523 results about "Hydrometry" patented technology

The application discloses a self-adaptive flood forecasting method, a terminal and a storage medium. The method comprises the following steps: generating a high signal-to-noise ratio local hydrological real-time data stream through adaptive sampling and a double-channel differential quantization technology; receiving an upstream input data stream, performing flood evolution calculation based on a viscoelastic dynamics model and fusing a dynamically identified input delay to obtain an original prediction value; for model parameters, a confidence ellipse is constructed based on ridge regression, the residual of real-time data and the prediction value is compared through statistical projection, the measurement noise and the river physical drift are intelligently distinguished, and the parameters are updated smoothly; finally, the original prediction value is corrected by solving a convex optimization problem subject to safety constraints, and a physically credible final prediction value is output. The corresponding terminal comprises corresponding modules for realizing the above steps. The application realizes high-precision, self-adaptive and intrinsically safe flood forecasting.

The present application relates to water level measurement technical field, especially a kind of for hydrological monitoring anti-silt water level measuring device, including, floating mechanism, including floating disc and the load-bearing disc of being fixed on the top surface of floating disc, the middle part of floating disc is opened with through groove along vertical direction, and through groove is provided with buoyancy measurer;Adjusting measurement mechanism, including anti-blocking component, auxiliary measurement component and first motor;And, set on the load-bearing disc above power supply control mechanism;Anti-blocking component includes first connecting ring, second connecting ring and third connecting ring from top to bottom sequentially, and first connecting ring, second connecting ring and third connecting ring are coaxially arranged from outside to inside, first connecting ring and second connecting ring are connected by first radial connecting strip along circumferential direction array arrangement, this water level measuring device can effectively avoid solid silt and affect the data of measurement, and the data accuracy of the device is good, and overall applicability is strong.

The application discloses a lake expansion contribution decomposition calculation method, extracts lake area and reverses water quantity change through remote sensing image and digital elevation model, discriminates precipitation phase state by using a temperature threshold method, estimates ice and snow ablation amount by combining a degree-day factor method, and calculates total glacier runoff; a single-layer evaporation model is constructed, net radiation, sensible heat, latent heat and water body heat storage and other fluxes are calculated based on a lake surface energy balance equation, and daily lake surface evaporation is obtained; quantile mapping and Bayesian fusion are combined to correct lake surface precipitation; non-glacier runoff is calculated according to a lake water balance equation; the stable period and the expansion period are divided according to the lake area trend, the stable period hydrological mean value is taken as a benchmark to calculate the expansion period abnormal value, and finally, the expansion contribution of each hydrological factor is determined according to the proportion of the abnormal value in the total water quantity increment. Through the method, the contribution intensity of different driving factors is clear, scientific support is provided for simulating lake change and predicting evolution, and ecological protection and management strategies are more targeted.

The present application belongs to the technical field of hydrological forecasting, and specifically relates to a multi-source adaptive transfer learning data-scarce watershed daily runoff prediction method, which comprises the following steps: selecting multiple source watersheds with long sequence observation data, independently training a basic prediction model based on a Transformer architecture by using historical hydrological and meteorological data of the source watersheds, and constructing a source watershed prior knowledge base containing diversified production and confluence mechanisms; adopting a parameter freezing and differential fine-tuning strategy, transferring the source watershed basic model to a data-scarce target watershed, fine-tuning the output layer by using limited measured daily runoff samples of the target watershed on the basis of keeping the parameters of the Transformer feature extraction layer fixed, and constructing multiple transfer branch models; establishing a dynamic attention module to realize real-time sensing of the evolution of meteorological environmental elements of the target watershed and self-adaptive calculation of the dynamic confidence weight of each transfer branch model at the current time; and based on the weight, weighting and integrating the preliminary prediction values of each branch model to obtain the final daily runoff prediction result of the target watershed.

