246 results about "Multiple-scale analysis" patented technology

The invention discloses a financial data risk control system and method based on big data, and relates to the technical field of financial science and technology, and the system comprises a multi-source data collection module which achieves cross-mechanism safety collection through federal learning; the data cleaning and preprocessing module is used for processing abnormal values and missing values by using an improved algorithm; the knowledge graph construction module is used for constructing a dynamic knowledge network based on an innovative algorithm; the risk assessment engine fuses various models to assess risks; the real-time monitoring and early warning module is used for realizing second-level response by utilizing multi-scale analysis; the decision support module is used for optimizing a strategy based on reinforcement learning; and the audit tracking module guarantees evidence storage and privacy through zero-knowledge proof, and all the modules cooperate to improve the risk control capability. According to the financial data risk control system and method, risks are accurately recognized, real-time monitoring and early warning are achieved, data security sharing is achieved, risk control strategies are dynamically optimized, risks and business development are balanced, the risk prevention and control capacity and economic benefits of financial institutions are improved, and data privacy and risk control transparency are guaranteed.

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 bridge structure optimization method based on BIM and a finite element method, and relates to the technical field of bridge structure optimization. According to the method, the finite element analysis model can be more accurately established, and the design precision is improved. Through finite element analysis and a digital simulation technology, structure response data is rapidly obtained, and design optimization is accelerated; the optimization of the final scheme is ensured by a multi-iteration optimization strategy, the extreme load response is predicted by introducing a machine learning algorithm, a high-risk area is identified in advance, and the overall performance is improved; based on a multi-scale analysis method, the durability and safety of the structure are comprehensively evaluated, weak links are identified, and long-term performance is guaranteed; meanwhile, the influence of environmental factors on material characteristics is considered, adjustment is performed according to climate data, the design scheme is closer to the actual environment, and durability is enhanced.

The invention relates to the technical field of cable partial discharge detection and diagnosis, provides a high-voltage cable multi-parameter intelligent diagnosis method, system and equipment and a storage medium, and aims to solve the problems of low accuracy and poor reliability of high-voltage cable multi-parameter diagnosis caused by multiple factors. The method comprises the following steps: decomposing a partial discharge signal after interference filtering, extracting a time domain amplitude change rate and an energy distribution parameter, and generating a multi-dimensional time-frequency domain feature vector; calibrating a multi-dimensional time-frequency domain feature vector, carrying out nonlinear manifold mapping, generating a low-dimensional feature vector matched with dielectric spectrum characteristics, matching a partial discharge type with the highest similarity with an identification result, judging an insulation degradation level in combination with a final loss threshold interval, and generating a comprehensive diagnosis report. According to the invention, the ultrahigh-frequency sensor array captures the partial discharge signal matched with the dielectric spectrum, precise identification of the discharge type and dynamic evaluation of the insulation degradation level are realized through multi-scale analysis and deep learning fusion, and the reliability of high-voltage cable diagnosis is improved.

The invention provides a fine-grained cultivated land protection potential optimization method, medium and system, and belongs to the technical field of cultivated land data processing.The method comprises the steps that firstly, multi-source remote sensing, soil and climate data are obtained, and a basic feature matrix is extracted through multi-dimensional decomposition; then, multi-scale analysis of soil quality features is realized by utilizing a pyramid neural network model, and adaptive fusion is carried out through a feature integration module; and then constructing a contribution degree optimization equation set, comprehensively considering four aspects of benchmark evaluation, space correlation, time sequence evolution and weight balance, realizing accurate evaluation of the cultivated land protection potential, and generating an optimized protection partition scheme. On one hand, the pyramid neural network model overcomes the problem of insufficient feature extraction of a traditional method through multi-scale feature extraction and adaptive fusion; and on the other hand, through multi-dimensional information fusion and dynamic optimization, the contribution degree optimization equation set solves the problem that accurate evaluation of the cultivated land protection potential is difficult to realize in the prior art.

