183 results about "Water balance" patented technology

The invention relates to the technical field of data processing, and discloses a water quality and water quantity accurate simulation method and system based on an SWAT model and a storage medium. The method comprises the following steps: analyzing rainfall and evapotranspiration data through a two-parameter trend test, and generating standard hydrological data by adopting a triple configuration algorithm; dividing sub-basins by combining topographic information, and calculating a hydrological response unit; performing water balance operation and water conservation evaluation; pollutant diffusion is analyzed, and a diffusion equation is solved; running state monitoring is implemented, and an early warning system is designed; and comparing and correcting measured data, and constructing a hierarchical monitoring mechanism. According to the invention, the whole-process precise management from hydrological data acquisition, water quality evaluation to early warning monitoring is realized, and the accuracy and reliability of water quality and water quantity monitoring are remarkably improved.

The invention provides an AI flood real-time simulation method for urban hydrological multi-physics field decoupling supervision, and the method comprises the steps: constructing an intelligent physical prediction model of urban flood, carrying out the layered decoupling of an urban hydrological hydrodynamic system, separating a one-dimensional drainage pipe network submodule from a two-dimensional surface flood module, designing a multi-scale physical constraint loss function for different links, and carrying out the real-time simulation of the AI flood. Microcosmic, mesoscopic and macroscopic multi-scale physical constraint fusion is realized, and residual constraints of the Saint-Venant equation are embedded in a neural network agent of a drainage system in a microcosmic level; in the mid-level, embedding the residual constraint of the shallow water equation in the surface flood routing system; in the macroscopic level, the water balance constraint of the whole system is realized; urban hydrology and hydrodynamic force are decoupled, and multi-scale physical field supervision constraint is achieved.

The invention discloses a channel flow evolution method and system based on a physical mechanism collaborative deep learning algorithm, and relates to the technical field of channel flow evolution, and the method comprises the steps: carrying out the data collection and preprocessing; carrying out feature extraction and constructing an evolution model; deducing a hydrological physical model through simultaneous operation of a Muskinggen tank storage curve equation and a river reach water balance equation; the section flow of each river reach is predicted through the hydrological physical model and the evolution model; and through the prediction result of the index evaluation model in each river reach, carrying out collaborative optimization intelligent evolution of each river reach through the evaluation result. The method guides the establishment of the constrained deep learning neural network model through a hydrological physical mechanism, organically fuses the hydrological physical model and the deep learning algorithm model, comprehensively applies the physical model and the mathematical model to better solve the hydrological problem of a complex system, gives full play to the respective advantages, and improves the reliability of the system. The method is suitable for flow simulation prediction of various river channels, operation is easy and convenient, training is fast, and results are reliable.

The invention discloses a method for predicting the long-term delivery probability of a dual-drive reservoir, and belongs to the technical field of intelligent scheduling of water resources. Comprising the steps of constructing and training a causal consistency probability inflow prediction model, eradicating quantile crossover mathematically by adopting a double-anchor symmetric chain output layer, and performing training by utilizing a gradient heterogeneous modulation normal form based on internal attention deconstruction to efficiently learn a hydrological process; a mechanism constraint outflow prediction model is constructed and applied, physical mechanisms such as a water balance equation and an ex-warehouse boundary are deeply transformed and embedded into a loss function of a gradient boosting decision tree, and the model is forced to self-learn and comply with physical rules in the training process. According to the invention, a reservoir outbound prediction scheme with probability self-consistency and physical reliability can be generated.

The invention discloses an irrigation area scale irrigation water consumption monitoring method based on physical constraint machine learning, and is applied to the technical field of irrigation water consumption monitoring. Comprising the following steps: constructing a space-time continuous sequence of irrigation response characteristics of an irrigation area by using a multi-source data integration and assimilation technology; the irrigation water amount balance equation and the earth surface energy conservation law are used as regularization terms of physical constraints to be embedded into the training process of the long and short term memory deep learning model, so that an irrigation water amount prediction model is established; and dynamically estimating the irrigation water consumption of the irrigation area based on the irrigation water consumption prediction model obtained by training. According to the method, the water balance equation and the energy conservation law are embedded into the machine learning model, so that the model prediction result follows the basic physical law, and water consumption estimation violating the physical common sense is avoided. The introduction of the physical constraint improves the robustness and generalization ability of the model under an unconventional climate condition or a limited training data condition, and enhances the credibility and interpretability of a result.

