63 results about "Scaled correlation" patented technology

The invention discloses an operation state monitoring method and system for AEM water electrolysis hydrogen production equipment, particularly relates to the technical field of electrolytic bath operation monitoring control, and aims to solve the problem of efficiency nonlinear fluctuation caused by polarization mode alternating dominance and microstructure degradation under a low current density working condition in the prior art. Extracting polarization fluctuation characteristics by generating a dynamic response sequence and calculating a dynamic coupling degree to identify a polarization alternating dominant mode; analyzing a local blocking probability based on frequency characteristics, positioning an active site sparse region in combination with temperature gradient spatial distribution characteristics, and generating an efficiency fluctuation evaluation coefficient; an active site sparse region and a dynamic compensation factor matrix of a cathode flow field pressure gradient are constructed to cooperatively adjust an anode current density distribution weight and a cathode pressure gradient parameter, so that an adjusting variable is accurately matched with a local blocking probability and a dynamic coupling degree; the multi-scale correlation analysis of the microcosmic degradation state and the macroscopic efficiency fluctuation of the electrolytic cell is realized, and the operation stability under the low current density is remarkably improved.

The invention relates to the technical field of concrete detection, in particular to a dynamic detection and analysis method and system for early performance of ultra-high performance concrete. According to the technical scheme, the method comprises the steps of establishing a raw material characteristic spectrum library based on multi-physics field coupling, constructing a dynamic detection array based on a time sequence, developing a multi-modal data fusion algorithm, establishing a nonlinear coupling effect decomposition model and implementing dynamic parameter inversion. According to the method, a technical chain of four-dimensional monitoring network-multi-physics field coupling analysis-cross-scale prediction-adaptive regulation and control is constructed, so that dynamic accurate detection and intelligent regulation and control of the early performance of the ultra-high performance concrete are realized, and the technical bottlenecks of a traditional method in the aspects of parameter coupling, cross-scale association and real-time regulation and control are solved; the early performance prediction precision and the maintenance efficiency are remarkably improved, the cracking risk is reduced, and reliable technical support is provided for application of the ultra-high performance concrete in major engineering.

The invention discloses a method for predicting the elastic property of a fiber reinforced composite material, and aims to solve the problems of low precision, low efficiency and difficulty in processing multi-scale correlation of a traditional method. The method comprises the following steps: firstly, generating a representative volume unit by using a random algorithm, and constructing a high-quality training data set by combining Sobol sequence sampling and an SMOGN data enhancement technology; secondly, designing and training a deep neural network embedded with residual blocks and physical constraints, and improving the generalization ability of the model through Bayesian optimization adaptive parameter adjustment; and finally, establishing a plurality of macrostructure models, and realizing end-to-end prediction of the multi-scale elastic performance. The method realizes breakthrough in prediction precision and calculation efficiency: the time consumed by single analysis is shortened from several hours to a minute-second level, and the average prediction error is lower than 5%. The technology can be widely applied to the fields of aerospace, new energy automobiles, wind power and the like, and intelligent support is provided for design and manufacturing of high-performance composite materials.

The invention relates to the field of all-weather safety monitoring in the chemical industry, and discloses an all-weather chemical plant monitoring method, which comprises the following steps: dynamically acquiring quantum state measurement data of environmental parameters through a quantum dot array, capturing parameters such as temperature, pressure and gas concentration in real time by the quantum dot array through a tunneling effect, the sensing principle is based on the displacement effect of the quantum limited energy level; mapping the measurement data to a five-dimensional AdS spatio-temporal manifold and calculating an Einstein tensor; based on the Einstein tensor, solving topology invariant and curvature evolution of the space-time manifold; generating an algebraic decision instruction according to the topology invariant; and the encrypted control signal is fed back to the execution mechanism through the topology photon link. Through dynamic coupling of the quantum dot array and the five-dimensional AdS space-time manifold, cross-scale correlation monitoring of quantum state fluctuation and macroscopic deformation is achieved, the recognition blind area of a traditional method for micro-nano defects is broken through, and the early detection rate of pipeline microcracks is increased by two orders of magnitude.

