22 results about "Water balance" patented technology

The application discloses a kind of substation flood intelligent monitoring method and system, it is related to electric power system safety monitoring field.The method is by building the multi-dimensional influence factor library including the bottom elevation of discharge port and pipe network permeation loss, combined with rainfall forecast executes dynamic water balance deduction, deduce according to the dynamic correction soil infiltration capacity of early rainless days, and according to the effective pressure difference of water level and the bottom of discharge port calculates net drainage capacity;Based on the deduction result, water level height field is generated by combining three-dimensional digital model for waterlogging overflow distribution;According to water level height field and equipment elevation, determine the flooding risk, and generate dispatching instruction by taking water center as target and combining prohibited operation area to optimize equipment layout coordinates;Through man-machine interaction correction parameter and trigger GPU acceleration recalculation to realize model online evolution.The application realizes the physical mechanism simulation and space intelligent scheduling under the microscale of substation, changes passive alarm into active risk deduction.

The invention discloses a crop irrigation short-term prediction method and system based on fuzzy control, and relates to the technical field of intelligent agriculture and irrigation control, and the method comprises the steps: collecting weather, multi-depth soil moisture content and field time sequence data of a crop growth stage, and obtaining irrigation feedback; processing the data, and inputting a time sequence prediction model to output water demand prediction values of one to three days and confidence obtained by mapping historical verification errors; fuzzifying the water demand predicted value, the real-time root zone moisture content and the growth stage index, performing weighted deduction and fuzzification based on a fuzzy rule base with a rule dynamic weight, and outputting planned irrigation duration or irrigation amount; and calculating the actual water consumption and updating the dynamic weight of the rule in the post-irrigation evaluation time window according to the water balance and the root zone soil moisture reserve variation. Through confidence risk suppression and post-irrigation closed-loop self-tuning, the stability of short-term prediction driving irrigation decision is improved, the over-irrigation and over-seepage risks are reduced, and the utilization efficiency of irrigation water is improved.

The application discloses a kind of rice irrigation area field dynamic hydrological process simulation method and system, method includes: the spatial correlation information base of rice field and ditch is constructed;With day as step simulation cycle, according to the dynamic adjustment effective field height of rice growth stage, based on field type and water level potential energy difference calculation lateral recharge or discharge;According to the exponential decay correction of plow pan saturated hydraulic conductivity of consecutive flooding days, calculate vertical infiltration amount;Overflow drainage, lateral discharge, vertical infiltration are updated into water balance equation field water depth;According to the topological network of confluence of field spatial position and elevation, consider the peak-shaving superposition of confluence time delay, obtain sub-basin export total runoff.It realizes the whole process fine simulation of field ridge water storage, field-ditch lateral interaction, plow pan nonlinear infiltration and distributed confluence, significantly improves the precision of rice irrigation area hydrological process simulation, provides scientific basis for irrigation water resources management.

This invention belongs to the field of monitoring technology and relates to an automatic monitoring system for rainfall and runoff balance in inland river basins in arid areas. The system includes a data acquisition module, a data synchronization module, a data processing module, and a visualization monitoring module. This invention constructs a comprehensive collaborative monitoring system integrating precipitation, runoff, infiltration, soil water, and evapotranspiration. The data synchronization module accurately matches multi-source monitoring data to the same timeline, achieving consistent spatiotemporal acquisition of the four core elements of water balance. The data processing module automatically calls the synchronized dataset and substitutes it into the water balance equation to calculate the water balance closure status of specific landscape types within the watershed in real time. Finally, the visualization monitoring module dynamically plots time-series curves and balance analysis charts for each element and displays them in real time on multiple terminals, forming a fully automated operation mode from data acquisition, synchronization processing, automatic calculation to visualization monitoring.

This invention discloses a method and system for dynamic assessment of groundwater over-extraction areas based on water volume tracking, belonging to the field of groundwater assessment technology. The method includes: acquiring over-extraction area distribution data within a target area based on groundwater extraction volume, surface water infiltration volume, and meteorological evaporation; constructing a groundwater cycle model to determine zonal control boundaries and optimize control strategies; performing water balance analysis to generate an over-extraction risk evolution map within a time sliding window; and setting up a water flow scheduling network associated with the over-extraction risk evolution map for water resource allocation control. This invention solves the technical problems in existing technologies where the assessment of groundwater over-extraction areas lacks dynamism and the accuracy of water resource allocation is insufficient, leading to a lack of rationality and timeliness in over-extraction control. It achieves dynamic assessment and precise water resource allocation of groundwater over-extraction areas, improving the rationality and timeliness of over-extraction control.

