7353 results about "Observation data" patented technology

The invention discloses a vehicle-mounted Beidou positioning deviation self-calibration system fused with inertial navigation information, and relates to the technical field of navigation and positioning. The method is used for solving the problems of positioning deviation accumulation and signal failure in a complex environment. The system realizes space-time alignment of inertial navigation and Beidou observation data through timestamp interpolation and coordinate system conversion, and constructs a multi-source synchronous data stream. A difference value between an inertial navigation calculation speed and a Beidou Doppler speed is analyzed based on a vehicle incomplete constraint model, and a weight factor is generated in combination with a confidence threshold to suppress abnormal observation. A geometric matching error is calculated through a high-precision lane map curvature and a vehicle steering angle, and candidate tracks are screened to generate transverse calibration parameters. The noise covariance is dynamically adjusted through a tight coupling filtering algorithm, the mode is switched to a lane constraint calibration mode when a Beidou signal is interrupted, inertial navigation errors are compensated through error boundary constraint, and stable output is achieved. Multi-source data deep fusion and adaptive robust calibration are realized, and system robustness and positioning continuity are improved.

The invention relates to the technical field of meteorological data analysis, and provides an aerosol prediction method and system based on artificial intelligence, which are used for accurately identifying an abnormal aerosol gathering mode in an extreme meteorological event. The method comprises the steps that an original multi-mode meteorological observation data set is acquired, and the original multi-mode meteorological observation data set comprises an earth surface aerosol concentration sequence, three-dimensional atmosphere state variables and boundary layer dynamic parameters; performing space-time heterogeneity correction on the original multi-modal meteorological observation data set to generate a space-time aligned target multi-modal meteorological observation data set; inputting the target multi-modal meteorological observation data set into a spatial-temporal feature fusion network to extract multi-scale spatial distribution features and cross-modal time sequence correlation features, and generating a spatial-temporal feature matrix based on the multi-scale spatial distribution features and the cross-modal time sequence correlation features; and performing multi-stage recursive optimization on the spatial-temporal characteristic matrix through a hierarchical time domain aggregation algorithm to generate an aerosol concentration prediction sequence of the target region.

The invention provides an ecological meteorology and satellite remote sensing combined dynamic environment monitoring method and system. Wherein spatio-temporal dynamic concentration data and spectral reflection characteristic data are obtained at a pollutant emission node; generating an associated data block by the spatio-temporal dynamic concentration data and the spectral reflection characteristic data according to a pollution concentration abrupt change event trigger time sequence, and performing chain storage on Hash fingerprints and pollution source geographic coordinate information through a distributed node consensus mechanism; integrating the ecological meteorological observation data to generate a pollutant migration path map; and establishing a pollution influence boundary judgment model according to the pollutant migration path map and the vegetation stress response characteristic data, and monitoring the diffusion range and influence boundary of pollutants in real time through the model to generate an environment monitoring report. According to the technical scheme provided by the invention, the industrial environment pollution dynamic monitoring precision and the decision response efficiency are remarkably improved.

Disclosed is a three-dimensional deformation field modeling method fusing global navigation satellite system (GNSS) / synthetic aperture radar interferometry (InSAR) observation data. The three-dimensional deformation field modeling method includes: five parts of solving an InSAR line-of-sight velocity field, unifying a spatial reference datum of the GNSS data and the InSAR data, verifying precision of a result of the InSAR data, solving a three-dimensional deformation field having high precision based on a least square method, and analyzing and verifying a result. According to the method, the InSAR data is processed according to a high-precision time series method, a long-wavelength error of the InSAR data is corrected on the basis of a difference of two data spatial reference datums, and the accuracy of the result is ensured through verification of internal coincidence precision.

Disclosed in the present invention are a danger test case generation method for a visual perception algorithm, and a related device. The method comprises: acquiring images captured by a visual sensor of a self-driving vehicle, cleaning and marking the collected images, and obtaining an observation data set according to no-reference image quality evaluation features; according to the observation data set and by means of performing fusion with domain knowledge, constructing a causal structure diagram that indicates the influence of environmental factors on perception performance; separating key environmental factors from the causal structure diagram, and quantitatively calculating the degree of influence of each environmental factor on visual perception performance; and on the basis of qualitative analysis of a causal relationship between a key environment variable and the visual perception performance, and quantitative estimation of a causal effect, proposing a challenge index, so as to generate, in batches, danger test cases for perception. The present invention has significant advantages in multiple aspects such as causal relationship mining, challenge index measurement, dangerous-scenario recognition and search space modeling, such that the performance of a visual algorithm for self-driving in a complex environment is evaluated and verified more accurately.

