote sensing data, and constructing a unified groundwater safety knowledge graph through standardized cleaning, semantic alignment and deletion completion; monitoring an extreme climate event in real time, and updating a node relation weight and sparsifying a transmission path based on knowledge graph dynamic evolution and a time sequence attention mechanism; performing risk propagation path reasoning on the dynamic knowledge graph in combination with an improved graph neural network, identifying key pollution nodes, and outputting a structured risk level and a coping suggestion; the system continuously optimizes atlas and model parameters based on evolution feedback, and high adaptability and reasoning precision of emergency response are achieved. According to the method, the intelligence, the real-time performance and the accuracy of underground water risk assessment are improved. The problems that the underground water pollution propagation path is difficult to dynamically identify and the decision adaptability is insufficient under extreme climate events are solved.

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 discloses an intelligent water affair monitoring and management system based on digital twinning, and relates to the technical field of intelligent water affair. The intelligent water affair monitoring and management system comprises a water affair monitoring and management platform, and the water affair monitoring and management platform is in communication connection with the following modules: a multi-source data acquisition module; the water affair monitoring system is used for collecting and preprocessing water affair monitoring data from a plurality of monitoring points of the water affair system, monitoring changes of a pipe network topological structure and obtaining dynamic data of a pipe network connection relation and geometric parameters. Through the digital twinborn technology, data of multiple monitoring points can be integrated in real time, abnormal events such as water quality pollution, equipment faults, water shortage and hydraulic change can be rapidly recognized in combination with the abnormal trend analysis module, early warning signals are automatically generated, the response time is remarkably shortened through an instant early warning mechanism, and the early warning efficiency is improved. Therefore, the management personnel can take measures at the initial stage of the abnormal event, the problem expansion is effectively prevented, and the timeliness and accuracy of water management are improved.

A water pollution risk early warning and tracing method based on multi-source data fusion belongs to the technical field of water pollution monitoring and early warning, and comprises the following steps: step 1, constructing a multi-source heterogeneous data acquisition network and realizing real-time data transmission; 2, performing multi-source data fusion and feature enhancement processing based on space-time reference; 3, constructing a water pollution risk dynamic early warning system based on a WOA-LSSVM model; fourthly, reverse positioning of the pollution source is completed on the basis of a CNN-GRU-SE Attention model; and 5, carrying out development and emergency response on a multi-dimensional visual decision support system, and positioning a pollution source. Multi-source information such as water quality sensor data, unmanned aerial vehicle image data and geographic information data is fused, and an intelligent monitoring network is constructed; through multi-source data fusion and an intelligent algorithm, water pollution risk early warning accuracy and traceability efficiency are effectively improved, and the method has the advantages of high response speed, wide monitoring range, accurate positioning and the like, can be widely applied to the fields of urban water supply, drainage basin management and the like, and meets the requirements of water environment safety guarantee.

The invention particularly relates to a water pollution intelligent traceability system based on a three-dimensional fluorescence spectrum, which relates to the technical field of environmental monitoring, and comprises a water sample collection and three-dimensional fluorescence data acquisition module, a data preprocessing and PARAFAC component decomposition module, a multi-source pollution characteristic fingerprint database module and an intelligent comparison and pollution source analysis module. According to the method, standard acquisition and preprocessing are adopted, and the original data quality is ensured by means of scattering rejection, instrument correction and the like; pure fluorescence components are decomposed through a PARAFAC algorithm, and feature extraction reliability is guaranteed through triple verification. In the comparison link, spectrum similarity, concentration proportion difference and space-time hydrological constraint multi-round screening are adopted, and irrelevant pollution sources are removed; and then the contribution proportion is quantified, and Monte Carlo simulation, water quality and specific pollutant cross validation are carried out, so that the crossing from suspected tracing to quantitative responsibility investigation is realized.

