540 results about "Water pollutants" patented technology

The invention discloses a method for inverting key parameters in an underground water pollutant biodegradation numerical model based on a neural network algorithm. Carrying out preprocessing and refined dimension reduction on the data, and processing high-dimensional multi-scale data; building a multilayer neural network architecture under a physical information neural network framework, embedding a related reaction equation of a pollutant natural attenuation process, and coupling a biochemical reaction mechanism and a data driving model; a neural network model is trained, errors in the training process are calculated, and the accuracy of an inversion result is improved by using a sub-when optimization algorithm; integrating observation data and a model prediction result, determining final parameter setting of a numerical model, and carrying out underground water damage real object quantification; comparative verification of a neural network parameter inversion model is carried out, and feasibility and accuracy of the parameter inversion method are ensured. According to the method, the problem that the key parameters of the biodegradation numerical model are difficult to accurately invert in the real object quantification process of underground water pollution under complex geological conditions is solved.

The invention discloses an intelligent monitoring and early warning system and method for agricultural non-point source pollution, and relates to the technical field of environmental monitoring, accurate prediction of water pollutant concentration is realized through multi-source heterogeneous data acquisition and fusion, a dynamic attention mechanism and a PINN-Transformer coupling model, the system extracts data spatial and temporal characteristics by using an optimized Transformer model, and the method is applied to the intelligent monitoring and early warning of agricultural non-point source pollution. A water pollution diffusion physical constraint is embedded, it is ensured that a prediction result conforms to an actual hydrodynamic law, and based on high-precision spatial-temporal distribution data, an intelligent algorithm is adopted to track a pollution diffusion path and rapidly lock a pollution source; meanwhile, the cellular automaton model simulates pollution risk dynamic diffusion and assists regional risk assessment, the system also combines a block chain technology to carry out credible evidence storage on key monitoring data, and real-time data processing and early warning pushing are realized through a cloud edge collaborative architecture. And an efficient and reliable technical solution is provided for agricultural water environment management and pollution prevention and control.

The invention relates to a water pollution event-oriented traceability analysis method, in particular to a water pollutant traceability method based on the combination of an improved attention module, an improved variational auto-encoder and a heterogeneous graph attention neural network model. According to the method, firstly, an improved attention module is utilized to extract correlation between water pollution characteristics, then data are input into an improved variational auto-encoder to reconstruct time sequence distribution, and whether a water pollution event occurs or not is judged. Then, a heterogeneous space-time diagram is constructed for the space and time sequence dependence of the pollution source and the water quality monitoring section, and finally, the abnormal upstream water quality monitoring section and surrounding pollutants are traced to determine the position of the pollution source.

The invention relates to the technical field of water quality detection, and discloses a water quality pollutant detection method, system and equipment based on spectral analysis, and the method comprises the following steps: carrying out real-time spectral data acquisition on a water body sample in a vehicle driving process through a multispectral sensor array in a vehicle-mounted water quality detection device; obtaining a water body spectrum three-dimensional data cube; carrying out anti-vibration wavelength calibration and ambient light source interference elimination processing to obtain a preprocessed spectrum data set; performing standard multivariate decomposition processing and horizontal local constraint optimization of a pollutant target wave band on the preprocessed spectrum data set to obtain enhanced feature fingerprint spectrums for different types of water quality pollutants; and inputting the enhanced feature fingerprint spectrum into a deep belief network model to perform pollutant feature matching analysis to obtain various pollutant types and corresponding concentration values in the water body sample, thereby effectively eliminating ambient light changes and other background interferences, not only determining the pollutant types, but also accurately quantifying the concentration values.

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 discloses an intelligent detection method for water pollutants, which comprises the following steps of: transmitting a signal of a specific frequency band to a water body through an electromagnetic wave transmitting module, acquiring reflected and refracted signals by using a multi-channel receiver, extracting the characteristics of dielectric constant, refractive index and the like of the water body, and analyzing the types and the concentration range of the pollutants by adopting a pattern recognition algorithm; the method comprises the following steps: selecting a micro-fluidic chip and an electrochemical unit, selecting a corresponding biosensor for targeted detection, processing detection data through the micro-fluidic chip and the electrochemical unit, predicting the concentration, spatial distribution and trend of pollutants through a data analysis module, setting a dynamic self-adaptive threshold by the system, realizing pollution early warning, and uploading data to a cloud platform through a remote communication module. And remote monitoring is supported. The method can be widely applied to the fields of water quality monitoring, environmental protection and pollution control, and the detection precision, the real-time performance and the intelligent level are improved.

