6132 results about "Stream flow" patented technology

The invention provides a flowmeter online calibration system and method, and the method comprises the steps: firstly collecting a real-time operation data set of a target flowmeter in a pipeline operation environment, and covering dynamic monitoring data and environment state data; preprocessing the data set to obtain an operation data sequence capable of describing the change trend of the operation state of the flowmeter at multiple continuous time nodes, inputting the operation data sequence into a pre-trained calibration parameter generation model, and generating a calibration parameter set comprising a flow correction coefficient, an environment compensation factor and an error adjustment threshold value; and adjusting and measuring output data in real time based on the calibration parameter set, generating a calibrated flow measurement result, comparing the calibrated flow measurement result with a standard flow reference value for verification, and if the deviation is greater than an error adjustment threshold value, regenerating the calibration parameter set and performing iterative adjustment until the deviation is within a threshold value range, thereby realizing high-precision and self-adaptive online calibration of the flowmeter.

The invention provides an intelligent management method for natural resource engineering data, and relates to the technical field of data processing, and the method comprises the steps: inputting a time-space coupling model into real-time weather forecast data, simulating water distribution schemes under different rainfall scenes, automatically recognizing a river reach position of which the ecological flow is lower than a design threshold value, and carrying out the calculation of the river reach position. In combination with a historical water replenishing strategy case library, generating a multi-objective optimization scheduling scheme including a water replenishing time sequence, a water replenishing path and a water replenishing amount; three monitoring points are arranged at a downstream section of an ecological protection area, a flood control section and a canal head of an irrigation area in a drainage basin, and ecological flow, flood control section water level and canal water level data are monitored in real time respectively; and dynamically correcting the water supplementing time sequence, the water supplementing path and the water supplementing amount in the multi-objective optimization scheduling scheme to obtain a corrected scheduling scheme. According to the method, refinement and toughness of drainage basin water resource management can be improved.

The invention discloses a reservoir scheduling decision support system for multi-objective optimization, and relates to the technical field of hydraulic engineering scheduling. According to the system, comprehensive optimization of flood control, power generation and ecological targets is realized through cooperative work of the multi-source dynamic constraint analysis module, the collaborative optimization module, the scheduling module and the correction module. A dynamic constraint boundary table is generated by collecting meteorological, hydrological and ecological data in real time. And matching a multi-target coordination rule through a physical constraint double-depth network algorithm, generating a Pareto feasible solution set, and mapping the Pareto feasible solution set into a coordination strategy of flood discharge flow and power generation output. In combination with a hydraulic model, flood control and power generation weights are adjusted, an executable dispatching instruction is generated, it is ensured that the ecological flow reaches the standard, and the influence of a downstream submerged area is optimized. And correcting the dynamic constraint boundary table according to actual monitoring data, and continuously optimizing a scheduling decision. Accurate management and multi-objective optimization of reservoir dispatching are realized, and the utilization efficiency of water resources is improved.

The invention relates to the technical field of town sewage pipe network detection, and discloses a town sewage pipe network leakage detection system and method. The system comprises a data acquisition module which uses a multi-source sensor to acquire real-time operation data such as pipe network pressure, flow and the like; the feature extraction module extracts spatio-temporal features based on the multi-scale convolutional neural network, and generates a pipe network state feature matrix; the anomaly detection module inputs the feature matrix into a pre-training model and marks a potential leakage area; the optimization analysis module constructs a multi-constraint dynamic optimization model, and pipe network pressure parameters are optimized by using an adaptive particle swarm algorithm; the hierarchical execution module generates a global regulation and control sequence, dynamically matches local pressure parameters and adjusts the valve opening and the pump station power through a decision layer, a region coordination layer and an execution layer. The system and the method are accurate in detection and reasonable in regulation and control optimization, leakage risks can be effectively reduced, the operation management level of a pipe network is improved, and water resource waste and environmental pollution are reduced.