The invention relates to an intelligent regulation and control system for multi-phase conversion of water quality of a gate-controlled river reach, in particular to the field of water treatment, and realizes accurate dynamic perception of the multi-phase conversion process of the water quality of the gate-controlled river reach by constructing a high-fidelity digital twinborn body and fusing a real-time data assimilation technology; by means of a time-space diagram neural network agent model based on an attention mechanism, the system can predict a complex time-space pattern of pollutant concentration field and phase evolution under different regulation and control strategies in an ultra-fast manner; on the basis, a multi-agent reinforcement learning algorithm is adopted to automatically generate a globally optimal gate group coordinated regulation scheme, and multiple targets of water quality improvement, ecological protection, engineering operation and the like are effectively coordinated; and finally, a theoretical strategy is reliably converted into a physical action through rolling optimization and a closed-loop execution mechanism, and adaptive adjustment can be performed according to environmental feedback, so that the emergency response speed of sudden water pollution events, the scientificity of regulation and control decision and the intelligent level of water quality management of the whole river network are comprehensively improved.

The invention specifically relates to an intelligent water affair dynamic supervision method and platform based on digital twinning, and relates to the technical field of intelligent water affair supervision. Risk early warning and situation deduction; emergency decision making and dispatching command; and post-treatment evaluation and knowledge precipitation. According to the method, a comprehensive and scientific risk quantification system is constructed through a combined weighting mechanism of multi-source data fusion, an analytic hierarchy process and an entropy weight method, and dynamic and accurate description of risks of each section of pipeline and each node of a pipe network is achieved in combination with a real-time working condition correction coefficient and a second-level updating mechanism; and grading accurate early warning triggered by a dynamic threshold value is matched with risk situation deduction of a simulation model, so that the influence range and the development trend of risks such as pipe explosion and water quality pollution can be pre-judged in advance.

The invention discloses a water pollution cross-domain traceability method based on multi-chain collaboration and stable isotope source analysis, and relates to the technical field of water environment monitoring, the method comprises the following steps: pollution source chemical fingerprint construction: collecting an environmental water sample and a potential pollution source sample in a drainage basin; adopting a mass spectrometer to detect stable isotope compositions such as delta 13C, delta 15N, delta 18O and the like in the pollutants; and performing morphological division on the pollutants by using a morphological grading extraction technology. Homology judgment precision is greatly improved through isotope and morphological analysis; cross-chain credible collaboration is achieved, multi-party data interaction is achieved, and timeliness and accuracy of pollution tracing are guaranteed; the space-time model traceability is combined with an evolution path and gridding mapping, so that the pollution inversion accuracy is improved; and ecological compensation support provides a quantitative basis for pollution intensity and diffusion range so as to support a compensation model.

The invention relates to the technical field of intelligent mine and environmental protection crossing, and discloses a mine development ecological data fusion and causal mining method based on a double-lineage knowledge graph. The method comprises the steps of constructing a double-pedigree domain ontology model comprising a production activity pedigree and an ecological response pedigree, carrying out multi-modal data space-time alignment and standardization, constructing a dynamic map reflecting the real-time state of a mine, predicting future ecological indexes by using a time sequence diagram neural network introduced with physical constraints, and carrying out real-time monitoring on the real-time state of the mine. And calculating the contribution degree of each production link to abnormity by utilizing anti-fact reasoning so as to lock a disaster-causing source, and generating a production regulation and control instruction according to a causal analysis result, and feeding back and executing the production regulation and control instruction. According to the method and the system, full-process traceability and accurate regulation and control of mine environment problems such as surface deformation and water and soil pollution are realized, so that the problems that data multi-source heterogeneous is difficult to fuse and the causal relationship is difficult to confirm in mine ecological environment monitoring are solved.

