39297 results about "Atmospheric sciences" patented technology

The invention discloses a tidal river reach storm surge rapid forecasting method based on deep learning and an AI large model. The method comprises a data acquisition and preprocessing step, a meteorological element forecasting step, a typhoon path and flow forecasting step and a storm surge forecasting step. Multi-source data fusion is realized through a four-dimensional variational assimilation method and an adaptive weight fusion algorithm; a multi-model integrated system such as FourCatNet and the like is used for forecasting meteorological elements; an improved XLSTM-Transformer model is adopted to predict the flow and the typhoon path; and carrying out storm surge forecasting based on a three-layer nested model system and a multi-process coupling technology. According to the method, full-process automation from data acquisition to forecasting is realized, the forecasting timeliness is improved by 60%, the average absolute error is reduced by 45%, and a reliable basis is provided for disaster prevention and reduction decisions.

The invention provides a drainage basin digital twinning construction method and system based on multi-source data fusion. The method is applied to the technical field of drainage basin digital twinning construction, and comprises the following steps: acquiring water level monitoring data, surface flood routing path data, weather forecast rainfall data and terrain slope change data; rainfall and topographic slope data are fused to generate a runoff distribution relation, and a high-risk area is identified in combination with a water level sudden change amplitude. A digital twinning preliminary framework is established, and related characteristic parameters are generated by utilizing reverse feedback of a flood path and water level data. And inputting meteorological data, constructing a twinborn model based on the boundary condition of the catchment area, and simulating flood direction abnormity and flooding early warning signals. And according to the early warning space-time distribution and the terrain elevation data, generating emergency schemes such as flood dividing gate scheduling and flood storage and detention area starting, and realizing risk hierarchical management and control. According to the technical scheme provided by the invention, the flood inundation prediction precision and the emergency response efficiency are improved, and precise decision support is provided for flood control of a drainage basin.

The invention provides a photovoltaic power station photovoltaic power prediction method and system. A cloud layer motion time sequence library is constructed based on a historical cloud picture, and a nonlinear model of cloud speed and output attenuation is established. The light sensor array monitors the cloud layer thickness and the aerosol density in real time, calculates the light intensity attenuation gradient of the shadow area in combination with the model, and predicts the remaining time of the cloud cluster reaching the core area. Dividing a power prediction confidence interval according to remaining time, inputting satellite data into a multi-scale meteorological fusion architecture, and adjusting a meteorological feature extraction path of an attention mechanism based on cloud density. And fusing the surface reflectance correction value calculated by the scattering data of the light sensor to generate a short-term power prediction sequence of the sudden change cloud layer scene. And inputting the sequence into an energy management system, and outputting an active and reactive double-target scheduling instruction in combination with a grid-connected point voltage deviation and a reactive compensation parameter constrained by a power grid rule. According to the technical scheme provided by the invention, the cooperative scheduling precision of the photovoltaic power station in the cloud abrupt change scene is improved.

The invention discloses a day-ahead and intra-day economic dispatching method for a wind and light storage system, which relates to the technical field of wind and light storage and comprises the following steps of: establishing a dynamic aging model based on historical operation data of a battery, fitting actual attenuation characteristics of the battery through machine learning, acquiring the health state of the battery in different operation states and the change rule of the battery along with time, and calculating the economic dispatching result. Therefore, the basis of the dynamic adjustment strategy is formed. Energy storage charging and discharging are optimized through health factors, the system monitors parameters such as SOH and SOC in real time, tasks are dynamically adjusted, battery aging is reduced, stability is improved, the SOC strategy is optimized in combination with deep reinforcement learning, the service life is prolonged, and the maintenance cost is reduced. Rolling optimization scheduling and experience playback are adopted, a system can dynamically adjust a strategy, the wind and light absorption rate is improved, power grid balance is optimized, thermal power dependence is reduced, scheduling cost is reduced, extreme weather adaptability is enhanced, and green and low-carbon development of electric power is promoted.