The application discloses an urban waterlogging intelligent prediction method and system based on a physically constrained enhanced video generative adversarial network. The method comprises the following steps: constructing a meteorological geographic data set containing static terrain data and dynamic rainfall data; generating waterlogging simulation data based on a physical numerical simulation model, and matching the waterlogging simulation data with the meteorological geographic data set into a comprehensive data set; constructing a physically constrained enhanced video generative adversarial network model, wherein the model comprises a generator and a discriminator; constructing a multi-angle loss function system that fuses an adversarial loss, a time sequence consistency loss, a data supervision loss and a water quantity balance constraint physical loss; and alternately optimizing the generator and the discriminator until the model converges, and outputting a spatiotemporally continuous waterlogging inundation depth prediction result. The application explicitly embeds a physical hydrological law into a deep learning network, and eliminates the physical logic contradiction of a pure data-driven model in continuous spatiotemporal sequence prediction through water quantity balance constraint.

This invention discloses a computer vision-based method for anomaly identification in smart water conservancy dams, belonging to the field of intelligent visual monitoring technology for water conservancy dams. The method includes acquiring a time-series image set covering the entire dam area, generating a standard observation dataset through multi-scale spatial registration and illumination normalization, constructing a digital twin of the dam containing topographic relationships, recording surface material properties, and elevation distribution information, mapping its surface grid data to the standard observation dataset, and generating a texture difference map with geographic coordinates. A rule library storing the evolution of dam deformation thresholds and seepage characteristic patterns under different hydrological conditions is loaded and stored. The texture difference map is input into the library for comparison and analysis, outputting anomaly candidate areas, aggregating the spatial distribution density and duration parameters of the anomaly areas, and generating a dam safety status assessment report. This method can mitigate the interference of illumination and viewing angle, achieving accurate anomaly location and systematic determination of the overall safety status.

This invention belongs to the field of coastal engineering and ecological restoration technology. It discloses a method for optimizing the design of beach ecological restoration trenches based on hydrodynamic simulation. The method includes: collecting beach hydrological datasets, topographic datasets, and ecological datasets, and preprocessing them. The hydrological dataset includes wave data, tidal current data, storm surge data, and sediment transport data. The topographic dataset includes shoreline slope, beach elevation, underwater topography data, and sediment source storage data. The ecological dataset includes tidal flat vegetation distribution, benthic organism density, ecologically sensitive area range, and water environment quality data. This invention, through the coupled processing of hydrological, topographic, and ecological multi-source datasets, obtains hydraulic reference values ​​and topographic adaptation values ​​for the trench, directly reflecting the regional dynamic conditions and the degree of topographic adaptation, significantly reducing the workload of manual calculation and trial and error.

The application discloses a flood forecasting method fusing a physical mechanism and a conditional denoising diffusion model, and comprises the following steps: obtaining multi-source hydrological data of a basin to be forecasted; driving a preset hydrological model to execute multi-stage sequential parameter optimization to obtain a basic forecasting sequence and an initial residual sequence; extracting multi-dimensional conditional variables based on the hydrological data and the initial residual sequence, and using a multi-dimensional evaluation criterion to perform dimension reduction screening to obtain a fusion conditional vector; inputting the fusion conditional vector as a guide condition into a pre-trained conditional denoising diffusion model, performing iterative denoising sampling through a multi-scale denoising operator to generate a forecasting residual probability sequence set; and coupling the basic forecasting sequence and the forecasting residual probability sequence set to obtain deterministic forecasting and probabilistic forecasting results of the basin to be forecasted. The application realizes a leap from single-point forecasting to probabilistic distribution deduction, can accurately obtain a flood deviation boundary under a changing hydrological environmental condition, and enhances the reliability of disaster prevention and mitigation decision-making.