The invention provides a soft measurement method and device for industrial multi-rate acquisition and a medium, and the method comprises the steps: collecting historical data in an industrial process, and dividing the historical data into a plurality of pieces of sampling rate data; feature extraction is carried out on different sampling rate data, and dimension regularization is carried out to obtain each scale feature; according to each scale feature and the query source, obtaining each bidirectional cross attention matrix, and performing fusion to obtain a bidirectional cross attention feature; dynamically calibrating the bidirectional cross attention features based on historical features to obtain a soft measurement model; and inputting to-be-predicted data in the industrial process into the soft measurement model to obtain a quality variable prediction result in the industrial process. According to the method, the device and the medium, the problem of low industrial process quality variable prediction accuracy caused by insufficient feature extraction, insufficient cross-scale association mining and limited utilization of historical target trend information when a soft measurement method of an existing industrial system processes multi-sampling-rate data can be solved.

The invention provides a dynamic sparse observation-oriented deep neural process ocean data assimilation method, and relates to the field of ocean data processing, and the method specifically comprises the following steps: constructing a training data set; simulating actually observed non-uniform and uncertain characteristics through Gaussian nuclear diffusion; building an ocean assimilation network oriented to sparse dynamic observation, outputting an analysis field and estimating uncertainty; and performing end-to-end training on the ocean assimilation network model by taking the reanalysis true value field as a supervision signal, and optimizing network parameters by combining a minimum error term and a structure constraint term. And after training is completed, inputting the background field in the test stage and sparse observation into the ocean assimilation network model for reasoning to obtain an ocean state reconstruction field conforming to the actual physical quantity scale. According to the technical scheme, the problem that in the prior art, calculation feasibility, cross-scale correlation modeling and credible uncertainty output cannot be considered under the real conditions of sparse observation and dynamic change of spatial-temporal distribution is solved.

The invention relates to a fermentation parameter intelligent decision-making method and device based on deep learning, equipment and a medium. The method comprises the following steps: collecting time series data of two modes in a microbial fermentation process and performing multi-dimensional feature analysis to obtain a morphological feature vector and a metabolic feature matrix; a fusion feature matrix is obtained through time dimension matching, and a dynamic correlation intensity curve is generated through nonlinear coupling modeling; metabolic fluctuation is marked abnormally, a cross-dimension anomaly recognition and multi-mode collaborative prediction model is constructed, data are fused and then input into the prediction model, and a regulation and control strategy is generated through long and short-term memory network optimization derivation. By adopting the method, multi-modal data fusion and cross-scale correlation analysis can be realized, the fermentation abnormity identification accuracy and parameter regulation and control scientificity are improved, and the product yield, purity and batch stability control capability are enhanced.

The invention discloses a motor stator winding degradation feature extraction method and system based on multi-dimensional multi-scale correlation sample entropy, and the method comprises the steps: collecting a multi-dimensional original time signal matrix of a motor stator winding according to the operation parameters of a motor; performing coarse-grained reconstruction on the multi-dimensional original time signal matrix to obtain a one-dimensional signal matrix after coarse-grained reconstruction; reconstructing the one-dimensional signal matrix to obtain a reconstructed signal matrix; constructing a nested matrix according to the signal matrix, and calculating the number of matrixes meeting conditions according to a set similarity threshold; and calculating a multi-correlation multi-scale sample entropy according to the number of the matrixes, and determining a real-time reliability index of the motor stator winding according to the multi-correlation multi-scale sample entropy so as to evaluate the degradation state of the motor stator winding. According to the invention, the cross correlation information of the signal can be captured among different dimensions, the complexity of the signal is accurately evaluated, the operation condition of the motor winding is detected in real time, and the motor winding fault and further deterioration after the fault are avoided.

The invention relates to the technical field of flue gas dilution sampling, and particularly discloses a flue gas dilution sampling method and system, and the method comprises the steps: carrying out the semantic analysis and multi-scale correlation analysis of sampling condition data inputted by a user through employing an artificial intelligence technology based on deep learning; according to the method, the semantic association information of the sampling conditions of the first scale and the second scale is captured, and fine-grained semantic interaction fusion is carried out on the semantic association information of the sampling conditions of the first scale and the second scale, so that deep semantic understanding of the sampling conditions is realized, and a suitable sampling mode is intelligently recommended. In this way, subjective judgment errors of operators can be effectively reduced, and the scientificity and accuracy of sampling mode selection are improved.