The invention discloses an inductive sensor calibration method, an inductive sensor calibration system, a medium and a product. The method comprises the following steps: acquiring a measured value data set of an inductive sensor to be calibrated and a measured value data set of a reference inductive sensor under a preset environment condition; performing difference operation on the measured value data set of the inductive sensor to be calibrated and the measured value data set of the reference inductive sensor to obtain a measurement deviation sequence; based on a preset environment condition and the measurement deviation sequence, determining an environment compensation coefficient under the preset environment condition; calculating calibration parameters of the inductive sensor to be calibrated according to the environment compensation coefficient; after the calibration parameters are applied to the inductive sensor to be calibrated, obtaining a standard measurement value of the reference inductive sensor and an actual measurement value of the inductive sensor to be calibrated under a preset environment condition; if the difference value between the standard measurement value and the actual measurement value is smaller than the preset error value, it is determined that calibration succeeds. By implementing the method, the calibration accuracy is improved.

The invention provides a smoke and fire detection method and system based on deep reinforcement learning and electronic equipment, and relates to the technical field of fire fighting. The method comprises the following steps: acquiring thermal imaging data of a forest area through a thermal imager, obtaining temperature distribution conditions of different positions, and determining position coordinates, the number and specific temperature values of temperature abnormal points for an area with abnormal temperature rise; if the temperature value of the temperature abnormal point exceeds a preset temperature threshold value, judging that the temperature abnormal point is a suspected fire point, and collecting a visible light video image of the area through a visible light camera according to the position of the suspected fire point; smog detection processing is carried out on the collected visible light video image, whether smog exists in the image is judged by analyzing the color, texture, motion and other characteristics of the image, and the concentration, morphological characteristics and diffusion speed of the smog are obtained; the meteorological parameters such as the wind speed and the wind direction of a fire occurrence area are obtained through the meteorological sensor, the meteorological parameters are input into the fire spreading model, and the spreading direction and the spreading range of the fire under the current wind field condition are simulated.

The invention discloses a forest fire risk assessment method based on a composite chain type disaster evolution mechanism, and relates to the field of forest fire risk assessment, and the method comprises the steps: carrying out the chain type feature field construction of multi-source heterogeneous data through the collection of the multi-source heterogeneous data, including remote sensing data, meteorological data, geographic information data and disaster loss data, obtaining a disaster chain core driving factor tensor, and carrying out the feature field construction of the multi-source heterogeneous data; performing composite disaster chain coupling analysis based on the disaster chain core driving factor tensor to obtain a disaster chain space-time cascade trajectory, performing dynamic risk assessment and grading based on the disaster chain space-time cascade trajectory to obtain a five-level risk zoning map, and performing intelligent early warning analysis based on the five-level risk zoning map to obtain a forest fire dynamic early warning map. According to the method, the chain type driving relation among drought, high temperature, vegetation dryness and fire danger is synthesized, the dynamic threshold value is adopted for risk grading, the dynamic influence of climatic variation and vegetation phenology on the fire danger can be captured, and the accuracy of forest fire risk assessment is improved.

The invention relates to the field of unmanned ship path planning, in particular to an autonomous water quality monitoring unmanned ship navigation control method and system based on multimode communication. The method comprises the following steps: collecting regional water quality monitoring parameters based on a shipborne water quality monitoring sensor, carrying out space pollution distribution excavation, and constructing a regional pollution concentration distribution diagram; obtaining a regional satellite remote sensing map, carrying out water flow distribution dynamic evolution, and constructing a water flow path distribution network; carrying out pollutant migration logic evolution and multi-region gradient diffusion prediction on the water flow path distribution network according to the regional pollution concentration distribution map, and constructing a water quality pollution diffusion prediction situation map; and according to the water quality pollution diffusion prediction situation map, carrying out highest diffusion progressive gradient identification, carrying out optimal navigation monitoring sequence analysis, and constructing an optimal navigation monitoring sequence. According to the invention, through dynamic and real-time unmanned ship water quality cruise path adjustment, the accuracy and efficiency of water quality monitoring are improved.

