1061 results about "Drainage basin" patented technology

The invention relates to the technical field of drainage basin water scheduling, and discloses a drainage basin water regulation and control optimization method based on ecological element changes. The method comprises the following steps: deploying a drainage basin monitoring system, and collecting ecological element real-time data such as a hydrological parameter sequence and a remote sensing image; after the data is cleaned and converted, hydrological trend features and spatial distribution features are extracted by adopting a feature learning model, and the hydrological trend features and the spatial distribution features are fused into unified ecological representation through a cross-modal alignment mechanism; inputting the unified ecological representation into a physically constrained neural network prediction model, and outputting a water regimen dynamic prediction value; and finally, based on the predicted value, a water resource regulation and control instruction is generated and executed by using a multi-objective decision algorithm so as to optimize the watershed water circulation process. According to the method, feature extraction comprehensiveness is improved through multi-source data fusion and cross-modal analysis, prediction reliability is enhanced in combination with physical constraints, reasonable allocation of water resources is achieved by means of multi-target decision, the ecological condition of a drainage basin can be improved, and the water utilization efficiency is improved.

The invention discloses a method for simulating and forecasting flood in a cold highland area based on hydrological and hydrodynamic coupling, and belongs to the technical field of disaster forecasting. The method specifically comprises the following steps: S1, multi-source basic data collection and preprocessing: collecting multi-type and multi-scale basic data for a target cold and cold mountainous area drainage basin; and S2, deep learning correction and fusion of the satellite rainfall data: aiming at the local overestimation and underestimation problems of the satellite rainfall data, a deep learning algorithm is adopted to carry out hour scale correction and fusion. Four types of core data of satellite remote sensing, reanalysis, ground observation and geographic space are collected, total factors of'rainfall-runoff-terrain-underlying surface 'required by flood simulation in the cold and cold mountainous area are covered, simulation one-sidedness caused by lack of data types in traditional modeling is avoided, rainfall and runoff abnormal values are eliminated by adopting a 3-sigma criterion, data formats and spatial-temporal scales are unified, and the modeling efficiency is improved. A standardized data set is formed, and interference of abnormal values, format incompatibility and space-time mismatching on subsequent model input is avoided.

The invention discloses a drainage basin maintenance intelligent management system based on the Internet of Things and intelligent decision, and relates to the technical field of computers. Comprising a multi-source perception and edge access module which is used for realizing multi-protocol access, time and session alignment, data quality labeling, breakpoint resume and edge side anomaly preliminary screening of industrial data, low-power-consumption point location data and video and thermal image data. According to the invention, through the multi-source sensing and edge access module, various types of data such as industrial data, low-power-consumption point location data, video and thermal image data can be processed, the technical problems of multi-protocol access, time and session alignment, data quality labeling and breakpoint resume and the like are solved, the integrity and reliability of the data on the edge side are ensured, and the service life of the data is prolonged. The edge side anomaly preliminary screening function significantly improves the timeliness of early fault discovery, reduces the network bandwidth pressure, effectively solves the problems of anomaly cooperative detection and alarm flooding in a complex system, and improves the accuracy and timeliness of fault early warning.

The invention relates to the technical field of water resource analysis, and discloses a water resource predictive analysis method based on artificial intelligence. The method relates to the technical field of water resource analysis, and comprises the following steps: acquiring an original hydrological data set including rainfall intensity, river flow and the like through a sensing terminal, and performing multi-modal data alignment to generate a hydrological space-time tensor; constructing a dynamic water level threshold response mechanism in combination with watershed topographic features to obtain a partition water level calibration matrix; inputting the hydrological feature map into a spatial-temporal feature coupling network containing a long-short-term memory module and a spatial self-attention module to generate a hydrological feature map; constructing a multi-dimensional abnormal association tensor based on the multi-dimensional abnormal association tensor, and identifying rainfall flood event nodes by using an adaptive sliding window detection algorithm; and an optimal hydrological parameter set is obtained through genetic algorithm optimization, and the three-dimensional hydrological dynamic model is driven to establish a mapping relation chain. The method can effectively fuse hydrological data spatio-temporal characteristics, and improves the accuracy and efficiency of water resource prediction analysis.