The invention provides an underground water pollutant concentration prediction method and system based on machine learning, and relates to the technical field of underground water pollutant concentration prediction.The method comprises the steps that historical data, hydrogeological parameters, meteorological data, human activity data and geochemical parameters of underground water pollutant concentration of a target area are preprocessed; dividing a training set, a verification set and a test set; constructing a preset resolution feature set based on a geochemical mechanism; selecting an adaptive machine learning model according to data characteristics and coupling a physical mechanism; performing hyper-parameter tuning by adopting Bayesian optimization, and supplementing small sample data in combination with transfer learning to complete model training; predicting the underground water pollutant concentration of the target area by using the trained model, and outputting a pollutant concentration prediction result with an uncertainty interval; the invention provides a technical scheme for predicting the concentration of underground water pollutants, which is efficient, accurate and high in adaptability.

The invention relates to the technical field of water quality detection, and discloses a water quality detection method and device based on an electrochemical sensor. The method comprises the following steps: deploying a multi-depth electrochemical sensor array in a target water body according to different water depth positions and carrying out real-time monitoring to obtain electrochemical response signal data; performing electrochemical fingerprint feature recognition on the electrochemical response signal data to obtain electrochemical feature vectors of water sample pollutants at different water depth positions; carrying out pollutant analysis through a hierarchical electrochemical transfer-attention network model to obtain types and concentrations of heavy metal ions and organic pollutants in water samples at different water depth positions; and executing Kriging space interpolation and environmental factor correction to generate a three-dimensional pollutant distribution dynamic map of the target water body. According to the method, high-precision identification of low-concentration multi-component coexisting pollutants is realized, and visual analysis of spatial distribution characteristics and migration rules of water pollutants is realized.

The invention discloses an industrial park groundwater pollutant screening and identification method, which comprises the following steps: firstly, scoring the source strength, toxicity and path of pollutants through a comprehensive scoring method, and carrying out preliminary screening according to the scoring result to determine preliminarily concerned pollutants, thereby being beneficial to narrowing the screening range and reducing unnecessary evaluation work; the evaluation efficiency is improved. Meanwhile, the ITD index model constructed based on the three dimensions of source intensity, toxicity and path can more comprehensively consider the potential risk of the pollutants, and a scientific basis is provided for an evaluation result. The toxicity test result is introduced, so that the evaluation result is more accurate, and experimental data support is provided for the evaluation result. The method can provide scientific basis and technical support for underground water pollution prevention and control in industrial parks. According to the comprehensive evaluation result, corresponding risk prevention and control measures are provided for the optimal control pollutants in the list, the underground water pollution risk of the industrial park is reduced, and the environment and human health are protected.

The invention discloses a simulation-optimization method for a pollution site groundwater pumping-injection-treatment pollution remediation system based on a multiform evolution search normal form. The method comprises the following steps: constructing a groundwater flow model and a pollutant solute transport model; the method comprises the following steps: determining a management objective function and constraint conditions of hydraulic control of an underground water pollution range, establishing an underground water pollutant treatment model, optimizing underground water pumping and injection treatment of a pollution site by adopting BLMFO, solving the problem of interdependence between an upper-layer global target and a lower-layer local target through nested optimization, and constructing an auxiliary task by utilizing MFO; state variables are updated through the groundwater flow model and the pollutant solute transport model, a management objective function value of the groundwater pollutant treatment model is calculated, and whether constraint conditions are met or not is judged; meanwhile, decision variables are selected by optimizing the groundwater pollutant treatment model, and state variables are updated by returning to the groundwater flow model and the pollutant solute transport model.

The invention provides a method for jointly repairing a coal mine wasteland by using plants and microorganisms, and aims to promote vegetation recovery, reduce water and soil loss, reduce leaching water pollutants and soil heavy metal content, improve treatment efficiency and effectively improve the ecological environment of the coal mine wasteland through planting of Juncao and using of Juncao nitrogen-fixing bacterial fertilizer. Reutilization of the waste land is achieved, and a new thought and approach are provided for treatment of the coal mine waste land.