The invention provides a sludge amount measurement method and system based on a reservoir model, and relates to the technical field of hydrographic survey, and the method comprises the steps: obtaining the operation data, basin hydrological data and reservoir topographic data of a target reservoir; based on the operation data, the watershed hydrological data and the reservoir topographic data, establishing a water balance equation and a sediment balance equation; establishing a reservoir model through a water balance equation and a sediment balance equation, and calculating a first predicted distribution quantity of sediment deposition in combination with a dispatching record and a sediment transportation quantity of a target reservoir; calculating a second predicted distribution quantity of the target reservoir at the prediction moment based on the historical sludge distribution quantity of the target reservoir; and if it is determined that the first predicted distribution quantity and the second predicted distribution quantity are within the preset range, determining that the sludge quantity of the target reservoir at the prediction moment is the first predicted distribution quantity. By introducing a coupling mechanism among multiple factors related to reservoir sludge amount prediction, the accuracy of reservoir sludge amount prediction is improved.

The invention discloses a staged design flood estimation method under the influence of climate change and reservoir regulation and storage. The method comprises the following steps: acquiring meteorological and hydrological data, human water consumption data and global climate mode output data of a reservoir; a reduced natural reservoir runoff series is obtained based on a water balance method, a GR4J-9 hydrological model is calibrated to achieve natural reservoir runoff simulation, and a long-short-term memory model is constructed to simulate the influence of human activities such as reservoir regulation and storage on runoff; based on a global climate mode set and a quantile deviation correction method, driving a GR4J-9 hydrological model and a long-short term memory model to obtain a reservoir runoff series under climate change and performing flood season staging; establishing a staging design flood calculation model of a future scene based on a Copula function, and deducing the most probable staging design flood; and constructing an emergence constraint model based on the dew point temperature so as to obtain a corrected stage design flood result. According to the method, the comprehensive influence of climate change and reservoir regulation and storage is considered, and a reference basis is provided for deducing stage design flood.

The invention relates to the technical field of ecology and hydrology, in particular to a terrestrial ecosystem water carbon flux estimation method considering water balance constraint, which comprises the following steps: estimating leaf scale photosynthetic rate and stomatal conductance by utilizing a photosynthesis model; the leaf scale photosynthetic rate and the stomatal conductance are upscaled into canopy vegetation total primary productivity and canopy conductance to construct an evapotranspiration model to estimate evapotranspiration data; constructing a water balance model according to the evapotranspiration data so as to estimate the available water amount of the soil; constructing a soil moisture stress function according to the available water amount of the soil so as to constrain the estimation process of the photosynthesis model and the evapotranspiration model; and acquiring historical water-carbon flux data and historical remote sensing observation data of a long sequence daily scale, and calibrating the model to obtain a water-balance-constraint-considered water-carbon flux estimation model of the terrestrial ecosystem capable of estimating the water-carbon flux data. Therefore, the problems that an existing water carbon flux prediction frame is difficult to adapt to climate changes, low in prediction accuracy and the like are solved.

The invention discloses a river spill flood routing rapid simulation method based on river adaptive segmentation and strong water balance constraint, and the method comprises the steps: collecting and processing basic data of a drainage basin, building a rapid calculation model of river spill flood inundation, and carrying out the rapid prediction and risk analysis of the river spill flood. Rapid and fine simulation of watershed river overflow flood is realized, four-pre requirements of watershed flood control fine simulation, real-time early warning and forecasting, rapid scene deduction and the like are met, and the method can also serve watershed flood control planning and design.