The invention relates to the technical field of image processing, in particular to a craniocerebral disease area identification and detection method and system based on an MRI (Magnetic Resonance Imaging) image, and the method comprises the steps: obtaining a plurality of sub-images with different scales according to a gray level image of the craniocerebral MRI image; performing multi-scale analysis on the gradient value of any pixel point according to each sub-image to obtain a multi-scale gradient coefficient of any pixel point; obtaining a multi-scale local anomaly degree according to the gray values of the pixel points in different local ranges of any pixel point and the distribution in the gradient direction; optimizing the gradient value of any pixel point according to the multi-scale gradient coefficient and the multi-scale local anomaly degree to obtain a self-adaptive gradient value, and performing image enhancement on the grayscale image by using an anisotropic diffusion filtering algorithm according to the self-adaptive gradient value of each pixel point so as to identify a craniocerebral disease region. And the effect of performing image enhancement on the MRI image by using the anisotropic diffusion filtering algorithm is improved.

The invention relates to the technical field of smartphone surface defect detection, and discloses a smartphone defect detection method and system, and the method comprises the steps: firstly collecting defect detection image data through multi-dimensional optical imaging, and dividing the defect detection image data into a plurality of detection sub-regions; extracting surface defect features based on a texture enhancement technology, and extracting defect difference indexes in combination with pixel distribution and spatial correlation; then, the regularity of defect modes is analyzed and evaluated through multi-scale decomposition, the defect fluctuation amplitude is dynamically analyzed and evaluated through a sequential sequence, and the stability of defect difference indexes is judged by integrating the regularity and the fluctuation amplitude; and finally, comparing the stable defect difference index with reference data, and determining a detection result. According to the method, through multi-dimensional imaging, multi-scale analysis and dynamic evaluation, the recognition precision and reliability of complex surface defects are improved, the method is suitable for surface detection of smart phones of different materials and structures, and the high-quality detection requirement in large-scale production is met.

The invention discloses a numerical control machining intelligent monitoring and self-adaptive control method and a related device, and the method comprises the steps: collecting a multi-mode original data set in a high-speed rotation state of a main shaft; performing time-frequency domain conjoint analysis on the data set to generate a time-varying feature matrix; reconstructing an inner hole state digital twinborn model based on the time-varying feature matrix; performing multi-scale analysis on the model, identifying macroscopic geometric deviation and microscopic material change, and generating a layering abnormal index; historical data and abnormal indexes are combined, a deep learning algorithm is applied to analyze the performance evolution trend of the spindle inner hole, and a multi-time-scale state prediction result is output; and according to the prediction result and the current processing task requirement, dynamically generating a decision scheme including processing parameter optimization and a customized maintenance plan, and feeding back the decision scheme to a processing control system in real time. According to the invention, real-time dynamic monitoring of the inner hole of the main shaft can be realized, accurate performance analysis and prediction are provided, and the stability and the machining precision in the machining process are ensured.

The invention relates to the technical field of data quality evaluation, in particular to a quality evaluation system and method based on river channel data. The method comprises the following steps: obtaining multi-source river channel data, and carrying out parallel data fusion to obtain river channel fusion data to be evaluated; performing error fluid propagation simulation based on the to-be-evaluated river fusion data to obtain an error statistical result set; performing spatial deviation multi-scale analysis according to the error statistical result set to obtain an elevation error distribution diagram; randomly selecting an underwater point cloud sample based on the to-be-evaluated river fusion data, and performing spatial interpolation point cloud precision calculation on the underwater point cloud sample to obtain a point cloud precision error matrix; performing quality detection and correction on the to-be-evaluated river fusion data by combining the point cloud precision error matrix and the elevation error distribution diagram to obtain quality optimization data; and based on the quality optimization data, carrying out river channel map profile visualization to generate a river channel optimization map profile. The method is helpful for improving the precision and consistency of the river surveying and mapping data.

