2084 results about "Water environment" patented technology

The invention relates to a water quality prediction method and system based on gating residual enhancement and feature fusion, and belongs to the technical field of water environment intelligent analysis and deep learning. Taking each water quality index as a node of the graph, and constructing two complementary variable relation graph structures by utilizing a Pearson's correlation coefficient and mutual information; respectively inputting the two graph structures into a graph convolutional network, extracting deep dependency features among indexes, and splicing and fusing the deep dependency features. A multi-head attention mechanism is used as a trunk to extract global time dependence, a GRU network is introduced to extract local time sequence features, GRU output is used as an adjustable residual term to be injected into the attention trunk through a residual gating mechanism, self-adaptive enhancement of local dynamic features is achieved, and finally a self-adaptive fusion mechanism is introduced to generate comprehensive representation. According to the method, the complex dependency relationship between the water quality indexes and the time dynamic evolution process can be modeled in a collaborative manner, the response capability to key local change and sudden change events is remarkably enhanced, and the accuracy and robustness of water quality prediction 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 provides an underground water environment automatic monitoring super station state supervision method and system. The method comprises the following steps: collecting a multi-source dynamic time sequence data set and carrying out time-space alignment; generating a dynamic characteristic index set by using a nonlinear dynamic characteristic extraction method; based on the index set, constructing a self-adaptive cooperative measurement network to perform anomaly monitoring, and generating an anomaly detection result and a cooperative control instruction; generating real-time monitoring and early warning information by using a dynamic threshold adjustment algorithm; and generating an adaptive control instruction and a dynamic resource allocation scheme by using a neural network adaptive control strategy and a resource scheduling optimization algorithm. According to the method, a dynamic characteristic index set is generated, a C-C method is adopted to reconstruct a high-dimensional phase space, a Wolf algorithm and a G-P algorithm are combined to calculate related indexes and dimensions, and a multi-scale fractal mode is analyzed through an R / S analysis method and wavelet transform. The methods capture complex dynamic behaviors of the underground water system, and solve the problem that the traditional method is insufficient in non-linear feature extraction capability.

The invention discloses a drainage basin water environment intelligent management method and system, and belongs to the technical field of environment monitoring and water resource management. Multi-source heterogeneous water environment data is obtained, and a multi-modal pollution sensing map is constructed through fusion; extracting a time mutation vector and a spatial offset path of a pollution abnormal diffusion source point based on the map, and generating a pollution propagation sensitive weight map; performing cross-space-time matching on the sensitive weight map and historical climate disturbance data, generating a pollution causal chain path map by using a deep causal learning model, identifying a potential hidden pollution source distribution unit, predicting a response trend of the potential hidden pollution source distribution unit to future water quality change, and forming a multi-scene pollution evolution simulation result; and finally, constructing a pollution response priority index map, and generating a treatment resource optimal allocation sequence and an intervention strategy in combination with the drainage basin management database. According to the invention, intelligent identification and dynamic treatment optimization of complex pollution diffusion behaviors can be realized, and scientificity and timeliness of water environment management are improved.

The invention provides a water environment model coupling data processing method and system, and relates to the technical field of hydrology and water quality monitoring. The method comprises the following steps: determining a soil permeability characteristic descending trend and estimating an uncertainty range of a runoff coefficient; obtaining source model data according to the uncertainty range of the runoff coefficient by using a regional pollution production and convergence model; using the one-dimensional river network model to obtain error information according to the source model data, and outputting prediction data; and determining a prediction deviation mode by comparing the prediction data with the real-time monitoring data, and correcting parameters of all related models in the whole model chain. The method aims at solving the technical problem that the permissible discharge amount set based on a three-model coupling system does not conform to the actual absorption capacity of a water body due to continuous nonlinear deviation of river network model prediction and actually measured water quality data in the three-model coupling system, and accurate analysis of the urban pollution load and water environment quality response relation is achieved. And real-time and self-adaptive adjustment of the permissible emission amount is carried out.

