13311 results about "Hydrology" patented technology

A scheduling method and system for the operation of reservoirs to recharge freshwater for repelling saltwater intrusion under changing conditions. Research data of a research area is collected, the research area is generalized, and the river network topology of the area is constructed; the research data acquire the seawater flow data of each station in the research area for N time periods, analyze the correlation between the flow of a predetermined station and the saltwater intrusion and build a seawater flow spatial-temporal evolution model; rainfall data and freshwater runoff of each station are acquired to analyze the variation trends of freshwater flow and hydrological regime of each station and a first ending point and build a freshwater flow spatial-temporal evolution model; and a scheduling model for recharging freshwater for repelling saltwater intrusion is built for simulation, a scheduling method is provided, and a scheduling scheme set is formed.

The invention discloses a stationary pollution source data acquisition and calibration method and system, a medium and a program product, and the method comprises the steps: carrying out the segmentation processing of a plurality of concentration change data, and obtaining a plurality of data segments; determining the concentration change trend of various pollutants, and calculating a correlation coefficient matrix; the Euclidean distance value between the correlation coefficient matrix of the current data segment and each correlation coefficient matrix before sorting is calculated; when the Euclidean distance is greater than a preset threshold value, marking the corresponding data segment as an abnormal data segment; determining the pollutant as a target pollutant, and establishing a material balance equation; based on the material balance equation, generating a theoretical concentration change curve of the target pollutant; calculating a deviation value, and when the deviation value exceeds a preset dynamic error limit value, determining a sensor correction coefficient according to the process parameters; and carrying out calibration processing on the acquired data of the target pollutant according to the sensor correction coefficient. According to the invention, the accuracy of data acquisition and calibration of the stationary pollution source is improved, so that the influence on formulation and execution of pollution prevention and control measures is reduced.

The invention discloses a slope rainfall infiltration stability dynamic evaluation and landslide prediction method. The method comprises the following steps: constructing a numerical model coupling rainfall infiltration and slope stress field response; integrating multi-source data, and updating model boundary conditions in real time; performing dynamic stability evaluation based on a local safety coefficient method; and training a landslide prediction model and realizing intelligent early warning identification. The seepage-stress-strength three-field coupling numerical model is provided, the physical response process of the unsaturated soil body is dynamically simulated, the precursor mechanism of rainfall-induced landslide can be truly restored, and prediction scientificity and accuracy are improved; remote sensing topographic data, soil property survey parameters and real-time meteorological monitoring information are integrated, model boundary conditions and initial states are dynamically updated through a system interface, real-time response to environmental changes is achieved, and simulation reliability is improved; a local safety coefficient evaluation mechanism is introduced, and a continuous weak region discrimination algorithm is combined, so that space identification and time sequence tracking of a potential slip region and a damage zone are realized, and the slope stability analysis refinement level is improved.

The invention discloses a multi-scale wave-flow-sediment coupling beach evolution prediction model method, and relates to the technical field of tidal flat landform morphology evolution simulation, comprising the steps of integrating a tidal current power module, a wave motion module and a sediment power module, solving, and predicting landform morphology evolution of a tidal flat area; establishing a coordinate system according to actually measured tidal flat topographic data, obtaining tidal current, wave and sediment data based on a hydrometric station, taking the data as boundary conditions, and importing the boundary conditions into the model; constructing a beach evolution prediction model based on integration of tidal current power, wave motion and sediment power modules, and setting parameters required by each module; a finite difference method is adopted to solve a fluid continuity equation, a wave energy conservation equation, a one-dimensional vertical average suspended sediment convection and diffusion equation and a bottom bed updating equation, and tidal flat section terrains at different stages are obtained, so that the tidal flat section form evolution process under the action of waves and flow is accurately simulated, and compared with a traditional tidal current sediment model, the simulation precision is higher, and the simulation efficiency is higher. And the calculation efficiency can be obviously improved.