The invention relates to an intelligent CNC (computer numerical control) machining method which comprises the following steps: dynamically monitoring a tool-workpiece interaction interface in a CNC machining process through a multi-dimensional torque sensor array to obtain a machining process feature data set, and analyzing a tool motion track in real time to obtain a tool motion state feature quantity; secondly, performing multi-scale analysis by adopting a wavelet packet decomposition technology, and determining processing quality evaluation data; and if the data does not reach the preset standard, performing microstructure reconstruction on the surface of the workpiece based on the data, and generating a workpiece processing state characteristic spectrum. Then, comprehensively analyzing the atlas by utilizing a multi-objective optimization technology to obtain a processing parameter optimization sequence; and finally, implementing closed-loop feedback control on the numerical control system according to the optimized sequence, and generating a real-time processing control instruction. The key problem of how to improve the automation level and the machining precision in the CNC machining process through an intelligent means is solved.

The invention relates to the technical field of defect detection, and discloses an industrial surface defect detection method, system and device and a storage medium. The method comprises the following steps: receiving an input surface image of a to-be-detected industrial product and carrying out image preprocessing to obtain a preprocessed surface image; performing multi-scale decomposition on the preprocessed surface image to obtain images of different scales; processing the images of different scales based on an adaptive local contrast enhancement algorithm to obtain enhanced images of different scales; performing multi-scale fusion on the enhanced images of different scales to obtain a target enhanced image; and performing defect detection on the target enhanced image based on an adaptive threshold segmentation method to obtain a defect candidate region. According to the method, industrial surface defect detection is carried out in a mode of combining multi-scale analysis and self-adaptive local contrast enhancement, so that the accuracy and robustness of defect detection are effectively improved, and the requirement of real-time detection in industrial production can be met.

The invention discloses a BGA welding spot bubble detection method and system based on a self-adaptive threshold value and multi-scale analysis. The method comprises the following steps: taking an obtained grey-scale map of a PCB device as an original image; the method comprises the following steps: carrying out linear gray transformation and binarization on an original image, carrying out morphological optimization on the binarized image, and carrying out connected domain analysis and area screening on the optimized image to obtain a candidate welding spot image; performing edge detection and contour screening on the candidate welding spot image to obtain a welding spot extraction image; traversing the contour in the welding spot extraction image, and intercepting a welding spot sub-image from the original image according to the circle center coordinate and the radius of the contour; dividing regions of the welding spot sub-graph, dividing each region into groups, and calculating an Otsu threshold value of a pixel gray value in each group; according to an Otsu threshold value, carrying out bubble judgment on the pixels in the group; taking the pixels meeting the conditions as bubble detection results; and splicing the bubble detection results of the welding spot sub-images according to the circle center coordinates of the contours on the original image to generate a bubble defect image. According to the invention, the detection accuracy and efficiency are improved.

The invention provides a three-dimensional visualization expression method for a multi-scale geological structure model, including: collecting geological exploration data which includes geophysical information, drilling information, outcrop survey information, seismic information and geological age data; performing interpolation simulation on the geological exploration data to obtain an exploration sample set; preprocessing the exploration sample set to obtain preprocessed data, the preprocessing process including data cleaning, format unification and noise removal; constructing a multi-scale geological structure model according to the preprocessed data based on a three-dimensional modeling technology; performing scale decomposition on the geological structure model using a multi-scale analysis method to form model representations at different scale levels; and converting the model representations into 3D visualization images using a 3D graphics processing technology. The invention can effectively improve the expression ability of geological model and user experience, and has important practical application value for geological exploration and resource assessment.