The invention relates to the technical field of water environment monitoring, and discloses a sewage detection and traceability system based on multi-source data fusion. The system comprises an unmanned aerial vehicle data acquisition module, an edge end preprocessing module, a cloud data fusion and modeling module, a traceability decision module and a closed-loop optimization module, and all the modules work cooperatively to realize sewage detection and traceability full-process automation. Hyperspectral, thermal infrared, LiDAR and meteorological data are collected in an integrated mode through a multi-source sensor, pollution area identification, pollutant concentration quantification, sewage draining exit positioning and pollution diffusion path tracing are accurately completed through edge end preprocessing and cloud deep fusion modeling, a detection strategy is dynamically adjusted through a closed-loop optimization mechanism, and the detection accuracy is improved. And high precision, high efficiency and real-time response of sewage detection under complex terrains are realized.

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 discloses a source network, plant, river and lake integrated model simulation method and device, a medium and equipment in the technical field of regional water environment pollution treatment. The method comprises the following steps: inputting a boundary condition and an initial condition of a first model into a pre-constructed surface runoff production and convergence model of a target area, and outputting a flow rate of surface runoff and a water quality index time sequence; inputting the boundary condition and the initial condition of the second model into a pre-constructed pipe network model of a target area, and outputting a flow and water quality index time sequence of key nodes in a pipe network system; and inputting the boundary condition and the initial condition of the third model into a pre-constructed riverway and lake grid model of a target area, and simulating the river and lake hydrodynamics and water quality change process of the target area. According to the invention, coupling simulation of surface runoff, pipe networks, rivers and lakes is realized, and a propagation path and an influence mechanism of pollutants are comprehensively reflected; and high precision and reliability of the model are ensured through parameter calibration.

The invention relates to the field of water environment monitoring and treatment, in particular to a lake and reservoir pollution flux dynamic evaluation and optimization system and method.According to the system, hydrological, water flow, meteorological and remote sensing data are collected in real time through a data processing module, and a pollution flux space-time matrix is generated; the atlas generation module constructs a mechanism model based on the matrix and outputs a pollution flux thermodynamic atlas; the scheme simulation module determines a pollution source emission reduction scheme according to the map and simulates the influence of the pollution source emission reduction scheme on lake and reservoir water quality and ecology; and the feedback optimization module performs treatment efficiency scoring on the simulation result, compares the evaluation result with a preset threshold value, and adjusts the emission reduction scheme and updates the thermodynamic map when the evaluation result does not reach the standard. According to the method, through data driving, map visualization, scene simulation and dynamic feedback closed loop, accurate evaluation and optimal regulation and control of the lake and reservoir pollution flux are realized, and a scientific basis is provided for pollution abatement decision.

The invention relates to the technical field of remote sensing data processing and water environment monitoring, and discloses a black and odorous water body intelligent identification method based on a multi-source remote sensing image, and the method comprises the steps: firstly obtaining a first high-resolution remote sensing image, and extracting a global water body mask through a TransUNet model; secondly, removing non-target plaques based on geometric features and a basic spectrum rule; then, calculating a black and odorous water body index by using the second multispectral remote sensing image, and screening suspected black and odorous water bodies by combining adaptive threshold segmentation; further, performing fine classification confirmation on the suspected target by using a third high-frequency remote sensing image and an Officient Net model; and finally performing fusion output on the identification result. Through multi-source data collaboration and a coarse screening-fine classification multi-stage strategy, the limitation of insufficient spatial-temporal resolution of a single data source is overcome, and the positioning precision and the anti-interference capability of urban black and odorous water body recognition are improved.

The invention discloses a high-frequency shallow water bottom reverberation signal modeling method, and belongs to the technical field of underwater acoustic engineering and signal processing. The method comprises the following steps: setting a modeling hypothesis; a cylindrical array geometric structure is constructed, scattering units are divided, and channel parameters and scattering functions are obtained in combination with a Bellhop tool and a GABIM model; generating a reverberation signal time domain expression, deducing an array receiving signal spatial domain expression and calculating a spatial spectrum; the model can also adjust underwater environment parameters to realize simulation under different conditions, and performs time domain and space domain theoretical analysis on reverberation signals based on a cylindrical array. Multi-dimensional underwater environment parameters are fused, and the modeling precision is improved; a receiving and transmitting combined cylindrical array is adapted, and spatial gain is embodied; experiments prove that the average error is within 3dB, the practicability is outstanding, support can be provided for design and optimization of the sonar system, and the method is suitable for underwater reverberation signal simulation and characteristic analysis of the sonar system in the high-frequency shallow water environment.