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 discloses a multi-scale fusion ecological hydrological interaction quantification method, and relates to the technical field of ecological hydrology, and the method comprises the steps: 1, collecting environment driving data and remote sensing data; 2, preprocessing the remote sensing data, and carrying out hydrological process dynamic monitoring and ecological parameter collaborative inversion in combination with environment driving data to respectively obtain hydrological data and ecological indexes; 3, constructing a spatio-temporal data set by using the hydrological data, the ecological indexes and the environment driving data; 4, constructing a bidirectional LSTM neural network model, and performing training optimization by using the spatio-temporal data set to obtain a water volume change predicted value, lag time and corresponding ecological variables; 5, lag effect analysis is carried out according to the water volume change predicted value, the lag time and the corresponding ecological variables, and an interaction quantification network diagram of the ecological hydrological elements is constructed. According to the method, the dynamic coupling relation of the ecological hydrological process of the sand lake basin is quantified, and theoretical support is provided for resource optimization management and ecological restoration engineering.

The invention discloses a reservoir group joint scheduling optimization method based on multi-agent deep reinforcement learning, and relates to the technical field of hydroelectric energy system optimization scheduling and control, and the method comprises the steps: dividing X reservoirs in the same drainage basin into J agent subsystems, each intelligent agent only senses the local water level-inflow state and outputs the target water level / discharge amount in the next time period; in the training stage, a centralized evaluation-distributed execution (CTDE) framework is adopted, a value function is constructed by combining a central Critic network with global state-action information, and iterative updating is performed on each Actor policy network by utilizing a multi-agent depth deterministic policy gradient (MADDPG); and the reward function integrates power generation benefits, ecological discharge and final water level penalty to realize global collaborative optimization. After the offline training convergence, the autonomous and complementary scheduling instruction of each reservoir can be obtained only by executing millisecond-level forward reasoning based on real-time monitoring data in the online deployment stage.

The invention discloses a river organic carbon flux inversion method based on multi-source data and related equipment. The method comprises the following steps: acquiring multi-source data of a river basin range in a target research area; obtaining a normalized difference index based on remote sensing image conversion; river parameters of the target research area are obtained based on the normalized difference water body indexes and multi-source data processing, and river runoff is obtained through conversion of a hydraulic geometrical relationship; on the basis of the remote sensing image, the normalized difference chlorophyll index and the normalized difference turbidity index, the particle organic carbon concentration is obtained through inversion by means of a first inversion model; based on the population number and the rainfall capacity, a second inversion model is used for inversion to obtain the concentration of dissolved organic carbon; and converting the river runoff, the particle organic carbon concentration and the dissolved organic carbon concentration to obtain the river organic carbon flux. The method can significantly improve the accuracy and reliability of monitoring on a high-dynamic river, accurately realizes the inversion of the organic carbon flux of the river, and can be widely applied to the technical field of data processing.

The invention discloses an inland river basin intelligent ecological scheduling rehearsal method fusing a scheduling process and an ecological process. The inland river basin intelligent ecological scheduling rehearsal method comprises the steps of multi-source data acquisition and digital twinborn construction; carrying out reservoir intelligent scheduling reinforcement learning modeling; ecological process lag response modeling is carried out; spatial diffusion modeling of ecological influence; performing cross attention guided ecological response interpolation; and carrying out rehearsal and visual display on the ecological scheduling scheme. According to the method, a hydrological-ecological response modeling mechanism is introduced, and a time-space response relationship between scheduling behaviors such as water level and water volume and ecological indexes such as vegetation indexes and habitat indexes is combined, so that lagging response characteristics of an ecological process to the scheduling behaviors can be quantitatively described, and the defect that a traditional scheduling model is insufficient in ecological expression capability is overcome. A reinforcement learning algorithm is utilized to fuse multi-source data for state perception and strategy iteration, and the scheduling strategy can be dynamically adjusted according to the current hydrological situation and ecological feedback result of the watershed. Compared with static rule type scheduling, the regulation and control efficiency and ecological adaptability are remarkably improved.

