2227 results about "Water pollution" patented technology

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 the technical field of water pollution traceability, and particularly discloses a river pollutant traceability system and method based on digital twinning, and the system comprises a water body sampling module which is used for obtaining the current water quality data of a preset region of a target river; the model construction module is used for constructing a digital twinborn model according to the river topographic data and the current water quality data and by fusing the historical hydrological data, the real-time meteorological data and the drain outlet distribution topological graph; the data analysis module is used for performing spatial-temporal feature extraction on a pollutant diffusion path in the digital twinborn model by utilizing a graph convolutional neural network, applying dynamic correction pollution source position probability distribution in combination with Bayesian inference, and generating a traceability path thermodynamic diagram; and the pollution traceability module is used for obtaining a pollutant traceability result according to the confidence coefficient threshold of the traceability path thermodynamic diagram. According to the method, the efficiency and the accuracy of tracing the river pollutants in the complex dynamic environment can be remarkably improved, and technical support is provided for ecological safety and precise treatment of a drainage basin.

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 relates to the technical field of water pollution detection, in particular to a water quality pollution multi-dimensional analysis system which comprises the following steps: synchronously measuring an ultraviolet-visible absorption spectrum of a water sample to be detected and scattered light intensity in an orthogonal direction, and deducting spectrum baseline interference caused by suspended particulate matters in the water sample through a correction algorithm; the interferents in the water sample are selectively converted and degraded by applying a programmed potential to the inflowing water sample to be detected; the reverse pulse is periodically applied, so that the online regeneration function of the electrode is realized; analyzing the spectral data by adopting a multivariate correction algorithm, and calculating the concentration of the target pollutant; the calculation residual error of the multivariate correction algorithm is analyzed, new interference and system performance drift of current measurement are diagnosed, and a corresponding system state code is generated; and responding to the system state code, automatically starting and carrying out state checking and multi-point calibration on the instrument, and using a calibration result to update the analysis model. Through multi-dimensional perception and closed-loop operation, accuracy and reliability of water quality monitoring are guaranteed.

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

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

The invention relates to the technical field of water pollution traceability, and discloses an underground water pollution traceability system based on simulation data, and the system comprises a data collection module which collects real-time data of an underground water monitoring well, soil sampling points and underground water monitoring equipment, and forms a multi-source heterogeneous database; the three-dimensional modeling module is used for constructing a layered geologic body three-dimensional model based on the drilling data, the geological section and the remote sensing image; and the numerical simulation module is used for solving an underground water seepage equation and a pollutant migration equation by adopting a finite difference method of GPU parallel acceleration on the basis of the constructed layered geologic body three-dimensional model. Multi-source monitoring data are accessed in real time through the data acquisition module, online dynamic calculation of an underground water flow field and pollutant migration is achieved in combination with the GPU accelerated numerical simulation module, and the efficiency is effectively improved compared with traditional offline analysis.

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.

The invention belongs to the technical field of environmental water quality pollution monitoring, and particularly relates to a drainage basin water environment monitoring and emergency pollution rapid tracing method and system based on water quality pollution detection, and the method comprises the following steps: constructing a multi-parameter cooperative monitoring model, and carrying out the training and deployment; through distributed sampling points and sampling stations, real-time data with verification marks and time-sharing data of watershed water environment water quality pollution detection are collected; mLP and LSTM / GRU models are adopted, EEM spectrum data and environment characteristics are fused, and multi-source heterogeneous data are fused and analyzed; whether an emergency pollution event occurs or not is automatically identified according to a preset condition, the pollution types and traceability results of water environment monitoring and emergency pollution are automatically output, manual further checking is carried out, and environmental law enforcement checking is carried out. According to the invention, drainage basin water environment pollution condition monitoring and emergency pollution rapid tracking and tracing can be completed in a large-range, low-cost and high-efficiency manner so as to support environment law enforcement.