The invention relates to the technical field of pathological section image splicing, and discloses a pathological section association-based pathological image splicing method and device. The method comprises the following steps: acquiring and preprocessing a serialized pathological sub-slice image; calculating a texture flow direction field representing microstructure arrangement of the tissue, and constructing a multi-scale feature descriptor; on the basis of the multi-scale features, the adjacency relation between the sub-slices is quantitatively evaluated by fusing gradient differences, texture distribution KL divergence and a multi-scale correlation degree function of frequency domain mutual information; constructing a global spliced graph model by taking the degree of association as an edge weight, establishing an energy function containing data fidelity and texture smoothness constraints, solving an optimal splicing parameter through iterative optimization, and controlling an iterative process according to convergence criteria of comprehensive splicing stability, residual matching potential and progress; and finally generating a seamless panoramic pathological image. According to the method, biological structure characteristics are utilized, robustness and matching accuracy under complex conditions are improved, and continuity and consistency of splicing results are ensured.

The invention relates to the technical field of materials and data processing, and discloses a method for constructing a damping layer material loss factor and temperature correlation model, which effectively overcomes the defect of insufficient adaptability of a traditional damping material model in a wide temperature range required by a thermal power plant by fusing a molecular chain segment dynamics mechanism and a macroscopic constitutive behavior. The method has the technical advantages that on the basis of the physical basis of a molecular motion energy barrier theory constraint model and in combination with a corrected temperature-frequency equivalent conversion mechanism, a cross-scale correlation framework with clear physical significance is constructed; through collaborative optimization of microscopic activation energy and macroscopic viscoelastic parameters, the reliability of thermal power plant all-working-condition temperature domain loss factor prediction is remarkably improved; an embedded real-time calculation framework is adopted, dynamic evaluation and compensation control of the material damping performance in an engineering scene are achieved, and more accurate technical support is provided for vibration reduction structure design, vibration fault diagnosis and prevention and service life prolonging of key equipment in the fields of thermal power plants, heavy machinery and the like.

The invention discloses a multi-modal geologic feature fusion method and a multi-modal geologic feature fusion system based on a discrete wavelet transform (CLIP)-Stable Diffusion model. The method comprises the following steps: reconstructing multi-modal geological data through differential wavelet transformation, extracting a low-frequency trend from earthquake and deposition data by adopting a Daubechies wavelet, capturing high-frequency details from deposition numerical simulation and logging data by adopting a Coiflets wavelet, and vectorizing a geological text through an orthogonal basis matrix; a CLIP-Stable Diffusion fusion model is constructed, a text encoder is utilized to analyze geological semantic features, an image encoder is utilized to extract spatial features, and a U-net diffusion generator realizes cross-modal alignment under the guidance of text conditions through a cross attention mechanism; and adopting a loss threshold termination mechanism constrained by a geological law in diffusion training, and finally outputting fusion data through wavelet inverse transformation. According to the method, cross-scale correlation deficiency and semantic segmentation limitation of a traditional method are broken through, and reservoir modeling precision and exploration efficiency are remarkably improved.

The invention relates to the technical field of copper-containing waste treatment, in particular to a method, system and equipment for dynamically detecting the ion content in the copper extraction process, and the method comprises the following steps: acquiring ion concentration time sequence data of a reaction system in real time, and extracting mass transfer kinetics correlation characteristics of the ion concentration time sequence data; constructing an ion content prediction model based on the mass transfer dynamics correlation characteristics, wherein the ion content prediction model represents a reaction interface mass transfer relationship through a topological network; generating an initial extraction parameter set through parameter traversal, and performing process simulation based on the ion content prediction model according to the initial extraction parameter set to obtain a dynamic prediction result; and verifying the precision of the ion content prediction model in combination with the dynamic prediction result and real-time detection data, and outputting an ion content dynamic change curve. By means of the method, the problems that in a traditional copper extraction technology, dynamic prediction deviation is large, abnormal response lags behind, and technology optimization lacks cross-scale correlation are effectively solved.