The utility model discloses a soil humidity monitoring devices for monitoring water quantity balance field ecology relates to ecological environment monitoring field, including humidity sensor, display device and detection probe rod, one end of detection probe rod is provided with conical head, and the other end of detection probe rod is provided with data line, and the outside of detection probe rod is equipped with the sleeve pipe, and the outside of sleeve pipe is provided with helical blade, and one end of detection probe rod is connected with the lengthening rod in screw thread, and the other end of lengthening rod is connected with the short tube in screw thread, and the side of short tube is connected with the handle in screw thread. The utility model discloses through setting the limiting rod outside detection probe rod, and then the outside of detection probe rod can be installed sleeve pipe, and the outside of sleeve pipe is provided with helical blade, and uses short tube to fix the sleeve pipe and press tightly, and then the staff can rotate handle and short tube and drive detection probe rod to rotate, and also can drive the sleeve pipe of being equipped with helical blade to rotate, and helical blade makes detection probe rod can be more relaxed and go into the inside of soil.

This invention provides an intelligent inversion method for inflow based on multi-source error constraint fusion, belonging to the field of hydrological forecasting technology. The method includes: constructing physical constraints based on long-sequence water level observation data, measured outflow sequences, and reservoir capacity-water level curves; constructing a composite optimization function including a reservoir capacity time-varying error compensation term, inflow continuity regularization constraints, and a water level observation data assimilation module; deriving the analytical inversion solution of inflow based on the Lagrange multiplier method; and automatically optimizing key parameters such as constraint weights and smoothing coefficients using intelligent optimization methods such as grid search and cooperative search algorithms to perform adaptive high-precision inversion under different hydrological scenarios. This invention, by dynamically adjusting the error weight coefficients, directly generates physically consistent inflow sequences without relying on the prior assumptions of traditional smoothing filters, significantly improving the performance of negative value rate control, water balance, and flood peak characteristic preservation.

This invention discloses a method for predicting extreme rainfall runoff and infiltration by fusing confluence calculation and neural networks. The method includes: constructing a two-way coupling mechanism between surface runoff and soil infiltration processes; dynamically allocating rainfall through runoff over-infiltration determination; feeding the runoff results as source terms to a GPU parallel confluence calculation framework; dynamically feeding soil moisture back to infiltration capacity calculation; and iteratively solving the water balance equation using an implicit or semi-implicit scheme. This method achieves dynamic coupling and collaborative simulation of multiple water cycle processes under a unified framework, solving the problems of separation between surface water dynamics and infiltration models, non-conservation of mass, and insufficient physical consistency under extreme conditions in traditional methods.

This invention discloses a method for calculating water volume in hilly irrigation areas, relating to the field of irrigation technology. It addresses the problems of large calculation errors and high implementation costs caused by complex terrain and dispersed water sources in hilly irrigation areas. The method first collects basic parameters such as terrain, water sources, meteorology, crops, and monitoring data. Then, based on the irrigation area slope, terrace levels, and pond / dam distribution density, a terrain correction coefficient model is constructed to correct the traditional crop water balance equation, resulting in a terrain-adaptive equation. Substituting water source parameters, the total input water volume is calculated. Combined with monitoring data from the beginning and end of ditches, water loss is calculated to obtain the net input water volume. The total return water volume and reuse volume are quantified based on field drainage flow, and then superimposed on the net input water volume to obtain the effective irrigation water volume. Finally, the total irrigation water volume, water source contribution ratio, and return water reuse rate are integrated and output. This invention adapts to hilly terrain characteristics, achieves multi-source coupling and quantitative utilization of return water, and offers accurate calculations with low implementation costs.

The application discloses a kind of groundwater reserves abnormal space-time evolution characteristic analysis method, it is related to groundwater monitoring technical field.The method includes: constructing GRACE data coordination processing flow, and fusing multiple-source hydrological climate factor generates training and target dataset.Constructs random forest and TabPFN parallel model, executes TWSA downscaling and optimization, and outputs two sets of high-resolution dataset.Subtract soil moisture and runoff, based on water balance extraction groundwater reserves anomaly, establishes three-level verification system to verify;The three-level verification system includes model accuracy, spatial consistency and measured water level comparison.The application can improve the inversion accuracy of groundwater reserves.

The application discloses a mountainous small watershed flood forecasting method and system, and relates to the technical field of intelligent water conservancy. The method comprises the following steps: acquiring multi-source hydro-meteorological data of a mountainous small watershed, and obtaining a multi-source heterogeneous data set through preprocessing. A long short-term memory network based on residual correction is constructed, the measured flow at the last moment is superimposed with the current moment prediction increment, and the current predicted flow is obtained. A physical constraint module is constructed based on the watershed water balance differential equation, and a physically consistent loss function is established with the change rate of water storage as an auxiliary variable. The data fitting error and the physical loss function are weighted and fused to train a residual physical constraint neural network model. Real-time monitoring data is used to deduce the watershed water storage dynamics, and flood forecasting is output. The application breaks the prediction inertia barrier of high-frequency data, eliminates the prediction lag, and actively provides a flood control safety margin when a sudden flood peak occurs, and realizes the reconstructability of the physical mechanism.