The invention relates to the technical field of migration simulation, in particular to a drainage basin water pollution migration and transformation simulation method and system based on an MT-DHM model. The method comprises the following steps: carrying out data acquisition on a watershed multi-source space, carrying out data preprocessing, and generating a hydrological response unit standard data set; performing vertical hierarchical calculation based on the hydrological response unit standard data set to obtain land runoff vertical analysis data; carrying out land pollutant migration simulation on the land runoff vertical analysis data, and carrying out real-time correction with an iteration step length of 1 h to obtain a dissolved state / adsorption state load matrix; therefore, by constructing a full-process multi-source heterogeneous data fusion and pollution migration simulation mechanism, the problems that a traditional model is insufficient in data uniformity, low in parameter calibration precision and weak in spatial distribution simulation capability are solved, and the precision, stability and management and control operability of drainage basin pollution load calculation 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 relates to the technical field of ecological environment monitoring, in particular to a multi-dimensional data analysis-based water body pollution monitoring and processing system, which comprises a dynamic data cube construction module, a pollution event slice extraction module, a multi-dimensional feature fingerprint coupling module, a pollution source reverse traceability module and a self-adaptive early warning trigger module. The method comprises the following steps: receiving water quality parameters of an internet of things sensor, meteorological data of a meteorological hydrological station and satellite remote sensing spectral data, and constructing a dynamic data cube of unified space-time coordinates and feature axes; identifying pollution event slices by using entropy change analysis and a self-adaptive threshold method; carrying out characteristic coupling analysis on the pollution event, and fusing a spectrum fluctuation mode, a physicochemical gradient and toxicity response to generate a pollution characteristic fingerprint; pollution source positioning and contribution weight estimation are realized by adopting a dynamic time warping algorithm, and corresponding disposal equipment is driven to start based on an early warning level, so that intelligent closed-loop control of pollution identification, source tracing and treatment is realized.

The invention provides an accurate transportation control method and system for a remediation agent for in-situ remediation of underground water, and relates to the technical field of underground water pollution remediation, and the method comprises the steps: collecting an image of a drilling rock core of a polluted site and sensor data, extracting features through a visual attention network and a time sequence attention network, and carrying out the feature extraction; the features are input into a conditional probability diffusion model and fused to generate hydrogeologic feature vectors, a high-precision hydrogeologic feature model is constructed in combination with space attention mapping and a generative diffusion model, the high-precision hydrogeologic feature model is input into a multi-region collaborative simulation environment for optimization calculation, and monitoring data are fused through edge calculation nodes. And training the lightweight control model to execute repair medicament transport control.

The invention belongs to the technical field of pollution tracing, and discloses a distributed water body pollution tracing method and system, and the method comprises the steps: constructing a plurality of directed node pairs according to a topological structure of each partition node in a monitored water area; according to the pollutant concentration data in the monitored water area, determining a time delay interval of a pollutant concentration peak value between each directed node pair; determining an effective node pair of which the time delay interval meets the space-time constraint from the plurality of directed node pairs, and constructing a plurality of backtracking paths according to the effective node pair; performing particle tracking simulation on the pollutant concentration data to obtain a plurality of simulation paths, and determining a particle intersection area of pollutants according to the plurality of simulation paths; and determining a pollution source area according to the plurality of backtracking paths and the space intersection of the particle intersection area, and further determining traceability positioning. According to the method, the backtracking path is constructed through dynamic time-delay analysis, high-precision identification of the pollution source is realized by combining particle tracking and gridding intersection positioning, and the problems of insufficient space-time dynamics and large positioning deviation of a traditional method are solved.

The invention discloses a boundary detection and positioning method suitable for trans-boundary water pollution, and relates to the technical field of river water quality monitoring. According to data obtained at the longitudinal and transverse positions of a river channel of each monitoring point of a selected drainage basin, a two-dimensional hydrodynamic model of the selected drainage basin is established; then, combining with the obtained target pollutant concentration to construct a diffusion model of the target pollutant, identifying and integrating a to-be-positioned region set in each time period, and screening out the concentration abrupt change regions which are spatially adjacent and consistent in concentration gradient change trend through clustering analysis by utilizing the spatial distribution positions and concentration gradient values of the concentration abrupt change regions; and the boundary contour of the pollutant is generated by fitting in combination with the direction information of the flow velocity field. According to the method, after concentration abrupt change areas identified in each time period are integrated, clustering analysis is carried out according to spatial distribution and gradient change trends of the concentration abrupt change areas, pollution boundary contour fitting is optimized in combination with flow velocity field direction information, and pollution boundary detection requirements in a complex water area environment are met.