The invention discloses a PM2.5 complex time sequence prediction method based on a double-path fusion architecture, and belongs to the technical field of PM2.5 complex time sequence prediction methods.According to the method, a double-path fusion structure comprising a local feature extraction path and a global time sequence modeling path is constructed, and combined modeling is carried out on PM2.5 time sequence data of multiple cities, the local path captures short-term fluctuation and high-frequency disturbance characteristics by using a convolution structure, and the global path models long-term trend and multi-scale correlation by using a neural network based on an attention mechanism, so that fine prediction of a complex non-stationary sequence is realized; according to the method, a reversible normalization mechanism is introduced to dynamically adjust input distribution, self-adaptive fusion of local and global results is realized in combination with a double-prediction-head weighted fusion strategy, so that the influence of abnormal disturbance on the model is effectively inhibited, meanwhile, the overall calculation complexity is reduced through a modular design structure, and the calculation efficiency is improved. And the deployability and the real-time performance of the method in a multi-scene air quality monitoring system are enhanced.

The invention discloses a computing power resource prediction method and device based on a multi-scale transformer model and a storage medium, and relates to the technical field of data processing. The method comprises the following steps: sampling a historical state information sequence of a target resource under each time scale according to a plurality of time scales corresponding to a target prediction time point, and constructing a plurality of sampling sequences of the target resource under each time scale; respectively carrying out attention aggregation on the sampling sequences under the same time scale through a transform model to obtain a first aggregation vector of the sampling sequences under each time scale; inputting the first aggregation vectors of the plurality of sampling sequences under the same time scale into a feature fusion layer to obtain a second aggregation vector fused with data of different time scales; and inputting the second aggregation vector into an output layer to obtain the state data of the target resource at the target prediction time point, thereby effectively capturing the scale correlation characteristics of the computing power network resource data, and improving the prediction precision.

The invention discloses a multi-scale time sequence prediction system and method for adaptive hierarchical frequency, which realizes standardized processes of data preprocessing, dynamic hierarchical sampling, frequency attention modeling, cross-scale attention fusion and prediction output through modular architecture design, is clear in interface between modules, is easy to deploy and expand, and is high in practicability. The problem of non-uniform system architecture in the prior art is solved; in the dynamic stratified sampling step, through region division and dynamic sampling rate distribution, adaptive matching of a down-sampling rate and a local frequency characteristic of a non-stationary sequence is realized; in the frequency attention modeling step, through frequency domain conversion and attention weight distribution, the sensitivity of the system to periodicity and frequency characteristics is enhanced, and key frequency components can be focused according to prediction task requirements; according to the cross-scale attention fusion step, through feature dimension unification and cross-scale correlation calculation, the dependency among the scale features is effectively modeled, redundant information is filtered, and the fusion efficiency and the prediction precision are improved.

The present invention provides a method for detecting the surface velocity of flotation foam by NSST-domain infrared target segmentation and improved SURF matching. First, the NSST decomposition is performed on two adjacent frames of foam infrared images, and the boundary, brightness, and saliency constraint terms of graph cut are constructed in the multi-scale domain to achieve the segmentation of merged and broken bubbles. Then, SURF feature point detection and localization are performed on the segmented background region. The main direction of the feature points is determined by statistically calculating the scale correlation coefficient within the fan-shaped region, and the feature descriptor is constructed by using the multi-directional high-frequency coefficients in the neighborhood of the feature points. Finally, the feature points of two adjacent frames of foam infrared images are matched, and the magnitude, direction, acceleration, and disorder degree of the foam velocity are calculated according to the matching results. The application of this technical solution can reduce the influence of noise and improve the segmentation accuracy. The improvement of the feature direction determination and feature point description method of SURF in the NSST domain not only greatly improves the operation efficiency and matching accuracy, but also enhances the overall robustness of the algorithm.

The invention discloses a human body posture estimation method based on multi-scale data self-adaption, and belongs to the technical field of computer vision. The invention aims to solve the problem that the precision is low because the same neural network model is adopted to carry out attitude estimation on original pedestrian image data with different scales. Comprising the following steps: preprocessing an original image to obtain an input image with a set scale, and obtaining a scale scaling factor; a low-level feature map is obtained from an input image through a first convolution module, and a scale normalized low-level feature map is obtained through a first scale correlation domain bridging module; a human body structure feature map is obtained through a second convolution module, and a scale normalization human body structure feature map is obtained through a second scale correlation domain bridging module; a human body joint point feature map is obtained through a third convolution module, and a thermodynamic diagram predicted value of human body joint points is obtained from the human body joint point feature map through a network head; and realizing human body posture estimation based on the thermodynamic diagram predicted value. The method is used for human body posture estimation.