The invention discloses a dredge pump control system and method for river regulation, and relates to the technical field of dredge pump control. The dredge pump control system comprises a sensing module, a centralized control module, an energy-saving execution module and a control parameter recording module; the sensing module is arranged on a multi-element sensor at a key part of a dredge pump for acquiring data, and the data is pre-processed by a microprocessor and then transmitted to the centralized control module at a high speed by a wireless transmission module, so that an accurate basis is provided for system control; according to the invention, by integrating multiple sensors and an advanced control algorithm, the dredging efficiency and accuracy of the dredge pump of the dredger are obviously improved; the sensing module can monitor key parameters of the dredge pump in real time, such as suction pressure, discharge flow, pump shaft temperature and sediment concentration, and it is ensured that the running state of the dredge pump is comprehensively known; and the centralized control module dynamically optimizes a control sequence by using a model predictive control and fuzzy logic control algorithm so as to adapt to variable working conditions.

The invention relates to a hydropower station safety comprehensive management system based on a digital twinborn scene, which designs a hydropower station data resource coding system, establishes a hydropower engineering digital twinborn base, integrates all related geospatial data, basic data, monitoring data and business management data of a hydropower station, and constructs the hydropower station digital twinborn scene. Predicting displacement and deformation conditions of a building structure of the hydropower station and a change trend of important facility and equipment state indexes of the hydropower station, and predicting and pre-judging safety risks therein; the reservoir flow of the hydropower station, the upstream and downstream river section water level, the flow change and the power generation output and the power generation capacity of the hydropower station are forecasted or predicted, corresponding plans are provided for flood early warning or meteorological disaster early warning or water level over-limit early warning, and key links of the plans are simulated in combination with a simulation engine; and performing simulation calculation on different power generation and reservoir scheduling strategies to obtain an optimal scheduling scheme. According to the invention, the intelligent management of hydropower station engineering safety, scheduling safety and power generation safety is realized.

The invention relates to the technical field of hydrological monitoring, in particular to a multi-satellite collaborative hydrological monitoring system. The method comprises the following steps that a water level height measurement module obtains radar pulse recovery time delay data through a height measurement satellite and calculates the water level height, abnormal points are removed to generate river water level data, a water remote sensing module collects river water images through a remote sensing satellite to extract water boundary features, water area change data are deduced, and the water level height measurement module calculates the water level height. The meteorological wind shear analysis module obtains river channel meteorological data through a meteorological satellite, microwave scattering measurement and wind speed inversion are carried out, wind-induced shear stress parameters are generated, and the flow estimation module carries out section dynamic analysis and estimates the water flow in combination with river channel water level and water area data. And the flow correction module corrects the river water flow based on the wind-induced shear stress parameter and carries out real-time monitoring, and data are synchronously uploaded to the control terminal, so that comprehensive dynamic monitoring of the hydrological state of the river is realized. According to the invention, a more efficient multi-satellite collaborative hydrological monitoring system is realized.

The invention discloses a plain water network area runoff simulation method which comprises the following steps: acquiring and preprocessing multi-source data to obtain a time-space consistency hydrological data set; based on the time-space consistency hydrological data set, obtaining polder area water level data and exchange flow data by adopting an enhanced polder system dynamic model; carrying out runoff production calculation in the plain area by adopting an improved XAJ model; constructing a multi-scale river network topological structure, and executing multi-directional flow state identification and prediction of deep space-time fusion; and constructing a time-varying structure adaptive BP neural network model to realize runoff simulation of the plain water network area. According to the method, the technical problems of reverse flow phenomenon identification in a plain water network area, polder area water exchange threshold effect simulation, distribution node dynamic distribution coefficient optimization, temporary moving pump station influence correction and the like can be effectively solved.

