276 results about "Source water" patented technology

The invention belongs to the technical field of hydrology and water resource intelligent management, and discloses an inter-basin water resource system cooperative scheduling and forward-reverse bidirectional coupling dynamic decision method and system, and the method comprises the steps: S1, constructing a multi-modal water conservancy infrastructure topological relation graph, and carrying out the bidirectional conduction optimization; s2, sensing hydrological gap risk situation and analyzing multi-source water supply potential coupling based on the multi-modal water conservancy infrastructure topological relation map and optimized bidirectional conduction; s3, based on hydrological gap risk situation and multi-source water supply potential coupling, generating an elastic grading water quantity distribution strategy and a cross-engineering replacement protocol; and S4, constructing and optimizing a positive-negative two-way rehearsal decision engine based on an elastic grading water distribution strategy and a cross-engineering replacement protocol. The technical problem that a traditional one-way scheduling model is difficult to deal with complex topological relations, sudden water shortage events and cross-administrative-region collaboration is solved.

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

The invention relates to the technical field of intelligent water affairs, in particular to an intelligent water affairs management and control system based on the Internet of Things, and aims to solve the problems that in the prior art, multi-source water quality data and user behavior characteristics can be fused to construct a risk assessment model based on fuzzy logic and a Bayesian network, dynamic assessment of a complex water quality state cannot be achieved, and the risk assessment accuracy is poor. Abnormity cannot be quickly recognized through a similarity matching mechanism, and the response speed and accuracy of early warning are reduced; multi-source water quality data and user behavior characteristics are fused through the water quality intelligent early warning module, a risk assessment model based on fuzzy logic and a Bayesian network is constructed, the method has high nonlinear modeling and uncertainty processing capacity, dynamic assessment of a complex water quality state is achieved, abnormity is rapidly recognized through a similarity matching mechanism, and the risk assessment efficiency is improved. The method improves the early warning response speed and accuracy, combines the countercurrent tracking and GIS technology, accurately locates the pollution source, and enhances the emergency disposal and decision support capability.

The invention relates to the technical field of marine surveying and mapping and submarine topography modeling, and discloses a submarine topography and landform construction method based on multi-source water depth data fusion, which comprises a multi-source data acquisition module, a data preprocessing module, a feature extraction module, a fusion algorithm matching module, a three-dimensional modeling module and an uncertainty evaluation module. A historical submarine topographic map is converted into grid data, namely, submarine topography is discretized into digital representation of regular grid units, water depths of other sources are converted into the grid data, the precision of the water depths of different sources is evaluated by adopting a superposition comparison method, and an error distribution model is established to reasonably correct and fuse the water depth data of various sources. The innovation of the method lies in that topographic feature parameters including the gradient, the curvature matrix and the bionic optimization algorithm are introduced, the defect that a single algorithm is preset in a traditional method is overcome, and the precision of coral reef and fault zone complex areas is improved.

The invention relates to the technical field of water conservancy informatization and intelligent early warning, in particular to a river network water regimen big data intelligent analysis early warning system which comprises a dynamic data fusion module, a topology sensing module, an overload propagation analysis module, an intelligent early warning decision module and an early warning execution module. Wherein the dynamic data fusion module is used for collecting multi-dimensional data of a river network area in real time; the topology sensing module is used for dynamically constructing a real-time topological relation graph of the river network connectivity; the overload propagation analysis module is used for extracting an overload coefficient of each node and outputting a risk diffusion path set; and the intelligent early warning decision module is used for generating a three-level early warning instruction by combining the positions of the engineering vulnerabilities in the real-time topological relation graph. According to the method, through fusion of the multi-source water regimen data and the dynamic topological structure, linkage of accurate identification and hierarchical response of the risk path is realized, and the intelligent early warning and rapid disposal capability of the sudden water regimen of the river network is remarkably improved.

The invention relates to the field of data processing, in particular to a data acquisition method and system based on a multi-source water affair sensor, and the method comprises the steps: calculating period relevance and trend relevance, and respectively obtaining abnormal probability predicted values based on a period rule and a trend rule, taking the period relevance and the trend relevance as weights, and carrying out weighted summation on the two abnormal probability predicted values to obtain a comprehensive abnormal probability predicted value; calculating the abnormal relevance of each kind of historical influence factor data, and obtaining the abnormal possibility of the water affair data according to the abnormal relevance; correcting the comprehensive abnormal probability predicted value according to the abnormal possibility of the water affair data to obtain a final abnormal probability predicted value; setting an acquisition frequency according to the final abnormal probability predicted value; and performing water affair data acquisition control based on the acquisition frequency. And the water affair data acquisition frequency is adaptively set, so that abnormal information can be grasped in time while data redundancy is reduced.