The invention discloses an aero-engine residual life prediction method based on multi-modal deep learning, and relates to the field of aero-engine prediction and health management. The method comprises the following steps: acquiring and preprocessing multi-sensor time sequence data; constructing a degradation sensitive feature set through multi-scale analysis of a time domain, a frequency domain and a time-frequency domain; constructing a multi-modal deep learning model comprising an original data processing module and a multi-scale feature processing module, and introducing an attention mechanism and an uncertainty quantization module into the model; the model is subjected to lightweight processing to support embedded deployment. According to the method, multi-scale features and multi-modal deep learning are fused, the uncertainty quantification capability is achieved, high-precision and interpretable residual life prediction with uncertainty quantification is achieved, and reliable support is provided for engine maintenance decision making.

The invention relates to the field of thermal power generation, and discloses a thermal power boiler combustion optimization method based on multi-parameter feedback control, which comprises the following steps: acquiring key parameters such as oxygen concentration, temperature, pulverized coal concentration, air flow, pulverized coal flow and boiler load in boiler operation; establishing a distributed parameter model for describing dynamic changes of oxygen concentration, temperature and pulverized coal concentration in the hearth along with time and space; key characteristic variables representing the combustion state are extracted through multi-scale analysis; dynamically optimizing multiple parameters of different areas in the hearth based on a distributed feedback controller; a global optimization objective function is constructed, and the ratio of the air flow to the pulverized coal flow is dynamically adjusted; and the optimized control instruction is sent to a boiler system, and combustion parameters are adjusted in real time through an execution mechanism. According to the method, through distributed modeling, multi-scale analysis and feedback control, the combustion efficiency is remarkably improved, pollutant emission is effectively reduced, and the method can adapt to coal quality fluctuation and load change.

The invention relates to the technical field of electrical measurement and power system monitoring, in particular to a cable insulation fault detection method. The method comprises the following steps: measuring an electric field signal and constructing an electric field distribution model in a cable to obtain a signal mapping result of a cable insulation state; the signal mapping result of the cable insulation state is mapped to a frequency domain, smoothing processing is carried out, multi-scale analysis is carried out on the signal after smoothing processing, fault features of the cable are extracted, the insulation state of the cable is quantified, the probability that the cable has an insulation fault is calculated, and a basis is provided for fault early warning and maintenance. The problems that most of traditional insulation fault detection methods depend on single electrical or spectral characteristic detection, so that detection information is incomplete; the stability is poor, and misjudgment or missed judgment is easily caused; in some methods, power failure or direct contact with a cable is needed for measurement, and continuous operation of the cable is affected. And complex electric field distribution and dynamic change thereof in the cable are difficult to accurately describe, and the fault development trend is difficult to predict.

The invention discloses a three-dimensional scene reconstruction and monitoring method based on multi-view fusion and deep learning. The method comprises the following steps: acquiring image data from different viewpoints through a plurality of cameras, and carrying out geometric calibration; denoising, correcting and enhancing the image; constructing a multi-scale convolutional neural network and a variational auto-encoder model, and extracting multi-level features; carrying out weighted fusion on the features, and carrying out three-dimensional reconstruction through sparse coding and a graph neural network; performing feature optimization by applying a dynamic graph convolutional network and a double attention mechanism, and performing model updating based on adversarial gradient descent; anomaly detection is carried out through multi-scale analysis and an adversarial variational auto-encoder, preliminary processing is carried out at a camera end by adopting an edge computing technology, and further analysis is carried out at a central server end through a heterogeneous graph neural network and sparse subspace clustering. According to the method, high-precision and intelligent three-dimensional scene reconstruction and monitoring are realized, and the method has a wide application prospect.

The invention discloses a GIS combined electric appliance operation on-line detection method, and relates to the technical field, and the method comprises the following steps: obtaining a partial discharge original time domain signal, carrying out the signal enhancement processing of the original time domain signal, extracting a weak discharge pulse signal, and generating an enhanced discharge pulse signal; constructing a holographic time-frequency matrix atlas according to the enhanced discharge pulse signals; the holographic time-frequency matrix atlas is used as an input item, and a weak discharge type is identified based on a weak discharge neural network identification model; based on the identification result and the sensor array data, acquiring space coordinates of a discharge source, and outputting a discharge type and a positioning result to the monitoring terminal; according to the method, the holographic time-frequency multi-scale analysis algorithm and the weak discharge neural network recognition mechanism are fused, so that the recognition capability, the anti-noise robustness and the positioning precision of the GIS combined electric appliance online detection system on weak discharge are remarkably improved.