Disclosed in the present invention are a rapid large-range asphalt-pavement quality assessment method and system based on high-resolution remote sensing. The method comprises the following steps: acquiring a high-resolution multispectral image and panchromatic image of a pavement area of a road network area to be subjected to assessment; generating superpixels of the pavement area on the basis of the high-resolution multispectral image and panchromatic image of the pavement area; removing superpixels of a non-asphalt pavement from the superpixels of the pavement area, so as to obtain superpixels of an asphalt pavement in the pavement area; on the basis of the superpixels of the asphalt pavement, performing calculation to obtain a brightness indicator, texture indicator, structure indicator and spectrum indicator of each superpixel of the asphalt pavement; and according to the calculated brightness indicator, texture indicator, structure indicator and spectrum indicator, performing calculation to obtain a remote-sensing quality assessment index corresponding to each superpixel of the asphalt pavement, and then obtaining a quality situation assessment diagram for the asphalt pavement. By means of the present invention, various assessment indexes are comprehensively used, thereby improving the precision and visibility of an assessment result, and reducing the manual intervention.

The invention discloses a thunder and lightning monitoring and early warning method and system based on multi-source data fusion, and relates to the technical field of thunder and lightning early warning, and the method comprises the steps: extracting electric field time domain and frequency domain features, magnetic field change features, lightning activity modes and meteorological change features through obtaining atmospheric electric field, magnetic field, lightning activity and meteorological environment data in real time; and constructing multi-source feature data. A time sequence analysis and Bayesian fusion technology is adopted to calculate a correlation weight between data sources, and a fusion feature vector is generated. And establishing a weighted regression model based on the vector, calculating thunder and lightning occurrence probability through dynamic weight distribution, and generating a risk distribution map in combination with geographic information. The method has a closed-loop feedback optimization mechanism, model parameters and weights can be adaptively adjusted according to prediction errors and early warning accuracy, the accuracy, timeliness and environmental adaptability of lightning early warning are improved, and the method is widely applied to the fields of electric power, aviation, buildings and the like.

The embodiment of the invention discloses a method for determining wind power in different weathers. The method comprises the following steps: collecting historical weather data and carrying out clustering classification on the historical weather data to obtain wind profile index data, and carrying out data enhancement on the wind profile index data to obtain a first data set; determining a plurality of wind profile indexes according to the first data set, and determining a corresponding wind profile index under an extreme weather condition according to a nonlinear relation model of the plurality of wind profile indexes and meteorological parameters; determining a dynamic prediction model of the wind profile index according to the corresponding wind profile index under the extreme weather condition by using a bidirectional long-short-term memory network; collecting ground wind speed data in real time, and determining the wind speed of the hub height of the wind turbine generator according to the dynamic prediction model of the wind profile index; determining power output curves at different wind speeds according to the corresponding relation between the historical ground wind speed data and the historical wind turbine generator power output data; and determining the wind power corresponding to the wind speed of the hub height of the wind turbine generator according to the power output curve.

The invention relates to the technical field of electrical system evaluation, in particular to a toughness evaluation method of a novel power system in extreme weather, which comprises the steps of constructing a digital twin model, collecting data in real time, performing fusion processing on multi-source data, identifying potential risks of the power system in extreme weather, determining system vulnerability and constructing an extreme weather scene. The method comprises the following steps: simulating weather events possibly occurring in the future, predicting the occurrence probability and influence range of extreme weather, analyzing system vulnerability, identifying key nodes and vulnerable equipment, defining and calculating a toughness index, optimizing an operation strategy, executing an emergency plan when the extreme weather occurs, evaluating a plan execution effect, and continuously optimizing the plan through feedback; according to the method, the dynamic response capability and the toughness evaluation accuracy of the power system in extreme weather are remarkably improved, the actual operation state of the power system is reflected in real time, the model parameters can be dynamically adjusted through the digital twin technology, and the real response of the system under the changing condition is simulated.