The invention provides a Beidou intelligent early warning analysis method and system applied to a drainage basin-level reservoir dam group, and the method comprises the steps: obtaining a dam body deformation time sequence record and a corresponding hydrological environment time sequence record which are collected by a monitoring system of the drainage basin-level reservoir dam group through a Beidou satellite positioning terminal; dam body deformation response mode self-encoding processing is carried out on the associated time sequence data set, a deformation response feature element set representing the dam body structure state is generated, the deformation response feature element set is input into a pre-constructed basin reservoir dam group topological relation graph, abnormal state propagation calculation is carried out through the state dependency relation between graph nodes, and the abnormal state propagation calculation is carried out. And obtaining a chain risk transmission path set, performing risk evolution trend deduction on dam nodes in each path based on the chain risk transmission path set, generating a risk evolution index matrix, and generating a threatened risk identification sequence for key dam nodes. According to the method, the structuralization and response operability of the early warning information can be improved, and the scientificity and practicability of early warning analysis of the basin-level reservoir dam group are comprehensively improved.

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

The invention relates to an inter-basin water saving amount transaction and scheduling optimization method, which comprises the following steps of: performing dynamic contrast analysis on water saving behaviors and water resource response on the basis of historical water consumption behaviors, resource regulation and control records and meteorological basic data, judging the autonomy and continuity of the water saving behaviors, and obtaining the right-confirmable water saving amount; according to the watershed water resource tension degree, the water ecology sensitivity and the water transfer cost, a cross-watershed conversion rule is adopted, a conversion scale factor is set, and resource value transverse butt joint of water saving behaviors of different areas is achieved; calculating the flow attenuation, the regional bearing threshold and the water quality compatibility in the allocation path based on the water-saving amount obtained by the water-saving transaction, and determining the available transmission range and the sustainable allocation amount of the water-saving amount; and according to the distribution of the water-saving quotas and the corresponding value grades, carrying out combined screening according to the use priority, the basin compensation relationship and the space-time adaptation requirements, outputting a resource allocation scheme, and forming a cross-basin water-saving-quantity-driven water resource optimal allocation structure.

The invention discloses a distributed flood forecasting scheduling model construction method based on sub-basins, particularly relates to the technical field of flood forecasting model optimization, and is used for solving the problem of local prediction failure due to spatio-temporal differences caused by neglecting parameter sensitivity of an existing flood forecasting model. A target drainage basin is divided into sub-drainage basins, hydrological and geographic feature parameters are extracted, and a cross-drainage-basin water conservancy facility topology network is constructed to dynamically correct river channel evolution boundary conditions; quantifying parameter sensitivity spatial diversity based on a Sobo index method, identifying a sensitive abnormal response region in combination with historical flood event distribution, and applying spatial lag correction through a river topology network to generate parameter sensitivity grade classification; constructing a dynamic weight matrix, and performing partition parameter calibration by adopting global optimization and local adjustment strategies respectively; and the hydraulic equilibrium constraint of the calibration result is verified based on the river topological relation, and a partition and grading flood discharge scheduling scheme is generated after iterative correction, so that the forecasting capability in a sudden flood scene is remarkably improved.

The invention discloses a drainage basin intelligent flood control scheduling method and system based on digital twinborn, and relates to the technical field of flood control and disaster mitigation, and the method comprises the steps: collecting static data and dynamic data of a drainage basin, building a hydrological and hydrodynamic coupling model based on the static data and the dynamic data, and forming a drainage basin digital twinborn body; inputting the received numerical weather forecast into the digital twin of the watershed for simulation, generating a plurality of flood routing scenes in a future time period, and calculating a dynamic flood risk probability graph; the method comprises the following steps: constructing a simulation training environment by using historical flood data and a high-precision drainage basin digital twinborn body, carrying out offline training on a scheduling strategy network in the simulation training environment based on a reinforcement learning algorithm, and outputting a scheduling instruction according to a real-time drainage basin state to complete training of the scheduling strategy network. According to the method, the core problem that the traditional method is insufficient in decision timeliness and weak in adaptive capacity in an uncertain environment is effectively solved.

The invention relates to the technical field of hydrological prediction, in particular to a drainage basin distributed runoff prediction method and system based on a graph neural network, and the method comprises the following steps: obtaining a drainage basin multi-source runoff data set to construct a multi-relation dynamic graph structure, and extracting node feature vectors; based on the node feature vector, obtaining a watershed evolution trend forward feature by establishing a watershed diffusion fitting architecture; constructing a distributed runoff probability prediction model, and taking the watershed trend forward features as model input to obtain runoff initial condition probability distribution of each sub-watershed in multiple periods in the future; establishing a mixed loss function, and performing physical constraint optimization on the runoff initial condition probability distribution to obtain distributed runoff optimization probability distribution; and performing uncertainty quantification on the distributed runoff optimization probability distribution to obtain a drainage basin distributed runoff prediction result. According to the method, the hydrological process simulation capability of the complex watershed is improved, and accurate prediction of the distributed runoff volume is realized.