The invention relates to the technical field of underground drainage pipeline monitoring and hidden danger analysis, and provides an underground drainage pipeline hidden danger data analysis method based on big data, which comprises the following steps: acquiring data such as flow, water level, pressure and pipeline structure by using a multi-source sensor to form an original monitoring data set; preprocessing the data, extracting time domain, frequency domain and spatial topological features, and constructing a multi-dimensional feature vector; and inputting the feature vectors into a pre-trained hidden danger prediction model, and outputting hidden danger types and position coordinates. And constructing a spatio-temporal data analysis model based on the hidden danger position, generating a spatio-temporal correlation feature matrix, and performing dynamic risk assessment to obtain hidden danger development trend parameters. And the hidden danger information is mapped to a geographic information system, a three-dimensional visual analysis result is generated, and a graded early warning instruction is transmitted to a drainage pipe network operation and maintenance management system through a communication interface. The operation and maintenance management efficiency of the drainage pipeline can be improved, the hidden danger checking cost is reduced, and the safety and reliability of the drainage system are enhanced.

Disclosed is a reservoir ecological operation method integrating ecological flow process and water temperature process requirements of fish species, including: establishing a habitat model for a target fish species and deducing an ecological flow process of a lift history of the target fish species; calculating a critical spawning water temperature threshold and a critical gonadal development accumulated temperature threshold; establishing a multi-objective reservoir operation model to obtain flow process and water level after optimized reservoir operation; establishing a multi-objective reservoir ecological operation model integrating both the ecological flow process and the water temperature process, and calculating reservoir outflow water temperatures before and after the optimized reservoir operation; and calculating an improvement difference between the critical water temperature threshold and the accumulated temperature threshold of the target fish species on arrival dates after the reservoir ecological operation and those before the optimized reservoir operation.

The invention relates to the field of hydrology and water resource management and karst hydrodynamics, and discloses a karst basin coupling hydrology model prediction method, which comprises the following steps: S1, collecting and standardizing multi-source heterogeneous monitoring data of a karst basin; s2, constructing a hydrological model for dynamic coupling of surface runoff, fracture seepage and a pipeline network; s3, performing global optimization on the multi-dimensional hydrological parameters by adopting a hybrid intelligent optimization method; s4, performing iterative correction on the model based on a multi-level verification system; and S5, outputting a hydrological prediction result and generating disaster early warning information. By adopting a dynamic adjusting technology of karst area underground river and surface water flow network joint modeling, multi-karst water-containing medium characteristic analysis and integration of underground river and surface water system response characteristics, the problems that a traditional method cannot accurately predict underground water flow and flow velocity, and is difficult to deal with complexity of a karst water system, lack of data and the like are solved.

The invention relates to a method, device and product for monitoring river flow in real time, and the method comprises the steps: obtaining tower footing video data of a target river reach which comprises a target section and a pier buoy; inputting the tower footing video data into a water level and flow velocity identification model, and obtaining the water level elevation and the surface flow velocity of at least part of the cross section in the target section through the water level and flow velocity identification model; inputting the water surface elevation into a three-dimensional digital river channel model to obtain a section water passing area of a corresponding cross section; and obtaining the cross section flow of the target cross section according to the cross section water passing area and the surface flow velocity. According to the embodiment of the invention, automatic flow estimation based on the tower footing video can be realized, the manual measurement cost is reduced, the spatial-temporal resolution of flow monitoring is improved, and the method can be used for large-range river monitoring, especially in areas lacking hydrometric stations, and can provide data support for drainage basin water resource management and flood prevention early warning.

The invention relates to the field of water conservancy and hydropower engineering, in particular to a multi-objective optimized hydropower ecological scheduling decision-making system and method, and the system comprises a data collection module, an ecological model module, an intelligent decision-making engine module, a scheduling execution module, an effect evaluation module and a knowledge base module. The data acquisition module collects multi-source data, the ecological model module generates a training data set, the intelligent decision engine carries out ecological process modeling and probability prediction based on a deep learning architecture and spatial-temporal feature extraction, and generates a scheduling decision, the scheduling execution module controls hydropower engineering operation, and the effect evaluation module monitors ecological and economic effects. The knowledge base module stores historical experience and provides optimization suggestions, and the system improves the simulation accuracy of the ecological system, especially when the flow changes suddenly; and through uncertainty quantification, the system reliability and the ecological safety guarantee rate are enhanced, and high efficiency, accuracy and sustainability of ecological scheduling of the hydropower engineering are realized.