The invention discloses a circulating water waste heat recovery and air cooling collaborative optimization method which is used for solving the problems that an existing air cooling system, an existing waste heat recovery system and an existing water treatment system normally operate independently and lack a unified coordination mechanism, when environment parameters or operation working conditions change, all the systems cannot respond collaboratively, and the overall energy efficiency is difficult to achieve. The method comprises the following steps: generating a system state data set according to acquired environment parameters, unit operation parameters, heat supply network parameters and whole-plant multi-water-source water quality parameters; according to the system state data set, a nozzle array operation strategy is determined through a preset total spraying amount calculation model; according to the unit operation parameters and the heat supply network parameters, waste heat recovery power is determined; determining a water source distribution strategy according to the multi-water-source water quality parameters; according to the nozzle array operation strategy, the waste heat recovery power and the water source distribution strategy, a waste heat recovery-air cooling collaborative optimization strategy is generated; and a cooperative control instruction set is generated according to the waste heat recovery-air cooling cooperative optimization strategy.

The invention discloses a water conservancy knowledge base management method and device based on a large language model and a medium, and relates to the technical field of water conservancy management. The method comprises the following steps: collecting multi-source water conservancy data, and preprocessing the multi-source water conservancy data to obtain standard water conservancy data; analyzing the standard water conservancy data based on a preset target large language model, and converting the unstructured text into a structured text; integrating the structured text, the structured table and the image data to obtain a target water conservancy data knowledge base; and when a data query instruction is received, based on query information contained in the data query instruction, performing similarity matching in the target water conservancy data knowledge base according to a preset query rule to generate a water conservancy plan. By means of the method, intelligent water conservancy plan matching and automatic query are achieved, and the water conservancy knowledge base management efficiency is improved.

The invention relates to the technical field of sewage treatment, and discloses a chemical industry park emergency sewage treatment device abnormal discharge tracking method, which comprises the following steps: data synchronization: synchronously obtaining multi-source water quality time sequence data and hydraulic operation data of multiple monitoring points of a park pipe network; anomaly detection: establishing a time-varying adaptive baseline model based on the time sequence data, and calculating a residual error of the time-varying adaptive baseline model to detect a water quality anomaly event; response core construction: constructing a time-varying transport response core from a potential source point to a monitoring point through a dynamic model and data assimilation technology based on pipe network topology and real-time hydraulic data; and traceability inversion: inputting the residual error and the time-varying transport response kernel into an inverse problem solving model in which physical constraint and sparse prior are introduced. The abnormal emission tracking method for the emergency sewage treatment device in the chemical industry park aims at solving the problem that it is difficult to achieve accurate and reliable source tracing of abnormal emission events under the condition that the hydraulic working condition of a pipe network of the chemical industry park changes dynamically.

The invention discloses an urban multi-water-source joint scheduling method based on a discrete scene set and a robustness decision, and the method comprises the steps: researching the characteristics of multiple uncertainty factors of urban multi-source water supply, and generating a multiple uncertainty scheduling scene set through a Latin hypercube sampling method; constructing a water supply city regulation and storage reservoir and external water source joint scheduling model and a water receiving unit internal optimization scheduling model; and constructing a robustness decision-making-based urban annual multi-water-source combined scheduling model. A multi-uncertainty scene set and a robustness measurement method based on an expected benefit index are utilized, the minimization of the annual water level fluctuation risk and the urban water shortage risk of a regulation and storage reservoir is taken as a scheduling target, an urban multi-water-source combined scheduling model based on a robustness decision is constructed, the rationality and the applicability of the robustness scheduling model are verified, and the urban multi-water-source combined scheduling model is established. And analyzing a robust decision scheduling result under an uncertain condition.