An early warning method for debris flow disaster in a small watershed, and a disaster reduction method for debris flows in a small watershed is disclosed. An early warning method for debris flow disaster in a small watershed is provided, in which parameters of a hydrological model are calibrated by rainfall, evaporation and runoff data of the watershed based on water balance of the watershed, and an early monitoring and warning scheme for debris flows, which takes water storage in the watershed as a core monitoring and evaluation indicator, is established. The GR4J hydrological model is improved by taking into account a watershed area, specific yield of an aquifer and several geological parameters, as well as an evapotranspiration effect of vegetation. A disaster reduction method by means of regulation of a drainage channel can provide a basis for mathematical model simulation studies of disaster reduction in small watersheds.

The invention provides a mid-term runoff prediction method and a mid-term runoff prediction system considering a time interval distribution rule, aiming at a research basin, collecting and arranging long-sequence hydrological runoff data of a main control section, including hydrological station flow data of day, ten days, month, season and year scales and reservoir station reservoir flow data, and based on a water balance principle, carrying out runoff reduction calculation, obtaining a natural runoff sequence of any node of the drainage basin; carrying out incoming water frequency analysis, and determining incoming water frequencies of any node in different incoming water years, seasons and monthly; calculating the distribution proportion of the upper, middle and last ten-day scales of the runoff of any node in each month, and defining the runoff time history distribution coefficient of the month; on the basis of monthly-scale natural flow of a certain section, historical years with similar characteristics are searched, time history distribution coefficients of historical runoff sequences are matched, and mid-term natural flow prediction is obtained. Historical runoff sequence time history distribution is matched, a monthly runoff ten-day scale distribution scheme is obtained, and important technical support is provided for medium and long term forecasting of hydro meteorology.

The invention relates to an LSTM (Long Short Term Memory) model-based reservoir rain receiving capacity analysis method, which comprises the following steps of: acquiring rainfall historical data and reservoir historical monitoring data, constructing an input characteristic matrix of an LSTM model, matching a data scale under a reservoir rain receiving situation, adjusting parameter quantity of the LSTM model, optimizing an adjustment strategy and an Early Stopping early stop strategy based on an AdamW optimizer, a CosineAnnealingLR learning rate, and analyzing the rain receiving capacity of a reservoir. Training an LSTM (Long Short Term Memory) model by using the input feature matrix to obtain optimal parameters of the model, finally, setting future rainfall, a rainfall pattern, a discharged flow and a target water level based on the trained LSTM model to carry out water balance calculation, trying to calculate the receivable rainfall by using a bisection method, and completing the rain receiving capability analysis of the reservoir. Factors such as future rainfall, drainage control and early-stage rainfall influence are fully considered, accurate and effective reservoir receivable rainfall is obtained, and a set of rapid and accurate reservoir rain receiving forecasting scheme is formed.

The invention relates to a reservoir dispatching management method and system based on digital twinning, and the method comprises the following steps: carrying out the real-time parameter collection of a target reservoir, and obtaining a reservoir characteristic parameter matrix; performing three-dimensional space modeling based on the reservoir characteristic parameter matrix and the reservoir hydrogeological environment in the target reservoir to obtain a reservoir digital twinborn model; performing drainage basin rainfall-runoff dynamic response analysis on the reservoir digital twin model to obtain a drainage basin hydrological response characteristic curve group; performing prospective water balance calculation on the watershed hydrological response characteristic curve group to obtain a multi-period reservoir flow prediction sequence; and formulating an optimal scheduling instruction set of the target reservoir based on the multi-period reservoir inflow prediction sequence, and regulating and controlling a reservoir gate control system in the target reservoir based on the optimal scheduling instruction set. And the method is difficult to adapt to hydro meteorological conditions and multi-target scheduling requirements which change in real time.