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 relates to the technical field of unmanned aerial vehicle searchlighting control, in particular to an unmanned aerial vehicle searchlighting control system, which comprises a multi-source data acquisition module for acquiring a water surface image and unmanned aerial vehicle flight data; the water surface type analysis module is used for judging a calm or wave water surface according to the image gray variance and the edge density; the searchlighting output sub-control module is used for selecting a lamp control strategy according to a water surface type, identifying a high-confidence target in combination with multiple features and determining a wick power distribution parameter; the searchlighting output execution module drives the lampwick to illuminate according to the parameters; the target real-time tracking module is used for calculating the projection angle adjustment amount of the searchlight to aim at the target; according to the system and the method, the problem that an existing searchlight system cannot dynamically adapt to water surface optical characteristics, suppress reflection interference and accurately illuminate in a complex water environment at night can be solved, and the accuracy and efficiency of target recognition are remarkably improved.

The invention relates to the field of water environment pollution detection and analysis, in particular to a water environment pollution detection and analysis system and method based on the Internet of Things, and solves the problems of sampling redundancy, missing detection, high-dimensional data over-fitting, difficulty in traceability and early warning lag in traditional detection. The system comprises a monitoring acquisition module, a water quality anomaly detection module, a pollution tracing module, a pollution early warning module and a database. The monitoring acquisition module adopts chaotic adaptive sampling to adjust sensor frequency, and entropy optimization water quality data is obtained by combining permutation entropy and collaborative filtering denoising; the water quality anomaly detection comprises the following steps: standardizing data into high-dimensional point clouds, calculating a Betti number and a non-equilibrium state entropy, and fusing to generate a comprehensive pollution topological entropy index; according to pollution traceability, a dynamic graph and a diffusion kernel matrix are constructed by using GNN, and the optimal source intensity is solved through quantum annealing; the range is predicted through a diffusion equation for early warning, and information is integrated to generate a report to be pushed to users. The method corresponds to a module flow and provides support for pollution prevention and control.

The invention discloses an ecological shoreline diagnosis method and system based on hydrological-biological communication, and is applied to the technical field of water environment ecological restoration and shoreline health assessment. Comprising the following steps: dividing intertidal zones of a shoreline research area, and generating uniformly distributed sampling points in each tidal zone; extracting a plurality of indexes related to hydrological and biological connectivity, and constructing a comprehensive hydrological-biological connectivity state index through principal component analysis; dam feature data of a shoreline area are obtained, correlation analysis and collinearity screening are carried out, and key shoreline features are screened through a random forest algorithm; constructing a mechanism connectivity index based on a circuit theory; constructing and training a Bayesian network prediction model; and performing connectivity state diagnosis, attribution analysis and restoration scheme effect simulation on the target shore section by using a Bayesian network prediction model. By coupling hydrological and biological connectivity, static element evaluation is changed into dynamic ecological process diagnosis, and dominant factors are accurately positioned.

The invention discloses a method for degrading antibiotics in wastewater based on chitosan aerogel. The method comprises the following steps: adding chitosan aerogel Co-PBA / CS and potassium hydrogen peroxymonosulfate into the antibiotic-containing wastewater, and degrading under stirring. The method provided by the invention has a ciprofloxacin degradation capability of 94.7%. In addition, the material also has excellent self-cleaning and photo-thermal conversion performance, and can realize surface cleaning. The multifunctional aerogel provides a new strategy for removing pollutants such as ciprofloxacin in a complex and severe water environment.