A multi-source water pollutant detection device based on multi-dimensional spectral information fusion comprises a base, a light shield, an electric signal detector, an absorption and fluorescence generation mechanism and a light filtering and light transmitting mechanism, the absorption and fluorescence generation mechanism and the light filtering and light transmitting mechanism are oppositely arranged on the base, and the light shield covers the base; the light filtering and transmitting mechanism comprises a plurality of light filters, through holes, light filter mounting fixing parts and photoelectric sensors; the photoelectric sensors are used for converting fluorescence signals filtered by the corresponding light filters and transmission light signals passing through the through holes into electric signals; the photoelectric sensors are connected with the electric signal detector; the absorption and fluorescence generation mechanism comprises a sample bin and a light source irradiation part for irradiating light sources with different wave bands in the sample bin, and a focusing part is arranged on the sample bin and used for focusing transmission light and fluorescence generated after irradiation and absorption of the light sources with the corresponding wave bands and irradiating the transmission light and the fluorescence to the through hole in the light filter mounting and fixing part and the light filter. According to the utility model, the advantages of absorption spectrometry and three-dimensional fluorescence spectrometry are fused, and rapid detection of pollutants is realized.

The invention discloses a water area pollutant emergency treatment system and equipment in the technical field of water pollution treatment, and the system comprises a plurality of water quality monitoring modules which are used for collecting water quality factors of a covered area and generating real-time water quality data; the data collection module is used for acquiring, storing and forwarding real-time water quality data; the prediction module is used for analyzing and predicting the pollutant component evolution condition based on the real-time water quality data by using a big data model, and generating a pollutant component evolution condition graph of the water area; the emergency preprocessing scheme generation module is used for generating a corresponding preprocessing scheme and a preprocessing position based on the pollutant diffusion range diagram and the pollutant component evolution situation diagram; the biological filtration assemblies are used for being deployed at the pretreatment position and filtering and cleaning the water area. Plants aiming at specific pollutants are planted on the biological filtration assembly, and long-term ecological restoration can be achieved while water area pollutants are emergently treated.

The invention discloses a high-entropy layered double hydroxide with a strong strain effect and a preparation method of the high-entropy layered double hydroxide. The preparation method comprises the following steps: firstly, preparing ferric salt, cobalt salt, nickel salt, copper salt and zinc salt into a mixed aqueous solution of five metal salts; preparing sodium hydroxide and sodium carbonate into an alkali source aqueous solution; then mixing and stirring the mixed aqueous solution of the five metal salts and the alkali source aqueous solution to gradually form yellowish-brown precipitates, then transferring the yellowish-brown precipitates into a polytetrafluoroethylene reaction kettle, and further reacting in a drying oven; and finally, carrying out centrifugal separation, deionized water and absolute ethyl alcohol washing and freeze drying on the fully reacted precipitate to obtain brown powdery solid, namely the high-entropy layered double hydroxide. The high-entropy layered double hydroxide can be used for advanced oxidation catalytic degradation of tetracycline antibiotic wastewater, the strong strain effect of the high-entropy layered double hydroxide enhances the benefit of activating peroxymonosulfate to generate active free radicals, the efficiency of degrading water pollutants is improved, and the high-entropy layered double hydroxide has good application prospects.

The invention provides a method for rapidly detecting wastewater pollutants by atomic fluorescence spectrometry, and belongs to the technical field of wastewater treatment.The method comprises the steps that atomic fluorescence spectrum data of a wastewater sample are collected to establish a spectrum interference matrix to characterize the spectral line overlapping condition between elements; abnormal values are recognized through statistical analysis, a spectrum outlier matrix is built, abnormal fluorescence intensity distribution is recorded, sample matrix non-characteristic fluorescence signals are measured, a background fluorescence interference matrix is built, background interference intensity is quantized, system signal change rules are continuously monitored and detected, and a signal drift tendency matrix is built to predict the signal drift tendency. A spectrum interference matrix and a background fluorescence interference matrix are processed through a spectrum analytic function, compensation factors are calculated, spectrum overlapping and background interference influences are corrected, system drift is corrected in real time, and a final wastewater pollutant concentration detection result is output, so that the technical problem of unstable detection precision caused by coupling of multiple interference factors is solved.

The invention relates to an electric flocculation reactor and an electric flocculation treatment method. The electric flocculation reactor is provided with a biochemical treatment unit and a pretreatment unit located in the front of the biochemical treatment unit, and an electrode assembly with adjustable electrode spacing is arranged in the pretreatment unit. According to the electric flocculation treatment method, the electric flocculation reactor is adopted for removing pollutants in water, the distance between electrode plates in the electrode assembly is set or adjusted according to the water flow of inlet water or the pollutant flow, the biochemical treatment unit comprises an aerobic tank, the electrode assembly is arranged in the aerobic tank, iron ions are released through the electrode assembly, and the water quality is improved. And an iron ion environment beneficial to growth and metabolism of aerobic microorganisms is formed. According to the invention, the electrode spacing of the electric flocculation reaction can be adjusted, and the electric flocculation reaction device can better adapt to actual requirements.