This application relates to the field of geological disaster early warning technology, and discloses a method for monitoring, predicting and warning of geological disasters based on artificial intelligence, including the following steps: data collection: collecting geological disaster monitoring data; data preprocessing: time alignment and missing completion of the collected data; spatiotemporal correlation matrix construction: analyzing the preprocessed monitoring data; feature extraction: extracting the nonlinear spatiotemporal characteristics of geological disaster monitoring data; time series prediction: outputting the geological disaster risk level in the future period; feedback optimization: based on the error between the prediction result and the actual observation data; warning release: generating geological disaster warning information based on the predicted risk level. Through data collection, preprocessing, feature extraction, prediction optimization and warning release, a complete geological disaster monitoring and prediction method is constructed, which integrates multi-source data, extracts key features and dynamically predicts risks, so as to ensure the accuracy of the model and provide a scientific basis for disaster prevention and control.

The invention discloses a method for inverting key parameters in an underground water pollutant biodegradation numerical model based on a neural network algorithm. Carrying out preprocessing and refined dimension reduction on the data, and processing high-dimensional multi-scale data; building a multilayer neural network architecture under a physical information neural network framework, embedding a related reaction equation of a pollutant natural attenuation process, and coupling a biochemical reaction mechanism and a data driving model; a neural network model is trained, errors in the training process are calculated, and the accuracy of an inversion result is improved by using a sub-when optimization algorithm; integrating observation data and a model prediction result, determining final parameter setting of a numerical model, and carrying out underground water damage real object quantification; comparative verification of a neural network parameter inversion model is carried out, and feasibility and accuracy of the parameter inversion method are ensured. According to the method, the problem that the key parameters of the biodegradation numerical model are difficult to accurately invert in the real object quantification process of underground water pollution under complex geological conditions is solved.

The invention provides a marine environment time sequence prediction method and system based on a recurrent neural network, and relates to the technical field of time sequence prediction, and the method comprises the steps: obtaining marine environment historical observation data, carrying out the preprocessing of the marine environment historical observation data, and removing abnormal values and missing values from the marine environment historical observation data; according to the preprocessed marine environment historical observation data, a recurrent neural network model is constructed, and the recurrent neural network model comprises an input layer, two stacked long short-term memory (LSTM) network layers and an output layer; acquiring marine environment observation data at the current moment, and inputting the marine environment observation data into the trained recurrent neural network model; predicting marine environment data of a plurality of time steps in the future through a recurrent neural network model, and obtaining a marine environment prediction result in a preset time; and according to the marine environment prediction result, identifying a hot spot region of marine environment change, and generating a visual result representing a dynamic change rule of the marine environment in the prediction time period.

The invention relates to the technical field of geometric quantity precision measurement based on satellite positioning, in particular to a pumped storage power station dam safety monitoring method based on Beidou positioning, and the method comprises the steps: arranging a Beidou monitoring station and base station array in a dam body and a slope region, synchronously receiving multi-band satellite signals, and carrying out the edge calculation processing; and the effective frequency band data are screened and fused by using a signal-to-noise ratio threshold to generate preprocessed observation data, and the preprocessed observation data are uploaded to a cloud server in real time through a 4G network and a Beidou short message dual-channel transmission link. A cloud server constructs a local enhanced network, ionosphere and troposphere errors are corrected by adopting a multi-base-station joint error modeling method, a closed-loop mechanism dynamically optimizes monitoring point distribution density, signal processing parameters and atmospheric refraction compensation coefficients, and data integrity is guaranteed in combination with a dual-channel redundancy check and interpolation completion technology. According to the invention, full-domain millimeter-level deformation dynamic monitoring of the dam under the complex terrain is realized, and the space-time reference uniformity, the data real-time performance and the structure safety evaluation precision are improved.

