71 results about "Multiscale decomposition" patented technology

The application provides a multi-parameter fusion real-time monitoring and early warning method and system for anesthesia depth, relates to the technical field of medical monitoring, and comprises the following steps: acquiring physiological parameter data, performing multi-scale decomposition and calculating sample entropy values, extracting baseline features and fluctuation features to form an anesthesia feature vector; an initial anesthesia depth index is output by using a deep learning model; a cause-effect correlation matrix is constructed by calculating time-varying transfer entropy based on intrinsic mode function decomposition, dominant cause-effect patterns are extracted, and the anesthesia depth index is corrected; and real-time monitoring and early warning reports are generated. The application can improve anesthesia depth evaluation accuracy and reduce anesthesia risks.

This application relates to a method and system for detecting art and design feature conflicts based on deep learning. The method includes: acquiring image data of the target artwork; extracting multi-dimensional feature vectors using a multimodal feature extraction network; generating a multi-level feature matrix through multi-scale decomposition; mapping the matrix to a preset semantic space to construct a semantic feature matrix; constructing positive and negative sample pairs using a triplet sampling strategy; calculating the cross-modal feature similarity matrix between the semantic feature matrix of the target artwork and a benchmark feature matrix library using a contrastive loss function; and generating a visualized detection report based on a similarity threshold. This technology solves the technical problems of low efficiency, strong subjectivity, and difficulty in quantifying and analyzing complex conflict relationships between design elements in traditional manual detection, achieving automated, multi-dimensional, accurate identification, and visualized presentation of art and design feature conflicts.

This invention discloses a timing-linked control system for the flow channel switching and sealing engagement actions of a screen switcher, relating to the field of industrial automation control technology. It includes a rhythm construction module, a multi-scale decomposition module, a dominant rhythm generation module, an action timing mapping module, and a dynamic correction control module. The rhythm construction module collects process change information during the continuous operation of the screen switcher, constructs a rhythm change sequence according to a unified time order, and divides the rhythm change sequence into segments based on the rate of change, obtaining slow-change segments and fast-fluctuation segments, forming a time reference result. This invention separates multi-scale changes and reconstructs the rhythm change sequence, enabling the control process to make judgments based on a continuous evolution path, reducing the interference of short-term fluctuations on time node identification, and minimizing action timing deviations. Simultaneously, the dominant rhythm sequence enables action timing mapping and dynamic adjustment, ensuring coordinated and stable progress and connection between the flow channel switching and sealing engagement processes.

This invention discloses a method and system for monitoring mechanical damage to batteries during electric vehicle transportation based on acoustic emission spectrum analysis. The method includes: performing multi-scale decomposition of the original signal sequence using wavelet transform to separate high-frequency transient components and low-frequency background noise, obtaining a denoised elastic wave signal; calculating time-domain features, including peak amplitude and duration, based on the denoised elastic wave signal, and combining this with frequency-domain features such as the dominant frequency component to obtain a comprehensive feature vector; if the peak amplitude of the comprehensive feature vector exceeds a preset threshold, it is judged as a potential damage event, and damage-related subsets are extracted from the feature vector to obtain damage candidate features; training the damage candidate features using a support vector machine classifier to obtain damage type labels; and fusing transportation environment data with the damage type labels, using sliding window analysis to track signal change trends and determine the degree of damage evolution. This invention achieves accurate identification, classification, and dynamic monitoring of battery pack transportation damage.

This invention relates to the field of electro-digital data processing technology, specifically to a method and system for monitoring the dispersion state of mesoporous carbon electrode slurry based on data fusion. The method includes: performing Granger causality tests on time-series monitoring data to identify target sensor pairs and their causal directions, determining the causal and consequential sensors; performing multi-scale decomposition; performing cross-scale correspondence analysis to determine the cross-scale correspondence layer; setting a prediction window and verifying cross-scale response during real-time monitoring; and calculating the dispersion state index of the mesoporous carbon electrode slurry. This invention establishes a causal correlation and transfer parameter model between monitoring data at the micro-particle scale and macro-flow behavior scale of mesoporous carbon electrode slurry through the coupling of Granger causality tests and multi-scale decomposition, enabling the dispersion state assessment to reflect cross-scale dynamic evolution rather than local information at a single scale.