The invention discloses a water quality monitoring method and system based on large model data analysis, and the method comprises the steps: collecting multi-dimensional environment index real-time data through a remote sensing device and an underwater mobile sensor, completing the noise filtering and format standardization through an edge calculation node, and inputting the data into a machine learning model; the method comprises the following steps: automatically extracting space-time distribution characteristics and intelligently identifying potential abnormal modes, immediately starting multi-parameter correlation analysis when an abnormal degree exceeds a threshold value, constructing a deviation correlation map to accurately invert pollution source space coordinates, further performing time sequence prediction modeling in combination with a historical data sequence, and deducing a future diffusion path track of pollutants. And finally, deeply overlapping and fusing the predicted trajectory and the ecological sensitive area map, quantitatively calculating the comprehensive risk score distribution of each intersection area by using a risk assessment algorithm, automatically generating a visual environment monitoring report, and updating a historical sequence to form closed-loop learning at the same time. According to the invention, the emergency response speed, the traceability accuracy and the risk prevention and control capability of the sudden pollution event of the water environment are greatly improved.

The invention discloses an underground water pollution diffusion model construction method based on multi-modal data fusion, and the method comprises the steps: fusing the dynamic time sequence characteristics of a monitoring well and the static space attributes of a geological field, and generating an initial state vector with rich information for a graph node; based on the instantaneous water level difference between the nodes and the equivalent permeability coefficient, a dynamic graph topological structure which evolves along with time and represents the hydraulic connection relation is constructed; deducing a future state through numerical integration by using a graph neural network model with a node state derivative as output; and constructing a composite loss function containing a data fitting item and a physical law residual item, and training the model through back propagation. According to the method, the dynamic graph of physical driving is constructed and the physical equation constraint is introduced, so that the model prediction has the data driving precision and the reliability of the physical mechanism, and the generalization ability and the prediction precision of the model under the complex hydrogeological condition are improved.

The invention relates to a microplastic pollution traceability method, system and device and a storage medium, and is applied to the field of water pollution traceability. Cleaning, standardizing and spatially aligning micro-plastic data and then storing; constructing a micro-plastic pollution source space-time diagram, and performing definition reinforcement on the micro-plastic pollution source space-time diagram to obtain a reinforced micro-plastic pollution source space-time diagram; performing space-time dependence modeling on the enhanced micro-plastic pollution source space-time diagram to obtain a dynamic pollution propagation probability diagram, extracting feature vectors of social economic plastic data and micro-plastic multi-modal feature data in the micro-plastic data, and performing fusion to obtain fused multi-modal features; performing model training by fusing the multi-modal features and the dynamic pollution propagation probability graph to obtain a traceability model; and setting a dynamic threshold value and a dynamic risk level, and performing microplastic pollution tracing and pollution alarm according to the dynamic threshold value, the dynamic risk level and the tracing model. The technical effect of the invention is that the microplastic pollution traceability precision is improved.

An intelligent early warning method for soil and groundwater pollution in an industrial gathering area belongs to the technical field of early warning of soil and groundwater pollution, and comprises the following steps: constructing an early warning index system for soil and groundwater pollution in the industrial gathering area and formulating a scoring rule; constructing an industrial accumulation area soil and groundwater pollution risk assessment model; constructing a pollutant migration prediction model; and carrying out early warning grade division by applying a density-based clustering algorithm. According to the method, a whole-process multi-level index system covering pollution generation, migration and action is constructed, factors such as enterprise emission behaviors, pollution characteristics, anti-seepage and hardening conditions and receptor sensitivity are systematically integrated, and panoramic quantitative characterization of industrial gathering area pollution is achieved. According to the method, the pollution risk index and the pollutant migration simulation prediction result are combined, the automatic division of the pollution early warning grade is realized by using the density clustering algorithm, a static division mode depending on a fixed threshold value is eliminated, and the scientificity and rationality of the early warning result are remarkably improved.

The invention relates to the field of water quality detection and analysis, in particular to a water quality detection method, device and equipment and a storage medium. The method comprises the following steps: performing multispectral remote sensing scanning on a to-be-detected urban water body to generate a standardized water body spectral image; performing mixed pixel component decomposition on the standardized water body spectral image, performing concentration spatial distribution calculation, and constructing a multi-pollutant concentration distribution diagram; a concentration diffusion rule is obtained based on pollutant motion vector estimation of the multi-pollutant concentration distribution diagram; collecting topographic surveying and mapping data of the urban water body, positioning and tracing potential pollution sources, and marking the positions of the potential pollution sources; and performing water pollution diffusion prediction calculation according to the concentration diffusion rule and the potential pollution source position, performing pollution risk comprehensive analysis, and generating a pollution risk analysis report. Through non-contact spectral image analysis, the water quality pollution concentration and distribution are accurately analyzed and calculated in real time.