The invention provides a river water quality monitoring method based on multi-source remote sensing data, and belongs to the technical field of river water quality monitoring. The method comprises the following steps: establishing an inherent optical characteristic model of a river water body based on a Hybrid water body radiation transmission model, optimizing a calculation path by adopting a shortest path algorithm, and carrying out water body optical component inversion by adopting a quasi-analysis algorithm and a generalized inherent optical characteristic algorithm in combination with a multispectral characteristic enhancement model to obtain absorption coefficients of all components; and constructing a multi-band combination index to realize optical coupling effect decoupling, and applying the fine-tuned water quality parameter inversion basic model to a target river area to output a suspended matter concentration distribution diagram, a chlorophyll concentration distribution diagram and a transparency distribution diagram. The technical problem of low precision of remote sensing inversion of water quality parameters caused by mutual coupling of multiple optical active components in a river water body is solved.

The invention discloses a high-precision topographic surveying and mapping system and method based on an unmanned aerial vehicle, and particularly relates to the technical field of unmanned aerial vehicle surveying and mapping, and the method comprises the following steps: carrying out the topographic modeling before a task, recognizing GNSS shielding, and dividing a navigation region; gNSS, INS and visual inertial navigation positioning information are fused in flight, the weight is dynamically adjusted, and historical trajectory constraint is combined; constructing a closed-loop detection mechanism to carry out error feedback and feedforward prediction; confidence is calculated according to image definition, IMU stability and time synchronization deviation, high confidence points are screened for DEM reconstruction, local heavy flight is triggered by low confidence points, and surveying and mapping precision and reliability are improved; according to the method, the autonomous navigation and positioning precision of the unmanned aerial vehicle in a complex environment is improved, the quality control of surveying and mapping data and the DEM reconstruction reliability are enhanced, the closed-loop self-correction and feed-forward error prediction capabilities are provided, and the stability, precision and intelligent level of the system are remarkably improved.

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 relates to the technical field of digital twinning, in particular to a high-density urban ponding evolution and early warning method based on digital twinning. The method comprises the following steps: real-time ponding depth, real-time river water level, real-time meteorological conditions and real-time drainage pipe network water level conditions corresponding to a city are collected in real time through ponding depth, river water level, meteorological monitoring and drainage pipe network sensors installed in the city, digital twinborn construction is carried out, and an urban inland inundation digital twinborn model is generated; obtaining drainage pipe network distribution and carrying out drainage grid division to generate an urban drainage grid model; acquiring a historical ponding event rule, and performing ponding area prediction analysis and ponding evolution coupling analysis to generate an urban ponding evolution distribution field; performing ponding space-time evolution analysis and ponding early warning processing on the urban ponding evolution distribution field so as to execute corresponding urban ponding emergency response work. According to the invention, accurate ponding evolution prediction and effective early warning can be provided.

The invention discloses a wind power plant generating capacity improving method based on active wake flow yaw control optimization. The method comprises the following steps: 1) acquiring basic information data of a wind power plant; 2) establishing a CFD simulation model of the wind power plant according to the basic information data; 3) optimizing the arrangement scheme of the laser wind finding radar, and obtaining the operation data of the wind turbine generator in real time; 4) constructing a single-machine analysis wake flow model and a multi-machine wake flow superposition model, respectively comparing with the flow field simulation result data and the wind measurement result of the laser wind measurement radar, and correcting a wake flow speed distribution expression to obtain wind speed and wind direction prediction data at the hub center height of each machine site in the wind power plant; and 5) constructing a whole field optimization control model, taking maximum output as an optimization target to obtain an optimal yaw control strategy set of each wind turbine generator, and enabling the related wind turbine generators to execute yaw actions. The invention belongs to the technical field of wind power generation control, and solves the problem that the wake flow loss among wind power units in a wind power plant is difficult to reduce by the existing control strategy.