The invention relates to the technical field of hydrological monitoring, and discloses a regional groundwater reserve change dynamic monitoring method based on gravity satellite time series data analysis. According to the method, the multi-source data scale effect and observation noise are effectively eliminated, the physical reliability and resolution of groundwater signal extraction are improved, and the recognition precision and early warning perspectiveness of the abnormal loss trend are enhanced. A full-chain monitoring closed loop of multi-source satellite observation, high-precision physical inversion and dynamic statistical benchmark is constructed, multi-dimensional environment data is aligned, a component separation strategy based on multi-element physical mechanism constraint is established based on a water balance principle, and anomaly diagnosis is performed in combination with a self-adaptively updated long-time-sequence historical database.

The invention discloses an irrigation index prediction method based on meteorological data and field moisture capacity, and relates to the technical field of intelligent agriculture, and the method comprises the steps: collecting real-time meteorological data, real-time soil volumetric water content data, soil matrix potential data and future precipitation forecast data, and carrying out quality control and time alignment processing to obtain a multi-source data set; the method comprises the following steps: calculating evapotranspiration of a reference crop according to real-time meteorological data, obtaining actual water demand of the crop in combination with a crop coefficient, calculating a moisture stress coefficient according to real-time soil volumetric water content data and a field moisture capacity static parameter value, and obtaining a basic irrigation demand index in combination with future precipitation forecast data. According to the method, the evapotranspiration amount is separated by adopting the Shuttleworth-Wallace model, the irrigation index integrating the crop evapotranspiration amount, the water deficit amount and the effective precipitation amount is generated on the basis of multi-period rolling water balance calculation and risk decision rules, and online calibration of soil parameters and optimization of irrigation decision are achieved.

The invention relates to the technical field of geological engineering and underground water simulation, and discloses a regional underground water simulation method and system based on a physical mechanism and deep learning, and the method comprises the steps: converting the static attributes of an observation well into the embedding of nodes and edges through a graph neural network, and obtaining spatial features; according to the dynamic input of the observation well, modeling time sequence response of the underground water system under external dynamic driving through a long short-term memory neural network; deep fusion of spatial features and time dynamics is realized through a feature interaction mechanism, and a groundwater level simulation value is generated; combining a water balance constraint with a relative water level consistency constraint between observation wells to jointly construct a composite loss function for quantifying a difference between an analog value and a real observation value, and guiding parameter updating; the time-space dynamic and physical mechanism can be sensed synchronously, and the method is more reliable and efficient.

The invention discloses an end-to-end drainage basin flood risk influence forecasting method based on physical constraints, and particularly relates to the technical field of meteorological and hydrological forecasting and artificial intelligence, and the method comprises the steps: S1, obtaining multi-source spatio-temporal data of a target drainage basin, constructing a drainage basin graph structure after spatio-temporal alignment and meshing, and constructing a drainage basin graph structure; and S2, constructing an end-to-end deep learning network comprising an adaptive multi-modal feature extraction module and an influence forecasting module, and training the network by using a loss function fused with physical constraints to obtain a flood risk influence forecasting model, the physical constraints at least comprising a water balance constraint. According to the method, the physical mechanism is deeply embedded into the data driving model, the key problems of error accumulation, poor physical consistency and disaster sample scarcity of a traditional cascade forecasting mode are solved, and the forecasting precision and reliability are remarkably improved.

The invention relates to a rapid calculation method and system for reservoir flood regulation calculation based on a dynamic reservoir capacity curve, and the method comprises the steps: collecting reservoir area data, and constructing an initial reservoir area hydraulic model; collecting alignment samples including a calibration sample and an inspection sample, and inspecting the initial reservoir area hydraulic model by adopting the calibration sample and the inspection sample to obtain a reservoir area hydraulic model; extracting actually measured flood data, dividing into a plurality of incoming water schemes, setting different reservoir outlet flows for each incoming water scheme, inputting the different reservoir outlet flows into a reservoir area hydraulic model for calculation, obtaining sample nodes, and constructing a dynamic reservoir capacity curve graph; on the basis that the water level calculation process is continuous and the highest reservoir water level is equal to the dynamic reservoir capacity curve calculation value, the process forecast of the flood regulation water level in the predicted period is obtained through backward calculation by adopting a water balance equation period by period. According to the method, rapid and accurate reservoir flood regulation calculation under the influence of dynamic reservoir capacity is realized, a session flood reservoir water level forecasting process is provided, and more accurate reservoir water level forecasting information support is provided for reservoir regulation and flood and drought disaster prevention.