The present application belongs to the technical field of hydrological prediction and water resources management, and specifically relates to a prediction method and system based on multi-scale decomposition of runoff sequence and dynamic weight reconstruction. The method first constructs a physical hydrological model based on meteorological driving data and generates a runoff simulation sequence; the simulation sequence is subjected to multi-scale decomposition by using variational mode decomposition, the decomposition parameters are adaptively determined by particle swarm optimization, and a plurality of mode components are obtained; a long short-term memory network is constructed to establish a mapping relationship between the contribution weights of the mode components, and dynamic weights varying with time and normalized are output; the mode components are weighted and reconstructed according to the weights, so as to realize deviation correction of the simulated runoff of the physical hydrological model on different time scale structures. In the prediction stage, the same decomposition is performed on the future runoff simulation sequence, and the trained model is directly used to output the runoff prediction result. The present application converts the runoff prediction problem into a dynamic weight distribution problem of multi-scale structure components, improves the prediction precision and migration ability while maintaining physical interpretability, and is suitable for scenarios such as basin runoff prediction, flood simulation, water resources scheduling and the like.

The present application relates to the technical field of biomedical signal processing and artificial intelligence, and provides a heart rate detection method based on a learnable wavelet transform and feature enhancement, comprising: acquiring radar phase signals collected by a millimeter wave radar and obtained after preprocessing; and performing a learnable wavelet transform, extracting time-frequency features, and through multi-scale decomposition on a physiological related frequency band, performing dynamic weighted fusion based on energy of each frequency band, and outputting a multi-scale fusion feature tensor; after projecting and fusing the multi-scale fusion feature tensor and the original radar phase signals, inputting the same into an LSTM-Transformer hybrid time series modeling network, and outputting a second-by-second heart rate estimation value sequence; in a training stage, a hybrid loss function is calculated by using a real heart rate label and the heart rate estimation value sequence, and an end-to-end optimization is performed on the network. Through the method, the accuracy and robustness of non-contact heart rate monitoring are improved.

The present application relates to the technical field of industrial vision detection, and discloses a green color printing surface defect detection method based on computer vision. The method comprises the following steps: collecting real-time image flow and performing multi-scale decomposition to generate a feature vector sequence; constructing a dynamic defect template set by using a generative adversarial network; encoding features by using a convolutional neural network, calculating a similarity matrix with the template set, and generating a defect matching index; adjusting the detection threshold value adaptively according to the defect matching index, and obtaining a defect probability distribution map through clustering analysis; identifying a defect aggregation area through space-time domain analysis, and starting a local re-inspection mechanism; updating a defect feature prototype library and optimizing network weights by using re-inspection data, and forming a self-learning closed loop; and finally performing full-process quality consistency inspection and outputting a report. The present application realizes dynamic adaptation and continuous optimization of the detection process.

This invention discloses a three-dimensional modeling and visualization method based on gravity and magnetic data, relating to the field of geophysical exploration technology. The method includes the following steps: performing multi-scale decomposition processing on gravity and magnetic anomaly data to weaken the volume field superposition effect; constructing a three-dimensional grid model of the study area based on the spatial distribution of surface observation data, using the density and magnetic susceptibility of each grid cell as model variables to be inverted, and reducing the impact of superposition effect on subsurface exploration modeling through multiple gravity and magnetic field components with different depth sensitivities, providing high-quality input for the constructed three-dimensional grid model; then inverting the three-dimensional grid model, and introducing differentiated weight constraints during the inversion process to improve the resolution and stability of the subsurface physical property distribution inversion, and enhance the accuracy of inferring subsurface geological properties.

The application provides a physical mechanism coupling data-driven fast prediction method, and belongs to the technical field of disaster prediction.The application extracts an invariant topological feature vector through topological data analysis driven phase space reconstruction and inputs a physical constraint identification model to obtain a conservation law deviation vector and a physical feasible region boundary parameter, adopts a multi-resolution adaptive grid technology to perform multi-scale decomposition, extracts long-term evolution trend features and spatial local features through a full connection layer network and a convolution layer network, combines the physical feasible region boundary parameter to perform projection gradient descent iterative optimization to generate a preliminary prediction field, performs Bayesian uncertainty quantization, adjusts an artificial intelligence model regularization coefficient according to a disaster precursor identification function value, and outputs a graded early warning result, so that the technical problem of insufficient prediction accuracy caused by insufficient coupling of a physical constraint and a data-driven model in a disaster prediction system is solved.