A method and device for inverting ocean-atmosphere optical parameters and storage medium. The method includes: obtaining a vector apparent reflectance ρl, ρQ, ρU of water-leaving radiance at top of atmosphere and observation geometry of a multi-angle polarized satellite, and inputting the vector apparent reflectance ρl, ρQ, ρU and the observation geometry into a trained ocean-atmosphere optical parameter model, to output a group of ocean-atmosphere optical parameters, the output ocean-atmosphere optical parameters comprise: PBOAw, FAOD / CAOD, FRm / CRm, W, CHL and SPM, the ocean-atmosphere optical parameter model is an XGBoost machine training model. In this way, the ocean-atmosphere optical parameters can be quickly and stably inverted.

The invention discloses a wind field wind resource fine simulation method, system and device based on WRF-CFD medium and micro-scale coupling and a storage medium, and belongs to the technical field of wind energy resource evaluation.The method comprises the steps that mesoscale meteorological analysis data is collected and calculated through a mesoscale WRF numerical simulation technology; performing fitting processing to obtain a fitting function between the wind speed component and the height; extracting wind speed contour information at a micro-scale modeling boundary based on the mesoscale meteorological analysis data and the fitting function, and determining a boundary condition of a micro-scale CFD model; performing directional calculation and correction on the wind speed of each sector by using a virtual anemometer tower; performing grid division on the wind power plant area to generate a refined grid corresponding to the wind power plant area; and driving numerical simulation of the micro-scale CFD model, and carrying out wind power plant regional range stroke resource evaluation. According to the method, the complex characteristics of the wind field can be comprehensively reflected, the wind resource evaluation precision and the calculation efficiency are remarkably improved, and the resource utilization is optimized.

The invention discloses a geological disaster deformation monitoring method based on remote sensing data, and relates to the technical field of environment monitoring, and the method comprises the following steps: S1, data source selection and preprocessing; s2, region feature extraction; s3, differentiating the multi-temporal remote sensing images; s4, analyzing a time sequence; s5, performing three-dimensional reconstruction on the deformation area; s6, settlement and landslide monitoring; s7, performing fusion analysis on environment and meteorological data; s8, performing data anomaly detection and automatic early warning; and S9, risk assessment and influence analysis. According to the geological disaster deformation monitoring method based on the remote sensing data, SAR, optical images and LiDAR data are combined, the defect of dependence of a single data source in the prior art is overcome, dynamic data fusion and time sequence analysis are used, multi-dimensional real-time monitoring of earth surface deformation is achieved, the monitoring precision and timeliness are improved, and the geological disaster deformation monitoring method based on the remote sensing data has good application prospects. More comprehensive geological disaster early warning can be provided, and the effectiveness of disaster prevention and control is greatly enhanced.

The invention relates to a real-time river water level monitoring and early warning method and system based on deep learning. The method comprises the following steps: acquiring a target watershed remote sensing image and real-time hydrological data of multiple monitoring points; performing water flow direction topology analysis on the remote sensing image of the target watershed, and constructing a water flow topology network; performing basin structure dynamic change feature analysis on the target basin remote sensing image to generate basin structure dynamic change data; carrying out topology situation evolution fitting on the water flow topology network based on the basin structure dynamic change data to obtain a dynamic topology evolution network; performing multi-scale time sequence trend evolution analysis on the multi-monitoring-point real-time hydrological data to generate water flow time sequence evolution characteristics; and evolution characteristic space position mapping is performed on the dynamic topology evolution network based on water flow time sequence evolution characteristics, and a three-dimensional hydrological evolution model is constructed. According to the invention, timely and accurate river water level monitoring and early warning are realized.