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 discloses a bed load particle starting test method and device, and relates to the technical field of hydromechanics, the method comprises the following steps: laying sediment particles in a wave water tank, and adjusting the water flow rate through flow control until the bed surface shear stress is less than the critical shear stress; tracer particles are excited to emit fluorescence through a laser beam, and a camera array is used for synchronously collecting instantaneous flow velocity of a flow field and displacement data of sediment particles; analyzing spatial and temporal distribution characteristics of turbulence energy dissipation and pressure energy dissipation based on a turbulence burst process, and establishing a sediment particle starting mathematical model; experimental data and computational fluid mechanics numerical simulation are combined, and the mathematical model is verified and optimized. According to the technical scheme, the analysis precision and the experimental convergence speed of the sediment starting criterion under the complex hydrodynamic force condition can be remarkably improved, and full-chain efficient cooperation from micromechanical response to macroscopic engineering application is achieved.

The invention discloses an integrated measurement method and system for hydrological elements in a high-salinity and high-sand environment, and relates to the technical field of hydrological monitoring, and the method comprises the steps: correcting the theoretical signal receiving strength of each sonar through an established sound attenuation model, and then calculating an effective signal-to-noise ratio, thereby achieving the measurement of the underwater topography in the high-salinity and high-sand environment. According to the method, a dynamic weight fusion algorithm is adopted to perform data fusion on the water depth data of each sonar to obtain fused water depth data, and an inversion method based on residual error minimization is adopted to perform iterative optimization on model parameters, so that the accuracy is relatively high. When the water velocity and the flow rate are measured in the high-salt and high-sand environment, iterative decoupling is carried out based on the sediment particle motion comprehensive model to obtain the sediment particle velocity, then the water velocity is updated based on the measured apparent velocity and the difference value, and after the updated water velocity is converged, the flow rate is calculated. And the accurate water flow rate can be calculated by combining the flow cross section area. The measurement precision of the unmanned ship on the hydrological elements in the saline-alkali area can be improved.

The invention discloses a valley-type tailing pond flood runoff prediction method based on underlying surface parameter dynamic correction, which comprises the following steps: S1, acquiring valley-type tailing pond multi-source data for preprocessing, and constructing a basic database; s2, underlying surface parameters of the valley-type tailings pond are obtained, and the initial value range of each parameter is determined; s3, establishing an underlying surface parameter dynamic correction model, setting differentiated production and confluence parameters for different areas, and performing dynamic correction; s4, constructing a coupled hydrological-hydrodynamic model, simulating a runoff forming process and time-varying characteristics, outputting predicted values of runoff flow, flood peak time and flood peak water level, and comparing the predicted values with actual measured values of a historical flood area of a satellite remote sensing image for verification and calibration; and S5, inputting the real-time data into the hydrological-hydrodynamic model, and predicting the submerging range, submerging time and submerging degree of the flood runoff based on a geographic space analysis method. According to the method, the timeliness and the accuracy of runoff prediction of small watershed areas without runoff data such as valley type tailings ponds are improved.

The invention discloses a water quality monitoring management system and method based on a municipal sewage pipeline, and relates to the technical field of water quality monitoring, the system comprises a data receiving module, a pipeline evaluation module, a region division module and a treatment estimation module; the method is technically characterized in that before water quality treatment, the current flow change coefficient of the sewage pipeline is compared with the flow change coefficient after last maintenance, so that the potential blockage risk in the pipeline can be effectively predicted, and preventive measures are taken in advance; based on coefficient calculation of sewage reserves, pipeline positions and sewage flow, the influence degree of different factors on environmental interference is comprehensively considered, for example, based on the coefficient of the sewage reserves, the proportion of the sewage borne by each pipeline in the total reserves is clearly reflected, and the potential interference is helped to be judged; the pipeline position coefficient considers the sensitive area distance and the special area crossing condition, and the influence of emission on the surrounding environment is effectively evaluated.