This invention belongs to the field of mining engineering technology, specifically disclosing a method and storage medium for the evaluation of curtain-type recharge and safety optimization of open-pit mine drainage systems. The method involves identifying groundwater source inflows, establishing a hydrogeological model of open-pit mine drainage systems, and defining boundary conditions and model scope; using monitoring data combined with guide points and regularized inversion to perform heterogeneous inversion of model parameters; extracting water levels at observation points from all simulation examples and interpolating to identify the critical time for curtain-type overflow. t lim The invention employs particle tracking to determine the safe reinjection zone; based on batch numerical simulation results, it utilizes three measures—coordinated optimization of curtain extension, addition of drainage wells, and coordinated pumping-injection control—to determine the optimal parameter combination; and uses the water balance method to assess the change in groundwater evaporation caused by reinjection under the optimal parameter combination, thereby determining the risk of regional salinization. This invention can effectively prevent curtain overflow and increased mine water inflow while ensuring the scale of drainage and reinjection.

This invention discloses a method for determining the geometric and temporal characteristic parameters of a landslide dam breach based on overtopping failure. The method includes collecting basic parameters and determining the reservoir capacity characteristic coefficient; determining the final breach morphology parameters; calculating the water depth in front of the dam corresponding to the peak flow rate based on the actual discharge volume at the end of the breach and its corresponding depth, the discharge volume at the peak flow rate, and the reservoir capacity characteristic coefficient; determining the breach bottom width at the peak flow rate by combining empirical coefficients with the final breach bottom width; calculating the vertical breach depth at the peak flow rate based on the sediment transport balance principle, the final breach bottom width, and the trapezoidal similarity principle; and solving for the duration of the rapid development stage of the breach based on the water balance equation and the peak flow calculation formula. This invention proposes a method for determining breach development parameters, including the duration of the rapid development stage reaching the peak flow rate, the breach morphology at the peak, and the total breach duration of this stage, enabling scientific prediction of the discharge flow process during a landslide dam overtopping failure.

This invention discloses a marginal monitoring method for reservoir water level forecasting. It includes: acquiring real-time monitored rainfall, externally forecasted rainfall, real-time reservoir water level, and real-time outflow from the watershed; generating an initial forecast inflow process based on the fused rainfall sequence and the current internal runoff state parameters of the hydrological forecasting model; inverting the actual inflow using water level fluctuations and outflow based on water balance constraints, and extracting a real-time forecast deviation coefficient in conjunction with the initial forecast results; using this coefficient to dynamically correct the initial forecast inflow process within the forecast period and synchronously update the internal runoff state parameters to obtain the corrected forecast inflow process; and deriving the reservoir dynamic balance to output the forecast water level process. This invention constructs a data-driven online closed-loop feedback mechanism, overcoming the error accumulation problem caused by the static solidification of traditional hydrological forecasting models, and improving the dynamic accuracy and timeliness of water level forecasting.

This invention provides a method for predicting hydrological variables based on a differentiable physical constraint multi-task learning framework. The method includes constructing a multi-task learning model to predict evapotranspiration and soil moisture in the surface, middle, and deep layers. The multi-task learning model is trained using a loss function based on the water balance equation, which includes parameters characterizing the influence of geomorphological factors on some hydrological processes. These parameters are obtained through the output of an LSTM network trained using the same loss function as the multi-task learning model. This invention employs differentiable modeling technology, replacing the structurally uncertain parts of the water balance equation with a neural network, thereby deeply integrating physical processes with the multi-task deep learning model to simultaneously predict multi-layer soil moisture and evapotranspiration. This allows for a more flexible and effective integration of physical knowledge to help the deep learning model improve the performance of hydrological variable prediction.

This invention provides a watershed water cycle simulation method considering lake water balance, comprising five steps: sub-watershed unit division and coding, data processing and model construction, water cycle process simulation, simulation result verification, and result statistical analysis. Based on a DEM (Digital Equation Model), river networks are extracted and distinguished between general river networks and lake river networks. Large water bodies within the same lake are divided into independent lake units, using a unified tributary topological code for encoding. Basic data is processed, and lake units are supplemented with specific information. Differentiated simulation logic is designed for river and lake units, and key hydrological parameters are calculated separately. After validating the model with measured data, it is used for long-term simulations across multiple scenarios. This invention solves the simulation distortion problem caused by generalizing lakes into rivers in existing technologies, improves simulation accuracy, is suitable for small watershed and lake-specific research, and is highly practical.