The invention discloses a flood discharge gate opening degree real-time automatic distribution method for a centralized control center, and relates to the technical field of water conservancy gate control, and the method comprises the steps: setting a monitoring point in each sub-region, and collecting the mechanical state, the vibration stress parameter and the surrounding geological parameter of a flood discharge gate, so as to construct a gate wear index Msz; therefore, the state of the gate can be monitored in real time, and abrasion and fault problems can be found and early warned in time. And establishing a sub-region prediction model, extracting water flow characteristics and river volume characteristics, calculating an average water flow index Slzs and a regional river clogging index Ydzs of each sub-region, correlating the obtained predicted river load index Fzz, and performing evaluation. And setting a first difference threshold value C1 and a second difference threshold value C2, calculating and obtaining a load difference value FCz according to the predicted river load index Fzz and the load threshold value R, generating an opening instruction, and setting a priority distribution mechanism. The opening degree of the gate is optimized, and the accuracy and response speed of flood discharge control are ensured.

The invention discloses a deepwater well drilling overflow early warning device and method, and relates to the technical field of petroleum engineering, the deepwater well drilling overflow early warning device comprises a data acquisition module, an environment compensation module, a data processing module, a data processing module and a data processing module, the data acquisition module is used for acquiring data during well drilling, and the data comprises underground bottom hole pressure, temperature, acoustic logging data, gamma logging data, underwater gas content data and overwater well fluid flow data; the environment monitoring module is used for monitoring environment changes and dynamically correcting data acquired by the data acquisition module by applying a real-time correction algorithm based on a neural network, and the data processing and analyzing module is used for cleaning the data of the data acquisition module, extracting features and transmitting the features to the early warning system module. Accurate environment data are obtained through the environment compensation module, the data are processed through a real-time correction algorithm, the influence of external interference such as water flow fluctuation and temperature change on sensor reading can be eliminated, and therefore the accuracy and reliability of the data are improved.

Disclosed herein are gutter guard systems arranged to efficiently manage the flow of rainwater across the system and prevent the matting of debris onto the system. In one exemplary embodiment, a gutter guard system includes a main body and a screen covering the main body. The main body includes a front receiver, a rear receiver, and a water management section positioned between and connecting the front and rear receivers. The screen spans the water management section and is secured to the main body by engagement on one end with the front receiver and on the opposite end with the rear receivers. When the gutter guard system is installed in a rain gutter, the front receiver engages with the front edge of the rain gutter, and the rear receiver engages with the rear portion of the rain gutter.

The invention relates to a method and system for monitoring the running state of a water pump unit based on the digital twin technology, and belongs to the technical field of pipeline pump monitoring. The method comprises the steps that a digital twin model fusing equipment parameters and environment sensing data is constructed, and multi-dimensional running data such as dynamic pressure, flow, electric energy quality and temperature are collected in real time in combination with the Internet of Things technology; a multi-dimensional correction algorithm is innovatively provided, fluid flow velocity calculation is corrected through pipe opening structure parameters, lift calculation is dynamically corrected through a temperature compensation algorithm and a mechanical wear factor, and dynamic compensation is conducted on the water pump efficiency by introducing a harmonic loss calibration mechanism. Based on a two-channel machine learning model, a fault incidence matrix is constructed by improving a weighted K-means algorithm to realize equipment health state clustering, an efficiency attenuation trend is predicted in combination with an LSTM network, static characteristics and dynamic time sequence data are fused by using an attention mechanism, and finally an accurate fault diagnosis report and a maintenance strategy are generated.

According to the technical scheme, the automatic feeding system comprises a feeding bin, a pneumatic conveying pipeline, an air source device and a discharging bin, and further comprises a distributed sensor set used for monitoring the flux, pressure, temperature and vibration frequency spectrum of a preset point of the pneumatic conveying pipeline; comprising a flow sensor, a pressure sensor, a temperature sensor and a vibration acceleration sensor which are arranged at a plurality of preset points of a conveying pipeline, and a digital twin model building module which is used for building three-dimensional modeling of the pneumatic conveying pipeline and mapping state parameters of all positions of a pipeline distribution sensor group in real time; the blockage analysis module is used for marking a blockage position to three-dimensional modeling in real time based on data acquired by the distributed sensor group, extracting a multi-parameter change curve in a time period T before blockage, constructing a blockage feature library, and generating a blockage risk level through a weight change rate difference value of a feeding bin and a discharging bin; and the graded dredging execution module is configured with a differentiated dredging strategy, and the differentiated dredging strategy is started according to the blockage risk grade.