The invention relates to the technical field of water resource scheduling, and provides an urban inland river system intelligent resource scheduling management system and method based on dynamic data collaboration, and the method comprises the following steps: S1, collecting river hydrological data, water quality indexes, urban water demands and water transfer facility state data in real time, and carrying out the time-space alignment and abnormal value filtering; s2, a hydrodynamic force-water quality coupling model of multiple water sources is established, a multi-objective optimization model is established in combination with constraint conditions of ecological base flow and an engineering upper limit, and the multiple water sources include but not limited to river water, underground water, reservoir water, reclaimed water, rainwater and desalinated seawater; and S3, carrying out global dynamic optimization on the water transfer proportion and the water transfer facility parameters by adopting a model algorithm, and minimizing the water transfer cost and the ecological influence. By quantifying the maximum flow upper limit and the ecological base flow threshold value of each water source and optimizing the water transfer proportion and facility parameters, the water resource utilization efficiency is improved, and it is guaranteed that the water transfer cost and the ecological influence are balanced in the urban river water dispatching process.

The invention discloses a reservoir group delivery flow prediction method considering climate change and artificial regulation and storage. The method comprises the following steps: collecting hydro meteorological data of a research basin; determining an upstream inlet reservoir, an interval reservoir and an outlet reservoir according to the reservoir position distribution of the research basin; determining reservoir inlet flow calculation modes of the reservoirs at different positions and reservoir outlet flow models of the reservoirs; and for the outlet reservoir, according to the outlet flow of the upstream reservoir and the hydrometeorological data, the reservoir inlet flow of the reservoir is obtained through a vector autoregression model or a multiple linear regression model, the outlet flow simulation of the reservoir is carried out, and an outlet flow residual prediction model based on deep learning is established, so that the outlet flow prediction of the outlet reservoir is realized. According to the method, the influence of the artificial regulation and storage effect of the reservoir on the downstream reservoir is considered on the basis of the inflow and outflow hydraulic relation between the reservoir groups, and the outflow process of the downstream reservoir is predicted by combining the conceptual rainfall runoff model, the reservoir regulation model and the deep learning model.

The invention discloses a regional water supply emergency scheduling method and system. The method comprises the following steps: fusing GIS and IoT data to construct a dynamic three-dimensional topology model; historical water consumption and meteorological data are decomposed through wavelet multi-scale decomposition, and a prediction model is input to obtain a water consumption prediction value of a scheduling day period; on the basis of reservoir water storage, flow, water quality, rainfall and pollution source data collected in real time, a flow-water quality coupling model is used for predicting the reservoir inflow and water quality of a dispatching day; the method comprises the following steps: establishing a multi-objective function by combining water consumption prediction, storage prediction and current water storage, embedding a water quality constrained multi-objective optimization model, and solving by adopting an NSGA-II algorithm to obtain a Pareto optimal scheduling scheme set; and finally, a scheme is selected to be issued and executed. According to the method, data-physical collaborative intelligent scheduling decision is realized, the water supply demand is met under the condition that the water supply quality is stable, the condition of water shortage of the user terminal is avoided, and the water quality of the user terminal can continuously reach the standard.

The invention discloses a reservoir multi-target optimization scheduling scheme optimization method and system based on multi-party coupling, and belongs to the field of reservoir scheduling. The system comprises the steps that firstly, boundary conditions of a simulation system are determined, and then a reservoir multi-target optimization scheduling model is constructed; and then solving the reservoir multi-objective optimization scheduling model by using a non-dominated sorting genetic algorithm of the reference points, verifying the effectiveness of the solving method by using a standard multi-objective test function, and evaluating and optimizing a reservoir scheduling scheme by using a method of combining a grey target theory, a cumulative foreground theory and a fuzzy mathematical method. According to the system, the multi-objective optimization problem among flood control, power generation and ecology of the reservoir is coordinated, decision support is provided for formulating a reservoir dispatching scheme of multi-objective optimization dispatching of the reservoir, the contradiction among flood control, power generation and downstream river ecology can be effectively coordinated, and the flood control and power generation benefits are increased on the basis of considering the downstream ecological flow demand as much as possible.