The invention provides a carbonate reservoir detection method and system based on resistivity anomaly inversion, and relates to the technical field of oil-gas exploration, and the method comprises the steps: obtaining historical data; performing multi-scale analysis and classification processing according to the resistivity data to obtain a resistivity anomaly distribution diagram; carrying out modeling processing according to the geological parameters and the carbonate rock resistance test data to obtain a geological-electrical coupling relation model; a resistivity three-dimensional distribution model is constructed according to the resistivity anomaly distribution diagram and the geology-electrical coupling relation model; performing resistivity prediction processing based on adaptive mesh generation and parallel calculation to obtain a resistivity space distribution prediction result; and performing three-dimensional geological modeling processing according to the resistivity spatial distribution prediction result to obtain an oil and gas reservoir detection result of the target area. According to the method, through multi-scale resistivity anomaly analysis, resistivity data processing is carried out by adopting high-order derivative filtering and wavelet decomposition technologies, and the resistivity anomaly region can be efficiently extracted.

The invention discloses a safety monitoring system and method for a new energy automobile battery. The safety monitoring system comprises a conventional vibration monitoring module, an impact capture module, a cross validation module, an accumulative influence module, an early warning module and a storage and learning module. The conventional vibration monitoring module obtains a vibration characteristic base line through multi-scale analysis, and the impact capturing module captures instantaneous impact and extracts characteristics such as a peak value, duration and energy concentration ratio. If it is judged that the loosening risk exists, electromagnetic field, acoustics and temperature information is further collected through a cross validation module, and whether substantial loosening occurs or not is comprehensively evaluated; if not, the accumulated influence module calculates residual influence values and accumulates the residual influence values into the historical impact parameters so as to capture the fatigue effect caused by accumulation of multiple small impacts. And the storage and learning module records each impact and evaluation result and optimizes a threshold value through a self-learning algorithm, so that the detection precision and the safety guarantee of the loosening risk of the battery fixing structure are improved.

The invention discloses an intelligent geophysical exploration geological exploration analysis system, and relates to the field of geological exploration, and the system comprises a collection and preprocessing module which is used for carrying out the adaptive collection of multiple types of geophysical field signals of a target exploration region, and synchronously carrying out the preliminary noise reduction and quality optimization of original signals through signal purification processing; the fusion module is used for receiving the preprocessed multi-type geophysical field signals and carrying out normalization and feature depth correlation fusion on coordinates of data of different dimensions through spatial-temporal feature correlation processing; the method can dynamically adapt to environmental noise optimization signal acquisition, extracts stratum recessive characteristics through multi-dimensional data fusion and intelligent mining, constructs an accurate three-dimensional geologic model, positions stratum attribute deviation and grades through multi-scale analysis, presents exploration results in combination with three-dimensional visualization, and generates target region optimization suggestions.

The invention discloses a composite material performance prediction method based on multi-field coupling multi-scale analysis and a knowledge graph, which integrates microstructure modeling, graph neural network representation learning, cross-scale parameter coupling modeling and robust optimization analysis. The method is suitable for prediction and design of key mechanical properties such as modulus, strength and toughness of a thermosetting / thermoplastic composite material under different working conditions. The method comprises the following steps: firstly, constructing a grain-level tissue knowledge graph based on an electron backscatter diffraction image, secondly, constructing a multi-scale input system comprising a microscopic variable (such as a fiber volume fraction), a mesoscopic variable (such as a layer thickness sequence) and a macroscopic variable (such as a load condition), and finally, performing robust optimization by utilizing a multi-objective evolutionary algorithm to obtain a multi-scale input system. And outputting a material performance prediction result and a knowledge graph associated with the ji structure-process-performance. According to the method, the accuracy and interpretability of performance prediction of the composite material can be remarkably improved, and data-knowledge dual-drive support is provided for design optimization of the high-performance composite material.