The application provides an improved method of a two-parameter monthly water balance model considering time-varying evaporation parameters, and aims at the problem that the simulation precision is limited due to the fixed value of the evaporation pan evaporation in the existing two-parameter monthly water balance model, and proposes a method for dynamically calculating time-varying evaporation parameters, constructs a time-varying evaporation parameter calculation model by fusing multi-source evaporation data, uses the time-varying evaporation parameter calculation model as a dynamic input to participate in the calculation of the evaporation and runoff of the two-parameter monthly water balance model, and uses the Bayesian optimization method for parameter calibration. The application can effectively improve the simulation precision of the model on the natural runoff of a basin.

The application discloses a farmland water demand prediction and irrigation control method. The application belongs to the technical field of farmland intelligent irrigation and water resource allocation. The method comprises the following steps: acquiring historical daily meteorological data, current annual daily rainfall, evaporation, soil moisture content and other basic data of a target farmland (irrigation area), and presetting meteorological data and planting structure of a prediction period; determining crop water demand and drainage capacity according to basic parameters, and correcting water supply parameters; constructing a daily water distribution model and completing model parameter calibration; determining initial conditions, combining dynamic changes of crops, and performing daily prediction by using a water balance model; generating an irrigation or drainage control scheme, and outputting an irrigation schedule in the whole growth period and annual total water demand prediction results and generating the irrigation schedule. The technical scheme can realize agricultural water demand prediction and intelligent irrigation control in an irrigation area, lays a foundation for guaranteeing food safety, and provides a decision basis for regional water resource allocation.

This application discloses a method, apparatus, system, and storage medium for determining a water balance scheme. The method includes: abstracting water-using and water-producing units within a target area as nodes based on an overall process system diagram of the target area; determining the unidirectional water flow connections between nodes to construct a water resource flow grid diagram; establishing water balance equations for each node in the water resource flow grid diagram based on the water inflow and outflow relationships of each node to form a set of water balance equations with the line flow rate as the unknown; determining an objective function related to water resource management in the target area and obtaining at least one system constraint condition characterizing external water input, output, or equipment performance; adding the system constraint condition to the set of water balance equations to construct a mathematical model, and solving the mathematical model to obtain a water balance scheme that satisfies the objective function requirement. The scheme provided in this application can accurately and quickly determine the optimal water balance scheme for complex water systems.

The application discloses a kind of reservoir water level prediction edge monitoring methods.It includes: obtaining the real-time monitoring rainfall of drainage basin, external forecast rainfall, real-time reservoir water level and real-time discharge flow;Based on the fusion rainfall sequence and the current internal runoff generation state parameter of hydrological forecast model, generate initial forecast inflow process;According to water balance constraint, use water level amplitude and discharge flow to retrieve actual inflow, and combine initial forecast result to extract real-time forecast deviation coefficient;Use the coefficient to dynamically correct initial forecast inflow process in the foreseeable period and synchronously update internal runoff generation state parameter, obtain corrected forecast inflow process;Derive reservoir dynamic balance, output forecast water level process.The application constructs data-driven online closed-loop feedback mechanism, overcomes the error accumulation problem caused by traditional hydrological forecast model static solidification, improves the dynamic accuracy and timeliness of water level prediction.

This invention discloses a method and system for dynamic inversion of soil moisture based on a general differential equation using hybrid physical sensing, belonging to the field of artificial intelligence hydrological remote sensing technology. The invention first completes multi-source spatiotemporal data preprocessing and hydrological clustering, then generates physically constrained soil hydrological parameters through a static hypernetwork. Dynamic hydrological flux is modeled using a combination of spatiotemporal KAN and FiLM-Mamba networks. A general differential equation is constructed based on water balance to deduce soil moisture. A joint loss model with physical constraints is used to train the model, and finally, explicit hydrological analytical expressions are extracted through KAN symbolic regression to achieve prediction. This invention deeply integrates hydrological physical laws with neural networks, ensuring physical consistency of predictions, eliminating anti-physical anomalies, improving generalization ability in extreme scenarios, and possessing both high computational efficiency and strong interpretability.

This invention discloses a method for constructing a flux equation for changes in groundwater storage on coral reef islands. The method includes the following steps: based on the principle of water balance, the change in groundwater storage is defined as the difference between effective rainfall infiltration and freshwater discharge; feature extraction is performed on the rainfall process on the coral reef to obtain rainfall characteristic parameters; the rainfall characteristic parameters are input into the effective rainfall infiltration equation to obtain the effective rainfall infiltration; the chloride ion concentration gradient of the freshwater-saline transition zone and the freshwater-seawater head difference are input into the freshwater discharge equation to obtain the freshwater discharge; the chloride ion transport control equation and the water balance equation are combined to construct a flux equation for changes in groundwater storage based on the effective rainfall infiltration and the freshwater discharge; using chloride ion concentration profile data and head change data collected from mechanistic experiments, machine learning methods are used to fit the undetermined parameters in the flux equation to obtain a calibrated flux equation.