The invention discloses a reservoir group-lake system flood control risk adaptive analysis method and system. The method comprises the following steps: acquiring a basin hydrological sequence and a pre-configured physical causal constraint matrix, and constructing a VineCopula joint distribution and initial Bayesian network conforming to physical topology according to the basin hydrological sequence and the pre-configured physical causal constraint matrix; risk sensitive weights of the sample points are calculated based on the Bayesian network, and weighted sampling density inclining towards the high-risk area is constructed; generating a self-adaptive scene set based on the density, solving an optimal scheduling scheme, feeding back a scheduling result to the Bayesian network to update parameters, carrying out closed-loop iteration of perception-sampling-scheduling-learning until risk assessment is converged, and determining a comprehensive flood control risk assessment result of the system based on the converged risk factor Bayesian network. According to the method, the problems of poor physical interpretability and scarcity of extreme risk samples of a traditional model are solved, and accurate capture and quantification of extreme risks of a complex flood control system are realized.

The invention discloses a drainage basin water quality pollution source flux identification method based on an SWAT model, computer equipment and a storage medium, and the method comprises the steps: obtaining topographic data, land utilization data, soil type data and meteorological data, and enabling the topographic data, the land utilization data, the soil type data and the meteorological data to form a drainage basin spatial database; based on the preprocessed digital elevation model, determining a water flow direction and setting a reasonable water collection area threshold value, and dividing sub-basins to construct a basin calculation unit network topology structure; obtaining pollution source data, and constructing an SWAT-CUP model according to the pollution source data; performing system calibration and verification on the watershed hydrological process by adopting an SUFI-2 algorithm in the SWAT-CUP model; carrying out system calibration and verification on the total nitrogen load of the drainage basin by adopting an SWAT-CUP model; and based on the calibrated SWAT hydrological model, the input condition of the pollution source is changed. The method can cover the whole watershed and quantify the contribution rate of different pollution sources; cost is high, operation is simple, and long-term change can be dynamically simulated.

The invention relates to the technical field of ship lock combined scheduling, and discloses a cascade ship lock combined scheduling method and system based on multi-basin input factors, and the method comprises the steps: obtaining a plurality of sub-basins with independent hydrological responses based on the hydrological flow information of basin units, and calculating the flow information of the sub-basins; performing calculation to obtain uplink and downlink flow imbalance of the sub-basins, collecting ship operation data and ship lock information of the step ship locks, and constructing a multi-basin input factor; and constructing a combined scheduling strategy based on the multi-basin input factors, performing combined scheduling on the cascade ship locks in different sub-basins, and generating scheduling actions of the cascade ship locks. According to the method, the ship operation data, the ship lock state information and the uplink and downlink flow imbalance of the cascade ship locks are combined, organic fusion of hydrological information and the shipping state is achieved, the cascade ship locks in different sub-basins can cooperatively decide scheduling actions, and the response ability and the regulation and control efficiency of the cascade ship locks to multi-basin complex changes are improved.

The invention discloses a driving mechanism analysis method of reservoir group scheduling for flood non-consistent evolution, which comprises the following steps: acquiring a smooth reservoir entering flood sequence by utilizing a topologically coupled physical manifold constraint denoising model, and constructing dynamic topological characteristics representing propagation time lag and sensitivity through a hydraulic propagation mechanism-based Figure ordinary differential equation; constructing a structural causal model comprising a scheduling capability index, ectogenic hydrological driving and topological characteristics, and fitting a nonlinear dependency relationship by using a causal generalized additive model; executing anti-fact inference, and calculating a local causal driving index and a global causal cumulative effect index through a dry budget; and generating an optimal scheduling strategy for suppressing variation based on the causal index. According to the method, systematic analysis from data physical restoration to causal mechanism decoupling can be realized, the driving contribution of a scheduling behavior to flood inconsistency is accurately quantified, and decision support is provided for scientific flood control of a drainage basin.

