692 results about "Weather station" patented technology

The invention discloses a power construction unmanned aerial vehicle path planning method and system, and relates to the technical field of space calculation, and the method comprises the following steps: determining a target region needing power construction and a preset construction point coordinate; obtaining a topographic map of the target area based on the geographic information database, constructing a three-dimensional map model, and marking the preset construction point coordinates; generating an initial construction path of the unmanned aerial vehicle; the method comprises the following steps: acquiring meteorological data in real time through meteorological data of a meteorological station in a target area, establishing a meteorological obstacle three-dimensional model, and dividing a preset radius threshold with a meteorological obstacle as a circle center into non-flying areas according to the severity level of the meteorological obstacle; adjusting the initial construction path based on the non-flying area; the cruise height is adjusted according to the meteorological data and topographic relief, a relief tracking path is generated, and the constant relative height is kept; and combining the battery capacity, the flight speed and the task priority, dynamically distributing inspection road sections, and generating a corresponding power construction unmanned aerial vehicle flight path.

The invention relates to a runoff hydropower station generating capacity prediction method based on depth data fusion, and the method comprises the following steps: S1, carrying out data layer fusion, integrating multi-source hydro meteorological data, obtaining multi-source data from a meteorological station, a hydrological station, a satellite remote sensing platform and a numerical weather forecasting system, carrying out the cleaning, interpolation and normalization processing of the data, and carrying out the prediction of the generating capacity of a runoff hydropower station; performing data space-time alignment; s2, carrying out feature layer fusion, firstly inputting a multi-source data set of a unified space-time reference, carrying out feature extraction, carrying out feature fusion based on a self-attention mechanism, embedding a water balance equation in a deep learning model, and obtaining a comprehensive feature vector; and S3, performing prediction, inputting the comprehensive feature vector in the step S2, and performing power generation prediction by using a deep neural network.

A regional extreme rainfall forecasting and early warning method based on a conditional generative adversarial network (cGAN) comprises the following steps: preprocessing multi-source meteorological data, including standardization and space consistency alignment, so as to ensure the availability of the data; and through a coding module of the cGAN, extracting and fusing interannual daily average features and horizontal features, constructing a multi-scale feature pyramid, and enhancing the characterization capability of a rainfall spatial mode. Forecasting data are generated through a cGAN generator and a condition discriminator, rainfall collaborative factors are introduced into the discriminator to serve as physical constraint conditions, and network parameters are optimized through an adversarial loss function and physical consistency loss. The forecasting data is post-processed through a graph neural network (GNN), and spatial dependency modeling and optimization correction are carried out by taking a meteorological station observation value as a true value, so that the forecasting precision and reliability of an extreme rainfall event are remarkably improved.

The invention discloses a radar lifting control method and system based on meteorological monitoring, and relates to the technical field of radar lifting control, and the method comprises the steps: completing the switching of a power supply and communication after a radar is powered on, initializing a controller, collecting the data of a meteorological station, and generating a future fusion wind speed in real time through a Kalman filtering physical model and a residual neural network; future fused wind speed is converted into wind pressure for evaluation, the risk degree is judged according to the evaluation result, early warning is given out, and the controller is preheated to enter a lifting preparation state. The input stability is improved through meteorological data sliding window smoothing and feature extraction, wind speed dynamic prediction and uncertainty quantification are achieved through XGBoost prediction and residual variance estimation, the time sequence consistency and robustness are enhanced through remote API interpolation correction and adaptive extended Kalman filtering, residual correction is conducted through a neural network, the prediction precision is improved, and the prediction accuracy is improved. And a reliable decision basis is provided for radar lifting control.