The invention discloses a river and lake water level and flow prediction method and system in the field of remote sensing hydrology and hydraulic engineering, and the method comprises the steps: obtaining satellite radar altimeter data, river and lake water system data, rainfall inversion data and surface temperature inversion data based on a target river, a target lake and a watershed range file; taking the cross interval as a virtual hydrometric station observation point, extracting and processing waveform data, calculating water surface elevation information through a wavelet tracking algorithm, constructing an elevation profile group to establish an initial water level time sequence, and generating a river and lake water level inversion data set through fitting and elevation reselection. Meanwhile, NDWI data are obtained from satellite radar altimeter data, water surface width data are extracted, an inversion data set is formed by combining actually-measured river channel section data and a Manning formula inversion river channel flow rate, and finally various kinds of data are input into a pre-constructed intelligent prediction model to obtain future river and lake water level and flow rate change conditions. According to the invention, the precision of river and lake water level and flow prediction in areas lacking data is effectively improved.

The invention relates to the technical field of metering pumps, in particular to an external meshing gear metering pump flow calculation method based on linear regression and curve fitting, which comprises the following steps: generating an integrated data set by monitoring the flow rate, pressure, temperature and viscosity in real time, setting flow rate, pressure and temperature thresholds based on the data set, judging the flow state and recording abnormity, and calculating the flow rate of the external meshing gear metering pump. Flow velocity and viscosity are selected for flow calculation, a corrected flow value is generated, whether flow is normal or not is analyzed and determined, a flow stability evaluation result is generated, and a visual interface is created to display parameters and a calculation result. According to the method, the multiple sensors are installed at the inlet and the outlet of the metering pump, the flow speed, the pressure, the temperature and the viscosity are monitored in real time, an integrated data set provides a precise basis for flow calculation, a threshold value is set, data are monitored, it is ensured that parameters exceeding the normal range can be recorded and analyzed in time, and the process improves the accuracy of flow calculation and improves the working efficiency. And flow evaluation is optimized, and the operation safety and efficiency are improved.

The invention discloses a high-precision flow online monitoring device and method for a siltation section, and belongs to the technical field of hydrological monitoring. The device comprises a sensing terminal, an edge terminal and a mounting mechanism, the sensing terminal is integrated with an upper and lower section layered speed measurement module, an underwater distance measurement module, a first absolute pressure type water level measurement module and an attitude and temperature sensor, and vertical section flow velocity data is obtained by receiving water body reflection signals in a time-sharing manner; and the dynamic deposition section area is corrected in real time in combination with ultrasonic ranging. And the edge terminal compensates water level measurement data by using atmospheric pressure, and calculates the flow through fusion of a telemetering module. The installation mechanism supports fixed or telescopic installation, and the height of the installation mechanism is automatically adjusted according to the distance between a deposition layer and equipment. The method comprises the steps of dynamically correcting the section, calculating the flow through layered integration and compensating errors of multiple sensors, the problems of large area deviation, high operation and maintenance cost and the like caused by sedimentation in a traditional flow measurement technology are solved, and high-precision online monitoring under the high-sand-content complex flow state is achieved.

The invention discloses a river flow missing data reconstruction method, which comprises the following steps of S1, constructing a river network topological structure, and quantifying hydraulic correlation; s2, spatio-temporal feature fusion and multi-source information extraction; s3, performing multi-task cooperative flow reconstruction and confidence coefficient prediction; s4, dynamic weighting and result correction of meteorological factors; and S5, performing anomaly detection and model dynamic updating. According to the method, a river flow missing data reconstruction method is set, the steps are mutually fused and used, and topology-aware space-time diagram convolution is performed: a river network topology structure is encoded into a weighted adjacency matrix for the first time, space correlation features are extracted through a diagram convolution network, and the problem that a traditional method ignores hydraulic connection is solved; a multi-task collaborative learning mechanism: synchronously outputting a flow reconstruction value and confidence, combining topological smooth constraints, and realizing reconstruction reliability quantification while ensuring precision; and meteorological dynamic weighted correction: dynamically adjusting the node weight based on the real-time rainfall intensity, and accurately adapting to the nonlinear response of the water flow in the heavy rainfall period.