This application provides a method for optimizing the tiered drought limit water level during the flood season, taking into account both supply and demand. It obtains the downstream control station of the target river channel, tiered water supply demand, initial tiered outflow parameters, and target tiered outflow parameters. Based on a preset low-water inflow sample and the target tiered outflow parameters, it calculates the initial tiered drought limit water level of the upstream reservoir during the flood season. It determines the drought resistance capacity parameters on the supply side based on historical reservoir operation data, and determines the target tiered drought resistance flow gap parameters on the demand side based on the actual outflow and the target tiered outflow parameters. Simultaneously, it determines the risk value of flood control loss conditions based on the reservoir's flood season operating water level and the river's excess discharge. A scheduling simulation model is constructed to perform multi-objective optimization of the initial tiered drought limit water level during the flood season, resulting in a target tiered drought limit water level that balances both supply and demand. This method is adaptable to complex hydrological scenarios such as flood season reversals and rapid shifts between flood and drought, and can collaboratively ensure downstream tiered water demand, reservoir drought resistance capacity, and flood control safety.

This invention discloses a method for optimizing and numerically simulating hydrological parameters of coalfield aquifers, relating to the field of coalfield hydrogeological exploration and groundwater numerical simulation technology. The method includes the following steps: Step S1: Obtain drilling monitoring data and historical hydrogeological data from multiple boreholes within the target coalfield area, construct an initial aquifer structure model, and generate an initial hydrological parameter field based on the initial aquifer structure model; Step S2: Based on the initial hydrological parameter field, construct an initial groundwater numerical simulation model, and calibrate and verify the initial groundwater numerical simulation model using preset observation well water level data to obtain a benchmark numerical simulation model; Through distributed fiber optic temperature and strain dual-field monitoring, and coupled Bayesian inversion of heat, solid, and fluid, this method overcomes the limitations of traditional pumping / pressure water tests, which suffer from single-point discretization, time lag, and the inability to obtain only static averages.

The application relates to a river flood discharge capacity dynamic evaluation and hydrological early warning method. The method comprises the following steps: based on a parameter sensitive observation subset, sequentially updating an initial hidden variable in a low-dimensional hidden space through an unscented particle filter algorithm to obtain an optimized hidden variable, and inputting the optimized hidden variable into a river dynamic resistance field generator based on an initial dynamic roughness field to perform decoding processing to obtain a dynamic roughness field of a current period; based on the dynamic roughness field and a state sensitive observation subset, performing analysis and update processing on a state variable field of a hydrodynamic model through a deterministic ensemble Kalman filter algorithm to obtain an assimilated water level field and a flow velocity field; according to the water level field, the flow velocity field and the dynamic roughness field, the maximum flood discharge capacity of a section is calculated in combination with dike top elevation data, and a flood discharge capacity dynamic index is calculated based on the maximum flood discharge capacity of the section and a current actual flow. The method can realize real-time dynamic quantification of river flood discharge capacity and predictive evaluation of flood risk.

The application discloses a kind of lake total phosphorus concentration spatio-temporal evolution and driving factor identification method, and the application relates to water environment monitoring technical field, constructs water body, sediment and in and out lake river multi-medium linkage analysis system, carries out multi-scale spatio-temporal evolution analysis and multi-path mechanism quantization, realizes the accurate identification of scene main control driving factor, and the standardization analysis process is formed by verification solidification, the application has the advantages that by constructing water body, sediment and in and out lake river multi-medium linkage full-dimensional analysis system, simultaneously integrate multi-source spatio-temporal data covering full lake area function partition, complete hydrological year cycle, carry out the coupling analysis of multiple time scales and multiple spatial dimensions, break through the industry common defects that the evolution law of total phosphorus concentration is not comprehensive, data is insufficiently representative, and the whole chain variation characteristics cannot be clarified, caused by the fact that the prior art only focuses on single medium of water body, uses scattered point position low-frequency monitoring data.