Provided in the present invention are a power-distribution-network self-healing method and system taking photovoltaic output into consideration. The method comprises: acquiring historical operation data of a photovoltaic power station and irradiance observation data from a meteorological station; on the basis of the historical operation data of the photovoltaic power station and the irradiance observation data from the meteorological station, predicting the generated power of the photovoltaic power station by using a convolutional long-short-term memory recurrent neural network model that takes sparrow search into consideration; on the basis of the generated power of the photovoltaic power station, a segment-switch state of a power distribution network and a network topology of the power distribution network, constructing an objective function and a constraint condition for a power-distribution-network self-healing model, and obtaining the power-distribution-network self-healing model; solving the power-distribution-network self-healing model by using a propagation search algorithm, so as to obtain an optimal recovery strategy; and executing the optimal recovery strategy by means of segmented switches and node loads. The present invention can realize self-healing of a power distribution network while ensuring the minimum power generation cost of a distributed power source, the minimum network loss and the minimum node voltage deviation.

The invention provides an inspection robot path optimization method and system based on dynamic obstacle avoidance, and the method comprises the steps: enabling an inspection robot to carry out the multi-layer convolution processing of gas and wind direction data through obtaining the turbulence data of a gas leakage source, the wind direction data of a meteorological station, and environment reflection feature data in real time, and generating a high-precision gas diffusion prediction map, meanwhile, a transparent obstacle optical contour and an unstructured obstacle geometric parameter are extracted from reflection data, and a three-dimensional space mapping relation with a gas diffusion diagram is established. On the basis of the mapping relation and the preset task priority, path control parameters are dynamically generated, global path node distribution is optimized, and the robot path is made to adapt to the composite risk environment of gas concentration sudden change and obstacle movement in real time. According to the invention, the dynamic obstacle avoidance precision and the safety response speed of the inspection robot path in a complex dangerous environment are improved.

The invention relates to the technical field of meteorological remote sensing, in particular to a self-adaptive partition water vapor chromatography method and system based on multi-scale Bayesian prior, and the method comprises the steps: S1, obtaining and processing multi-source observation data of a to-be-analyzed region; s2, constructing a multi-scale grid of the tomographic inversion region; s3, multi-scale representation of the water vapor density is established, and a Bayesian prior constraint model is constructed; s4, constructing a three-dimensional chromatography inversion equation set; and S5, carrying out Bayesian inversion solution. According to the method, global navigation satellite system observation data, meteorological station data and sounding station data are fused, Bayesian prior constraints are constructed, and a multi-scale representation and self-adaptive partitioning method is adopted, so that the number of parameters is effectively reduced, and the stability, precision and calculation efficiency of tomography inversion are improved.

The invention discloses an urban blue-green space microclimate prediction system and method based on AI fusion. The method comprises the following steps of: 1, acquiring field microclimate measured data through linkage of a portable weather station and a GPS (Global Positioning System); 2, utilizing a machine learning downscaling algorithm and a deep learning semantic segmentation technology to extract high-resolution land surface temperature and urban land surface coverage classification data from the remote sensing image; 3, constructing a time-space aligned multi-modal GIS data set, and constructing a high-precision microclimate prediction model by using an ensemble learning algorithm; and 4, introducing an SHAP interpretability framework, carrying out deep analysis on the model, and quantifying the contribution degree of various environmental elements to a microclimate prediction result and the complex nonlinear influence of the contribution degree. According to the method, unprecedented high-precision and explainable scientific decision support can be provided for formulating urban planning, landscape design and thermal environment mitigation strategies, and development of healthy, sustainable and climate-flexible cities is powerfully promoted.

The invention discloses a new energy station intelligent micrometeorological monitoring device suitable for extreme weather and a power prediction method, and relates to the technical field of new energy station meteorological monitoring and power prediction. The device integrates a plurality of meteorological sensors, combines satellite meteorological data and ground meteorological station data, improves meteorological monitoring accuracy through data fusion, and gives 8-15-day long-time scale weather forecast. According to the method, based on meteorological data collected in real time and historical meteorological data of a new energy station, a long-short term memory network model and a segmentation multi-target risk method are adopted to carry out risk assessment on extreme weather, and an extreme weather early warning signal is given. A deep neural network is utilized to establish a power nonlinear prediction model of a new energy station, and a higher-precision power prediction result is given through a random forest dynamic correction algorithm. The meteorological and generated power prediction accuracy of the new energy station is improved, and the operation safety of the new energy station is improved.