The invention discloses an urban rainwater pipe network blockage risk early warning method and system based on edge calculation, and relates to the technical field of urban drainage monitoring. The problems that existing pipe network blockage detection lags behind, and the early warning precision is insufficient are solved. Dynamic hydraulic parameters and sediment migration state data of a pipe section are collected in real time through edge calculation equipment deployed at a pipe network node, the hydraulic state deviation rate is calculated, and the local blockage risk is rapidly recognized; when the deviation rate exceeds a threshold value, a dynamic sensing network is established by the trigger nodes, and a pipe network hydraulic topological relation is established by integrating the liquid level and flow velocity characteristics of the upstream and downstream nodes; executing distributed collaborative analysis based on the topological relation, identifying an abnormal attenuation area, calculating a sediment dynamic equilibrium index, and generating a blockage diagnosis parameter; and further combining real-time rainfall intensity to predict an overflowing capacity attenuation curve, generating a multi-stage early warning instruction according to an attenuation slope, and distributing the multi-stage early warning instruction to an operation and maintenance terminal, thereby realizing accurate early warning and active regulation and control of the blockage risk of the rainwater pipe network.

The invention provides a hydraulic engineering information management method and system based on big data. The method comprises the following steps: firstly, uploading flood flow velocity dynamic data, terrain elevation data and spectral feature data to a cloud server, then carrying out multi-dimensional superposition on the flood flow velocity dynamic data, associating with a historical inundation path, and generating a superposition association result; constructing a three-dimensional geographic model based on terrain elevation data and spectral feature data, generating a spatial superposition map according to boundary features of a water body and a terrain abrupt change area and a flood routing path, performing position association with a material reserve position, and performing dynamic priority ranking according to a submerging probability gradient, and finally, according to the resource scheduling instruction sequence, correcting the flight path of the unmanned aerial vehicle cluster and the data acquisition area to form a closed-loop feedback mechanism. According to the technical scheme provided by the invention, the accuracy and timeliness of real-time data updating are ensured.

A water filter system includes an upper chamber removably coupled with a lower chamber. In implementations the lower chamber has a sidewall and a rim wider than the sidewall. A filter medium filters water from the upper to the lower chamber. A seal couples between the chambers and has a circular cross-section and a gap receiving a portion of the sidewall. The seal may also have a cavity receiving the rim. The seal includes one or more arced flaps contacting the upper chamber, a tip of each arced flap defining a circular inner opening of the seal. The seal includes one or more first barbs contacting the upper chamber, each first barb forming a circle. The seal includes one or more second barbs contacting the lower chamber, each second barb forming a circle.

The invention discloses a soil cadmium pollution risk identification method based on a spatial information enhancement algorithm, and relates to the field of soil cadmium pollution risk prediction and risk identification, and the method comprises the steps: predicting a constructed soil cadmium pollution data set through employing a machine learning algorithm, and comparing the prediction precision of the machine learning algorithm, selecting an optimal model as a soil cadmium concentration prediction model; dividing a target area into grid units, utilizing a soil cadmium concentration prediction model to perform prediction, outputting soil cadmium concentration spatial distribution data, utilizing a Bayesian information criterion BIC to determine the optimal clustering number of soil cadmium pollution, adopting a Gaussian mixture model to perform clustering on the soil cadmium concentration, obtaining soil cadmium clustering features, and determining the soil cadmium pollution. Identifying and evaluating a soil cadmium pollution risk area; and carrying out overlay analysis on the risk receptor and the soil cadmium pollution risk area, and verifying the accuracy of identifying the risk area by the soil cadmium concentration prediction model. According to the method, accurate identification and reliable evaluation of the soil cadmium pollution risk are realized.