The invention discloses a thermal power generating unit load distribution optimization method and system, and relates to the technical field of load optimization distribution, and the method comprises the steps: quantifying the contribution degree of the position of each operation state monitoring device to a load prediction error based on operation influence data, and screening out a representative monitoring point set covering a unit core energy consumption node; constructing a multi-scale influence analysis model, identifying the change influence degree of the operation state data of each representative monitoring point on the load data of the thermal power generating unit, and generating a load distribution initial scheme; running state data of representative monitoring points are monitored in real time, a multi-target optimization mechanism is introduced, the weight value of each target is dynamically adjusted, and the load distribution of the thermal power generating unit is optimized in real time in combination with a load distribution initial scheme. Accurate dynamic load distribution based on multi-scale analysis and multi-objective optimization is realized, and the comprehensive benefit of a unit is improved.

The invention belongs to the field of hydroelectric generating set monitoring, and discloses a set vibration signal noise reduction and feature extraction method based on IAVMD and WOA, and the method comprises the steps: constructing a comprehensive evaluation index comprising an energy difference, an average energy entropy and a center frequency variation coefficient; combining an adaptive variational mode decomposition method and a whale optimization algorithm to obtain an optimal decomposition layer number K and a penalty factor; calculating an energy entropy and a Pearson correlation coefficient of each intrinsic mode component; screening the K intrinsic mode components through a double-threshold mechanism; and calculating to obtain a denoised unit vibration signal time sequence and fault features according to the screened intrinsic mode components. According to the method, the problems of large noise interference, fuzzy characteristics, parameter dependence on experience and the like in a traditional method are solved, the precision and efficiency of unit vibration signal processing are remarkably improved, nonlinear dynamic characteristics in unit vibration signals are captured through multi-scale analysis, and the sensitivity and diagnosis accuracy of early weak faults can be improved.

The invention provides a light source control method and system based on data analysis, and the method comprises the steps: carrying out the multi-channel spectral characteristic collection of an LED light source through a spectrum analyzer array and an optical fiber probe, obtaining a spectral response curve and an interference image, carrying out the multi-scale analysis processing, and generating a multi-dimensional data tensor; performing interference mode and defect signal decoupling analysis on the multi-dimensional data tensor to obtain an interference mode feature and an interference-defect discrimination result; performing spatial distribution analysis on the interference sensitivity of the wafer surface by adopting preset kernel density estimation and a graph neural network according to the interference mode characteristics and the interference-defect judgment result, and generating an interference sensitivity distribution matrix; according to the interference sensitivity distribution matrix, the current waveform and light beam phase distribution of the LED light source are accurately modulated, intelligent control over the LED light source is achieved, the influence of multi-mode interference on semiconductor detection is effectively eliminated, and the accuracy of nanoscale defect detection is improved.

The invention relates to the technical field of network security, in particular to a security monitoring method and system for network traffic. The method comprises the following steps: capturing a network flow data flow in real time, and extracting a network entity and a direct communication relationship to construct a basic communication graph; identifying and quantifying a high-order interaction mode between network entities, and taking the high-order interaction mode as an implicit feature enhanced basic communication graph to generate an enhanced security graph; processing the enhanced security map by using a multi-scale time sequence diagram neural network, and capturing a short-term burst mode and a long-term evolution mode at the same time; a dynamic anomaly score is calculated based on the network entity historical behavior baseline and the current network situation, and a security alert is generated when an adaptive threshold is exceeded. The system correspondingly comprises a flow capture module, a feature extraction module, a high-order mode analysis module, a security map construction module, a multi-scale analysis module, a dynamic risk assessment module and an intelligent alarm module, and comprehensive and accurate network threat detection is realized.