This invention proposes a collaborative reconstruction method for multimodal images from unmanned aerial vehicles (UAVs) for all-weather perception. First, it uses an Adaptive Degradation Perceptual Block (ADPB) to perform spectral analysis on degraded visible light features to perceive the degradation type, and utilizes a learnable frequency mask to decouple features, providing a clear, high-fidelity structural prior for subsequent modal interactions. Next, it leverages the multi-scale decomposition capability of wavelet transform to decouple multi-frequency sub-band signals, and learns local contraction mappings within each sub-band through deep convolution, effectively preserving structural integrity while suppressing blur and noise. Finally, addressing the challenge of simultaneous degradation and cross-modal information fusion in multimodal images, it generates dynamic modulation factors in spatial and channel dimensions to guide adaptive complementary interaction and deep fusion of cross-modal features, strengthening the representation capabilities of global and local features. This significantly improves the reconstruction quality and multi-task generalization ability of the unified multi-task recovery model in complex scenarios.

The application relates to the technical field of fan blade fault diagnosis, and provides a fan blade intelligent online fault diagnosis and positioning method and system. The method is characterized in that: a feature vector is formed by acquiring multi-dimensional vibration and load signals and performing multi-scale decomposition and reconstruction; the feature vector is matched with a fault mode feature library to obtain a fault type; a nanosecond-level emission signal is used to identify a fracture signal vibration waveform of a guide strip with a length of hundreds of meters, and a fault candidate area is determined in combination with amplitude and phase difference of the vibration signal; a high-resolution analysis is used to determine a fault space position; and a parameter library is corrected by using a maintenance result feedback. The application realizes accurate identification and accurate positioning of fan blade faults, and improves fan maintenance efficiency.

This invention discloses a method and system for detecting defects in precision-diameter seamless steel pipes based on laser scanning, belonging to the field of non-destructive testing technology. The method includes: acquiring raw laser scanning data of the inner wall of the steel pipe containing distance, angle, and displacement information; constructing regular grid data through data conversion and spatial mapping; obtaining a reference cylindrical model through cylindrical fitting; performing coaxial error compensation and calculating radial residuals; expanding and correcting the residuals to generate an inner wall residual map; performing multi-scale decomposition on the inner wall residual map to obtain high- and low-frequency components; combining the two to complete defect identification and output the results. This invention solves the technical problems of coaxial error interference, difficulty in distinguishing defects from geometric deviations, and low accuracy in identifying minute defects in the inner wall defect detection of precision-diameter seamless steel pipes. It achieves accurate compensation for coaxial errors in the detection system, effectively separating the macroscopic geometric features and local defect features of the inner wall of the steel pipe, thereby meeting the technical requirements for quantitative and accurate judgment in precision testing.

The application relates to the field of communication and specifically provides a Fresnel reflection optical fiber diagnosis method and system, which comprises the following steps: collecting a Fresnel reflection signal of a to-be-detected optical fiber to obtain a one-dimensional original time domain signal sequence; adopting a multi-scale decomposition algorithm, taking an asymmetric fractional B-spline wavelet low-pass filter as a convolution kernel to carry out smooth filtering and two-division down-sampling processing on the signal; combining up-sampling and filter difference operation to solve detail components layer by layer until a preset decomposition layer number is reached; calculating a normalized interlayer difference kurtosis to construct a noise suppression factor and weighting denoising of the first scale detail component; up-sampling each scale detail component after denoising to the original signal length, and generating a multi-dimensional feature vector by aggregating components at each time point; calculating the L2 norm square of the time gradient of the feature vector to obtain corresponding fault characteristic values and synthesize a one-dimensional fault feature map; setting a judgment threshold value, positioning the Fresnel reflection position, identifying faults, and realizing optical fiber fault detection and positioning.

This invention relates to the field of visual inspection technology for electroacoustic devices, and discloses a method and system for quality inspection of horn drum paper based on visual inspection. The method separates a base layer, a detail layer, and a noise layer through multispectral image fusion and multi-scale decomposition. A three-dimensional mesh model of the horn drum paper is reconstructed based on the base layer, and the detail layer is mapped to the surface texture of the model, realizing the correlation analysis of defects in three-dimensional morphology and texture features, thus improving the ability to identify minute three-dimensional defects. By analyzing the local energy distribution of candidate defect regions in the noise layer and combining it with the local curvature of the mesh for authenticity judgment, it can accurately distinguish between real material damage and imaging artifact interference, reducing the false detection rate. Finally, repair instructions are generated based on the determined defect set. This method overcomes the limitations of two-dimensional image detection, has higher accuracy and reliability, and is suitable for automatic quality inspection on production lines.