The invention relates to the technical field of water quality monitoring, in particular to a water quality monitoring method and system based on the Internet of Things technology. The method comprises the following steps: acquiring multi-source water quality monitoring data of a monitoring area; analyzing the multi-source water quality monitoring data, and determining whether illegal pollution discharge possibility exists or not; if yes, water pipe layout information and area information of the monitoring area are obtained; analyzing the water pipe layout information, and determining water flow path association; determining pollution dissipation path information based on the water flow path association according to the multi-source water quality monitoring data; analyzing the regional information, and determining factory distribution characteristics; and determining an illegal pollution discharge point according to the water flow path association, the plant area distribution characteristics and the dissipation path information. The spatial consistency of the final positioning result and the physical pipe network layout is ensured, and the false alarm caused by the model error is eliminated.

The invention relates to the technical field of water quality prediction. The pollution risk prediction method and device based on multi-source water quality data fusion, the equipment and the medium are provided, and the method comprises the following steps: carrying out multi-source collaborative acquisition processing on hydrological data, meteorological data and real-time water quality monitoring data to generate a time-space association data set; carrying out pollution scene feature coupling analysis processing on the space-time correlation data set to generate a pollution type identification matrix and a dynamic driving parameter; collaborative activation processing of the process modules is carried out through a scheduling engine, and a target simulation module topology network is generated; performing adaptive parameter configuration processing on the topological network of the target simulation module to generate a migration and transformation parameter tensor; performing dynamic evolution processing on the migration and transformation parameter tensor to generate a risk early warning hologram spectrum; and carrying out multi-dimensional parallel rehearsal processing on prevention and control measures to generate a treatment efficiency quantitative report so as to improve the pollution characteristic analysis precision and optimize the accuracy of a prediction result.

The invention discloses a port sewage treatment intelligent control and water resource efficient utilization system and method, belongs to the technical field of water conservancy projects, and collects multi-source water quality, water quantity, equipment operation state, pipe network and environment data in real time by deploying an environment data sensing module in a port area. And the multi-source data fusion and analysis module cleans, analyzes and predicts the data and generates an optimization decision basis. The flow decision control module generates an intelligent control instruction based on the flow decision control module, and controls sewage treatment facilities and water resource scheduling. And the intelligent execution and feedback module executes the instruction and visually presents the instruction. Intelligentization and energy consumption optimization of port sewage treatment are effectively realized, and through linkage of dust concentration and reclaimed water dust suppression, intelligent allocation of multi-source water bodies and the like, the utilization efficiency of water resources is greatly improved, the operation cost is reduced, dependence on municipal water is reduced, and sustainable development of port water affairs is realized.

The invention discloses an intelligent water storage irrigation system suitable for ecological restoration of an abandoned mine, and relates to the technical field of ecological restoration, and the system comprises a multi-source water quality regulation and control module which is used for providing an irrigation water source adaptive to plant growth and soil improvement requirements in different restoration subareas; the ecological perception decision module is used for monitoring ecological indexes in real time; generating an irrigation decision executed according to the global scheduling plan through cloud data fusion analysis; the geology and hydrology safety module is used for constructing a digital twinborn model of a mine area and simulating restoration scenes under different irrigation decisions; the distributed irrigation module is used for building a distributed irrigation pipe network; and an energy circulation module. According to the method, fundamental transformation from single water supply to water-soil-plant synergistic remediation is achieved, the integrity of ecological remediation is improved, the vicious circle that traditional irrigation aggravates soil hardening or pollution leaching is effectively broken through, and a stable foundation is built for survival and growth of vegetation in the severe mine environment.

The invention relates to the technical field of integrated wall-mounted laundry closestools, and discloses a distributed multi-water-source cooperative control system based on an integrated wall-mounted laundry closestool, which comprises a multi-source water quality monitoring module, a flow dynamic distribution module, a cleaning strategy generation module, a pipeline deposition correction module and a water source switching optimization module. The multi-source water quality monitoring module acquires water quality parameters in real time and constructs a multi-layer evaluation topology, the flow dynamic distribution module performs pipeline modeling and pressure optimization, and the cleaning strategy generation module combines water quality and pressure information to generate a flushing framework and sorts the flushing framework. The pipeline deposition correction module corrects the deposition rate and balances pressure parameters, and the water source switching optimization module detects path deviation and feeds back iteration. The system realizes multi-source water quality accurate monitoring, flow dynamic distribution and cleaning strategy intelligent generation, improves the utilization efficiency of water resources, ensures stable operation of the system, and solves the problems of extensive water quality monitoring, static flow distribution, lack of intelligent cleaning strategy and the like of a traditional system.