The invention relates to the technical field of Internet of Things, in particular to a water meter data acquisition method based on an open source gap, which comprises the following steps of: based on an open source gap distributed architecture, acquiring water meter sensing and communication data, analyzing flow fluctuation, marking low-efficiency nodes, optimizing communication and energy consumption matching, screening abnormal links, and identifying over-limit nodes. And generating an exception and switching control table. According to the method, intelligent division of a data fluctuation interval is realized by analyzing a flow change trend in unit time, on the basis, a data acquisition task and an energy consumption state are matched in combination with an operation period and electric quantity distribution, meanwhile, signal strength and delay in a transmission path are comprehensively evaluated, and the flow output change is continuously tracked, so that the flow output change is accurately determined. And abnormal operation nodes are identified and switching control signals are triggered, so that the stability and accuracy of data acquisition in a complex environment are improved, the coordination and execution efficiency of the whole process are enhanced, the information loss risk caused by communication faults is reduced, and the intelligent response level is improved.

The invention relates to the technical field of data driving and landslide surge disaster prevention and control, and discloses a landslide surge height prediction method and system based on a sparrow optimization random forest model.The method comprises the steps that for a target landslide area, a corresponding landslide surge physical model is built, and test groups with different landslide volumes and water depths are set for testing; a corresponding test data set is obtained, and equal-proportion scaling is carried out according to Froude similarity criteria; the method comprises the following steps: preprocessing test data to obtain a corresponding data set, and dividing the preprocessed data set into a training set and a test set; building a sparrow optimized random forest model, setting input values of various parameters, enabling the training set to train the sparrow optimized random forest model, and predicting the maximum height of the landslide surge by using the label data set and the trained sparrow optimized random forest model. The system comprises a data acquisition module, a data division module and a landslide surge prediction module. According to the method, the maximum height of the landslide surge can be accurately predicted.

The utility model relates to the technical field of model tests, and discloses a landslide damage simulation test device which comprises a model test box, a rainfall frame, a hydraulic assembly, a plurality of spraying assemblies, a water tank assembly and a constant temperature system. The angle of the landslide model box can be adjusted through the hydraulic machine so as to change the slope of a model test landslide soil body; the multiple spraying assemblies are arranged at the top of the rainfall frame and located over the model test box, the output ends of the spraying assemblies aim at a test soil body in the model test box, the influence of rainfall on landslide slippage is simulated by adjusting the spraying size, and the constant temperature system is arranged at the top of the model test box and used for regulating and controlling the temperature of the test soil body. The constant temperature system is used for measuring the influence of the dry-wet cycle effect on the landslide soil body.

The invention discloses a gravity type submerging net cage parameter design optimization method, and relates to the field of mariculture equipment structure optimization, and the method comprises the steps: collecting the multi-period marine environment data of a target sea area, and constructing an environment load sequence with a time-varying characteristic; establishing a three-dimensional coupling finite element model, performing disturbance analysis on a plurality of structure control parameters, and identifying a key design parameter set based on a response sensitivity function; and constructing a multi-objective optimization model including floating and sinking time control, maximum stress minimization and attitude offset constraint, performing global optimization by adopting an adaptive genetic algorithm, obtaining an optimal parameter solution, substituting the optimal parameter solution into the simulation model for dynamic response verification, and if a response index is converged to a target interval, outputting the optimal parameter solution as final design configuration. According to the method, systematic optimization of structural parameters of the submerging and surfacing net cage can be achieved, the stability and safety of the submerging and surfacing net cage under complex sea conditions are improved, and the method has high engineering applicability.