The invention provides a sewage treatment multi-objective optimization method and device and a storage medium, and relates to the field of sewage data analysis and processing.The method comprises the steps that firstly, multiple types of sewage data are collected in a time sequence mode at a specified sewage data station and preprocessed to form a data set, then a sewage prediction model is constructed based on a deep learning model, and a sewage prediction result is obtained; and inputting the data set for training, and predicting the energy consumption of the sewage treatment equipment and the effluent pollutant concentration. Then, a multi-objective optimization function is constructed, so that the total energy consumption is minimized, and the effluent pollutant concentration reaches the standard; and finally, solving the function by using a multi-objective particle swarm optimization algorithm to obtain an optimal solution set, and screening out an optimal operation parameter from the optimal solution set. According to the method, two important targets of energy consumption minimization and effluent pollutant concentration standard reaching can be considered at the same time, the relation between energy consumption and water quality can be balanced, the optimal operation parameters are screened out from the optimal solution set, and the overall performance of sewage treatment equipment is improved.

The invention discloses a high-valued graded utilization method of marine shells, and belongs to the technical field of comprehensive utilization of biological resources and preparation of functional materials. The method comprises the following steps: cleaning, air-drying and superfine grinding marine shell wastes such as abalone shells, and extracting water-soluble organic components in the marine shell wastes by adopting mild water extraction, so that the obtained extract is rich in protein, polysaccharide or small molecule peptide, has good biological activity, and can be used for preparing functional materials such as biomimetic mineralization, tissue repair and the like; extracted shell powder residues are subjected to heat treatment or chemical activation treatment and then compounded with natural polymer materials such as chitosan and sodium alginate, and the composite adsorbent which is loose in structure and large in specific surface area is prepared and can be used for removing dye, heavy metal, antibiotics and other water pollutants. According to the invention, separate utilization of organic components and inorganic framework materials in the marine shells is realized, and the method has the characteristics of environmental protection, efficient resource conversion and multifunctional application, and has a good industrial popularization prospect.

The invention relates to the technical field of water pollutant concentration detection, and discloses a water pollutant concentration detection system and method based on big data analysis, and the water pollutant concentration detection method based on big data analysis comprises the following steps: constructing a multi-level sensing network, the multi-layer sensing network comprises a fixed monitoring station layer, a water surface unmanned ship layer and an underwater robot layer; carrying out multi-modal data fusion by utilizing a knowledge constraint data assimilation algorithm to generate consistent water quality parameter estimation; a double-layer cognitive structure combining a knowledge graph and a causal model is constructed, and water pollutant propagation understanding and tracing are achieved; continuous optimization of system configuration is realized through a multi-objective reinforcement learning framework; through collaborative optimization of a multi-layer sensing network, the system realizes three-dimensional monitoring of a water body, the monitoring coverage range is expanded, and meanwhile, the high-precision detection capability is kept.

The invention belongs to the technical field of water pollutant identification, and particularly relates to a water pollutant distinguishing method and system based on multi-source spectrum fusion. According to the method, ultraviolet visible spectrum data and three-dimensional fluorescence spectrum data are effectively integrated in a multi-source spectrum data fusion mode, so that the characteristic information of water pollutants is captured, stably appearing characteristic absorption wavebands can be positioned by utilizing the ultraviolet visible spectrum data, and the water pollutants can be accurately detected. The method comprises the following steps: identifying a characteristic sub-region capable of representing the fluorescence response characteristic of a pollutant through three-dimensional fluorescence spectrum data, in a characteristic extraction stage, performing first-order derivative operation on an ultraviolet visible spectrum to position an absorption peak position, calculating curvature distribution of a three-dimensional fluorescence spectrum to identify the characteristic sub-region, and when a fusion characteristic is generated, performing first-order derivative operation on the three-dimensional fluorescence spectrum to identify the characteristic sub-region; according to the method, the key operation parameters are subjected to grid search optimization, and the optimal key operation parameter combination is screened out by performing multiple rounds of iterative training and evaluation on a preset training set and a verification set, so that the recognition performance of the real-time collected water sample is improved.