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

The invention provides a power station water regimen trend prediction method and system based on big data analysis, and relates to the technical field of power station water regimen prediction.The power station water regimen trend prediction method comprises the steps that firstly, multi-source water regimen associated data in a power station jurisdiction is obtained; the method comprises the following steps: generating a water regimen multi-dimensional evolution correlation graph presenting dynamic action relationships of different evolution units, then mining a key evolution path influencing a water regimen trend from the water regimen multi-dimensional evolution correlation graph, and constructing a water regimen trend dynamic deduction model containing action transfer rules based on the key evolution path; and finally, inputting the current water regimen, weather and equipment evolution unit data into the water regimen trend dynamic deduction model, executing trend deduction operation through an action transfer rule, generating a power station water regimen trend prediction sequence, and effectively improving the accuracy and reliability of power station water regimen trend prediction.

The invention relates to the field of water quality prediction, and particularly discloses a water supply pipe network water quality prediction method and system based on big data analysis. The input mixed water quality signal is decoupled into a plurality of independent component branches in one-to-one correspondence with the water sources on the characteristic level. And then, through a time-space diagram convolutional network sharing parameters, parallel and independent evolution derivation is carried out on each component flow, so that pervasive transportation and attenuation laws followed by different source water in a pipe network can be respectively captured, and the fundamental defect that a traditional single model cannot be distinguished and processed is overcome. Finally, all independently predicted future component states are subjected to self-adaptive nonlinear fusion with definite physical significance according to the accurate volume ratio of the future component states at the target point, so that a final mixed water quality prediction result with physical interpretability and high precision is generated.

The invention relates to the technical field of water purification treatment, discloses a comprehensive optimization method, device, equipment and medium for rural water supply and water purification, and aims to improve the efficiency and reliability of rural water supply and water purification. According to the method, a water source water quality prediction model and a water purification process optimization model are constructed and are respectively used for predicting future water quality conditions and optimizing a water purification treatment process; and constructing a deviation correction model for correcting an output result of the water purification process optimization model. And in combination with the real-time environmental factor data and the historical water quality data, generating initial operation parameters and water purifying agent dosage of the water purification treatment equipment so as to guide water purification treatment. Real water quality of the water source is monitored in real time and compared with a prediction result, and if the deviation exceeds a threshold value, the deviation correction model is triggered to recalculate. Water source water quality prediction, water purification process optimization and real-time deviation correction can be comprehensively considered, operation parameters of water purification treatment equipment and the adding amount of a water purifying agent are dynamically adjusted, and the water quality safety and stability of rural water supply are ensured.

The invention discloses a water pollution traceability analysis and ecological restoration decision support system. The system comprises a data acquisition layer, a data management and fusion layer, a pollution traceability analysis layer, an ecological restoration decision layer and a user interaction and visualization layer. The system collects multi-source water quality, hydrology and discharge data by deploying a distributed water quality sensing network, remote sensing and unmanned aerial vehicle monitoring equipment and a pollution discharge source online access module; through data cleaning, fusion and knowledge modeling, a pollution propagation model and a traceability inversion algorithm are constructed, and a pollution source and a contribution rate are identified; and simulating a multi-class repair measure response process in the digital twin water environment, and automatically recommending an optimal repair scheme in combination with a multi-objective optimization algorithm. The method has the advantages of accurate traceability, scientific decision, eco-friendliness, dynamic optimization and the like, and is suitable for pollution treatment and ecological restoration scenes of various water bodies such as drainage basins, lakes, urban rivers and the like.

The invention discloses a transmission and distribution pattern optimization method for a multi-water-source water supply pipe network system, and the method comprises the steps: solving a multi-water-source initial ratio at a hour level, and determining the boundary of a water supply region of each water source as an energy storage regulation and control static frame; minute-level reversible flow-water head scanning is carried out on the high-level water tank and the two-way pump station to generate a virtual pressurizing unit, reverse matching is carried out to form an energy storage dispatching pre-instruction, and the pre-instruction is locked as an executable instruction after returning to a static frame to verify water source reverse flow, water hammer overpressure and negative pressure pollution; a valve opening and closing sequence and a pump station rotating speed are coded into time-state vectors, the time-state vectors are input into a graph convolutional network transient pressure surrogate model to deduce a pressure envelope in the third hundred seconds in a rolling mode, only when the whole process is in a pipe allowable interval, the pressure envelope is issued to the site, and closed-loop collaborative optimization of the hour-level water source ratio and the minute-level energy storage scheduling is achieved. According to the method, the problems of complex calculation and control in multi-water-source, multi-energy-storage and multi-time-scale coupling are solved.