The invention discloses a multi-vegetation salt marsh biological landform evolution prediction method and system. The method comprises the following steps: constructing a coupling model based on a water and sediment dynamic model and a vegetation dynamic model; performing water and sediment flow simulation by using the water and sediment dynamic model, obtaining a water and sediment flow simulation result after the simulation is finished, calculating characteristic values of stress factors, and interpolating the characteristic values of the three stress factors into a vegetation grid of the vegetation dynamic model from a water and sediment dynamic grid; performing vegetation distribution simulation by using a vegetation dynamic model, completing planting, succession, pressure death, annual death and expansion processes of vegetation on a vegetation grid, performing weighted averaging on a vegetation distribution simulation result from the vegetation grid to a water and sand dynamic grid after simulation is ended, and inputting the vegetation distribution simulation result to a water and sand dynamic model; long-term simulation of the biological landform of the salt marsh is achieved through a plurality of cycles. The simulation prediction method can solve the problems that an existing model can only consider a single vegetation species, tidal power is excessively simplified and the like, and the scientificity and accuracy of the simulation process are guaranteed.

The invention discloses an urban inland inundation optimization transformation method and system based on a pipe network earth surface coupling hydrodynamic model, and the method comprises the steps: firstly generalizing a pipeline and a catch basin, constructing a pipe network confluence model according to a pipe network hydrodynamic method, then dividing a sub-catchment area according to a river network flood routing method, building an urban comprehensive drainage model through the sub-catchment area, and carrying out the optimization transformation of the urban inland inundation. Then establishing a surface runoff model by using a surface flood routing method and a Horton penetration method; according to the system, the function of accurately and efficiently simulating the urban inland inundation process is achieved, urban rainwater circulation is completely simulated, an urban drainage pipe network and a river channel model are integrated, and the interaction between an urban drainage system and a ground surface storage water body is truly simulated; and necessary support is provided for disaster situation rehearsal and rainwater engineering planning and layout, so that urban inland inundation points can be removed in advance, influences on production and life of people are avoided, and the method is suitable for being widely popularized and used.

The utility model relates to a thermostatic controller for controlling water temperature, which is characterized by comprising a valve seat, and a cold water inlet flow channel, a hot water inlet flow channel, a first valve core mounting cavity, a second valve core mounting cavity, a cold water outflow channel, a hot water outflow channel, a water mixing cavity, a third valve core mounting cavity and a mixed water outflow channel are arranged in the valve seat; a first movable valve plate of the first flow adjusting valve core is driven by a first motor; the second flow regulating valve core is mounted in the second valve core mounting cavity, and a second movable valve plate of the second flow regulating valve core is driven by a second motor; the cold water outflow channel is communicated with a cold water inlet of the balance valve element, the hot water outflow channel is communicated with a hot water inlet of the balance valve element, and a cold water outlet and a hot water outlet of the balance valve element are both communicated with the water mixing cavity. The flow of cold water and hot water is adjusted by balancing movement of the piston in the valve element, and the problem that the fluctuation range of the water yield is enlarged due to the water pressure difference is effectively solved.

A system and method for determining optimal sampling parameters is described. The method gathers soil data from a soil data source, which is associated with a soil organic carbon (SOC) project area. The crop prediction engine then determines that the soil data is less than optimal, but that the soil data is sufficient to generate an optimal sampling plan. The method completes a Monte Carlo simulation, which generates an empirical sampling distribution. The optimal sampling plan is determined by defining a margin of error, which provides a deviation from a predictive analysis of measured soil chemistry for a plurality of collected soil samples, and performing Monte Carlo simulations that include one Monte Carlo simulation having a lowest sampling density that satisfies the margin of error. The optimal sampling plan has the lowest sampling density and includes one or more sampling locations.

The invention relates to the technical field of soil erosion monitoring, in particular to a shallow mountain belt abrupt slope shrub land soil erosion monitoring method and system, and the method comprises the following steps: S1, collecting topographic point cloud data and vegetation reflectivity data; s2, spatial interpolation is carried out on the terrain point cloud data to generate three-dimensional terrain grid data, and meanwhile, boundary coordinates of a shrub coverage area are taken; s3, calculating a root system corrosion resistance strength value of each grid; s4, generating a real-time erosivity index of each grid; s5, extracting a real-time erosivity index of the grid where the corresponding node is located and a terrain parameter of the associated grid; and S6, calculating the soil erosion amount grid by grid, and generating a three-dimensional erosion space distribution diagram. According to the invention, through integration of multi-source data acquisition, real-time data processing and three-dimensional visual monitoring, accurate evaluation and dynamic early warning of the soil erosion process of the shrub forest land with the abrupt slope in the shallow mountain are realized, so that decision support is provided for water and soil conservation and ecological restoration.