The invention provides a multi-source data fusion-based high-wind disaster risk assessment method along a high-speed rail, and the method comprises the steps: obtaining the meteorological temperature distribution, the wind speed peak record, the rainfall cumulative value and the wind direction angle change of a meteorological station, carrying out the format conversion and time-space alignment of multi-source data through a preset standardization protocol, and obtaining a multi-source data format; generating a first data set with a unified spatial-temporal scale; acquiring a corresponding wind speed peak record, wind direction angle change and disaster loss degree through a similar case set in the third data set, calculating a first risk probability under the current condition by adopting a weighted average method based on historical cases, and generating a first risk value; updating a line state factor in the fourth data set through a fused probability value in a risk assessment result, adjusting a weight parameter of a wind direction matching coefficient by adopting an online learning method, and generating an optimized fifth data set; according to the continuous dynamic risk assessment sequence, the operation strategy of the high-speed rail line is adjusted in real time, and line safety is ensured.

The invention relates to the technical field of port operation state sensing and monitoring, and particularly discloses a port operation state sensing and monitoring system based on a data center, which monitors a wind speed value and a visibility value at each current time point in real time through a micro weather station array, and synchronously obtains tidal phase data of an observatory. Comprehensively generating multi-dimensional meteorological data in a set time period; dynamically calculating the berthing safety coefficient of the target ship through a multi-dimensional constraint condition in combination with the load tonnage of the target ship; according to the target ship berthing safety coefficient, a dynamic ship berthing strategy of the target ship is generated; through comprehensive application of multi-dimensional meteorological data acquisition, berthing safety coefficient calculation, dynamic ship berthing strategy generation and a feedback terminal, a port manager can monitor and optimize the ship berthing process in real time. Therefore, the operation safety and efficiency of the port are improved, a more scientific and flexible berthing scheme is provided for the ship, and the sustainability and economic benefits of port operation are ensured.

The embodiment of the invention provides a multi-element deep learning correction method and device for numerical mode forecasting. The method is applied to the technical field of data processing, and comprises the following steps: marking observation points, and performing loss function construction on the positions of the observation points to obtain a model optimization target; carrying out iterative updating on model parameters, and carrying out training process optimization by adopting a dynamic learning rate adjustment strategy to obtain a forecast field correction model; and applying the prediction field correction model to target numerical mode prediction data acquired in real time, performing prediction effect evaluation, and performing standardization processing and anti-standardization processing to obtain a correction prediction result. According to the method, the information extraction capability of a deep learning visual algorithm model is utilized, fine deviation correction of data of a numerical mode forecast space grid by meteorological station observation data is completed, meanwhile, multiple types of meteorological elements are fused, the deep learning model is utilized to learn mutual influences and restrictions between related elements, and the prediction precision of the numerical mode forecast space grid is improved. And finer deviation correction is completed.

The invention relates to the technical field of microwave communication, and discloses a microwave communication data transmission method, which comprises the following steps: S1, sensing real-time rainfall intensity and atmospheric turbulence intensity parameters through combination of a multi-band radar and a meteorological sensor; s2, establishing a fractional order random channel model of a rainfall and turbulence coupling effect; s3, generating a dynamic coded modulation control strategy based on deep fractional order reinforcement learning; s4, optimizing a programmable metasurface reflection coefficient matrix according to the dynamic coded modulation control strategy and a channel state to reconstruct an electromagnetic propagation path; s5, executing power-codebook joint optimization of space-time coding and non-orthogonal multiple access; and S6, the system stability is monitored on line, and a degradation fault-tolerant mechanism is triggered. According to the invention, a Ku / W dual-band radar and meteorological sensor variational Kalman fusion technical scheme is adopted, and compared with a single-band radar or sparse meteorological station isolated observation scheme in the prior art, the problem of signal sudden drop misjudgment in a rainstorm scene is solved.