The invention discloses a shallow lake water quality pollution treatment system and method, and relates to the technical field of water pollution, and the method comprises the steps: S1, building a disturbance intensity index DIX and ammonia nitrogen concentration change curve, achieving the quantitative recognition of bottom mud disturbance and pollution release behaviors, training a rebound risk prediction model according to the quantitative recognition, and outputting a rebound risk prediction coefficient Ft; and the capability of pre-judging the rebound trend of the ammonia nitrogen pollution is effectively improved. In the step S2, the adjusted aeration response frequency BAF is dynamically generated based on the rebound risk prediction coefficient Ft value, so that the aeration behavior is converted into a risk-driven system from a traditional timing system, the energy consumption and disturbance intensity are further reduced, and the treatment efficiency is improved. And S3, further introducing a governance residual coefficient GRC, constructing a complete feedback closed loop, and realizing accurate evaluation and secondary strategy optimization of the governance effect of each round, thereby avoiding the problem of effect back-off or excessive disturbance after governance.

The invention relates to the technical field of environmental monitoring, in particular to an ecological environment and environmental pollution monitoring system and method, and a multi-source heterogeneous data acquisition module comprehensively acquires water quality, weather and space monitoring data. The system comprises a topological mapping data preprocessing module which is used for abnormal data detection and data vacancy filling; the state evolution analysis module is used for constructing an environment state transition network and analyzing an environment state evolution rule; the environment comprehensive evaluation module is used for calculating a water safety level index and a water pollution condition index and generating an environment quality evaluation report; the pollution diffusion prediction module is used for predicting a pollutant diffusion path and concentration distribution and identifying a high-risk area; the intelligent early warning and visualization module is used for generating graded early warning information and visually displaying the environment state and the pollution diffusion condition through multi-dimensional data, so that the coverage and the data integrity of environment monitoring are remarkably improved, the environmental pollution problem can be timely found and dealt with, and the ecological environment safety is guaranteed.

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 provides a method and a system for intelligently interactively controlling water production equipment. Wherein heavy metal ion concentration data is collected in real time through a multi-node sensing array, concentration difference fluctuation of adjacent monitoring points is analyzed, and a pollutant diffusion track is deduced; when the monitoring data exceeds the standard, switching of a standby water source is triggered based on a diffusion trajectory analysis result, and a reverse cleaning instruction is generated in combination with the dynamic ratio of the concentration gradient change rate to the diffusion speed; dynamically correcting the concentration deviation by utilizing fluid mechanics parameters in a high-pressure impact process in cooperation with multi-dimensional sensor data, and generating environmental response type pollution grade parameters; finally, a pollutant migration path in the three-dimensional diffusion model is matched according to the pollution level, a decision signal containing emergency water source coordinates and the cleaning priority is output, and full-process closed-loop intelligent control from pollution monitoring to emergency decision is achieved. According to the technical scheme provided by the invention, the response speed and the disposal accuracy of the water quality sudden pollution event are improved.

The invention belongs to the technical field of farmland tail water pollution load analysis and evaluation, particularly relates to a farmland tail water pollution load analysis and evaluation method, and aims to overcome the obvious defects of an existing monitoring and evaluation method in the aspects of monitoring point layout, index coverage, data processing, analysis depth and the like. In order to solve the problems that farmland tail water pollution load analysis and evaluation cannot meet accurate and dynamic farmland tail water pollution load analysis and evaluation requirements and pollution prevention and control decisions are difficult to effectively support in the prior art, the invention provides the following scheme: the method comprises the following steps: S1, carrying out multi-source acquisition on farmland information data, and carrying out fusion processing; s2, setting a fixed sampling unit and a movable sampling unit according to farmland information setting; according to the method, multi-source data such as farmland basic information, agricultural production activity data, meteorological data and water quality monitoring data are comprehensively considered, and comprehensive and accurate quantification and evaluation of farmland tail water pollution load are realized through a data preprocessing and fusion technology.