The invention provides lipase biological agent integrated sewage treatment equipment for degrading oily sewage, and relates to the technical field of water pollution treatment equipment. Comprising a treatment tank, one side of the treatment tank is fixedly connected with a treatment box, the top of the treatment box communicates with a fixed cylinder, the outlet end of the treatment tank communicates with an inclined connecting pipe, one end of the inclined connecting pipe penetrates through one side of the treatment box and then communicates with the interior of the treatment box, and a filtering assembly is installed in the treatment box; a backwashing unit and a pressurizing unit are mounted in the treatment box; in the descending process of the lifting frame, in the initial stage, a horizontal part of a transverse stop block stops a push plate from moving downwards, the lifting plate compresses a vertical spring to store elastic potential energy, when the lifting frame continues to descend, an extrusion block guides and pushes a push wheel through a first slope part, a transverse plate is pulled through an L-shaped rod, the transverse stop block is separated from the push plate, and at the moment, the vertical spring releases the potential energy to push the push plate to move downwards quickly; and the water flow is accelerated to enter the hollow cavity through the communicating pipe and the transverse pipe and is sprayed out from the water outlet holes, so that the cleaning effect is improved.

The invention discloses an early warning grade evaluation method based on an urban pollution industrial site, relates to the technical field of industrial site early warning evaluation, and solves the technical problems of incomplete overall evaluation and lack of systematic analysis process and data processing correction caused by analysis of a single evaluation index. According to the method, pollution factor indexes are integrated in a three-dimensional mode through the pollutant standard exceeding multiple mean value, the specific pollutant toxicity coefficient and the pollution plume diffusion area proportion, the pollution intensity is reflected, toxicity hazards and spatial diffusion risks are considered, the soil pollution area is calculated through the Kriging interpolation method, the groundwater pollution plume area is predicted through the MODFLOW model, and the groundwater pollution plume area is predicted through the MODFLOW model. The overlapping area is processed through a maximum area method, repeated statistics is avoided, the accuracy of pollution range data is improved, different threshold values are set, standardized matching of comprehensive indexes and early warning levels is achieved, corresponding management and control measures are defined for all the early warning levels, abstract comprehensive indexes are converted into specific practical operation guidance, and hierarchical management and control can be conveniently carried out.

The invention discloses an underground water pollution dynamic monitoring and risk early warning system, and relates to the technical field of underground water pollution monitoring and early warning. Comprising a data monitoring module used for collecting pollution index data of underground water in real time and sending the collected data to a data transmission module; the data transmission module is used for transmitting the received data to the data processing module; the data processing module is used for preprocessing and analyzing the received data and sending the processed data to the risk assessment module; the risk assessment module is used for carrying out pollution risk assessment based on the processed data and sending an assessment result to the early warning module; and the early warning module is used for sending out corresponding early warning information according to the evaluation result. According to the invention, the dynamic and real-time monitoring of groundwater pollution is realized, the accuracy of data processing is improved, the timeliness of early warning is ensured, and the loss caused by groundwater pollution is reduced.

The invention discloses a water quality inspection system based on a full-spectrum unmanned aerial vehicle, and relates to the technical field of environmental monitoring, the system comprises a spectrum acquisition module for acquiring an original spectrum data set containing related information of pollutant types and concentrations to obtain preliminary spectral feature distribution, a data preprocessing module for preprocessing the initial spectral feature distribution according to the preliminary spectral feature distribution, and a water quality inspection module for inspecting water quality according to the data preprocessing module. The system comprises an original spectrum data set, a feature extraction module for denoising the original spectrum data set to obtain a pure spectrum signal set, and a feature extraction module for extracting specific signal features of pollutants for the pure spectrum signal set and determining a spectrum band range corresponding to each feature to obtain a characterized spectrum signal subset; the water quality inspection system based on the full-spectrum unmanned aerial vehicle realizes real-time monitoring and analysis of water body pollution, can quickly identify the types, concentrations and diffusion conditions of pollutants, provides scientific basis and decision support for water body pollution treatment, and has important environmental protection significance.

