287 results about "Multiple-scale analysis" patented technology

The invention provides a coal mill fault diagnosis and prediction method and system based on big data analysis, and relates to the technical field of big data analysis, and the method comprises the steps: collecting a vibration signal of a coal mill bearing through a vibration sensor, carrying out the multi-scale analysis through wavelet decomposition, extracting features, constructing a deep convolutional neural network model, and carrying out the fault diagnosis and prediction of a coal mill bearing. A maximum mean value difference adaptive layer is introduced to carry out feature alignment on data of a source domain and a target domain, identification of fault types and degrees is realized, a health state evaluation index including a fault severity coefficient and a development trend coefficient is established, and the remaining service life is calculated and early warning is carried out. According to the invention, the accuracy and generalization ability of fault diagnosis are improved, accurate prediction and timely early warning of the bearing fault of the coal mill are realized, and the service life of equipment is prolonged.

The invention discloses a concrete surface intelligent construction method and system based on multi-modal sensing. The method comprises the following steps: firstly, receiving multi-source sensing data, and generating standardized feature data and defect feature vectors through standardization processing and feature extraction; performing time sequence alignment processing on the sensing data, and generating time sequence alignment data in combination with multi-scale analysis; constructing a state evaluation model based on the dynamic Bayesian network, and generating an environment state vector and a construction difficulty graph; carrying out region segmentation and optimizing task allocation by adopting a spectral clustering algorithm, and generating a task allocation scheme and a collaboration strategy; then, construction parameters are determined through multi-objective optimization, tool configuration is planned, and an optimization parameter set and a tool configuration scheme are generated; and finally, trajectory planning is carried out based on an improved A * algorithm, and a multi-robot cooperative construction trajectory is generated through comprehensive optimization. Full-process control of intelligent construction of the concrete surface is achieved, and the construction quality and efficiency are improved.

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 discloses a multi-dimensional time sequence photovoltaic grid-connected harmonic current prediction control method, and particularly relates to the technical field of power system control. Grid-connected original multi-dimensional electrical signals are collected and decomposed into multi-dimensional frequency signals through Fourier transform, the characteristic components of an independent harmonic source are separated through the blind source separation and non-negative matrix factorization technology, and the frequency and amplitude characteristics of the characteristic components are extracted; then, a multi-dimensional time sequence prediction model is constructed through a recurrent neural network, dynamic behaviors of multiple harmonic sources are predicted at the same time, and frequency domain data are reconstructed and frequency aliasing errors are corrected in combination with high-frequency compensation and multi-scale analysis; and finally, inputting a prediction result into a photovoltaic inverter control system, dynamically generating a compensation current opposite to a target harmonic wave, realizing effective offset of the harmonic wave, accurately processing the multi-source harmonic wave problem in a complex power grid environment, and greatly improving the harmonic wave suppression capability, the electric energy quality and the system operation stability of a photovoltaic grid-connected system.

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 discloses a fracture structure multi-scale modeling method based on multi-source data fusion, and belongs to the field of geological modeling, and the method comprises the following steps: S1, collecting and preprocessing multi-source data; s13, aligning the data, and integrating the data from different sources together; s2, three-dimensional geologic modeling: S21, generating an initial three-dimensional grid by using geologic modeling software to obtain an initial three-dimensional geologic body model; s22, dynamically adjusting the density of the initial three-dimensional grid by using a self-adaptive grid division technology; s23, using a machine learning algorithm to identify and classify faults; s3, expanding the three-dimensional geologic body model macroscopically and microcosmically; and S4, performing dynamic simulation and prediction by using the three-dimensional geologic body model. By adopting the fracture structure multi-scale modeling method based on multi-source data fusion, the accuracy and reliability of the fracture structure model are improved by integrating multiple data sources and multi-scale analysis.

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 discloses a sea ice target detection method based on multi-dimensional feature extraction, and the method specifically comprises the following steps: S1, data preprocessing: carrying out the denoising of an original sea ice image through a Gaussian filter, carrying out the multi-scale analysis of the sea ice image through an image pyramid, and obtaining sea ice images under different scales; s2, local feature extraction: extracting texture features of each local region by using a gray-level co-occurrence matrix; s3, performing enhanced texture feature processing on the extracted texture features through a directional filter and an enhancement algorithm to obtain final texture feature data; and S4, performing feature fusion on the obtained multi-dimensional features and the texture features to form a comprehensive feature image, and obtaining a classification result of the sea ice target. According to the sea ice target detection method, through multi-scale analysis, image representation is obtained under different scales by using an image pyramid transformation method, changes and expressions of texture features under different scales are captured, and the stability of sea ice target detection 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 a cancer image contrast enhancement method and system based on deep learning, belongs to the technical field of medical image processing, and adopts an adaptive histogram equalization algorithm for preprocessing to enhance the contrast of a local region. Texture, shape and edge features are extracted through multi-scale analysis, and feature descriptors are constructed. And based on the feature descriptor, segmenting the image by using a region growing segmentation algorithm to obtain a focus region boundary. A self-adaptive contrast enhancement technology is adopted for the segmented focus area, and the focus display effect is highlighted. In consideration of noise interference, a denoising method based on wavelet transform is introduced, noise is suppressed in a high-frequency sub-band, and details are reserved in a low-frequency sub-band. Through the organic combination of the above technical schemes, the contrast and definition of the cancer image can be effectively enhanced, the focus area is highlighted, and noise interference is inhibited at the same time.

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 provides an entropy-based muscle fatigue assessment method and application thereof, and particularly relates to the technical field of signal processing, and the method comprises the steps: firstly collecting a surface electromyogram (sEMG) signal, and carrying out the preprocessing; then, fractional calculus is combined with a fluctuation dispersion entropy (FDE) algorithm, and a multi-scale processing process is introduced, so that an RCMFFDE algorithm is innovatively proposed. Performing multi-scale analysis on the preprocessed sEMG signal by using the algorithm to generate a plurality of entropy curves; the entropy curve meeting the preset condition is selected as the final entropy curve, and the final entropy curve can accurately reflect the muscle fatigue state. According to the method, the muscle fatigue degree can be effectively and reliably evaluated, and the method has important kinematics and medical significance for preventing human body muscle diseases and sports injuries.

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.