The invention relates to a water quality micro-station space-time correlation traceability analysis method based on multi-scale feature fusion. The method comprises the following steps: step (1), preprocessing and hierarchical dimensionality reduction of multi-source water quality data; step (2), measuring relevance of time-space fusion; and step (3), constructing and analyzing the space-time correlation network. According to the method, the association precision is improved through multi-scale feature fusion, the association deviation is reduced through spatial topology correction, the efficiency and the precision are considered through hierarchical dimensionality reduction, management and control are facilitated through structured network output, the precision and the practicability of water quality micro-station association mining are comprehensively improved, and a new breakthrough is brought to water quality monitoring traceability analysis.

The invention discloses an intelligent water tank water inlet valve control system comprising a data acquisition and preprocessing module used for acquiring multi-source time sequence data through a multi-source water affair sensor unit and preprocessing the multi-source time sequence data; the water level prediction modeling module is used for constructing a water level prediction neural network model; the control objective function construction module is used for setting an optimization objective function containing a valve control performance index according to the liquid level prediction result and the operation state; the valve control optimization module is used for generating an optimal valve control strategy sequence by adopting an improved cat swarm algorithm; the electric valve execution control module is used for sequentially inputting the target opening degree instructions into the electric control valves, sequentially executing the instructions according to a control cycle and adjusting the opening degree of the electric control valve group in real time; and the Internet of Things visual management module is used for carrying out visual management through the Internet of Things monitoring operation and maintenance platform. Accurate intelligent water tank water inlet valve control is provided for a user.

The invention provides an underground water source water quality early warning method and system for environmental safety, and relates to the technical field of environmental protection and water quality safety, and the method comprises the steps: obtaining a plurality of water quality indexes of an underground water source in different historical periods and index values corresponding to the water quality indexes; screening the water quality indexes by combining a Lasso regression model and an FISTA algorithm, and determining key water quality indexes; constructing a two-dimensional tensor according to the key water quality index and the index value; constructing a water body pollution prediction model based on CNN and LSTM in combination with an attention mechanism; inputting the two-dimensional tensor into a water body pollution prediction model, and determining a pollution factor concentration; calculating the deviation between the pollution factor concentration and the pollution factor threshold value, and determining the water quality risk grade; and performing early warning on the water quality of the underground water source by combining an early warning grading strategy according to the water quality risk grade. The water quality safety of the underground water source can be ensured, and potential threats to the environment and human health are reduced.

The invention discloses a multi-sensor water level automatic monitoring and early warning method, device and equipment and a storage medium, and belongs to the field of hydraulic engineering, and the method comprises the steps: monitoring water level source data through monitoring station water level multi-sensor equipment, and carrying out the preprocessing (data interpolation and abnormal value correction) of the water level source data, so as to achieve the continuity of the data; the method comprises the steps that firstly, source data are obtained, CEEMDAN decomposition is conducted on the source data, effective signals are extracted, the extracted effective signals serve as characteristic values to be substituted into a GRU to be trained, predicted IMF component results are overlapped to obtain a post-rental predicted value, WMAE, WRMSE and WR2 serve as water level early warning precision evaluation factors to monitor water level abnormity, and finally early warning data are stored in a ground database; according to the method, accurate prediction of the large water level is achieved, the characteristics of source water level data are reserved, the method has obvious advantages in the aspect of intelligent water level prediction, and a reliable scientific basis is provided for establishing a scientific reservoir water level prediction and early warning decision model and strengthening system management and operation data.