The invention discloses a river water pollution real-time tracing method, which comprises the following steps: obtaining a pollution source position of a river basin, obtaining a pollutant type discharged by a pollution source, and obtaining pollutant particle parameters and pollutant types; obtaining a river connection relation of the river basin, and confirming an input flow and an output flow of pollutants; based on the output flow of the pollutants, the pollutant type of the river basin is obtained, and the input flow of the pollutants is obtained; obtaining a theoretical pollutant level based on the theoretical water flow velocity and pollutant particle parameters of the river basin; based on the actually measured pollutant concentration and the actually measured water flow velocity, acquiring an actually measured pollutant level; and obtaining the pollutant source based on the actually measured pollutant level and the theoretical pollutant level. The problems of difficulty in real-time detection and insufficient traceability precision in a water pollution analysis method based on sampling in the prior art are solved. The traceability speed and precision are fully improved, and various hydrological conditions in rivers can be handled.

The invention discloses a water environment pollution water treatment and purification device and a purification method thereof, and belongs to the technical field of water treatment, the water environment pollution water treatment and purification device comprises a water inlet tank, a treatment tank is fixedly connected to one side of the water inlet tank, a water outlet tank is fixedly connected to one side of the treatment tank, and water pumps are fixedly connected to the water inlet tank and the water outlet tank; the input end of the water pump is fixedly connected with a water pumping pipe. According to the conveying device, the first motor drives the transmission wheels to rotate, the transmission wheels drive the belt and the other transmission wheel to rotate synchronously, then the connecting shaft rotates, the connecting shaft drives the reciprocating lead screw to move in the fixing plate, the reciprocating lead screw pushes the threaded cap to move, the threaded cap drives the transverse plate to move synchronously, and then the conveying cylinder moves stably in the horizontal direction. Chemical agents can be evenly distributed in the treatment box, the chemical agents and sewage are fully mixed through continuous stirring of the stirring blades, the purification effect is achieved, and finally treated water is sucked into the water outlet box through the water pump and discharged through the water outlet valve.

The invention discloses a self-adaptive grid dynamic encryption method for underground water pollution migration simulation, which belongs to the technical field of underground water pollution migration simulation, and comprises the following steps of: forming 0.05-meter super-resolution grids at the periphery of a dispersed pollution source through a local grid dynamic encryption technology triggered by a pollution source intensity gradient threshold; the pollutant frontal surface tracking error is greatly reduced, and the high-precision pollutant migration simulation capability is realized. Moreover, based on an intelligent grid reconstruction algorithm of a flux threshold, the number of calculation units is reduced by 62%, the time consumed by single simulation is shortened to 3.3 hours, and the overall mass conservation error is kept to be smaller than 1% while the calculation efficiency is improved. Meanwhile, for a heterogeneous aquifer, through a pollution flux and stratum permeability coefficient dual-drive encryption strategy, the solute transport prediction precision of the model at the boundary of a clay lenticular body is greatly improved, and the problems that in the prior art, the simulation precision of the periphery of a pollution source is insufficient, and the calculation efficiency is low due to global encryption are effectively solved.

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 discloses a water quality monitoring method and system based on big data analysis, and the method comprises the steps: obtaining water quality parameter data collected by multiple types of sensors, carrying out the format unification processing of the water quality parameter data through a standardization algorithm, and carrying out the elimination through a median filtering algorithm if abnormal data points are detected, thereby obtaining a standardized data set; aiming at the standardized data set, performing feature extraction on the chemical parameters, the spectral features and the biological indexes by adopting a principal component analysis algorithm to obtain feature vectors, and if the variance contribution rate of the feature vectors exceeds a preset threshold value, retaining the feature vectors and generating a dimension reduction feature data set; according to the dimension reduction feature data set, a random forest algorithm is adopted to construct a pollution evaluation model, a pollution index is calculated, if the pollution index exceeds a preset threshold value, a high pollution state mark is generated, and a pollution evaluation result is output. According to the invention, real-time monitoring, evaluation, early warning and traceability of water quality pollution are realized, and comprehensive technical support is provided for water environment protection.

The invention discloses a water pollution monitoring method and device based on an intelligent sensor, and the method comprises the steps: collecting the real-time water temperature, pollutant concentration and environmental parameter data of a water body through a sensor network, and storing the data into a distributed database to form a multi-dimensional time series data set; a feature extraction and dimension reduction module is used for obtaining key feature vectors, a potential pollution event marking module is used for screening out a preliminary event candidate set, and an event detection model is trained by means of a random forest algorithm. And when the model judges that a pollution event occurs and the confidence reaches the standard, generating an early warning signal. The water temperature abnormal trend is predicted through a long and short term memory network, Kalman filtering smoothing processing is carried out, finally, a pollution event early warning report is generated according to abnormal points in a stable water temperature change trend sequence, and efficient and accurate monitoring and early warning of water pollution are achieved.