The invention provides a multi-source data fused water quality pollution monitoring method and device and a storage medium, and relates to the technical field of water quality monitoring. According to the method, through the key steps of data preprocessing, cross-modal feature fusion, pollution concentration estimation, graph neural network traceability and the like, multi-source water quality data and monitoring point space topology information are deeply integrated, and a whole-process automatic monitoring system is constructed. According to the method, end-to-end processing from data standardization to pollution emission source positioning is realized, a multi-source heterogeneous data barrier is effectively broken, the complementary value and the time-space association rule of different modal data are fully mined, the integrity, accuracy and efficiency of water quality pollution monitoring are improved, the emission source position and the pollution diffusion path can be accurately positioned, and the water quality pollution monitoring efficiency is improved. Full-link and targeted technical support is provided for water quality pollution control and treatment decision, and the problems that traditional monitoring data is low in utilization rate and insufficient in traceability accuracy are solved.

The invention discloses a lake pollution traceability analysis method based on multi-source data assimilation and reverse diffusion, and relates to water pollution traceability analys.The lake pollution traceability analysis method comprises the steps that chemical pollution is monitored at the downstream of a lake, monitoring data are imported into a hydrodynamic force-water quality model after Kalman filtering, and spatial and temporal distribution data of pollutants in a water body are obtained; pollution source inversion is carried out based on a differential evolution Markov chain Monte Carlo method, and pollution source parameters are obtained; forward simulation is carried out, a flow velocity field and water depth are solved through a hydrodynamic force-water quality model, then a pollutant transport model is generated, pollution source parameters are used as input, a finite volume method discrete control equation is used based on the pollutant transport model to predict pollutant concentration space-time evolution, and pollution duration under natural attenuation is evaluated; and comparing real-time monitoring data with an expected effect, and evaluating a pollution traceability analysis effect. Pollution source positioning errors are reduced, pollution contribution rate calculation accuracy is improved, traceability efficiency is improved, the risk of excessive reagents is avoided, and treatment cost is reduced.

The invention discloses a water pollution treatment device and method for a water supply pump station, and the device comprises an oil scraping plate which is arranged below an oil skimming rod, keeps synchronous rotation with the oil skimming rod, and is used for pushing scum and sump oil on the surface of sewage in a sedimentation tank; the oil skimming part is arranged in the sedimentation tank, rubs the oil scraping plate in the rotating state and is used for collecting scum and dirty oil on the surface of the sewage and discharging the scum and the dirty oil out of the sedimentation tank; the vibration assembly is arranged in the oil scraping plate and is used for driving a plurality of vibration rods to vibrate so as to crush and gather emulsified oil particles in the sewage; the gathering assembly is arranged above the oil scraping plate and is used for generating air flow, blowing the surface of the sewage by utilizing wind power, and driving the scum and the sump oil to gather towards the center direction of the oil scraping plate. According to the invention, the technology spanning from only oil slick treatment to emulsified oil treatment is realized, the oil content in the effluent is obviously reduced, and the efficiency of the subsequent treatment unit and the final water supply quality are improved.

The invention belongs to the technical field of water pollution composite treatment, and discloses a water pollution composite treatment method and system based on water quality pollution real-time monitoring. The system comprises a pollution responsibility traceability identification module, a multi-scene risk early warning simulation module, a comprehensive treatment strategy intelligent generation module, an instruction processing output module and a treatment effect evaluation optimization module, a pollution source analysis report is obtained, and risk prediction is performed based on a water quality space-time matrix. Future meteorological data is obtained according to the sensor network for simulation prediction, an active early warning report is obtained, processing is carried out based on the active early warning report, a comprehensive treatment strategy report is obtained, the comprehensive treatment strategy report is processed, an equipment control instruction set is obtained and output, and a treatment optimization report is generated. The method has the remarkable advantages of high reliability of governance decision basis data, good active effect of pollution early warning and great synergistic effect of governance means.