The invention relates to the technical field of information, in particular to a vegetation water conservation amount change monitoring system and method, and the system comprises a data obtaining module which is used for obtaining vegetation coverage data and hydrological element data of a monitoring region; the data processing module is used for inputting the vegetation coverage data and the hydrological element data into a preset water conservation amount estimation model and outputting a water conservation amount estimation result, and the water conservation amount estimation model is constructed by adopting a water balance equation according to historical vegetation types, areas and meteorological and hydrological data of the monitoring area; and the abnormity early warning module is used for carrying out abnormity early warning and change trend prediction based on the water source conservation amount estimation result, and completing vegetation water source conservation amount change monitoring of the monitoring area. According to the invention, accuracy and time-space continuity of vegetation water source conservation amount monitoring can be improved, and a scientific basis is provided for basin water resource management decision.

The invention discloses a flood forecast evaluation method adapted to reservoir dispatching characteristics, which comprises the following steps: respectively driving the same hydrological model by using historical observation rainfall and forecast rainfall, quantifying the error contribution of the hydrological model and rainfall forecast through double-track difference, and constructing a sample evaluation weight in combination with a rainfall score; constructing a weighting condition error distribution model based on the weight, and generating a reservoir flow forecasting scene set containing physical cause characteristics; and inputting the scene set into a reservoir scheduling model containing flood control and benefit-making rules, and outputting a comprehensive scheduling risk index containing a downstream over-alarm risk and water abandoning loss through water balance and downstream evolution calculation. According to the method, the statistical weight is corrected through error attribution, the abstract forecast error is converted into specific engineering risk and economic loss, the hidden risk working condition which is difficult to find by a traditional statistical index is effectively identified, and a quantitative basis is provided for risk decision making of reservoir dispatching and targeted optimization of a forecast system.

The invention discloses a physical constraint fused deep learning gravity satellite groundwater vertical signal separation method, which comprises the following steps: S1, acquiring and preprocessing basic data: calculating to obtain total groundwater reserve abnormities needing vertical separation, unifying all data to a preset temporal-spatial resolution, and removing abnormal values; s2, constructing a deep learning model, which is configured to receive the preprocessed time sequence including the total groundwater reserve abnormality and meteorological data as input, and output shallow groundwater reserve abnormality and deep groundwater reserve abnormality at corresponding time; s3, the deep learning model is trained, optimization is carried out by adopting a composite loss function, and the composite loss function comprises a data-driven loss item Ldata and a physical constraint loss item Lwater based on water balance; and S4, after training is completed, inputting a total groundwater reserve abnormal signal into the deep learning model for processing, and outputting a separated shallow groundwater reserve abnormal time sequence and a separated deep groundwater reserve abnormal time sequence.

The invention discloses a multi-level water supply and drainage balance monitoring method and system for the water affair industry, and relates to the technical field of water affair monitoring. The method comprises the following steps: deploying a multi-level monitoring network; the method comprises the following steps: collecting real-time data of a multi-level monitoring network, executing space-time alignment operation, and preprocessing the data; constructing a quarterly three-dimensional water balance equation, and dynamically estimating through Kalman filtering; dBSCAN clustering is implemented based on pipe network topology, and leakage positioning is carried out to generate a leakage probability thermodynamic diagram; and establishing a hierarchical response mechanism to carry out multi-level early warning linkage, and rendering a multi-layer perspective view in a GIS (Geographic Information System) engine. According to the method, a quarterly three-dimensional water balance equation is constructed through a city-region-enterprise three-level dynamic weight model and a hierarchical penetration algorithm, and dynamic estimation is performed through Kalman filtering; dBSCAN clustering is implemented based on the pipe network topology, leakage positioning is performed, a leakage probability thermodynamic diagram is generated, the water affair monitoring efficiency is improved, and the manual troubleshooting workload is reduced.