The invention provides a freezing circle watershed hydrological prediction method and system based on multi-source data fusion, and relates to the technical field of hydrological monitoring and sediment monitoring, and the method comprises the steps: obtaining a vibration signal of the interaction of a water flow and a riverbed and meteorological data in a region, and carrying out the preprocessing; the preprocessed meteorological data and vibration signals are input into a multi-source data fusion model, sequential feature extraction is conducted on the meteorological data and the vibration signals through a multi-layer perceptron in the multi-source data fusion model, the extracted sequential features are fused through a gating circulation unit, and multi-source data fusion features are obtained; and carrying out empirical mode decomposition and Hilbert spectrum analysis on the preprocessed vibration signal to obtain a plurality of signal characteristics such as instantaneous frequency and instantaneous amplitude of the vibration signal, inputting the signal characteristics and the multi-source data fusion characteristics into a hydrological parameter prediction model, and outputting corresponding predicted values of the water flow velocity, the flow and the sediment transport capacity.

The invention discloses a reservoir water regimen analysis method and system based on artificial intelligence, and the method comprises the steps: collecting original signals from multi-source monitoring indexes, such as water level, flow, rainfall and water quality, carrying out the standardized conversion through employing distributed calculation nodes, and forming a unified multi-source data set; based on this, using a feature extraction network to fuse upstream rainfall and reservoir flow, extracting space-time correlation features, and determining a short-term water level change trend; historical water quality abnormal data are integrated through a sequence prediction network, a time sequence is modeled, and potential pollution risks are judged; when the risk exceeds a threshold value, dynamically adjusting the weight of the prediction model, and generating an optimized water regimen simulation scene; finally, resource scheduling logic is fused, multi-scene risks are evaluated, and an optimization management strategy is output. Through deep fusion of spatial-temporal feature extraction and dynamic prediction, accurate water regimen prediction and flood control water supply decision support are realized, and the water resource management efficiency and the pollution prevention and control capability are improved.

The invention relates to the technical field of intelligent water affair and urban water supply system informatization, in particular to a water supply pipe network hydraulic model calibration and leakage locating method and system, and the method comprises the steps: obtaining the pressure and flow data of a plurality of monitoring points in a pipe network in real time; taking the model as a boundary condition to drive a hydraulic model to carry out real-time simulation, and calculating a theoretical value; calculating a residual error between a theoretical value and an actual value of the monitoring point; judging a triggering model calibration event or a leakage suspicion event based on a residual error abnormal mode; if calibration is triggered, inverting and updating global parameters of the model through an optimization algorithm to realize self-calibration; if leakage is triggered, a suspected area is determined by combining pressure space distribution analysis, and a leakage pipe section is accurately positioned through analog simulation and correlation analysis. Through closed-loop feedback of real-time data and the model, the model can adapt to changes of a pipe network system, dynamic self-calibration of the water supply pipe network hydraulic model is achieved, and the problem of precision attenuation of a traditional static model is solved.

The invention discloses a mountain torrent debris flow early warning method and system based on multi-source data comprehensive analysis. And the monitoring system carries out multi-source monitoring on rainfall, material source and mud level change characteristics of the ditch domain. The method is based on double-threshold rainfall intensity parameters, a quantitative analysis model of channel mud level and flow, and a blocking and collapsing mechanism of object source deformation. The threshold value of each element is determined according to the disaster history, a disaster outbreak probability formula is established, and key factor weights are screened through a random forest algorithm. And data preprocessing is carried out through cross validation and dynamic adjustment. And substituting monitoring data into the analysis model, and comprehensively judging the early warning level of the mountain torrent debris flow. And issuing an early warning signal of a corresponding level to the object in the threatened area through the early warning system. After operation, an incremental random forest method is adopted to update the model through online learning, data redisk is carried out, and model threshold correction and parameter inversion analysis are carried out. According to the pre-warning system, a multi-index combined action comprehensive monitoring and pre-warning evaluation system for quantitative analysis is established.