The invention relates to the technical field of nuclear power internal disaster analysis, and discloses a nuclear power plant internal flooding experiment verification method, device, equipment and medium, and the method comprises the steps: carrying out the modeling analysis of a target plant prototype, and determining a plurality of model parameter values of an experiment scaling model; constructing an experiment scaling model according to the plurality of model parameter values, and determining experiment parameters of an internal water logging experiment; performing an internal water logging experiment on the experimental scale model by using the experimental parameters to obtain an internal water logging experiment result of the experimental scale model; and obtaining an internal water logging simulation result of the numerical model, comparing the internal water logging simulation result with an internal water logging experiment result, and verifying the accuracy of the internal water logging simulation result according to a comparison result. According to the method, the experiment scale model of the nuclear power plant prototype is constructed, the flooding spreading conditions under different working conditions are simulated and compared with the flooding simulation result, the accuracy of flooding simulation calculation is verified, and more accurate guidance is provided for follow-up nuclear power project internal flooding analysis.

The invention relates to the technical field of monitoring and identification, and discloses a rainfall type group landslide risk area and hidden danger point comprehensive identification and evaluation method. The method comprises the following steps: determining a landslide danger index and a disaster-bearing body vulnerability index of each calculation unit in a target area according to an updated comprehensive database; determining a risk evaluation result according to a landslide risk index of each sub-region determined by the landslide risk index and the disaster-bearing body vulnerability index, wherein the risk evaluation result comprises a landslide risk grade corresponding to each sub-region; determining a deformation area in the target area according to synthetic aperture radar data; determining a hidden danger point identification result corresponding to the deformation area, wherein the hidden danger point identification result comprises disaster hidden danger points in the deformation area and hidden danger risk levels corresponding to the disaster hidden danger points; adjusting a hidden danger point identification result according to the risk evaluation result; and adjusting a risk evaluation result according to a hidden danger point identification result and a disaster hidden danger point treatment result. According to the method, dynamic feedback and optimization of risk and hidden danger assessment can be realized, and misjudgment is reduced.

The invention provides a high-resolution ocean thermocline depth rapid reconstruction method based on a random forest. The method is used for carrying out rapid reconstruction and quasi-real-time acquisition on the depth of an ocean thermocline by using sea surface data. A random forest model is established, sea surface data such as sea surface temperature, salinity and sea surface height abnormity are utilized, historical data of thermocline depth calculated based on actually measured temperature data are combined, and regression relation fitting is carried out on sea surface elements and thermocline depth, so that the sea surface elements and the thermocline depth are obtained. Therefore, the targets of multi-source ocean data fusion and high-resolution thermocline depth rapid reconstruction are achieved. In order to train the model, a large amount of measured data is used for sample fitting and optimization, so that the prediction accuracy is improved; the method has the advantages of being accurate, efficient and wide in applicability, the temporal-spatial resolution of ocean thermocline depth calculation can be improved, the ability to understand and predict an ocean temperature field is enhanced, and the method has important significance in the fields of ocean engineering, physical ocean and the like.

The invention relates to the technical field of reservoir dam safety monitoring, and discloses a reservoir dam siltation dynamic monitoring and early warning system. Multi-dimensional sensing node arrays of the system are arranged at key positions of a dam body structure and a reservoir area, and sediment thickness distribution data, water flow velocity field data and sediment concentration gradient data are synchronously collected. And the edge computing node receives the original monitoring data, executes data cleaning and space-time alignment processing, and generates a standardized siltation feature data set. And the cloud analysis platform receives the data set, calculates a deposition evolution trend matrix through a space-time coupling prediction model, and outputs a reservoir area deposition risk level distribution map. And the dynamic visualization engine analyzes the risk level distribution map, generates a three-dimensional dynamic deposition situation model, and marks the space coordinates of the abnormal deposition area. And the early warning decision center generates a graded early warning instruction set according to the space coordinates of the abnormal region, and triggers a corresponding emergency response strategy.