The invention provides a watershed artificial precipitation enhancement operation effect evaluation method based on hydrological simulation, and belongs to the technical field of artificial influence weather, and the method comprises the steps: obtaining first data and second data; based on the first data, settling a rainfall-runoff process, selecting a preset hydrological model to carry out a drainage basin hydrological batch simulation test, calibrating hydrological parameters, and constructing a drainage basin hydrological simulation forecast model; based on the second data, calculating the natural precipitation of the non-operation area and the operation precipitation of the operation area, and constructing a natural precipitation-operation precipitation data sequence; based on the natural precipitation-operation precipitation data sequence, inputting the natural precipitation into the watershed hydrological simulation forecasting model to obtain runoff before precipitation enhancement operation, and inputting the operation precipitation into the watershed hydrological simulation forecasting model to obtain runoff after precipitation enhancement operation; and based on the runoff before the rainfall enhancement operation and the runoff after the rainfall enhancement operation, carrying out quantitative evaluation on the artificial rainfall enhancement operation effect of the drainage basin. Scientific quantitative evaluation of the artificial precipitation enhancement operation effect of the watershed is realized.

The invention relates to an intelligent drainage basin management method, device and equipment based on digital twinning and a medium of the intelligent drainage basin management method and device based on digital twinning. The method comprises the steps that core terrain attributes are extracted through terrain feature decoupling and coding, and a disaster response function of an optimal source drainage basin is migrated based on terrain and rainfall similarity; a hydrodynamic model and a migration function are combined to generate a physically constrained synthetic disaster situation data set, a two-channel neural network model fusing topographic features and rainfall dynamics is constructed, and model parameters are dynamically calibrated through Bayesian continuous learning by using real-time monitoring data of a target drainage basin. Finally, a digital twinborn system with flood routing prediction capability is formed, the problem of rapid construction of a flood prediction model under the condition of no historical data is solved, and the timeliness and accuracy of early warning of sudden flood in small and medium-sized watersheds are remarkably improved.

The invention relates to the technical field of digital twinborn and drainage basin environment monitoring simulation, in particular to a drainage basin environment data simulation deduction system based on a digital twinborn technology, which comprises a digital twinborn management center used for calling multi-source heterogeneous monitoring data of a drainage basin environment to obtain an aligned environment data sequence; the causal coupling analysis unit is used for obtaining a steady-state logic signal or a dynamic decoupling signal; when a dynamic decoupling signal is generated, the topology adaptive reconstruction unit is used for performing structural mutation recognition feedback analysis on a calculation map of the digital twin model to obtain a reconstructed topology map; when the steady-state logic signal is generated, the simulation deduction execution unit is used for performing environment evolution deduction analysis on the aligned environment data sequence based on the current calculation map to obtain a simulation deduction result; according to the method, model calculation divergence or misleading output caused by sensor faults is effectively avoided, and the calculation problem caused by multi-source data heterogeneity is solved.

The invention provides a drainage basin level dam monitoring data standardized integration and cooperative processing method and device, and the method comprises the steps: building a four-dimensional unified data model, and carrying out the standardized mapping of space, time, equipment and business dimensions of multi-source heterogeneous monitoring data; deploying a self-adaptive protocol analyzer, automatically analyzing an equipment document based on a natural language processing technology, generating a protocol conversion rule, and realizing dynamic conversion of various industrial protocols; performing local cleaning, compression, encryption and time sequence feature extraction on the standardized data through an edge computing node to generate a data packet in a unified format; a transmission path is dynamically optimized according to channel quality by using an intelligent routing selection model based on Q-learning, and multi-mode hybrid transmission of a data packet among LoRaWAN, 5G and Beidou short messages is realized. Standardized integration and intelligent cooperative processing of multi-source heterogeneous dam monitoring data in a drainage basin are achieved, information islands are effectively eliminated, the comprehensive operation and maintenance cost is reduced by 30% or above, and the security situation awareness timeliness is improved to the minute level.