This invention discloses a short-term intelligent numerical weather prediction method and system based on environmental perception, belonging to the field of meteorological management technology. The system includes a data acquisition and fusion module, an intelligent zoning and feature mining module, a short-term forecasting and risk assessment module, and a visualization and focusing module. The data acquisition and fusion module collects and fuses environmental data from meteorological radar and weather stations, as well as GIS maps. The intelligent zoning and feature mining module divides the data into grids based on the GIS map, analyzes the rainfall synergy between grids based on historical environmental data to construct analysis groups, and selects and constructs an impact set for each analysis group. The short-term forecasting and risk assessment module fits near-term precipitation intensity relationships and short-term precipitation intensity relationships based on the impact sets of each analysis group. The visualization and focusing module calculates and outputs the near-term and short-term predicted precipitation intensities for each analysis group based on the relationships, identifies key areas of concern using topographic and hydrological models, and enhances visualization.

The application relates to the technical field of hydrological monitoring and discloses an intelligent hydrological monitoring system based on Internet of Things, which comprises an upper computer, a flow measurement control platform, a network communication module, a PLC controller, a mechanical system and a flow measurement system. The flow measurement control platform respectively instructs the PLC controller and the flow measurement system through the network communication module. The flow measurement system is used for acquiring hydrological monitoring data and uploading the flow measurement control platform. The flow measurement system comprises an ADCP flow measurement, a radar flow measurement, a time-difference method ultrasonic flow measurement, a propeller current meter and a field water level meter. The flow measurement system selects a flow measurement method according to needs at different measuring point positions in the same section to jointly measure the flow. The flow measurement system is equipped with multiple flow measurement methods and is uniformly controlled and dispatched by the flow measurement control platform, so that the monitoring system has good expandability. On this basis, a corresponding flow measurement scheme can be automatically generated according to different water level levels, the advantages and disadvantages of various flow measurement methods are complementary, and the detection precision is further improved.

The application discloses a kind of based on AI hydrology prediction watershed cascade reservoir joint scheduling optimization system, belong to watershed cascade reservoir joint scheduling technical field.The system is connected by data fusion, hydrology prediction, collaborative scheduling and scheduling correction module in turn communication connection composition.Data fusion module processes multiple-source hydrology meteorological data to generate standardization dataset;Hydrology prediction module constructs the AI dynamic prediction model of fusion of LSTM and GRU, combines dynamic error correction and extreme scene reinforcement learning, outputs future 96 hours time-to-time hydrology prediction result and confidence;Collaborative scheduling module establishes dynamic scheduling rule base and upstream and downstream water power time difference grading compensation mechanism, and distributes scheduling authority according to flood control> ecology> power generation priority;Scheduling correction module generates regular and extreme scene differentiation instruction, and is based on execution deviation grading dynamic correction.The application solves the problems of low precision of traditional hydrology prediction, cascade reservoir collaborative response lag, realizes multi-objective overall optimal scheduling, and significantly improves water resource utilization efficiency and watershed flood control safety.

The application discloses a kind of spatiotemporal continuous land surface water body generation methods based on multi-source geospatial data, belong to remote sensing information processing and geographic information technical field.The method includes: obtaining the multi-source geospatial data related to water body distribution, including climate, hydrology and topographic data;It is spatiotemporal resolution and coordinate system, constructs pixel level characteristic variable;Based on existing water body data constructs training sample library;Using random forest or gradient boosting tree and other machine learning methods trains water body distribution reconstruction model;The model is applied to historical or missing time node, and water body distribution prediction result is generated every time phase;Finally form long time sequence, spatially continuous, resolution consistent land surface water body grid data set.The application can effectively fill in remote sensing data missing, improve the spatiotemporal continuity and consistency of water body data, suitable for wide range, long time sequence water body dynamic monitoring and analysis.