The invention provides a low-cost and high-integration meteorological detection system for low-altitude meteorological service, and belongs to the technical field of meteorological detection. The low-cost and high-integration-level meteorological detection system for the low-altitude meteorological service comprises a plurality of small meteorological stations and MEMS sensors integrated with temperature, humidity, air pressure, wind speed, wind direction and the like. A plurality of small laser wind finding radars are based on an optical fiber laser and a VCSEL (Vertical Cavity Surface Emitting Laser) technology and adopt a modular design. And the data fusion module is used for fusing multi-source data of a ground station, a laser radar, a satellite and the like by adopting a four-dimensional variation assimilation algorithm to generate a 100-meter-resolution grid meteorological product. The technical bottleneck of traditional low-altitude meteorological detection is broken through, and a meteorological service solution is provided for the fields of urban fine management, navigation safety, intelligent agriculture and the like. The system has the characteristics of high cost performance, high flexibility and high resolution, and promotes low-altitude meteorological observation to be upgraded from sparse monitoring to global sensing.

The invention discloses a hybrid photovoltaic prediction method based on SARIMA-LSTM, and the method comprises the steps: carrying out the calculation according to historical photovoltaic power generation data in an SCADA system, wherein the historical photovoltaic power generation data comprise the power generation power and the accumulated power generation amount, and the meteorological data comprise the irradiance, the temperature, the humidity, the wind speed, the cloud cover and the like, and are obtained from a meteorological station or an open-source data platform; statistics is carried out by combining an SARIMA model with a seasonal prediction function and an LSTM model for processing a time sequence, and a more accurate photovoltaic power generation prediction value is obtained after combined prediction and evaluation. Compared with the prior art, short-term prediction and long-term prediction can be considered, and seasonal change data prediction is more accurate.

The invention relates to the technical field of weather forecast, discloses a method for classifying severe convection weather forecast, and aims to solve the problem that complexity of severe convection weather requires multi-dimensional data support, so that a three-dimensional data acquisition network covering'ground-air-sky 'needs to be constructed. Ground observation data need to include minute-level rainfall, hourly air temperature and humidity (emphatically paying attention to humidity difference between 850hPa and 500hPa and reflecting unstable stratification) and 10-minute average wind speed of a meteorological station, and high-altitude detection data need to extract temperature vertical profiles (calculating convective condensation height LCL) and wind speed vertical shear (shear values of 0-3km and 0-6km) at 08 o'clock and 20 o'clock every day. According to the method for classifying severe convection weather forecast, new signals (such as sudden cloud top brightness temperature drop) observed in real time are rapidly absorbed, and meanwhile, the method is adaptive to severe convection characteristic differences of different areas (such as mountainous areas and plains) and different seasons, so that the forecast precision is improved, and the requirements of refined disaster prevention for high-accuracy and high-timeliness forecast are met.

The invention relates to the technical field of water resource management, in particular to a cascade reservoir ecological scheduling system based on artificial intelligence, which comprises a multi-source data integrated processing module, an ecological influence prediction module, a scheduling strategy optimization module, a self-adaptive water quality management module and an intelligent user interaction module. Wherein the multi-source data integrated processing module is used for collecting various data of a meteorological station, a hydrometric station and an ecological monitoring point; the ecological influence prediction module is used for constructing an ecological influence model; the scheduling strategy optimization module is used for formulating a reservoir scheduling strategy; the self-adaptive water quality management module is used for adjusting the reservoir outlet flow and the adjusting strategy of the reservoir; and the intelligent user interaction module provides a human-computer interface. According to the invention, by integrating multi-source real-time data, applying reinforcement learning to optimize the scheduling strategy and providing an efficient user interaction interface, the real-time responsiveness and decision-making efficiency of reservoir management are significantly improved, and optimal utilization of water resources and protection of the ecological environment are ensured at the same time.