The invention discloses a sewage treatment system based on crowd sensing, and relates to the technical field of pollution prediction.A pollution prediction module is based on a two-dimensional instantaneous pollution source diffusion model and combines sensor point location information provided by a sensor screening module and water depth influence data provided by a water depth measurement and calculation module; the dynamic simulation and prediction module is used for carrying out dynamic simulation and prediction on pollutant diffusion, calculating a water pollutant concentration prediction value of each selected sensor point location, and sending the pollutant concentration prediction value to the feedback determination module; correcting the position of the pollution source according to the deviation by using an inversion algorithm, specifically, continuously correcting the predicted pollution data value again by adjusting the position data of the pollution source and changing the values of x, y and b, and comparing the predicted pollution data value with an actual measurement value until an error requirement is met; therefore, the prediction precision of the pollution condition of the position, which is not covered by the sensor, of the monitoring area can be ensured by continuously calibrating the prediction value and the actual measurement value.

The invention provides a rapid detection method for water pollutants, which comprises the following steps: removing large-particle impurities and suspended solids in a water sample to be detected to obtain a target water sample; the method comprises the following steps: synchronously detecting multiple pollutant indexes of a target water sample through a multi-source sensor array to generate an initial detection data set; performing noise filtering and baseline correction on the initial detection data set to generate a target detection data set; performing space-time dimension dynamic fusion and feature decoupling on the target detection data set to generate water quality pollutant data and dynamic risk assessment indexes; and constructing a multi-dimensional risk assessment report by adopting the water quality pollutant data and the dynamic risk assessment indexes. By adopting a dynamic gradient filtering technology, impurity interference is effectively removed, dynamic risk assessment of a time dimension is realized, the accuracy and reliability of detection in a complex water quality environment are remarkably improved, and rapid and accurate water quality pollutant detection and risk assessment are realized.

The invention provides a pollutant flux method based on full-section sampling and water quality automatic station data fusion. The method comprises the following steps: coupling full-section sampling and water quality automatic station water pollutant concentration data; assimilating water pollutant concentration data and full section of the water quality automatic station; carrying out full-section water pollutant concentration process distribution; calculating the flux of river water pollutants; the problems that the frequency of full-section sampling data adopted in river water pollutant flux calculation is insufficient, and the change process cannot be reflected are solved.

The invention relates to the technical field of water pollutant removal, in particular to a UiO-66 / sodium carboxymethyl cellulose / polyvinyl alcohol / melamine sponge composite material and a preparation method and application thereof.The preparation method comprises the steps that zirconium chloride and terephthalic acid are dissolved in an organic solvent to obtain a mixed solution, the mixed solution is transferred into a reaction kettle, and through solvothermal reaction, the UiO-66 / sodium carboxymethyl cellulose / polyvinyl alcohol / melamine sponge composite material is obtained; the preparation method comprises the following steps: preparing UiO-66 powder; putting UiO-66 powder into the sodium carboxymethyl cellulose solution, and uniformly stirring the UiO-66 powder and the sodium carboxymethyl cellulose solution to obtain a UiO-66 / CMC mixed solution; placing the UiO-66 / CMC mixed solution in a polyvinyl alcohol solution, and uniformly stirring the UiO-66 / CMC / PVA mixed solution to obtain a UiO-66 / CMC / PVA mixed solution; the preparation method comprises the following steps: dipping a melamine sponge in a UiO-66 / CMC / PVA mixed solution to obtain an adsorption saturated melamine sponge; the UiO-66 / CMC / PVA / coated MS composite material is obtained by repeatedly freezing and thawing the adsorption saturated melamine sponge for multiple times and then freeze-drying, the composite material can be used for rapidly, efficiently and selectively removing phosphate or fluorine ions and the like in water and is convenient to separate and recycle, and a new thought is provided for development of efficient water treatment adsorbent materials.

The invention relates to the technical field of pollution treatment of livestock and poultry breeding, in particular to a pollutant emission prediction and management system for a livestock and poultry farm, which comprises a data acquisition and storage unit, a data processing unit, a data processing unit and a data processing unit, and is characterized in that the data acquisition and storage unit is used for acquiring multi-source data of the farm in real time; the influence factor calculation unit obtains key influence factors influencing water quality change and malodorous gas based on production data and facility operation data of the farm; the model prediction analysis unit generates a predicted value of pollutant emission by using a dynamic multi-dimensional pollutant emission prediction model, and key influence factors are introduced in the prediction process for correction; and the management decision-making unit judges the early warning level and generates a control instruction according to the early warning level. According to the system, accurate prediction of emission of malodorous gas and water pollutants is realized by constructing a dynamic multi-dimensional pollutant emission prediction model, and meanwhile, the system not only prevents excessive emission of pollutants in advance, but also is deeply integrated with aquaculture production management to guide actual aquaculture operation.