This invention discloses a method and system for dynamically determining the impact of construction projects on hydrological monitoring, relating to the field of hydrological monitoring technology. The specific steps are as follows: constructing an evaluation index system that couples static and dynamic indicators; assigning static weights to static indicators using the analytic hierarchy process (AHP) and dynamic weights to dynamic indicators using the entropy weight method; calculating the total weight based on the static and dynamic weights; constructing and training a machine learning model; inputting the static, dynamic, and total weight data into the trained machine learning model; and outputting the impact level; automatically matching response measures based on the impact level and trend prediction results. This invention proposes a dynamic weight correction algorithm based on project stage and hydrological conditions, applying machine learning algorithms to determine the impact level of construction projects on hydrological monitoring, achieving a closed-loop linkage from level determination to measure matching, and solving the problems of singular and disconnected evaluation in existing technologies.

This invention relates to the field of urban flooding prediction technology, and discloses a method, system, and medium for constructing a training dataset for urban flooding prediction. The method includes: acquiring historical rainfall data, performing event-based segmentation, and extracting multi-dimensional features of each rainfall event; calculating the distance matrix between any two rainfall events in each dimension of features; weighting and fusion of the distance matrices to obtain the comprehensive distance between any two rainfall events; then selecting typical rainfall events through cluster analysis to generate a representative rainfall event set; simulating each rainfall event in the representative rainfall event set using a hydrological and hydrodynamic mechanism model to generate flooding depth distribution labels; and pairing the multi-dimensional features of each rainfall event with the flooding depth distribution labels to generate a training dataset. This invention improves the accuracy of urban flooding prediction by constructing a training dataset closely related to physical processes and capable of enhancing the predictive performance of machine learning models.

The application discloses a kind of arid region reservoir water and sand prediction and coordinated regulation potential evaluation method and system, belong to water resources, silt treatment and reservoir optimization scheduling technical field.The technical problems of traditional model prediction accuracy insufficient caused by arid region hydrology data scarcity, water and sand prediction process is fragmented and reservoir desilting and silt reduction and water storage benefit difficult to be synergistically optimized, its gist is that: long short-term memory network is used to predict future runoff sequence by fusing attention mechanism, and the sediment concentration is calculated by combining the water and sand relationship formula;By setting the critical sediment concentration threshold, the high sediment concentration period is identified and the available water for dredging is calculated;At the same time, the flood resource potential is evaluated by combining reservoir capacity and water demand constraints;Finally, the coordinated scheduling scheme is generated by comparing the two potentials.The application realizes the integrated accurate prediction of water and sand process in arid region and the simultaneous quantitative evaluation of regulation potential, and provides intelligent decision support for multi-objective optimization scheduling of reservoir.

The application provides a hydropower station reservoir flow prediction and scheduling optimization method, which separates different scale hydrological characteristics through multi-time scale processing of multi-source data, extracts reservoir inflow characteristics by cooperating with gray correlation analysis, avoids time structure aliasing, and makes the hydrological law clearer; the prediction interval is corrected by the deviation of the observed value of the reservoir inflow, the prediction result is dynamically adjusted with the actual hydrology, the error of the initial prediction is made up, the prediction accuracy is twice improved, and the problem of inaccurate reservoir inflow prediction is solved. In addition, the corrected prediction interval is used as an uncertainty set, a double-layer robust optimization model of safety margin constraint is combined with a confidence parameter adjustment, scheduling safety and benefit are balanced; different risk level strategies are generated by weighting and combining scheduling decision variables through a risk preference parameter, and the scientificity and stability of reservoir scheduling are improved.

The present application relates to the technical field of hydrological resources, and specifically includes an intelligent short-impending fusion flood forecasting method based on a rain measurement radar, which comprises: receiving rain measurement detection information, performing prediction effect feedback in combination with a short-impending flood monitoring task at an edge adaptive correction node, configuring flood identification features based on a hydrological fusion prediction model, determining a flood peak time combination and a flooding risk, and generating a graded short-impending flood warning signal. The technical problem of the initial configuration of the hydrological fusion prediction model not matching the rain measurement data characteristics and the prediction target, the prediction deviation being unable to be corrected in time, and the deviation accumulation affecting the prediction reliability is solved by using a general hydrological model deployment mode, the technical effect of adaptively deploying the hydrological fusion prediction model by binding the core detection information of the rain measurement radar and the flood prediction accuracy requirement, the cooperative mechanism of the edge adaptive correction node and the RS485 bus interface, effectively avoiding deviation accumulation, and improving the stability of runoff simulation is achieved.