The invention provides a dynamic aggregation peak regulation method and system based on distributed photovoltaic power prediction and computing equipment, and is applied to the technical field of dispatching and control of a power system. The dynamic aggregation peak regulation method comprises the following steps: acquiring data of a photovoltaic power station through a photovoltaic power station monitoring system, a meteorological station and a geographic information system; performing photovoltaic power prediction by using the distributed photovoltaic power prediction model; load demand prediction is carried out on the load in the future time period; performing network division and dynamic aggregation on the photovoltaic area; and carrying out peak regulation strategy optimization based on the output of the dynamic aggregation. According to the method, the peak regulation pressure of power grid operation can be reduced, and the stability and economical efficiency of the system are improved.

The invention discloses a rainfall forecast correction method based on CNN-BiLSTM-At and ST-CFormer. The rainfall forecast correction method comprises the following steps: acquiring a meteorological radar echo sequence image and meteorological station observation data, and preprocessing the meteorological radar echo sequence image and the meteorological station observation data; constructing a CNN-BiLSTM-At rainfall forecast model structure, training the structure by using the preprocessed meteorological radar echo sequence image, optimizing hyper-parameters by using an adaptive genetic algorithm, and obtaining a trained rainfall forecast target model; the method comprises the following steps: constructing an ST-CFormer rainfall forecast deviation correction model structure, training the ST-CFormer rainfall forecast deviation correction model structure by using data obtained by fusing a rainfall forecast image and observation data of a meteorological station, and optimizing hyper-parameters by using an NSGA-II algorithm to obtain a trained rainfall forecast deviation correction model. According to the method, secondary correction is carried out on the forecast result by utilizing meteorological station observation data and the global modeling capability of the Swindow-transformer, so that the rainfall forecast precision is effectively improved.

The invention relates to the technical field of snow melting disaster early warning, in particular to a snow surface rain disaster identification method based on a high and cold mountainous area and a related product, and the method comprises the steps: constructing a snow surface rain disaster single-point identification rule capable of being physically explained by using meteorological observation data and remote sensing inversion data of a small amount of weather stations in the high and cold mountainous area; the method is advantaged in that classification is carried out without depending on a black box model, precision and interpretability of single-point snow surface rain event discrimination are improved, regional popularization is carried out through dynamic downscaling simulation of a regional climate mode WRF coupling land surface process model Noah-MP, a point-surface fusion snow surface rain disaster discrimination and identification system is constructed, and the method is advantaged in that the method is simple and convenient to operate. The method is suitable for identifying snow rain disasters in complex terrain areas with high and cold areas, high altitude, large gradient, lack of data, even no data and the like, and solves the technical problems of insufficient snow rain disaster space identification capability and low precision caused by sparse observation data in the high and cold mountainous areas in the prior art.

The invention provides an unmanned aerial vehicle autonomous obstacle avoidance and dynamic path planning method and system based on multi-modal perception and hybrid intelligent decision. Environmental parameters such as wind speed, illuminance, temperature and humidity and image quality indexes are collected in real time through an airborne weather station, an IMU and a visual sensor, a flight parameter-image quality coupling model is established, and multi-target optimization is achieved through support vector regression (SVR) and particle swarm optimization (PSO). And designing a dynamic strategy optimization module based on Q-learning, and designing a reward function in combination with image quality, obstacle avoidance safety and energy consumption. An obstacle three-dimensional model is constructed in real time through ORB-SLAM3, a dynamic danger coefficient is calculated, and an obstacle avoidance track is generated by adopting an improved APF-RRT * algorithm. And the online cooperative control module optimizes the control quantity by using a BFGS algorithm so as to ensure the flight stability and the task efficiency. The method realizes high-precision obstacle avoidance and path planning of the unmanned aerial vehicle in a complex environment, has the characteristics of high robustness and wide adaptability, and is suitable for practical application scenes such as routing inspection, surveying and mapping and the like.