The invention provides an underground water environment monitoring system and method, and the method comprises the steps: carrying out the three-dimensional fluorescence spectrum full-wave band scanning of a known type of pollution source reference sample in a target monitoring region, and obtaining a three-dimensional fluorescence spectrum data set; performing characteristic analysis on the three-dimensional fluorescence spectrum data set to obtain fluorescence characteristic components, and determining a characteristic excitation wavelength corresponding to each fluorescence characteristic component; the classification contribution degree of each characteristic excitation wavelength is evaluated, and a characteristic excitation wavelength combination during source tracing of the pollution source is obtained through screening; performing fixed wavelength scanning on the to-be-detected underground water sample in the target monitoring area according to the characteristic excitation wavelength combination, and determining the initial type of the pollution source; and determining the relative contribution degree of the type of the pollution source of the to-be-detected underground water sample, simulating the migration trend of the pollution plume according to the underground water migration and diffusion model, and generating the position of the pollution source of the to-be-detected underground water sample. By adopting the scheme of the application, the pollution source of the underground water can be quickly identified and positioned at low cost.

The invention relates to the field of environment management, and discloses a watershed digital twinning environment real-time simulation and interaction platform fusing cloud edge collaboration and intelligent modeling, and the platform comprises the steps: carrying out the dynamic collection of the Yangtze River and Yellow River watershed, and generating environment state information organized according to the temporal-spatial resolution; associating the generated environment state information with multi-source environment data to form a multi-source knowledge basis of the drainage basin pollution field; establishing a rule knowledge base covering pollution identification, early warning and prevention and control business processes by utilizing a generative knowledge representation method, and generating a pollution event candidate identification result; carrying out fusion calculation on the pollution event candidate recognition result and multi-source heterogeneous data, constructing a digital twin simulation model, and eliminating data islands between modules and data sources; and executing linkage calculation on the pollution area and the risk node calibrated in the digital twinborn simulation model, generating environment state interaction information and synchronously updating the environment state interaction information to the digital twinborn environment. The method has the advantage that the regional water environment risk prevention and control capability is improved.

The invention discloses a water environment pollution traceability system and method. The system comprises a multi-level monitoring module, a flow field reconstruction module, an inversion solving module, an intelligent identification module and a decision judgment module which are connected in sequence. The multi-level monitoring module acquires pollutant concentration and a three-dimensional flow field parameter time sequence in real time, and performs spatial interpolation and time filtering processing on flow field data; the flow field reconstruction module establishes a dynamic source-receptor response matrix according to the pollutant transmission track network; an inversion solving module calculates posterior probability distribution of the parameters; the intelligent identification module identifies the pollution source type through a pre-training classification model; and the decision judgment module comprehensively judges the effective pollution source according to the posterior probability confidence coefficient and the spatio-temporal evolution characteristics. According to the method, flow field reconstruction and probability inference technologies are fused, high-precision identification and parameter estimation of the dynamic pollution source of the water body are achieved, and the problems that in the prior art, mobile pollution sources cannot be effectively treated, and intermittent emission cannot be achieved are solved.

The invention provides a water chlorophyll a concentration inversion method and system based on multi-modal data and a lightweight model, and relates to the technical field of water environment remote sensing evaluation. The method comprises the following steps: firstly, acquiring a Gaofeng No.5 satellite remote sensing image, a sentinel No.3 satellite image and ground actual measurement data, and completing image preprocessing and water body pixel extraction; constructing a hyperspectral index and an aquatic vegetation index, and fusing the hyperspectral index and the aquatic vegetation index with the water body temperature, the pH environmental factors and the spectral reflectivity to form a multi-dimensional feature sample set; a core feature subset is obtained through random forest and XGBoost coupling feature selection, and a lightweight student model is trained based on knowledge distillation; and constructing a to-be-predicted feature sample for the to-be-predicted time phase image and the environment factor, inputting the to-be-predicted feature sample into the lightweight student model to obtain a chlorophyll a concentration predicted value, and generating a spatial distribution map and a quality control map layer. According to the invention, high-precision, low-redundancy and efficient deployment chlorophyll a concentration inversion is realized.