A system includes a plurality of satellites orbiting around a celestial body in a plurality of orbital planes. Each satellite includes an imaging device having a field of view (FOV) to capture an image of a sky that includes celestial image features of resident space object (RSO), stars, and / or planets. The satellite processor is configured to input the image data for each image into a pre-processing software pipeline to generate for each image a contrast-enhanced image data replica of each image, and an enhanced image data replica of each image, to receive from an output of a known-unknown RSO split data processing pipeline, a determination that a candidate RSO is a known RSO stored in an RSO catalog, or an unknown RSO, and to assign to the candidate RSO based on the determination, an RSO ID of the known RSO listed in the RSO catalog, or a new RSO ID.

The invention relates to the technical field of material physicochemical property monitoring of a multi-source sensing network, in particular to a multi-source sensing storage environment collaborative monitoring and early warning method, which comprises the following steps of: dividing grid units according to material storage types, binding sensor node coordinates, constructing a grid region reference model, and constructing a grid region reference model; integrating the volatilization characteristic parameters analyzed by the laboratory, historical monitoring data and an environment threshold value to generate a substance and environment relation matrix; the method comprises the following steps of: calling model parameters to invert a theoretical gas concentration value, and triggering finite element physical field simulation through residual analysis to generate a three-dimensional simulation field data set containing a temperature gradient and a diffusion path. And the dynamic correlation analysis precision and the early warning reliability of storage environment monitoring are improved.

The invention provides a meteorological element three-dimensional analysis method combining remote sensing and ground observation, and relates to the technical field of meteorological monitoring, and the method comprises the steps: obtaining remote sensing and ground observation data, and obtaining meteorological element data of a unified space-time reference; dividing the data into space-time grid units and performing quality evaluation to obtain a quality evaluation index; calculating a fusion weight coefficient according to the quality evaluation index based on an adaptive fusion algorithm of a dynamic weight, and performing weighted fusion on the meteorological element data in the grid units to generate an initial three-dimensional meteorological field; performing scale decomposition and reconstruction by adopting a spectrum analysis method to obtain an optimal three-dimensional meteorological field, constructing a self-adaptive tree-shaped composite analysis grid, analyzing evolution characteristics of a weather system in a multi-layer progressive mode, determining topological evolution parameters, and outputting a three-dimensional analysis result of meteorological elements.

The present invention belongs to the field of new-energy power prediction of electric power systems. Disclosed is a short-term wind-photovoltaic power prediction method based on high-dimensional multivariate meteorological data fusion. In the present invention, an acquired meteorological data set ERA5 is fully utilized, a massive number of meteorological factors are greatly expanded in terms of physical causality, spatial scales, and temporal scales, and characteristic relationships between the meteorological factors and new-energy power are fully explored, thereby improving the prediction accuracy. Power-related meteorological factors are greatly expanded by using a physical causality method and a spatial longitude and latitude envelope method, so as to form a high-dimensional multivariate meteorological factor set. Dimensionality reduction is performed on ultra-high-dimensional meteorological data for comprehensive correlation analysis and fusion to prevent correlations and information overlaps between multi-dimensional meteorological data from affecting a power prediction model; and then, a sliding time window method is used to form a power prediction sample set. The prediction model is fitted according to meteorological data and actual power station operation data, and is applied to actual engineering projects in Dali, Yunnan for verification. The prediction accuracy of the present invention far exceeds that of conventional prediction methods, thus showing relatively strong robustness and universality.

The invention discloses a cascade reservoir collaborative flood control scheduling decision-making method of a coupling meteorological-hydrological-hydraulic model. The method comprises the steps of multi-source meteorological data fusion and probability forecast generation, dynamic coupling hydrological-hydraulic simulation, risk entropy driven collaborative optimization decision-making, digital twin platform verification and correction, instruction execution and closed loop feedback. Through multi-technology fusion and an intelligent optimization mechanism, the scientificity, timeliness and safety of flood control scheduling are remarkably improved. In the weather forecast stage, multi-source data are integrated to output ensemble rainfall forecast scene data, rainfall input uncertainty is reduced from the source, it is ensured that initial driving precision of flood simulation is improved, and a reliable input basis is provided for subsequent model coupling; in the hydrological-hydraulic dynamic coupling stage, a coarse-fine grid dynamic division strategy is adopted to reduce redundancy calculation, the asynchronous pipeline technology enables the flood routing calculation efficiency to meet the real-time scheduling requirement, and the simulation speed is greatly accelerated.