The invention provides a watershed water right marketization configuration method and device under water-sediment coupling constraint, and the method comprises the steps: constructing a water-sediment coupling constraint model which comprises a plurality of constraint conditions, and the constraint conditions are used for constraining the sediment transportation and deposition conditions in a water transportation process; establishing a multi-target water right optimization configuration model based on the importance of various water consumption subjects in the drainage basin and a preset water right distribution strategy; solving the multi-objective water right optimal configuration model by adopting a multi-objective evolutionary algorithm and constraint conditions, and generating a candidate water right configuration solution set; selecting a feasible solution set from the candidate water right configuration solution set based on constraint conditions; scoring each solution in the feasible solution set, and sorting according to a scoring result; and obtaining an optimal water right configuration solution set based on a sorting result. According to the method, the safety and ecological requirements of a watershed water-sediment system can be guaranteed while the water right is reasonably distributed.

The invention provides a hydrological forecasting method and system based on an adaptive correction chain, and relates to the field of data processing, and the method comprises the steps: generating a first reservoir inflow forecasting result according to a rainfall deviation traceability-dynamic correction chain and rainfall observation data; multi-dimensional features of the current scene are determined, and a second reservoir inflow forecasting result is generated according to the historical similar sample matching-rule application chain and the multi-dimensional features of the current scene; according to the confluence and production knowledge-threshold constraint chain, the underlying surface condition, the rainfall spatial distribution and the basin state, a third reservoir inflow forecasting result is generated; and determining the dynamic weights of the first reservoir inflow forecasting result, the second reservoir inflow forecasting result and the third reservoir inflow forecasting result, and generating a final reservoir inflow forecasting result, thereby improving the accuracy of the reservoir inflow forecasting result.

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

The invention relates to the technical field of water resource management and forecasting, in particular to a deep learning model building and forecasting method based on hydrological mechanism fusion. The method specifically comprises the following steps: constructing a snow melting calculation module and a soil calculation module, splicing to form a runoff production model, constructing a confluence calculation module, executing confluence evolution simulation of a calculation result of the runoff production module by using the confluence calculation module, and constructing a confluence model. In confluence model simulation calculation, according to the number of calculation units, a broadcast calculation strategy and an array-based ordinary differential equation solving method are adopted, parallel calculation of the multiple calculation units is achieved, and runoff production calculation results of the multiple units are obtained; and integrating and calculating runoff production calculation results of each unit through a confluence model to obtain a runoff prediction result of the modeled drainage basin after confluence. The method provided by the invention solves the problems of low model construction efficiency and poor practical application effect faced by the application of the deep learning technology in the hydrological model at present.

The invention relates to the technical field of big data management, in particular to a collaborative optimization decision-making system for electric power-environment coupling big data, and the system comprises a drainage basin coupling module which is used for fusing multi-source ecological data obtained in real time with a preset mechanism model, and constructing a drainage basin water-energy-grain-ecological coupling model; the hierarchical decision-making module comprises a coordination layer agent representing a top layer strategy target and execution layer agents representing different benefit parties; the decision attribution analysis module is used for analyzing the collaborative scheduling strategy and acquiring a key driving factor causing a preset scheduling behavior; the anti-fact deduction optimization module is used for defining an anti-fact scheduling scene based on the key driving factors, calling a watershed water-energy-grain-ecology coupling model to perform rapid deduction, quantitatively comparing the comprehensive influence of the anti-fact scheduling scene and the collaborative scheduling strategy on water, energy, grain and ecology under a preset time scale, and performing collaborative scheduling on the water, the energy, the grain and the ecology. And optimizing the collaborative scheduling strategy.

The invention discloses a surface water and underground water combined regulation and control method considering runoff and underground water level recovery, and belongs to the technical field of comprehensive regulation and control of water resources. According to drainage basin water resource supply and demand conditions and engineering characteristics, on the basis of fully considering multiple factors such as economy, society and ecology, organic combination of multiple regulation and control measures is implemented, and the method becomes an important way for achieving efficient combined utilization of water resources, restoring the underground water level, improving the regional ecological environment and guaranteeing food safety. Besides, in order to comprehensively evaluate the combined regulation and control effect of the surface water and the underground water, an evaluation index system of the system needs to be established and is used for evaluating the recovery conditions of the river runoff and the underground water level. By quantitatively evaluating actual effects of different regulation and control measures, a decision maker can be helped to judge whether water resource distribution and use are reasonable or not, the regulation and control measures are adjusted and optimized according to evaluation results, an appropriate surface water-underground water combined regulation and control scheme is selected, and reference can be provided for sustainable management of drainage basin water resources.