The application relates to an infrared and visible light image fusion method based on multi-scale decomposition and partial differential equations, and belongs to the technical field of image fusion. The method solves the problems of blurred target edges and details in a current fusion image and large resource consumption. The method comprises the following steps: obtaining an infrared image and a visible light image including a target object through synchronous acquisition, and carrying out pretreatment and adaptive multi-scale decomposition; the number of layers of a Gaussian pyramid is obtained based on the complexity of the visible light image and the infrared image; the pretreated visible light image and the infrared image are subjected to multi-scale decomposition by using the Gaussian pyramid corresponding to the number of layers; each decomposition image is subjected to smoothing filtering and detail enhancement to obtain an enhanced image of each scale; the enhanced images of the infrared and visible light of the same scale are subjected to dynamic fusion, and all the scale images obtained through the fusion are reconstructed to obtain a final fusion image. The method realizes the reservation of target edges and details in the fusion image by using small resources.

The application relates to the technical field of computer vision and digital image processing, and discloses a multi-band fusion processing method and system for seamless splicing of overlapping images, a computer readable storage medium and a computer program product, the method comprising the following steps: performing multi-band decomposition on overlapping area images to obtain sampling images of K different frequency bands; based on a set fusion weight, performing weighted fusion on the sampling images of two overlapping area images, and reconstructing the fusion result to generate a fusion image of the two overlapping area images; and using the fusion image to replace the overlapping area images to splice two original images to be spliced, thereby obtaining a spliced image. The overlapping area images are subjected to multi-scale decomposition, and a differentiated fusion strategy is adopted in different frequency bands, so that low-frequency brightness smooth transition and high-frequency detail retention are realized, thereby obtaining a spliced image which has no obvious joint, consistent brightness and clear details.

This invention provides a geometric correction method for high-orbit staring SAR based on multi-angle weighted image simulation in mountainous areas. The method involves selecting a DEM image D for the corresponding region; determining the coordinates of the four corner points of image D; calculating the corresponding simulated image value for each pixel in image D; performing multi-scale decomposition on each SAR multi-angle image using a non-downsampling pyramid to obtain high-frequency and low-frequency sub-bands for each image; fusing the high-frequency and low-frequency sub-bands using Gaussian blur; calculating the fusion coefficients of the high-frequency and low-frequency sub-bands; and then determining the grayscale value of the fused image; finally, matching the simulated image with the SAR image using the SIFT algorithm to obtain the geometrically corrected image. This invention effectively alleviates the problem of the significant impact of elevation on the geometric correction accuracy of images during high-orbit SAR imaging in mountainous areas.

This invention discloses a method and system for pontine infarct segmentation and END prediction based on multimodal joint learning. The method includes acquiring and preprocessing multimodal data; constructing a wavelet transform-based feature encoding network to perform multi-scale decomposition and detail preservation of features; constructing a dual-task guided fusion module to align the deep semantics of clinical variables and imaging features and generate task-specific representations; constructing a Mamba-based global feature aggregation module to model sequence dependencies using a state-space model; constructing a multimodal second-order fusion classifier to enhance the clinical-image interaction modeling using second-order statistics; and employing a two-stage joint training strategy for training and prediction, and outputting the prediction results. This invention utilizes the DWT / IWT mechanism to significantly improve the accuracy of capturing small pontine infarct lesions; it achieves explicit interaction between segmentation evidence and prediction signals, significantly improving the segmentation accuracy of small lesions and the reliability of stroke risk assessment.