The invention relates to the field of water quality monitoring, in particular to a water area environment water quality on-line intelligent monitoring device and method, and the water area environment water quality on-line intelligent monitoring device comprises a shell, a defoaming mechanism and a monitoring mechanism; a water inlet and a water outlet are formed in the shell, and a water flow channel is formed in the shell; a water inlet valve is mounted at the water inlet; the defoaming mechanism comprises a negative pressure cylinder arranged in the shell and a piston assembly arranged in the negative pressure cylinder; the piston assembly moves upwards to suck the water flow channel to negative pressure, bubbles in water flow are promoted to be separated out, the pressure difference between the two sides of the water inlet valve is increased, and the water flow is promoted to enter the water flow channel through the water inlet valve. The opening threshold value of the water inlet valve is adjusted according to the gas amount in the water flow, so that new water flow enters from the water inlet valve when the piston assembly sucks the water flow channel till the absolute value of the negative pressure is large enough, and the separation effect of bubbles in the original water flow and the new water flow in the water flow channel is guaranteed. And the water quality monitoring accuracy of the monitoring mechanism is improved.

The utility model provides a submerged plant diversity restoration biological window which is provided with a frame structure and a supporting structure, the supporting structure is arranged on one side or two sides of the frame structure, the frame structure is provided with a through containing cavity, and one or more submerged plants are planted in the containing cavity of the frame structure. A plurality of biological windows are arranged in a combined gathering manner or are detachably removed to form a repairable submerged plant group. According to the method, an economical and efficient engineering technology path can be provided for restoration of diversity of submerged plants in the damaged aquatic ecosystem and optimization construction of the healthy aquatic ecosystem, and the method has important significance on restoration of health of the aquatic ecosystem and improvement of water environment quality.

The invention discloses a culture tail water resource treatment system based on pearl mussels, which belongs to the technical field of aquaculture and water environment protection and comprises a culture pond, a plurality of first treatment ponds, at least one second treatment pond, a third pipeline and a control unit. Each first treatment pond is connected with the culture pond through a first pipeline, activated sludge is laid in each first treatment pond, and an aeration device and an adsorption filter with the height capable of being adjusted through a lifting mechanism are arranged on each first treatment pond. The second treatment pond is connected with the first treatment pond through a second pipeline, the interior of the second treatment pond is divided into a phycomycete symbiotic area and a water quality purification area, the water quality purification area sequentially comprises a supporting layer, an adsorption layer and a substrate layer from bottom to top, and various aquatic plants are planted in the substrate layer. The third pipeline is connected with the second treatment pond and the culture pond to form a closed water treatment circulation loop. The control unit is responsible for monitoring and regulating the operation state of the system. According to the system, efficient purification and resource recovery of aquaculture tail water are realized through multi-stage treatment and cyclic utilization.

The invention belongs to the technical field of water environment algae monitoring, and provides an algae multi-dimensional feature intelligent identification system and method based on a large model. Comprising a data acquisition module, a preprocessing module, a feature extraction module, a feature library module, a large model reasoning module, a result verification module, an incremental training module and a real-time monitoring terminal module. According to the method, the problems of efficiency, precision, adaptability, updating capability and the like of a traditional algae recognition technology are effectively solved, the algae recognition efficiency and precision are remarkably improved, the scene adaptability and the dynamic updating capability are enhanced, and the method has wide application prospects in the fields of water environment monitoring and the like.

The invention discloses a water quality probability forecasting method based on Bayesian multi-time-sequence deep learning. The method comprises the following steps: S1, determining a forecasted water environment water ecological index, a driving index and a forecasting day number; s2, collecting time sequence data monitored by the forecasting indexes and the driving indexes, and after data preprocessing, constructing a data set required by model construction; s3, carrying out data division on the time sequence data, constructing a driving index forecasting model by adopting a multi-time sequence deep learning method, and carrying out parameter learning by selecting a Bayesian random discarding method; s4, performing effect evaluation on the accuracy and precision of the model, and adopting a hyper-parameter optimization method to improve the simulation forecast effect; s5, carrying out model training by adopting all data without segmenting the training set and the test set, carrying out water quality probability forecasting by utilizing the trained model, and outputting a forecasting mean value and a confidence interval; according to the method, the confidence interval is output while high-precision prediction is provided, and the scientificity and stability of prediction are improved.