The invention provides a drought and flood sudden change prediction method and system based on artificial intelligence, and the method comprises the steps: collecting the in-out reservoir runoff observation data of a reservoir in a target region, obtaining a restored natural reservoir runoff series based on a water balance method, calibrating a long-short-term memory model through a minimum batch gradient descent method, and carrying out the prediction of the drought and flood sudden change. Based on the natural reservoir runoff, the actual reservoir runoff and meteorological data, constructing a long-short-term memory model to simulate the influence of water conservancy project regulation and storage on the runoff; based on an earth system mode set and a multivariable deviation correction method, performing spatial downscaling on output data of earth system modes to obtain corrected meteorological variables; driving a multi-member set of an earth system mode, and separating by adopting a detection attribution technology to obtain a contribution rate of man-made climate compulsion to drought and flood sudden turning event evolution; and predicting a daily runoff process under future climate change, and predicting a future drought and flood sudden turning event by adopting deep learning and superposition of regulation and storage influence of a water conservancy project.

The invention provides an arid valley collapse body vegetation restoration method and device, and belongs to the technical field of ecological governance, the arid valley collapse body vegetation restoration method comprises the following steps: obtaining environmental data of a collapse body, determining candidate plant species based on the data, and generating a plurality of candidate restoration schemes; plant combinations of different ages are covered, long-term ecological benefits are ensured, water resource changes are dynamically simulated through a water balance model, water resource data in a collapsed body can be updated in real time, the long-term water resource changes can be predicted, and the adaptability index is determined in combination with water demand data of the plants at the current age. According to the method, the performance of the plant in the restoration scheme can be accurately evaluated, and then the most suitable scheme is selected from the plurality of candidate restoration schemes, so that the restoration plant can adapt to the environmental condition of the collapse body, and the long-term stability and the restoration effect of vegetation are improved.

The invention discloses an efficient regulation and control method and system for water resources in a cold region under soil erosion, and the method comprises the steps: collecting regional weather, soil and crop data, and dividing a space grid unit; constructing a freeze-thaw period water balance model, and simulating day-by-day soil water content and snow melting water dynamics; an irrigation-yield correlation model and an improved soil erosion model are established, and the driving effect of irrigation water quantity on crop growth and soil loss is quantified; designing a yield, water saving amount and erosion amount three-objective optimization function, generating a Pareto optimal solution set by adopting a third-generation non-dominated sorting genetic algorithm (NSGA-III), and optimizing an irrigation scheme in combination with a decision algorithm. The method has the advantages that the limitation of traditional single target regulation and control is broken through multi-model coupling, and accurate quantification of the contribution rate of the irrigation water amount in soil erosion is achieved; the water and soil loss risk of the high-gradient area is effectively reduced based on the differential regulation and control strategy of the space grid, and meanwhile the water demand of crops is guaranteed; the black soil area water resource utilization efficiency is improved, and cultivated land degradation is inhibited.

The invention discloses an underground water reserve inversion method and system based on multi-source data, relates to the technical field of hydrological data processing, and solves the technical problem that underground water reserve changes are difficult to accurately and finely describe due to multi-aspect limitations in the prior art. The method comprises the following steps: acquiring and preprocessing underground water reserve related data; the land water reserves are from a plurality of mechanisms, uncertainty evaluation is carried out on the land water reserves of data of each mechanism, a synthesis coefficient is calculated, and then synthesis is carried out according to the synthesis coefficient; carrying out interpolation on the missing data of the land water reserves; carrying out downscaling processing on the land water reserve data and other groundwater reserve related data after interpolation; performing underground water reserve inversion based on a water balance formula by using the downscaled underground water reserve related data; according to the method, the time-space consistent land water reserves data are obtained through data synthesis, and the accuracy of the data is improved.