Embodiments are provided for a LIDAR system comprising a laser emission unit configured to generate at least one laser beam; a scanning unit configured to project the at least one laser beam toward a field of view of the LIDAR system; and at least one processor programmed to: determine at least one indicator of a current yaw orientation of the LIDAR system based on analysis of point cloud representations of at least one stationary object in an environment of a host vehicle and based on detected ego motion of the host vehicle; determine a difference between the current yaw orientation and a target yaw orientation for the LIDAR system; and adjust at least one scan range limit associated with the scanning unit to at least partially compensate for a difference between the current yaw orientation of the LIDAR system and the target yaw orientation for the LIDAR system.

The invention relates to a sponge city design optimization method and system based on rainfall calculation, and the method comprises the following steps: predicting the occurrence probability of a rainfall event in a target sponge city based on rainfall analysis data and climate data, and obtaining the probability distribution of future rainfall and the future calculated rainfall; performing spatial analysis on the target sponge city through a preset geographic information system to obtain an easy water accumulation point distribution map; adopting a Bayesian network algorithm to evaluate the waterlogging risk level of each region in the target sponge city based on the distribution map of the points prone to water accumulation, the future measured rainfall and the probability distribution of the future rainfall, and obtaining a waterlogging risk map; and performing layout optimization design on the target sponge city based on the waterlogging risk map by using a preset multi-target optimization algorithm to obtain an optimal configuration scheme, thereby solving the problem of how to scientifically and reasonably predict a rainfall mode and influence, and effectively relieving the urban waterlogging problem.

The invention provides a particulate matter and ozone source monitoring method and system based on a distributed sensor, and relates to the technical field of pollution treatment. According to the invention, sensor nodes with geographic perception capability are deployed in a monitoring area in a high-density manner, pollutant concentration and meteorological parameters are collected in real time, and data are uploaded to a cloud platform for preprocessing and dynamic calibration; a machine learning model is constructed based on the combined features of the pollutants and the meteorological factors, a driving relation is mined, and pollutant influence factors are extracted; further performing joint modeling on the influence factors and regional emission source data, identifying the coupling strength between pollutants and emission sources by adopting a classification or clustering method, and judging the categories of main sources; backward trajectory simulation, source fingerprint analysis and multi-source regression decomposition are combined to realize pollution path inversion and source contribution rate quantification; and finally, constructing a geographic information visualization platform, displaying a pollution thermodynamic diagram, a contribution change diagram and an evolution path diagram, and providing support for multi-source pollution traceability and scientific management and control.

The invention relates to the field of water quality evaluation, in particular to a rain source type river water quality evaluation method and system.The method comprises the steps that a sensor of a water quality monitoring system monitors micro-rain detection data of a micro-rain event, and the water quality monitoring system adjusts the sampling frequency of the sensor through an adaptive algorithm; obtaining water quality monitoring data through a micro-pollution mutation detection method; constructing a water quality recovery model of the micro-rain event through nonlinear kinetic analysis, and predicting a peak value and recovery time of water quality change of the micro-rain event; identifying a significant sudden change point of water quality change through a chaos theory algorithm, and adjusting the sampling frequency of the sensor through fuzzy logic control; key pollutants are obtained according to the peak value of the water quality change and the recovery time, and the key pollutants and pollution source data are subjected to data correlation analysis to judge the pollution source. The problems that the impact and the influence of a micro-rain event on the river water quality are not fully considered, so that the water quality evaluation is not timely and accurate enough, and the water quality change cannot be effectively captured and a pollution source cannot be analyzed are solved.