The invention discloses a preparation method of a composite material for improving the utilization rate of solar blue-green photocatalytic degradation of water pollutants, which comprises the following steps: firstly, preparing a Cu-ZnO nano composite material by doping zinc oxide with copper ions, and reducing the forbidden band width, so that the Cu-ZnO nano composite material can utilize sunlight with longer wavelength, and the absorption peak of the Cu-ZnO nano composite material generates red shift; secondly, decolorizing the wood by adopting an alkaline sodium sulfate method, removing lignin in the wood to leave a cavity, and growing the Cu-ZnO nano composite material in the treated wood and on the surface of the treated wood by adopting a hydrothermal method to form a composite material with a catalyst growing in a similar resonant cavity, as shown in Figure 1; the utilization rate of a semiconductor catalyst to blue-green light is improved, a cavity structure formed by the treated wood can have a certain local effect on long-wavelength light, the purpose of fully utilizing long-wave sunlight for multiple times is achieved, and the blue-green photocatalytic efficiency is improved.

The invention belongs to the technical field of water environment monitoring and simulation analysis, and aims to provide a water pollutant flux prediction method and system, electronic equipment and a product. According to the invention, the final water pollutant flux prediction model is obtained based on historical data, and the real-time water monitoring data obtained by water quality monitoring is used as the input data of the final water pollutant flux prediction model, so that the real-time water pollutant flux prediction value is obtained. And finally, performing correction processing on the real-time water body pollutant flux prediction value to obtain a corrected real-time water body pollutant flux prediction value of the target water body area, thereby realizing pollutant flux prediction of the target water body area. According to the method, the flux of the water pollutants can be predicted, and the prediction precision is high.

The invention discloses a soil and underground water pollutant monitoring method and a digital early warning platform, and relates to the technical field of data processing, and the method comprises the following steps: S1, obtaining a monitoring data set and an influence data set of soil and underground water; s2, utilizing the influence data set of the underground water and the topographic parameters of the soil to construct a migration and flow model for the underground water of the soil; and S3, determining the heavy metal concentration of the soil groundwater at the next moment by using the migration flow model and the monitoring data set of the soil groundwater, and performing early warning when the heavy metal concentration at the next moment exceeds a preset threshold value. Through multi-parameter fusion and model construction, accurate prediction of the underground water heavy metal concentration can be realized, the early warning precision is improved, a scientific basis is provided for environmental protection and water resource management, and sustainable development is promoted.

The invention discloses a device and a method for monitoring the content of pollutants in oilfield wastewater by using hyperspectral data, and relates to the technical field of water sampling and water quality pollutant monitoring. Comprising a sampling shell, a rotating plate is rotatably connected in the sampling shell, the upper side of the rotating plate is fixedly connected with a circumferentially distributed negative pressure sampling barrel, the outer side of the sampling shell is provided with a sliding cylinder, the outer side of the sliding cylinder is slidably connected with a buoy, and the lower side of the buoy is rotatably connected with a circumferentially distributed circular gear. Circumferentially distributed racks are fixedly connected to the outer side of the sliding cylinder, the circumferentially distributed racks are engaged with the adjacent circular gears, and driving rods are fixedly connected to the circular gears. The five racks move downwards and drive the adjacent driving rods to rotate, impurities on the water surface are shifted to the side, and therefore the situation that in the process of lowering the sampling shell, the impurities on the water surface are attached to the outer side of the sampling shell, and the sampling result is affected is avoided.

The invention discloses a method for detecting the content of tributyl phosphate in salt lake brine, and belongs to the field of environmental chemical analysis and detection. The method for detecting the tributyl phosphate in the salt lake brine specifically comprises the following steps: transferring the salt lake brine, adding water, diluting and uniformly mixing to obtain a sample solution; then loading the sample solution to an activated C18 solid-phase extraction column, leaching with water, discarding effluent, eluting with an acetone ethyl acetate solution, collecting eluant, blowing to be nearly dry, redissolving, and passing through a membrane to obtain a to-be-detected sample, determining by adopting GC / MS, and calculating to obtain the content of tributyl phosphate in the to-be-detected sample according to a standard curve model of an internal standard method. The method has good sensitivity, stability and repeatability, is suitable for accurate determination of tributyl phosphate in salt lake brine, and provides necessary technical support for detection and monitoring of salt lake brine pollutants.