The invention provides an emergency broadcast intelligent triggering system based on multi-source seismic data fusion, and relates to the technical field of seismic monitoring and early warning, and the system comprises a data receiving module which is used for receiving multi-source seismic early warning information in real time, including seismic source parameter data, strong vibration observation data and real-time early warning parameters, and transmitting the multi-source seismic early warning information through a built-in communication protocol; and outputting the standardized multi-source seismic data. And the localization calculation module is used for carrying out real-time analysis on the standardized multi-source seismic data, extracting a P-wave initial parameter, a seismic motion acceleration predicted value and a seismic source fracture feature, carrying out dynamic calibration on an analysis result by combining actual measurement data of a built-in intensity meter, and outputting a multi-dimensional seismic data stream verified by the intensity meter. Through multi-source earthquake data fusion, real-time analysis and calibration, dynamic risk modeling and intelligent broadcast scheduling, generation and efficient propagation of earthquake early warning information are realized, emergency response timeliness and pertinence are improved, and earthquake disaster risks are reduced.

A method and a system for reconstructing aerosol chemical components based on CNN-BiLSTM-BO, including collecting multi-source environmental observation data through observation equipment, preprocessing and extracting key characteristic variables. The pre-treated multi-source environmental observation data are input into the CNN-BILSTM model for feature analysis, and the CNN-BiLSTM hyperparameters are adjusted by Bayesian optimization algorithm to generate a reconstructed model of aerosol chemical components. After verifying the performance and stability of the reconstructed model, the predicted results of the chemical components of the aerosol are output. On the basis of not relying on traditional chemical analysis technology, the invention can accurately reconstruct various aerosol chemical components, greatly reduce the cost and time of chemical analysis, effectively solve the problems of variable inconsistency, data missing, and spatio-temporal mismatch in multi-source observation data, and automatically adjust hyperparameters through Bayesian optimization algorithm to ensure that the output prediction results are more accurate.

The invention discloses a multi-dimensional carbon flux monitoring method, and particularly relates to the technical field of carbon flux monitoring, and the method comprises the following steps: collecting multi-source heterogeneous sensor data, and carrying out spatial resolution unification, time synchronization alignment and data format standardization preprocessing to ensure data consistency; executing fusion validity detection, and correcting time synchronization deviation and data redundancy conflicts; according to an abnormal detection result, dynamically adjusting a fusion weight or determining a weight based on historical clustering, and combining spatial adjacency interpolation and time interpolation to realize data fusion; and finally, inputting a carbon flux inversion model, and outputting a high-precision carbon flux monitoring value to realize dynamic monitoring and prediction. According to the method, a dynamic fusion weight adjustment mechanism is adopted, the fusion weight of each data source is dynamically optimized, adaptive fusion of multi-source observation data is realized, the problems of data discontinuity and insufficient data coverage in the prior art are solved, and a multi-dimensional, full-coverage and high-resolution carbon flux fusion data set is ensured to be formed.

The invention relates to the technical field of data processing, and discloses a quality control method and system based on buoy observation data. The method comprises the following steps: performing multi-dimensional anomaly detection processing on preprocessed standard format data to obtain an anomaly detection report marked with abnormal and suspicious data points; performing multi-source data fusion and cross validation processing on the data in the anomaly detection report to obtain an initial data quality score; performing intelligent correction processing on the anomaly detection report to obtain a corrected data set and uncertainty estimation data corresponding to the corrected data set; performing consistency analysis processing on the corrected data set in a space-time dimension to obtain a space-time consistency score and abnormal region marking data; and carrying out comprehensive evaluation processing on the abnormal region mark data to obtain a target data quality grade identifier and a target quality report, and carrying out quality control strategy analysis to obtain a target quality control strategy. According to the invention, the accuracy of quality control based on buoy observation data is improved.

The invention provides an unmanned aerial vehicle multi-target tracking method and system based on visual and millimeter-wave radar information fusion, and the method comprises the steps: obtaining an association result through employing a space-time consistency model, constructing a self-adaptive joint probability data association frame according to the association result, so as to obtain an initial target state estimation value, and carrying out the recognition of the initial target state estimation value. A prospective path planning algorithm is adopted to obtain an optimal observation angle flight path, a Bayesian network is utilized to obtain initial observation data, based on the initial observation data, an optimal target state estimation value is generated, a distributed cooperative sensing mechanism is used to generate an initial tracking task allocation scheme, and an intelligent resource allocation strategy is implemented. Generating an initial resource allocation scheme, starting a quick response protocol to obtain an optimal tracking task allocation scheme, and generating an optimal resource allocation scheme based on the optimal tracking task allocation scheme; according to the technical scheme provided by the invention, the accuracy of target identification is improved, the environmental adaptability is enhanced, the tracking efficiency is improved, and the working time is prolonged.