The invention provides a flood reservoir flow prediction method and device and related equipment, and relates to the technical field of disaster prevention and mitigation, and the method comprises the steps: obtaining a plurality of training data pairs corresponding to a target water conservancy facility, the plurality of training data pairs respectively corresponding to a plurality of floods experienced by the target water conservancy facility in a historical period, the training data pair comprises rainfall data indicating the rainfall of the corresponding flood and storage data indicating the storage flow of the target water conservancy facility in the corresponding flood; the in-storage data is data obtained through flood regulation calculation back calculation under the condition of water balance based on the out-storage data of the corresponding flood and the storage capacity data of the corresponding flood; performing data analysis on the plurality of training data pairs to obtain a target mapping relation; and according to the target mapping relationship and the rainfall of the current flood, predicting reservoir flow information of the current flood in the target water conservancy facility. According to the invention, more accurate storage flow information can be predicted.

The invention discloses a surface water-underground water-mine water coupling simulation scheduling method and system, and the method comprises the steps: obtaining a drainage basin around a target mine in a research region, and screening a reference point closest to the target mine; calculating a water quality fluctuation coefficient of a drainage basin where a reference point closest to the target mine is located on the influence drainage basin; calculating the influence weight of the influence basin on the target mine; and constructing a water balance equation of each influence drainage basin and an underground water control equation associated with the target mine water and the underground water, taking the amount of water permeating into the underground water in the influence drainage basin as an underground water source convergence item, simulating the change state of the mine water, and planning the water amount which can be scheduled at the current simulation scheduling moment of the target mine. The system comprises a water quality sensor, a water level sensor, a data acquisition module and data processing equipment. The method is suitable for dynamic optimization management of mining areas and surrounding complex water resource systems, and is especially suitable for water resource safety scheduling and water disaster prevention and control of mining areas with large water inflow.

The invention discloses a water pollution traceability monitoring method and a monitoring platform based on water balance, and the method comprises the steps: arranging a flowmeter in a rainwater / sewage channel maintenance well, dividing a detection region by taking the flowmeter as a reference point through a GIS system, screening a stable time period when the rainwater flow is stable through a sliding window algorithm, and carrying out the tracking of the rainwater flow. Calculating the total sewage inflow and the actual sewage / rainwater total flow of the area according to the flow meter data in the time period, judging whether the area is normal or not according to a balance value, distinguishing the mixed connection or leakage type according to an abnormal judgment value when the area is abnormal, generating a coincident control taking the flow as the diameter on a monitoring platform, and visually displaying the pollution load difference value; flowmeter redundancy configuration is adopted to ensure data continuity, and an abnormal diffusion path prediction map and a thermodynamic diagram are generated through a wavelet transform algorithm to assist accurate positioning. The monitoring platform is composed of a readable storage medium and a processor, accurate monitoring, intelligent anomaly classification and visual early warning of sewage seepage are achieved by executing the method, and the operation and maintenance efficiency is improved.

The invention discloses a dynamic flood risk evaluation method based on flood inundation simulation, and relates to the technical field of natural disasters. The method includes: acquiring meteorological and hydrological data of a target area; constructing a flood risk model according to the meteorological and hydrological data, and carrying out flood risk model parameter calibration by utilizing actually measured rainfall data and flow-water level data; based on a D8 diffusion algorithm and water balance equivalent analysis of a GIS spatial analysis technology, converting the water amount, higher than the river embankment, of the target area into a submerged water depth sum by using the constructed flood risk model, obtaining a submerged water depth map of the target area, and generating a flood deduction result; and according to the flood deduction result, determining flood disaster risk grade division of the target area, and evaluating the flood disaster risk. According to the method, the flood risk model and the flood rehearsal model of GIS spatial analysis are constructed, and the flood risk model can well reflect the flood rehearsal result.