The invention discloses an irrigation decision determination method considering a multi-source water conversion process, and relates to the technical field of agricultural water conservancy irrigation, and the method comprises the steps: obtaining a multi-spectral image and a thermal infrared image of crops in a target irrigation region, calculating a vegetation index and a leaf area index, and combining with the actually measured growth vigor data of the crops, a growth vigor model of crops is constructed by establishing a mapping relation, the rainfall, the irrigation volume, the canal system infiltration replenishment volume and the groundwater capillary rise volume of a target irrigation area are obtained, and a soil moisture income and expenditure model is constructed based on a water circulation process. According to the method, a plurality of paths such as rainfall, irrigation, canal system leakage and groundwater capillary rise are considered through the constructed soil water volume income and expenditure model, crop physiological moisture inflection points are accurately recognized by simulating the root zone volumetric moisture content and growth vigor response curve, threshold control irrigation based on crop requirements is achieved, and the crop yield is improved. And the multi-source input and conversion process of the farmland hydrological system is comprehensively reflected.

The invention relates to the technical field of intelligent early warning, and discloses a multi-source data driven intelligent early warning method and system for the abnormal state of a water quality meter, and the method comprises the steps: carrying out the multi-source data fusion of the collected real-time data of the water quality meter, and obtaining the multi-source water quality data of the water quality meter; performing multi-dimensional feature extraction on the multi-source water quality data to obtain water quality feature data; based on the change trend of the water quality features, performing adaptive fusion on the water quality feature data to obtain feature vectors of the multi-source water quality data; performing abnormal state association on the feature vector to obtain an abnormal probability score of the feature vector; comparing the abnormal probability score with a preset early warning grade, and performing abnormal threshold adjustment on the abnormal probability score according to a comparison result to obtain a graded early warning signal of the water quality meter; step-by-step coding is carried out on the graded early warning signal to obtain an early warning signal instruction of the water quality meter; according to the invention, the early warning efficiency of intelligent early warning of the abnormal state of the water quality meter driven by multi-source data can be improved.

The invention relates to the technical field of hydrology and water resource monitoring and emergency scheduling, in particular to a water source multi-modal monitoring fusion method and system, and the method comprises the steps: firstly obtaining multi-source water environment observation, and forming evidence graph initialization and assimilation initialization; secondly, carrying out cross-modal evidence fusion, generating topological boundary constraints, flow direction constraints and assimilation time windows, and compiling the topological boundary constraints, the flow direction constraints and the assimilation time windows into a constraint set; then executing data assimilation and source item joint inversion under the constraint of a partial differential equation to obtain a source item confidence set, the arrival time of a pollution front outer envelope and a key position, and a quantile interval of peak concentration and standard exceeding duration; and finally, constructing a risk functional and an opportunity constraint based on a quantile interval, and generating a scheduling instruction and partition early warning by adopting rolling predictive control, and performing closed-loop recharge. The method gives consideration to accuracy, robustness and performability.

The embodiment of the invention provides a pollutant source analysis method and device for underground water, equipment and a medium, and relates to the technical field of environmental chemistry analysis. The method comprises the following steps: carrying out water sample collection at candidate pollution source sampling points of target underground water to obtain candidate pollution source water samples, and carrying out water sample collection at downstream sampling points of the target underground water to obtain downstream water samples; wherein the target underground water flows from the candidate pollution source sampling point to the downstream sampling point; performing chemical component feature extraction according to the candidate pollution source water sample to obtain a first chemical feature, and performing chemical component feature extraction according to the downstream water sample to obtain a second chemical feature; performing similarity calculation according to the first chemical feature and the second chemical feature to obtain a target chemical feature similarity; and determining a target pollution source sampling point from the candidate pollution source sampling points according to the target chemical characteristic similarity. According to the embodiment of the invention, the accuracy of pollutant source analysis can be improved.

The invention discloses a fusion method and device based on multi-source water conservancy data, equipment and a medium, and relates to the technical field of water conservancy data processing, and the technical scheme is characterized in that the method comprises the steps: collecting water conservancy original data, carrying out the classified storage of the water conservancy original data according to a water conservancy data specification, and obtaining a water conservancy classification data set; preprocessing the water conservancy classification data set to obtain a preprocessed water conservancy classification data set, combining similar data of multiple sources, and carrying out missing compensation processing on the similar data to obtain a fused similar data set; and carrying out hierarchical modeling on the fused data sets of the same kind by utilizing distributed processing and a multi-bin hierarchical architecture design to obtain a standardized unified water conservancy data mart. The system has the advantages of high-efficiency water conservancy data acquisition, high-quality similar data fusion, high-standard heterogeneous data fusion, high-availability water conservancy data service and the like.