The invention relates to the technical field of communication, in particular to a low-delay data transmission system and method for marine observation data, and the system comprises a data collection module which collects the multi-modal data of a marine environment, and carries out the priority classification of the data according to the real-time performance and importance; the route optimization module dynamically adjusts a multi-path fragmentation strategy and single-path task distribution in combination with a reinforcement learning algorithm and path optimization data; the communication module supports multi-mode communication modes such as laser communication and low-orbit satellite communication, and monitors the link state in real time; and the data fusion and analysis module performs dimension reduction, fusion and real-time analysis on the data by using manifold learning and a graph neural network, and meanwhile, predicts a long-term trend through time sequence analysis to generate observation report data. The method effectively reduces the transmission delay of high-priority data, improves the response efficiency of the system to abnormal events, and is suitable for the scenes of marine disaster early warning, environment monitoring and the like.

A method of modeling interactive intelligent three-dimensional implicit structure based on deep learning is provided, including: generating a plurality of geological simulation structural models by using a data simulation technology, to construct a geological simulation structural model library, where the geological simulation structural model has a diversified fold and a fault feature; acquiring, for each, model, geological fault data and unevenly distributed geological horizon data to obtain a training sample data set; training a neural network by using the training sample data set; inputting multi-source heterogeneous data of a target region into a trained neural network, so as to output a geological structural model corresponding to the multi-source heterogeneous data, where the multi-source heterogeneous data includes fault interpretation data and horizon interpretation data, and the multi-source heterogeneous data includes at least one selected from: geological outcrop observation data, well logging data, various geophysical data or empirical knowledge data.

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 invention relates to the technical field of unmanned aerial vehicle positioning, in particular to an unmanned aerial vehicle flight path positioning method and system based on multi-source information fusion. The method comprises the following steps: acquiring an original observation data stream of an unmanned aerial vehicle in real time through a sensor array, and preprocessing to output a multi-source data sample set; calculating relative motion increment between adjacent key frames through an IMU pre-integration model, and extracting local pose estimation through a visual geometric calculation method; evaluating the confidence of each sensor in real time to dynamically generate an adaptive weight coefficient; and constructing a tight coupling fusion filter taking position, speed and attitude errors as core state quantities, and outputting a global optimal trajectory of the unmanned aerial vehicle. According to the invention, the weight is dynamically distributed through the real-time confidence of the sensor, the multi-source data association is fully mined, and the flight path positioning precision and environmental adaptability of the unmanned aerial vehicle are improved.

The invention belongs to the field of power distribution network low-carbon economic optimization scheduling, and provides a power distribution network source network load storage low-carbon optimization scheduling increment reinforcement learning method and system, and the method comprises the steps: carrying out the intelligent agent division of a power grid, obtaining a plurality of intelligent agent regions, and collecting the observation data of each intelligent agent region; on the basis of the observation data, constructing a low-carbon economic dispatching model of source-grid-load-storage cooperation by taking the operation cost and carbon emission of each agent regional power grid as objective functions; designing the source-network-load-storage coordinated low-carbon economic dispatching model into a reinforcement learning model in a multi-agent environment according to a state space, an action space, an environment and a reward function; and solving the reinforcement learning model in the multi-agent environment to obtain an optimal scheduling solution. According to the incremental optimization method, the environment can help the intelligent agent to detect environment changes in training, excessive exploration risks are avoided, the training time is shortened, and meanwhile the flexibility of the system is improved.

The invention provides a dam intelligent early warning method and system based on a depth time sequence attention network, and the method comprises the steps: carrying out the time alignment and sliding window segmentation of environment variables and historical displacement data of dam monitoring, extracting multi-scale statistical features, fusing the multi-scale statistical features with original features, carrying out the unified normalization processing of a spliced high-dimensional vector, and carrying out the calculation of the unified normalization processing; model input is generated; based on the constructed DSA-Net, carrying out local feature extraction, bidirectional time sequence modeling and key moment weighting on an input sequence, and jointly outputting horizontal and vertical displacement predicted values; fusing double deformation prediction results into a unified radial deformation index, and dynamically setting an early warning threshold value band according to a historical residual error to realize self-adaptive deformation early warning; quantitative evaluation is carried out on the model prediction precision, online prediction and threshold determination of real-time observation data are realized by using the qualified model and an adaptive threshold mechanism, abnormal early warning is triggered, and alarm information is recorded. According to the invention, deep coupling of deformation dimensions and dynamic threshold early warning are realized.