The invention relates to the technical field of underground water prevention and control, in particular to a tunnel two-step grouting plugging method for wide crack water gushing, which comprises the following steps of: constructing a grouting hole intersected with a wide crack or a concentrated water gushing channel according to a geological survey result; first rapid plugging slurry is injected into the crack through the grouting hole, and the first rapid plugging slurry can form a temporary water stopping barrier in the flowing water environment; the water outlet amount change and the grouting pressure change of the adjacent observation holes are monitored, and when the water outlet amount is remarkably reduced and / or the grouting pressure stably rises and forms a platform, it is judged that the temporary water stop barrier is formed; and second reinforcing grout is injected into the crack through the grouting holes, and after the second reinforcing grout is solidified, a permanent reinforcing body with mechanical strength is formed. By optimizing the grouting material and the process time sequence, effective combination of rapid plugging and long-term reinforcement is achieved, the grouting reliability is improved, and the engineering cost is reduced.

The invention provides a solar seawater desalination photothermal conversion efficiency optimization method and system based on machine learning, and the method comprises the steps: firstly collecting the solar spectrum distribution data, seawater turbidity spectrum data, real-time heat flow data and semiconductor nanomaterial data of a target sea area; and constructing a material spectrum mapping library based on the light absorption characteristics of the nano material. Through coupling analysis of a solar spectrum and a seawater turbidity spectrum, an environmental constraint condition is formed, a Bayesian optimization model is combined with a material spectrum mapping library and real-time heat flow data, and an optimal nano material mixing ratio is generated under environmental constraint. Then, the interface evaporation rate is predicted through the deep residual network, an optimal material morphology configuration scheme is obtained, and finally the morphology configuration is applied to semiconductor nanomaterial preparation. The photo-thermal conversion efficiency of the solar desalination system in the high-turbidity seawater environment is improved.

The invention relates to the technical field of sewage / wastewater denitrification, in particular to a method for predicting and explaining denitrification performance and microbial abundance of a single-stage PNA system, which comprises the following steps: constructing an ML model of nitrogen removal rate and functional microbial abundance, and incorporating key water quality and operating parameters into the model to improve the interpretability and prediction accuracy of the model; and the influence of each input feature on the model and the optimal range of the environmental parameters are determined, so that targeted operation adjustment is realized. Quantitative analysis is carried out through the model, potential synergistic effects among the characteristics can be explored, and therefore the optimal characteristic range can be found, the wastewater treatment efficiency can be improved, and the optimal water environment can be obtained.

The invention relates to the technical field of aquaculture, and particularly discloses paracoccus pantotrophus P.p-O8 and application of the paracoccus pantotrophus P.p-O8, the paracoccus pantotrophus P.p-O8 is preserved in the China General Microbiological Culture Collection Center (CGMCC), and the preservation number of the paracoccus pantotrophus P.p-O8 is CGMCC NO.26522. The invention relates to a microbial inoculum prepared from paracoccus pantotrophus P.p-O8. The method has a huge application prospect in improving the quality of aquaculture water and repairing the polluted sediments in the cage culture area, improves the water environment quality and the survival conditions of aquaculture organisms, repairs the eutrophicated polluted ecological environment, and has high environmental protection value and economic benefit.

The invention relates to the technical field of water environment remote sensing monitoring, solves the technical problem of systematic overestimation or underestimation under the condition of algae bloom outbreak or strong stratification due to the fact that a water body is regarded as an optical uniform monolayer parameter in a traditional method, and particularly relates to a stratified light field correction chlorophyll-a remote sensing inversion method and system for an eutrophic lake. Performing vertical type identification and three-layer layering by using multispectral / hyperspectral remote sensing reflectivity, a synchronous chlorophyll-a vertical profile and a diffusion attenuation coefficient, calculating light field weight and light path weighted concentration of each layer, constructing a layered light field correction coefficient, performing layered light field correction on the remote sensing reflectivity, and establishing an empirical chlorophyll-a inversion relationship; and generating a chlorophyll-a spatial distribution map and an algae bloom risk map. According to the method, the layered light field correction coefficient with clear physical significance is constructed to correct the water surface remote sensing reflectivity, so that the inversion precision and robustness under strong layering and complex optical conditions are improved.