The invention relates to the technical field of air pollution prediction, and provides an air pollution prediction system and method based on meteorological data. The data acquisition and fusion module is used for acquiring meteorological data, air pollutant data and traffic data and carrying out fusion processing on the data; the feature extraction module is used for filling missing data, normalizing the data and extracting features; the pollution prediction module is used for carrying out short-term and long-term prediction on air pollution based on the processed data, and carrying out visual display and air pollution early warning notification on a prediction result; and a model integration module. By integrating data of a weather station, an air pollution monitoring station, a traffic monitoring system and satellite remote sensing equipment, the system realizes comprehensive monitoring of air pollution. The spatial-temporal data fusion unit is used for performing data interpolation and consistency processing, so that the coordination of data from different sources is ensured, more comprehensive and accurate input data is provided, and the reliability of a prediction result is improved.

The invention discloses a high-resolution peak accumulated snow depth inversion device and method based on a geographically weighted random forest, and the device comprises a data obtaining module which is used for obtaining the multi-source data of a research region, and the multi-source data comprise MODIS remote sensing accumulated snow data, Sentinel-1VV / VH backscattering data, topographic data, and meteorological station snow depth observation data; the high-resolution snow coverage data set construction module is used for processing the MODIS remote sensing snow data to obtain a high-resolution cloudless binary snow coverage data set; the feature construction module is used for constructing a comprehensive feature set containing a snow space-time process feature index, a back scattering feature index and a topographic feature index; and the model construction and analysis module is used for training a geographically weighted random forest model through the comprehensive feature set, and inverting the peak snow depth of the research area according to the optimal model. According to the invention, estimation of high-resolution peak snow depth can be realized.

The invention provides a three-dimensional triple configuration potential evapotranspiration fusion method and system considering neighborhood space-time non-stationary errors, and relates to the technical field of potential evapotranspiration estimation. The method comprises the following steps: obtaining a potential evapotranspiration observation value of a target watershed meteorological station and three sets of independent potential evapotranspiration data sets, and unifying temporal-spatial resolution; calculating a Gaussian kernel function matched with the predetermined space-time window, and performing data standardization by using the kernel function; constructing a variance calculation model of the three-dimensional triple configuration error, and obtaining a variance of an error of neighborhood dimension spatio-temporal information contained in each data set under a predetermined spatio-temporal window; and constructing a data fusion model based on a least square framework, determining an optimal fusion weight and realizing fusion of the multi-source potential evapotranspiration data. According to the method, the problems that local space-time non-stationary errors exist during fusion of the potential evapotranspiration data and the fusion precision is influenced by the scale effect of the space-time window are solved, and the precision of the potential evapotranspiration fusion data is effectively improved.

The invention discloses a multi-sensor fusion meteorological data comprehensive analysis platform, and relates to the technical field of meteorological monitoring. The platform comprises a three-level sensing layer, a dynamic collaborative architecture and a meteorological data analysis layer. The third-level sensing layer works cooperatively through a ground total-factor meteorological station, an unmanned aerial vehicle cluster and a FY-4 satellite and is integrated with a bimodal ultrasonic wind measurement sensor, a miniature meteorological sounding cabin and an HRIT / LRIT data receiving module, so that multi-dimensional data acquisition from the ground to a 5km high altitude is realized; the dynamic collaborative architecture is based on an edge computing node and a cloud fusion module, adopts a lightweight LSTM (Long Short Term Memory) model to detect abnormity in real time, and dynamically optimizes the weight of multi-source data through Kalman filtering; and the meteorological data analysis layer is combined with the element coincidence index, the layering stability index and the risk assessment model to generate a graded early warning signal. According to the scheme, the problems of vertical data fault, low multi-source data fusion efficiency, early warning lag and the like in the traditional technology are solved, and the severe convective weather prediction precision is remarkably improved.

The embodiment of the invention provides a method and a device for monitoring abnormity of an automatic ocean weather station based on multi-dimensional configuration characteristics, which are applied to the technical field of meteorological monitoring. The method comprises the following steps: acquiring meteorological monitoring data and position calibration data in a target station area; grouping stations in the target station area according to the meteorological monitoring data and the calibration data; respectively inputting the meteorological monitoring data of the stations in each group into a preset performance health degree model, and correspondingly outputting performance health indexes; and / or respectively inputting the meteorological monitoring data of the stations in each group into a preset configuration health degree model, and correspondingly outputting configuration health indexes; and determining abnormal sites according to the performance health indexes and / or the configuration health indexes of the sites in each group. In this way, real-time monitoring of the operation health degree of the coastal automatic weather station equipment, abnormal early warning and automatic recognition of fault points can be achieved.