The utility model discloses a non-contact drinking water system for non-human primates, which comprises a cage, a main line water supply unit and a branch line water supply unit, the main line water supply unit comprises a constant-pressure water source and a drinking water main pipeline, and the drinking water main pipeline is communicated with the interiors of the constant-pressure water source and the branch line water supply unit; one end of the branch water supply unit is communicated with the drinking water main pipeline, and the other end of the branch water supply unit is communicated with the interior of the cage; a water inlet hole is formed in one side of the cage, and the water inlet hole is communicated with the interior of the branch water supply unit. The non-human primate breeding device can well adapt to the breeding scale, funds and breeding technology of the non-human primates, is simple in structure, low in cost, energy-saving and environment-friendly, and can avoid breeding water supply pollution so as to be beneficial to the physical health of the non-human primates.

The invention discloses a closed pit mine water pollution four-water conjugate cooperative monitoring and early warning method and system, and relates to the technical field of water quality analysis, and the method comprises the following steps: collecting water level and water quality data of corresponding positions of atmospheric precipitation, surface water, underground water aquifer and goaf ponding; performing format standardization, exception elimination, deletion processing and space-time alignment on the monitoring data, and constructing a four-water monitoring space-time data set; a four-water conjugate coupling analysis and prediction model is established, after training is completed based on historical monitoring data, the water level and water quality of each monitoring point are predicted, and pollution transmission paths and pollution lag characteristics among four types of water bodies are identified; and performing over-threshold judgment by combining the monitoring index and the early warning threshold, and outputting graded early warning and intervention regulation and control suggestions. According to the technical scheme of the invention, four-water integrated dynamic monitoring, pollution evolution rule identification and prospective early warning of the closed pit mine are realized, and the water pollution risk management and control precision and response efficiency are improved.

The invention relates to an organic combined polluted underground water in-situ remediation method based on a micro-nano iron-based material. The method comprises the following steps: acquiring underground water pollution information; establishing a composite reaction zone at a preset position, and constructing a reduction-oxidation chain reaction system; and operating the composite reaction zone, collecting monitoring data, and optimizing the composite reaction zone until the composite pollution is degraded and removed to a preset value. Through series reaction of powerful reduction of the micro-nano iron-based material and advanced oxidation of the iron-based activated persulfate, the performance of the micro-nano iron-based material serving as a strong reduction material and an active agent is fully played, pollutants such as halogenated hydrocarbon in underground water are sequentially subjected to reduction degradation, pollutants such as benzene series and volatile phenol are sequentially subjected to oxidative degradation, and the pollution to the underground water is reduced. Effective management and control of underground water combined pollution of medical and chemical parks and enterprises are realized; a monitoring well is configured based on a composite reaction zone, and remediation materials are discontinuously added in combination with management and control conditions, or other management and control materials are flexibly selected, so that long-term management and control of groundwater pollution risks are realized.

The invention discloses a river-into-sea pollution source segmented detection device, and belongs to the technical field of water pollution detection, the river-into-sea pollution source segmented detection device comprises a clear water tank fixedly mounted on a ship body, a mounting rack is fixedly mounted on one side of the clear water tank, and a hoisting mechanism, a water sample extraction mechanism, a water sample detection mechanism and a pipeline air drying mechanism are fixedly mounted on the top of the mounting rack. According to the invention, through cooperative use of the pipeline flushing mechanism and the cleaning member, water samples remaining in pipelines of the water sample detection mechanism and the water sample extraction mechanism can be flushed and cleaned; clear water left in the cleaned pipeline of the water sample detection mechanism and the water sample extraction mechanism can be subjected to hot air drying, so that the condition that a water body in a target detection area is mixed with a water body in a non-target detection area when the pollution conditions of different water areas are subjected to multi-point segmented detection is avoided; and the accuracy of a water pollution source detection result is improved.