This invention provides a method and system for weighted dynamic adjustment of unmanned surface vessel (USV) path segments, relating to the field of intelligent control technology. The method includes: Step 1, acquiring the USV's navigation environment information and mission requirements; determining a segmentation strategy based on obstacle distribution density and water complexity in the navigation environment information; and dynamically dividing the global navigation area into multiple navigation segments of varying lengths according to the segmentation strategy; Step 2, for each segment, real-time acquisition of water flow velocity data sensed by three fixed-position hydrological monitoring buoys within the segment; and constructing a water flow velocity field grid data for the segment based on the spatial coordinates of the three hydrological monitoring buoys and the water flow velocity data. This invention improves the navigation safety, efficiency, and autonomous navigation capabilities of USVs in complex waters.

This invention discloses a method for assessing farmland inundation. The method includes: evaluating multiple precipitation prediction models based on precipitation indices, and selecting the optimal model from among them; correcting the precipitation sequence of the optimal model, extracting extreme precipitation sequences, analyzing precipitation data based on the extreme precipitation sequences, and inputting the precipitation data into a preset hydrological-hydrodynamic coupling model; overlaying the inundation results output by the hydrological-hydrodynamic coupling model with pre-stored farmland spatial data to identify farmland inundation units and clarify the farmland inundation situation; classifying the farmland inundation situation based on a preset hierarchical index system to generate farmland inundation risk zones; and forming differentiated management strategies based on different farmland inundation risk zones. The technical solution of this application can conduct accurate quantitative assessments of watershed flood inundation and farmland exposure under multiple scenarios, providing scientific support for watershed flood control and disaster reduction and agricultural adaptability planning.

This invention provides a method and system for calculating pile foundation length, relating to the field of pile foundation design technology. Specific steps include: calculating environmental characteristic parameters based on the maximum flow velocity and highest water level under a preset return period using the moment estimation method to accurately quantify extreme hydrological evolution trends; constructing a formula for calculating the maximum scour depth by combining water-blocking characteristic parameters to obtain the scour reference surface, thereby overcoming the defects of physical boundary failure; reconstructing the effective stress field of the underlying soil layer based on the scour reference surface and stress release logic to realistically restore the physical unloading effect; calculating the side friction resistance at each depth using nonlinear strength decay logic to accurately assess the degradation loss of pile-soil bearing capacity; and outputting the optimal pile foundation length using a dynamic step-size iterative algorithm under a preset load safety threshold, based on the preset ultimate end resistance of the pile end bearing layer, ensuring the bottom line of flood control mechanical safety while avoiding over-design of the structure.

An urban-flooding-oriented optimization method for real-time control rules for a river network. The method comprises: S1, collecting data of a river network; S2, constructing an urban hydrological-hydrodynamic coupling model; S3, on the basis of a rainstorm intensity formula, simulating and calculating flooding processes in different design rainstorm scenarios, and comprehensively identifying drainage outlets within a study area that are susceptible to backwater effects from water levels in the river network; S4, using a clustering algorithm to perform spatial clustering on the backwater-affected drainage outlets, so as to define regulation and control zones, further performing feature clustering on the backwater-affected drainage outlets on the basis of spatial clustering, and outputting a cluster center with the greatest influence and using same as a river network regulation and control point; S5, using the river network regulation and control point as a regulation and control reference object, and formulating optimized gate and pump control rules on the basis of warning water level data and ecological water level data of river channels; and S6, inputting the optimized gate and pump control rules into the urban hydrological-hydrodynamic coupling model, using a rainstorm event to evaluate the performance of the method, and quantitatively analyzing the response of flooding to the gate and pump control rules. The method enables the regulation and control of water levels in a river network during a rainstorm, thereby mitigating the risk of flooding.