The invention relates to the technical field of data processing, in particular to a chemical industry park groundwater pollution risk dynamic assessment method fusing multi-source heterogeneous data. The method comprises the steps that multi-source heterogeneous data of a chemical industry park is acquired and preprocessed, and preprocessed multi-source heterogeneous data is obtained; performing preliminary feature extraction on the preprocessed multi-source heterogeneous data, and performing fusion processing to obtain fused data; based on the fused data, constructing a bidirectional coupling groundwater pollution migration simulation model, calculating a groundwater flow rate and a pollutant concentration, and constructing pollutant three-dimensional concentration distribution; based on the three-dimensional concentration distribution of pollutants, a weighted heterogeneous graph is constructed, a pollution propagation path is identified, a path potential score is calculated, a pollution risk index is calculated, and the pollution risk is dynamically evaluated. The technical problems that a traditional groundwater pollution risk assessment method is not accurate enough in multi-source heterogeneous data processing, not comprehensive enough in analysis and poor in self-adaptability of a chemical industry park are solved.

The invention discloses a complex water area water pollution monitoring and early warning system based on image recognition, which relates to the field of water pollution monitoring and comprises an image acquisition module, an image preprocessing module, a data fusion module, a water pollution detection module and an early warning response module. A multispectral camera and an unmanned aerial vehicle are combined to carry out all-dimensional and multi-dimensional image acquisition of a complex water area; a multi-task visual representation learning framework is adopted to carry out processing and feature extraction on images, and image data from different devices are integrated through data fusion, so that the comprehensiveness and reliability of water pollution detection are improved; the early warning response module discovers the water pollution problem in time and quickly responds and treats the water pollution problem; the invention solves the problem that the existing complex water area water pollution monitoring and early warning technology cannot comprehensively acquire the water area pollution information in real time, and can efficiently, accurately and comprehensively identify and monitor the water area pollution condition and timely send out the early warning information.

The invention relates to a water quality parameter monitoring and intelligent dosing method and system based on the Internet of Things, and the method comprises the steps: obtaining water quality parameter data of a time sequence through a multi-point sensing device, and carrying out the data preprocessing of the water quality parameter data, and obtaining a first water quality data set; performing time sequence analysis on the first water quality data set to obtain a water quality trend feature set; inputting the water quality trend feature set into a preset water quality state prediction model to obtain a predicted water quality parameter value in a preset future time period; performing anomaly analysis on the predicted water quality parameter value and a preset parameter threshold range to obtain early warning data, the early warning data including a pollution risk area, an anomaly time interval and a risk category; and according to the early warning data and the historical governance data, performing optimization calculation on the dosing parameters through a tree model to determine a first dosing scheme. According to the application, the accuracy and timeliness of water pollution monitoring and treatment can be effectively improved, and an intelligent solution is provided for water environment management.

The invention provides grouting and filling equipment for water-preserved mining of a coal mine, and relates to the technical field of mining equipment, and the grouting and filling equipment comprises a base frame piece, a grouting guide pipe piece, a slurry post-processing assembly and a plugging driving assembly. A plurality of groups of outer ring parts with hole wall pressure sensors are arranged on the outer wall of the grouting guide pipe piece, and a pipe end micro sensor is arranged at the grout outlet end; the inserting and pulling driving assembly drives the stirring assembly to push the outer ring part through the motor, and inserting and pulling of the grouting guide pipe piece are controlled. During working, the guide pipe piece is inserted into a grouting hole to a goaf, the sensor monitors rock mass pressure, temperature and sound wave signals in real time, the grouting pressure is dynamically adjusted in combination with formation resistance, and grout fluid channeling or insufficient filling is avoided. Through precise pressure regulation and automatic insertion and extraction, the problems of material waste, underground water pollution and insufficient goaf filling in traditional grouting are solved, and the environmental protection property and the engineering efficiency of water-preserved mining are remarkably improved.