The invention discloses a forest fire monitoring method based on a low earth orbit satellite, and the method comprises the steps: obtaining data through multiple sensors, and carrying out the denoising and smoothing processing, and generating a fusion data set; a convolutional neural network is combined with multi-scale analysis to enhance fire point features, and a feature set is formed. And matching the multi-satellite collaborative observation data with the feature set, and generating a high-frequency observation enhanced data set through time sequence alignment. A deep learning and transfer learning optimization model is adopted, key area features are extracted, multi-satellite data are shared through distributed calculation, and a coverage enhancement data set is obtained. And dynamically adjusting a multi-satellite scheduling strategy based on the timeliness evaluation model, and outputting a scheduling result. The robustness of an algorithm is optimized through reinforcement learning, a robustness enhancement model is obtained in combination with complex meteorological condition simulation, the data fusion precision is verified by using a multi-source data verification technology, and a final monitoring result is output. According to the method, the accuracy, timeliness and coverage integrity of fire point detection are remarkably improved.

The invention relates to the technical field of design and layout of a building photovoltaic system and renewable energy source evaluation, in particular to a mountainous area building surface solar energy potential evaluation method and device and a storage medium. The building surface solar energy potential evaluation method does not depend on traditional meteorological monitoring data and is suitable for coupling influence of cloud layer dynamic change and complex terrain shielding. By combining MODIS data, a digital elevation model and a machine learning algorithm, solar energy potential evaluation of the outer surface of a mountainous area town building without meteorological data can be realized, so that the problem that a traditional method depends on basic meteorological data is solved. Through the combination of three-dimensional modeling and multi-scale analysis, the limitation of an existing method under the influence of clouds, complex terrains and building shielding is overcome, particularly, the dynamic influence of cloud change and the shielding influence of the complex terrains can be effectively considered, the calculation efficiency and precision are improved, and the method is suitable for popularization and application. And a new technical path is provided for solar energy resource evaluation in mountainous cities and towns.

The invention discloses an abnormal transaction dynamic detection method and system fusing multi-scale analysis and information entropy, and the method comprises the steps: S01, constructing a dynamic heterogeneous graph which comprises account nodes, equipment nodes and IP nodes; s02, extracting node features of each node, extracting a spectrum feature matrix of the dynamic heterogeneous graph, and forming a node feature vector of each node; step S03, clustering the dynamic heterogeneous graph to screen out candidate detection sub-graphs, inputting node feature vectors of nodes in the candidate detection sub-graphs into a pre-trained graph classification model, and identifying an abnormal account node set; and S04, calculating a corresponding dynamic risk score in real time according to the memory state vector of each abnormal account node in the time sequence diagram network and the diagram information entropy change rate so as to evaluate a real-time risk and identify a core abnormal account. According to the method, the group type abnormal transaction account can be quickly and accurately identified, and the core abnormal account can be positioned.

The invention discloses a landform extraction method in a power transmission line wind disaster assessment model, and relates to the technical field of power transmission line wind disaster assessment, and the method comprises the following steps: 1, collecting geographic data; step 2, data processing; 3, topographic factor mathematical expression; and 4, FME-based topographic factor parameter extraction. According to the terrain and landform extraction method in the power transmission line wind disaster evaluation model, fine extraction of complex terrain and landform elements in a power transmission line corridor is realized based on high-resolution remote sensing image data in combination with a digital elevation model (DEM) and a geographic information system (GIS) technology by adopting advanced technologies such as multi-scale analysis and object-oriented classification; the method provides reliable data support for accurately evaluating the wind disaster risk of the power transmission line, has the advantages of high automation degree, high precision, high efficiency and the like, and can be widely applied to the fields of wind disaster risk evaluation of the power transmission line, line site selection, disaster prevention and reduction and the like.