The invention relates to the technical field of geological disaster monitoring and early warning, and discloses a railway tunnel portal geological disaster deformation early warning system based on an SAR radar, and the system comprises a digital twinborn body construction module which constructs a digital twinborn body with initial parameters based on basic data; the SAR deformation monitoring module is used for acquiring SAR deformation observation data; the digital twinborn body dynamic optimization module is used for inverting and updating parameters by using an optimization algorithm based on the SAR data, and generating an optimized twinborn body; a risk prediction and key area identification module which deduces a disaster scene based on the optimized twinborn body, predicts the risk and identifies a key risk area; and the intelligent early warning module is used for generating early warning information based on the prediction risk and the key risk area. According to the method, the geomechanical digital twins are dynamically optimized by adopting the SAR data, accurate prediction, key area identification and intelligent grading early warning of the geological disaster of the railway tunnel portal are realized, and the initiative and accuracy of risk cognition and early warning are remarkably improved.

The invention discloses a precipitation estimation method and system based on territorial rainfall multi-data fusion and application, and relates to the technical field of weather forecast, by fusing radar, satellite and ground observation data, the accuracy and reliability of precipitation prediction are remarkably improved, the fusion of multi-source data can make up for the deficiency of a single data source, and the prediction accuracy and reliability are improved. Through space-time registration and a weighted average fusion algorithm, a macroscopic rainfall background is provided by introducing territorial rainfall data, and radar, satellite and ground observation rainfall data are combined, so that rainfall estimation is more comprehensive, ground observation stations are supplemented and fused, effective data of the stations are increased, the real time of rainfall can be reflected more accurately, and the rainfall estimation accuracy is improved. In addition, the fusion of multi-source data can make up for the deficiency of a single data source, and the advantages of different data sources can be effectively integrated through the space-time registration and weighted average fusion algorithm.

The present disclosure provides devices, systems, and methods for capturing and analyzing behavioral and physiological data of subjects for treatment, modification, and manipulation discovery. In some embodiments, a method for classifying a drug is provided. The method includes obtaining observational data concerning an animal subject to which the drug is administered, the observational data acquired using an enclosure for the animal subject, the enclosure instrumented with at least one sensing device. The method also includes extracting features by applying the observational data to a machine-learning feature-extraction component. The method further includes predicting a class label of the drug by applying the features to a machine-learning classifier component, the machine-learning classifier component trained to predict the class label of the drug from, at least in part, the features. The method further includes providing an indication of the class label.

The invention relates to the technical field of satellite positioning navigation enhancement, in particular to a global ionosphere inversion method based on BP neural network fusion multi-source data, and the method comprises the steps: inverting STEC through ground-based GNSS observation data, then employing a projection function to project the STEC to a vertical direction of an ionosphere puncture point, and obtaining VTEC; a BP neural network and a Kriging interpolation method are utilized to fuse foundation observation data into an IRI-2016 ionosphere model to solve the problem that GNSS tracking stations are distributed unevenly in the world, so that the inversion precision of the ionosphere is improved, a spherical harmonic function is adopted to express VTEC space distribution, and then the ocean altimetry satellite observation data is utilized to achieve the purpose of solving the problem that the GNSS tracking stations are distributed unevenly in the world. According to the method, the problem of deficiency of ground-based GNSS observation data in an ocean area is solved by using ground-based GNSS observation data, DORIS observation data and COSMIC occultation data, a global ionosphere observation equation is established, a normal equation is constructed, and finally global ionosphere grid data VTEC is generated through Helmert variance estimation precise weighting calculation, so that the global ionosphere observation precision is improved.

The invention provides a weather forecast learning system based on an artificial intelligence algorithm, and the system comprises a data access collection module which collects a multi-source data set; the data fusion and processing module is used for carrying out data fusion and processing to obtain a multi-source fusion data set; the AI model design module is used for constructing a multi-model collaborative architecture and carrying out multi-model parallel training and optimization; the model fusion module is used for carrying out multi-model weighted fusion to obtain an AI model; the real-time prediction module is used for updating a prediction result according to the real-time data; the visualization and interpretability module is used for designing a visualization interface and carrying out interpretability verification; and the evaluation and iteration module is used for carrying out comprehensive evaluation and continuous improvement on a prediction result in combination with evaluation indexes. According to the method, accurate and reliable observation data can be obtained, massive meteorological data are efficiently processed by combining an artificial intelligence algorithm, trend analysis and prediction are automatically carried out, and a reliable prediction result is generated.