The application discloses an infrared image non-uniformity prediction and correction method based on wavelet transform, which comprises the following steps: adopting double-density dual-tree complex wavelet transform to perform multi-scale decomposition on an input infrared image; in a high-frequency subband, detecting a blind element position based on a local variance statistical method; in a low-frequency subband, constructing an autoregressive model, predicting non-uniformity of a background region, and performing accurate processing on a prediction result; combining the prediction results of the high-frequency subband and the low-frequency subband to generate a pre-correction coefficient; and adopting the pre-correction coefficient to perform correction processing on an original infrared image to obtain a corrected image. Through the combination of an adaptive fusion strategy, space-time consistency constraint and dynamic gain and bias updating technology, the problems of non-uniformity noise, balance between details and background, device drift compensation and consistency in a dynamic scene in infrared image processing are solved, and the accuracy and stability of infrared image processing are significantly improved.

The application discloses a kind of semiconductor silicon single crystal growth process V / G value prediction and quality evaluation method, belong to the technical field of preparation and intelligent process control of semiconductor material;Including the following steps: obtaining process parameter time series data;Its fragment division is generated continuous data unit, and time series dependence modeling is generated time series characteristic sequence;While it is carried out multi-scale decomposition and obtains high-frequency and low-frequency component, corresponding frequency domain characteristic sequence is generated by fragment division;Time series and frequency domain characteristic sequence are spliced into time-frequency fusion characteristic sequence;Based on the sequence, cross-domain correlation coding and prediction mapping are carried out, and V / G value prediction sequence is output;Finally, based on the prediction sequence, trend category and process quality grade are determined;The application realizes the high-precision trend prediction of V / G value and the intelligent online evaluation of process quality.

This invention discloses an automatic identification method and system for rock mass discontinuities based on density peak clustering. The method includes: acquiring three-dimensional point cloud data of the rock mass; acquiring a normal vector field based on the three-dimensional point cloud data; automatically determining the number of clusters of dominant groups of discontinuities using a density peak clustering algorithm based on the normal vector field to obtain cluster centers; performing spatial-directional ensemble clustering on the three-dimensional point cloud data according to the cluster centers and the normal vector field to obtain an initial clustering result; optimizing the boundaries of the initial clustering result using a region growing algorithm to obtain an optimized clustering result; and performing multi-scale decomposition on the optimized clustering result to obtain the attitude parameters of the rock mass discontinuities.

This invention provides a method and system for monitoring and early warning of transformer tap changers in converter stations. The method includes: First, classifying multi-source monitoring signals into different evidence categories according to fault mechanisms, and combining operating condition normalization and reliable gating mechanisms to distinguish between equipment anomalies and sensor anomalies. Second, employing a multi-scale decomposition method for different signal characteristics, and introducing a probabilistic prediction model to output the mean and variance, achieving anomaly detection with uncertainty. Third, unifying the dimensions through standardized residuals and robust anomaly mapping, and combining consistency counting, persistence counting, and smoothing processing to form category-level and system-level risk assessments. Finally, mapping the early warning results into hierarchical operation and maintenance suggestions, and using maintenance events to update thresholds and model parameters online, forming a self-optimizing closed-loop system that balances accuracy, interpretability, and engineering practicality.

This invention provides a lightweight intrusion detection method, system, and storage medium based on frequency domain gating and dynamic head attention distillation. The method includes: Step 1: Constructing a teacher model, which includes a frequency domain wavelet analysis module and a dynamic head interaction attention module. The frequency domain wavelet analysis module performs multi-scale decomposition and information enhancement of network traffic features in the frequency domain through a two-level Haar wavelet transform and adaptive gating mechanism. The dynamic head interaction attention module treats the feature dimension as a token sequence and models the global dependencies between features by introducing position-aware embedding and multi-head self-attention mechanisms; Step 2: Designing a dynamic entropy adaptive topology distillation strategy, enabling the lightweight student model to inherit the discriminative features and sample relationship structure of the teacher model while compressing the feature space; Step 3: Transferring the knowledge of the teacher model to the lightweight student network. The beneficial effect of this invention is that it achieves more efficient and more complete knowledge transfer.

This invention discloses a visual inspection method and system for PTC starter production, relating to the field of automated visual inspection. It involves acquiring original images of the PTC starter to be inspected, enhancing them to generate standardized inspection images, extracting geometric structure and surface texture features based on a multi-scale decomposition algorithm to construct multi-dimensional feature vectors, inputting these vectors into a deep belief network model, and outputting defect classification results and location information. Based on the defect classification results, it generates control commands for qualified release, unqualified rejection, or adaptive adjustment of process parameters. This invention improves the accuracy and intelligence of PTC starter defect detection, standardizes defect judgment, promotes the linkage between inspection and production processes, and adapts to the online quality control needs of large-scale automated PTC starter production.