This invention discloses an intelligent scheduling method for reservoir water supply safety based on deep reinforcement learning, comprising: 1. Constructing a reservoir scheduling environment model and acquiring historical hydrological data and engineering constraints of the reservoir; 2. Establishing an agent based on a deep Q-network, defining a state space, action space, and a composite reward function; wherein the composite reward function includes a water supply guarantee reward, a water level control reward, and a water replenishment reward; 3. Training the agent using an experience replay mechanism and a target network separation method, and updating the network parameters by minimizing the loss function; 4. Outputting the optimal scheduling scheme through the trained agent. This invention can significantly improve water supply reliability while ensuring reservoir water level safety, thereby providing a reliable guarantee for reservoir water supply safety.

The present application belongs to the technical field of hydrological prediction and water resources management, and specifically relates to a prediction method and system based on multi-scale decomposition of runoff sequence and dynamic weight reconstruction. The method first constructs a physical hydrological model based on meteorological driving data and generates a runoff simulation sequence; the simulation sequence is subjected to multi-scale decomposition by using variational mode decomposition, the decomposition parameters are adaptively determined by particle swarm optimization, and a plurality of mode components are obtained; a long short-term memory network is constructed to establish a mapping relationship between the contribution weights of the mode components, and dynamic weights varying with time and normalized are output; the mode components are weighted and reconstructed according to the weights, so as to realize deviation correction of the simulated runoff of the physical hydrological model on different time scale structures. In the prediction stage, the same decomposition is performed on the future runoff simulation sequence, and the trained model is directly used to output the runoff prediction result. The present application converts the runoff prediction problem into a dynamic weight distribution problem of multi-scale structure components, improves the prediction precision and migration ability while maintaining physical interpretability, and is suitable for scenarios such as basin runoff prediction, flood simulation, water resources scheduling and the like.

This invention relates to the fields of reservoir scheduling, flood control and disaster reduction, and hydrological forecasting, and particularly to a method for calculating reservoir rainfall carrying capacity based on a dynamic runoff coefficient. The method includes the following steps: collecting data including the target reservoir's catchment area, characteristic water level, and water level-capacity relationship curves; establishing a dynamic equation for rainfall-runoff coefficient based on reservoir storage using the initial water level, characteristic water level, and storage capacity relationship curves; collecting and analyzing historical flood data for the target reservoir to generate parameter statistics tables for each flood event; establishing a dynamic equation for rainfall-runoff coefficient based on underlying surface conditions based on the parameter statistics tables for each flood event; and solving the dynamic equations based on reservoir storage and underlying surface conditions simultaneously. This invention relies on real-time water level and real-time rainfall to dynamically calculate reservoir rainfall carrying capacity, thus improving the accuracy of reservoir rainfall carrying capacity calculation.

The application discloses a rainfall monitoring data anomaly identification and data fusion model optimization and parameter calibration method, belongs to the field of hydrological monitoring, collects multi-source heterogeneous data and standardizes processing to generate a unified input sequence; through double verification screening dynamic updating reference station; through four-layer progressive logic identification and marking abnormal data, forming quality control data after classification rejection or correction; taking radar data as the initial field and the quality control data as the calibration point, adaptively adjusting parameters to carry out multi-source data fusion; two types of models are included in the same framework, a comprehensive objective function is constructed to simultaneously optimize the partition parameters, and closed-loop optimization is completed; after independent sample verification, it is deployed, periodically iteratively updated. The application solves the problems of traditional methods, such as incomplete anomaly identification, contaminated fusion results and poor regional adaptability, improves the rainfall data quality and fusion accuracy, provides stable and reliable data support for mountain flood disaster monitoring and early warning, and has good practical value and application prospect.