The invention relates to the technical field of water body pollution monitoring, in particular to a multispectral image fused water body pollutant accurate recognition system, which comprises a data acquisition module, a cloud processing module, a boundary processing module, a pollution recognition module, a diffusion prediction module and a visualization module, the system constructs sub-pixel representation of a water pollutant boundary by using a differential geometry manifold theory, and realizes high-precision pollutant boundary description through a multi-scale analysis and curvature flow optimization technology; enhancing pollutant characteristic expression by adopting multispectral image fusion and an optimal wave band selection technology; using a support vector machine model to accurately identify various pollutant types such as oil films, oil spots, algae blooms and the like; based on a boundary fine description result and a pollutant type identification result, the diffusion trend of pollutants is accurately predicted in combination with historical flow, wind direction and wind speed data, and the system improves the water pollutant boundary identification precision and enhances the identification capability of complex boundary forms and low-contrast regions.

The invention discloses a water pollution prediction method based on transfer learning, and relates to the technical field of water pollution prediction. The method comprises the following steps: carrying out missing value interpolation and data normalization on water quality parameter time sequence data of a source domain and a target domain; constructing a water pollution prediction model based on GRU and RNN, and training the water pollution prediction model; migrating the pre-trained water pollution prediction model to a target domain based on transfer learning, fixing bottom-layer structure parameters in the pre-trained water pollution prediction model, and finely adjusting top-layer structure parameters in the pre-trained water pollution prediction model to obtain a target water pollution prediction model; and obtaining a water pollution prediction result based on the real-time water quality parameter time sequence data of the target domain and the target water pollution prediction model. According to the method, the water pollution prediction model trained in the source domain is migrated to the data target domain, so that the prediction problem in a small sample scene is effectively solved, the prediction precision and generalization ability of the water pollution prediction model are improved, and the training time of the water pollution prediction model and the consumption of computing resources are reduced.

The invention relates to the technical field of water supply network pollution monitoring, in particular to a water supply network water pollution rapid detection method. The method comprises the following steps: acquiring water supply pipe network operation data, extracting pipe network structure information, detecting a water flow direction, and constructing a water supply pipe network topological graph; on the basis of the topological graph and the operation demand data, the negative pressure overload condition of the pipe network is judged, the pipeline damage condition is estimated, then the conditions of external pollutant invasion and hydraulic unbalance are analyzed, and the pollutant backflow trend and the water flow retention area are determined; the conditions of microorganism breeding and internal water quality deterioration are evaluated by combining a water flow retention area and a pollutant backflow trend, and tracing and positioning of a pollution source are carried out; pipe network structure adjustment, water flow regulation and control and pollution prevention and control measures are implemented according to the pollution source tracing result, and optimization treatment of the water supply pipe network is achieved; optimization treatment of the water supply pipe network is achieved through pollution monitoring of the water supply pipe network.

The invention relates to the technical field of water pollution monitoring and big data analysis, and discloses a water pollution early warning method and system based on big data, and the method comprises the steps: collecting water and electricity use data and water quality monitoring data, and carrying out the standardized fusion, and obtaining an integrated data set; performing abnormal clustering analysis and multi-dimensional parameter relevance verification according to the integrated data set to obtain an abnormal linkage distribution result; performing historical behavior pattern matching and identity recognition based on the abnormal linkage distribution result to obtain hidden abnormal recognition data; performing reference coordinate retrieval and cross validation according to the hidden anomaly identification data, and determining specific position information of a potential pollution source; and carrying out pollution diffusion simulation on the position information, determining distribution information of an affected area and generating early warning notification data. According to the method, hidden pollution discharge behaviors can be effectively identified, and accurate positioning of pollution sources and dynamic evaluation of diffusion risks are realized.