The present application relates to the technical field of gravity measurement, in particular to a method for long-mid zone terrain iterative compensation of gravity anomaly based on a dynamic apparent density model, comprising: performing multi-scale decomposition on initial Bouguer gravity anomaly and DEM data respectively to obtain gravity anomaly components and terrain components corresponding to different wavelengths; screening at least one type of target wavelength according to the correlation coefficient between the gravity anomaly components and terrain components under the same wavelength; reconstructing the gravity anomaly components under all target wavelengths to obtain a pure terrain gravity anomaly field; and performing multi-round iterative compensation correction based on the pure terrain gravity anomaly field until the termination condition is met, and outputting the final updated Bouguer gravity anomaly. The present application realizes high-precision variable-density terrain compensation by constructing a complete technical link of adaptive anomaly separation, multi-constraint density inversion, partition coupling compensation and iterative optimization closed loop.

This invention relates to the field of electromagnetic resistivity logging technology, and in particular provides a deep learning inversion method combining multi-scale decomposition. The method includes acquiring nine components of the electromagnetic response signal and constructing a dataset; employing a multi-scale decomposition-local feature extraction-global sequence modeling architecture to construct an inversion model consisting of Maximum Overlap Discrete Wavelet Transform (MODWT), Temporal Convolutional Network (TCN), and Long Short-Term Memory (LSTM); training the inversion model and obtaining analysis results. This method can more effectively extract local non-stationary features near formation boundaries, improving the inversion accuracy and stability of the lower boundary region in highly deviated or horizontal wells.

This invention relates to the field of geological monitoring technology, specifically to an intelligent analysis method and system for geological monitoring data. The method first deploys multiple types of monitoring terminals, including those for seismic waves, displacement, seepage, and stress, in the exploration area. Then, it performs multi-scale decomposition on the time-series data, matching the geological body evolution process feature library to divide the evolution stages and generate feature threshold intervals. Anomaly feature extraction and verification are completed through a dual-branch cross-validation architecture, obtaining anomaly feature matching degrees and anomaly point identification sequences. A neural network with geomechanical constraints is constructed to fit the evolution trend and predict instability time intervals. Finally, through multi-dimensional index weighted fusion, a quantitative value of geological activity risk and a risk level identifier are output. This invention can achieve spatiotemporal benchmark unification of data collected by multiple types of monitoring terminals and basic geological exploration data, complete the normalization and integration of multi-source data, quantify the reliability of measurement point data, and improve the integrity and reliability of monitoring data.

The application relates to the field of image processing, and discloses a remote eye optometry and treatment intelligent service method and system and a storage medium. The method comprises the following steps: acquiring an eye fundus image to be processed, and performing standardization processing on the image, wherein the standardization processing comprises normalizing a gray value of the image and performing geometric correction; performing edge detection processing on the image after the standardization, extracting a structural feature region contained in the image, and dividing an image subregion based on the structural feature region; the system comprises the following modules: an image acquisition and processing module, an encoding and transmission module, a decoding and recovery module, and an evaluation and diagnosis module. By introducing a region weight map and a multi-scale decomposition technology, the image reconstruction precision is significantly improved, the detail retention capability is optimized, higher-quality image restoration is realized, and the system performs excellently in complex images and scenes with rich details.

This invention discloses a method, apparatus, equipment, and medium for identifying potential hazards of slope-type geological disasters. The method includes: acquiring multi-source remote sensing data of a target slope area; performing hierarchical preprocessing on the multi-source remote sensing data; adaptively filtering fusion bands of the spatiotemporally registered multi-source remote sensing data; and performing feature enhancement processing on the fusion bands after multi-scale decomposition; acquiring terrain sensing information of the target slope area; constructing a heterogeneous feature matrix based on the feature-enhanced multi-source remote sensing data and terrain sensing information; inputting the heterogeneous feature matrix into a deep learning model to extract slope hazard feature vectors; inputting the slope hazard feature vectors into a pre-trained hazard detection model to locate the hazard area of ​​the geological disaster and identify the type of geological disaster in the located hazard area, thereby obtaining the hazard identification result for slope-type geological disasters. This invention improves the accuracy of slope hazard identification under complex terrain.