The invention discloses a flood disaster monitoring and early warning system and a flood disaster monitoring and early warning method. A cloud, rain, water and I integrated sensing network is constructed through a full-chain monitoring capability; a hybrid prediction model coupled with HEC-HMS and SWMM physical models and an LSTM-Transformer deep learning architecture is established, parameter deviation is dynamically corrected through NSGA-II and a symbolic regression multi-objective optimization algorithm, the flood prediction period is prolonged to 10 days (the precision of the southern watershed is larger than or equal to 90%, and the precision of the northern watershed is larger than or equal to 70%), the flood peak time error is compressed to be within 30 minutes, and compared with a scheme based on a static flood risk model, the method has the advantage that the flood prediction efficiency is greatly improved. The false alarm rate is reduced from 20% to 5% through the dynamic threshold calibration technology; hierarchical response and survivability communication are adopted, Beidou satellite and NB-IoT dual-channel redundant transmission is deployed, and a Mesh ad hoc network and frequency modulation subcarrier technology are combined, so that the direct rate of early warning information in extreme weather is ensured to be greater than or equal to 99%; and three-dimensional GIS platform dynamic rendering is supported, and collaborative visualization of a submerging thermodynamic diagram, a material scheduling path and ecological flow monitoring is realized.

The invention discloses a multi-source data-combined pit-pond water depth calculation and storage capacity calculation method, which comprises the following steps of: acquiring actually measured water depth data, performing data preprocessing, and dividing the data into a training sample point set and a test sample point set; acquiring a multispectral remote sensing image of the reckoning area, and executing image preprocessing; unifying to the same coordinate system; fractal dimensions of each wave band and the combined wave band are calculated, and a water body region is segmented; establishing a mapping relation between the water depth values of the training sample points and the standardized multispectral apparent reflectance; establishing a water depth inversion model, and verifying the water depth inversion model by using the test sample point set; calculating discrete water depth points of the pit pond by using the optimal water depth inversion model; and establishing a minimum energy curved surface to obtain the water storage amount of a single pit pond, and accumulating to generate the total water storage amount. According to the method, actual measurement and remote sensing data are fused, the energy minimization water depth inversion model is constructed, accurate calculation of the water storage capacity of the pit pond is achieved, and the method has the advantages of being high in precision and high in applicability.

The present invention relates to urban rainfall runoff pollution prediction in urban water systems, and provides an urban rainfall runoff pollution prediction method based on integrated rolling decomposition method and deep learning algorithm. A rolling decomposition method is firstly used to decompose rainfall runoff sequence data into different sub-sequences; then decomposition is sequentially performed on added data, and future data is excluded, to prevent information leakage; a recurrent neural network is used to model and predict the sub-sequences; and finally, predicted results of the sub-sequences are summed to obtain the predicted result of rainfall runoff pollution.

The invention discloses a method and a system for analyzing a pollution source of a rainwater pipe network. The method comprises the following steps of: 1, obtaining a first key fluorescence characteristic parameter combination according to basic physicochemical indexes by adopting a fluorescence characteristic parameter prediction model; 2, three-dimensional fluorescence characteristic parameters of the pollution source are obtained, and a pollution source three-dimensional fluorescence characteristic data set is obtained; screening by adopting a multivariate statistical method to obtain a second key fluorescence characteristic parameter combination; 3, determining a fluorescence characteristic parameter combination according to the first key fluorescence characteristic parameter combination and the second key fluorescence characteristic parameter combination; 4, inputting the fluorescence characteristic parameter combination into the Bayesian mixture model to obtain the contribution rate of the pollution source to the receptor water quality, and completing the analysis of the pollution source of the rainwater pipe network; according to the method, the DOM fluorescent fingerprint information is predicted by adopting basic data; and based on the three-dimensional fluorescence characteristic parameters and in combination with a Bayesian mixture model, precise quantitative analysis of the pipe network pollution source is realized.