The invention provides an energy coordination control system and scheduling method for multi-energy complementary new energy storage, and belongs to the technical field of new energy storage and energy technology management. The data acquisition and processing module is connected with a current and voltage sensor, a power transmitter, a meteorological station sensor and a battery management system sensor, and is used for acquiring voltage, current, power, state of charge and environmental data of photovoltaic power, wind power, energy storage and load through the sensors; and sliding window filtering and wavelet de-noising processing are carried out through the embedded processor. The method has the beneficial effects that the uncertainty of wind and light output is effectively processed by establishing a layered distributed optimization architecture and adopting a method of combining multi-objective optimization and robust optimization, and the consumption rate of renewable energy sources can be remarkably improved, the net load fluctuation can be reduced and the energy consumption can be reduced on the premise of meeting various system operation constraints. Therefore, dependence on conventional fossil energy is reduced, and the economic operation level and the environmental protection benefit of the whole energy system are improved.

A meteorological-driven intelligent tracing method for leakage of hazardous chemical substances belongs to the technical field of hazardous chemical substance leakage detection, and comprises the following steps: deploying miniature meteorological stations and gas sensor arrays in a monitoring area according to a fixed interval to obtain meteorological and concentration data streams; and cleaning and structuring the meteorological and concentration data flow to generate a meteorological-concentration structured parameter table. And assimilating the structured parameter table to a lattice Boltzmann CFD model to recalculate the structured parameter table and output a gridding concentration distribution. And extracting out-of-standard region boundaries from the gridding concentration distribution, and generating a high-risk region boundary list. And generating a visual layer on a GIS platform based on the high-risk region boundary list, and triggering early warning information. Wherein the boundary of the exceeding region is a region in which the meteorological and concentration data in the gridding concentration distribution exceed a set threshold value. According to the method, the leakage source can be positioned at the first time after the hazardous chemical substance leaks, and the leakage source positioning efficiency and accuracy are improved.

The invention discloses a multi-scale spatial-temporal feature extraction and fusion weather prediction method and system, and the method comprises the steps: constructing a multi-scale spatial structure through employing a hierarchical clustering algorithm, extracting a trend component according to node data, carrying out the detrending of the node data, obtaining a season component, and obtaining a final time feature; calculating delay values of sub-nodes in the multi-scale space structure relative to father nodes, sorting the delay values to obtain a sequence index, and further obtaining space features; performing feature fusion on the time features and the space features to obtain space-time fusion features; and fusing the space-time fusion features of different scales to obtain a multi-scale feature fusion result, and performing linear mapping on the multi-scale feature fusion result to obtain a weather prediction result. According to the method, the calculation cost and the prediction error are reduced, the irregularly distributed meteorological station data can be flexibly processed, and the processing capability of the complex station layout data is improved.

The invention discloses a hydropower station operation state intelligent prediction method and system based on multi-source data fusion. The method comprises the steps that heterogeneous data including but not limited to water level, flow, generating capacity, equipment operation parameters, environmental meteorological data, hydrological data and the like are collected in real time from multiple data sources of a sensor network, an SCADA system, a meteorological station and a hydrological station of a hydropower station; the method comprises the following steps of: preprocessing collected original data, extracting time sequence features, fusing multi-source data, taking a fused multi-dimensional feature vector as input, and learning and training through an encoder-decoder architecture of a model so as to capture a complex dynamic mode of an operation state of a hydropower station; and inputting the data acquired and preprocessed in real time into the trained AI prediction model, and generating prediction results of key operation parameters, equipment states, residual life and potential risks of the hydropower station. The method can more accurately capture the complex nonlinear relation in the operation of the hydropower station, and significantly improves the prediction precision of key parameters and risks.