The invention relates to the technical field of data processing, and discloses a salinity drift correction method and system for buoy observation data. The method comprises the steps that historical buoy observation data and current environment parameters are collected and preprocessed, and a multi-source heterogeneous data set is obtained; performing multi-dimensional environmental factor analysis on the multi-source heterogeneous data set to obtain a salinity influence factor weight; drifting detection and quantification are carried out on the buoy salinity data through the salinity influence factor weight, and an accumulated deviation integral value is obtained; performing multi-scale correction calculation on the accumulated deviation integral value to obtain preliminary correction data; performing dynamic space-time weight optimization on the preliminary correction data to obtain optimized correction data; and performing real-time performance evaluation and automatic threshold adjustment on the optimized correction data to obtain target correction data and a parameter updating strategy. According to the invention, the accuracy of salinity drift correction for buoy observation data is improved.

The invention discloses a power grid dynamic modeling method based on a physical information neural network and a related device, and the method comprises the steps: obtaining power grid observation data, inputting the power grid observation data into a neural network for power grid dynamic behavior prediction, and obtaining a power grid state prediction value; calculating data item loss through a power grid state prediction value and a power grid state actual measurement value, and calculating physical residual item loss through power grid observation data; the physical residual item loss comprises current conservation constraint loss, voltage closed-loop constraint loss and generator dynamic response loss; and network parameters of the neural network are updated through the data item loss and the physical residual item loss until the neural network converges, and a power grid state model is obtained. According to the method, the mapping relation between the state variable and the system input is established by using the neural network, and the physical rule of the power grid is introduced into the loss function as a hard constraint term, so that physical consistency control during dynamic behavior modeling of the power system is realized, and the accuracy of dynamic behavior modeling of the power grid is improved.

The invention discloses a wind energy resource prediction method, system and device and a storage medium, and relates to the field of renewable energy sources, and the method comprises the steps: obtaining the three-dimensional wind field observation data of a target region; performing space-time alignment processing operation on the three-dimensional wind field observation data to generate a space-time sequence; performing multi-source data fusion on the space-time sequence, and constructing a multi-dimensional wind field matrix; establishing a wind energy potential prediction model based on the multi-dimensional wind field matrix; and predicting to-be-predicted wind energy data based on the wind energy potential prediction model to obtain a wind energy resource prediction result. And efficient, accurate and reliable resource evaluation is provided for wind energy development of various terrain areas by constructing a machine learning model.

The invention relates to the technical field of meteorological data processing, provides an artificial influence weather operation optimization method and system based on multi-scale analysis, and is used for improving the operation efficiency of artificial catalysis operation. The method comprises the steps that multi-source meteorological observation data of a target area are acquired, and the multi-source meteorological observation data comprise cloud layer dynamic distribution data, atmosphere vertical motion data and water vapor flux data; performing multi-scale space-time fusion processing on the multi-source meteorological observation data to generate space-time distribution data corresponding to different meteorological scales; based on the spatial and temporal distribution data, multi-scale meteorological characteristic parameters associated with the artificial catalysis potential tag are extracted, and the multi-scale meteorological characteristic parameters comprise a cloud phase state distribution parameter, a water vapor transmission intensity parameter and a vertical movement rate parameter; and inputting the multi-scale meteorological characteristic parameters into the operation catalysis effect prediction model, and outputting an artificial catalysis operation optimization scheme of the target area.

The invention discloses a spaceborne interference source Doppler observation strategy optimization method, which comprises the following steps: acquiring an original radio frequency signal and satellite orbit data, predicting mutation characteristics in Doppler data, and generating a self-adaptive observation scheme; according to the self-adaptive observation scheme, energy-aware resource scheduling is carried out, observation is executed, and enhanced Doppler observation data is obtained; and carrying out positioning calculation and performance evaluation based on the enhanced Doppler observation data, and generating parameters for iteratively optimizing an observation strategy. According to the invention, intelligent matching of the observation resource and the signal information content is realized, and the resource utilization efficiency, the key event capturing capability and the final positioning precision are improved.