The present invention belongs to the technical fields of resource exploration, geological mapping and environmental monitoring, and discloses a method, device, system and storage medium for enhancing geological table data based on deep learning. The method retains the physical properties and multi-scale correlations of continuous feature vectors in the data set as conditional inputs; and constructs a classification voter based on random forest, SVM and XGBoost to achieve the prediction and completion of discrete features, effectively addressing the problem of insufficient discrete labels for small samples. In order to systematically evaluate the performance of the model, a multi-dimensional evaluation system was also constructed. The experimental results using multiple sets of core analysis data as an example show that compared with the currently optimal CTGAN model, the data distribution generated by ICG-GAN is closer to the original data, and significant performance improvements are achieved in all six application indicators.

The present invention provides a low - illumination image enhancement and denoising method combining NSST domain, GAN and scale - related coefficient. First, collect the weak - light image and normal - light image datasets, convert the images from the RGB space to the HSV space, and construct a training set using the low - pass sub - band images obtained by decomposition. Secondly, construct a low - frequency sub - band image enhancement model LF - EnlightenGAN based on GAN, and train the model using the low - frequency sub - band image training set. Then, perform NSST decomposition on the low - illumination image to be processed, use the trained LF - EnlightenGAN model to enhance the low - frequency sub - band image, and use the scale - related coefficient to remove noise from each high - frequency direction sub - band. Finally, perform NSST reconstruction on the processed high - and low - frequency sub - band images, restore the image to the RGB space, and obtain the enhanced and denoised image. Applying this technical solution can lay a foundation for subsequent tasks such as image recognition, image classification, and target detection, and has a great improvement both in terms of visual effect and objective evaluation indicators of image quality.

The invention discloses a new material performance prediction method and system based on artificial intelligence, and belongs to the technical field of new material performance prediction. The system comprises a memory, a processor and a computer program, and the processor executes the program to realize the performance prediction method. The method comprises the following steps: obtaining target new material component data and preparation process parameters, and constructing a material feature matrix containing atomic bonding and microstructure features by means of a multi-modal feature extraction network; correcting process parameters through a process stability evaluation model, generating an optimization decision vector, dividing a matrix region, and monitoring phase transition temperature and grain boundary energy data of a key region; inputting a co-evolution network to obtain a performance influence factor sequence, and carrying out weighted fusion to generate an optimized feature tensor; and the cross-scale correlation model matches historical data and outputs a prediction interval, and the customer interaction unit displays and adjusts the matching dimension according to the demand and outputs again.

The application discloses a method for extracting middle layer top temperature multi-period characteristics and analyzing influencing factors, and belongs to the technical field of atmospheric detection and meteorological data analysis. The method first extracts time series data of the middle layer top temperature based on the 90km height and the lowest temperature point standard, then completes time domain trend and mutation analysis through seasonal departure and Mann-Kendall test, adopts discrete wavelet decomposition to obtain four main oscillation periods of 3 years, 7 years, 11 years and 22 years, then uses continuous wavelet to obtain the length of the fine-calibrated period, and finally performs cross wavelet analysis on the quasi-biennial oscillation, the El Nino effect and the solar activity index to obtain the final correlation and time lag results. The application realizes joint time-frequency domain analysis of discrete wavelet transform, continuous wavelet transform and cross wavelet transform, effectively improves the cycle recognition accuracy, quantifies the multi-scale correlation and time sequence hysteresis of each factor, and provides a new research idea for high-altitude atmospheric climate influencing factor analysis.

The invention discloses an intelligent concrete slump detection method based on multi-modal information fusion, and belongs to the technical field of concrete quality detection. The stirrer video data and the main shaft current signal are synchronously acquired. For video data, video image sequence apparent features are extracted and fused with dense optical flow field features, and a video fusion feature sequence is obtained. Specifically, the method comprises the following steps: extracting inter-frame dense fluid motion features by adopting a Farneback algorithm; and synchronously extracting image apparent visual features by using a deep network. The two are cooperated to realize cross-scale correlation of the material surface morphology and the pixel-level fluid flow state, and a video fusion feature sequence with both microscopic dynamic and global appearance is formed. And for current data, features are synchronously extracted through a deep network, a video and current parallel processing structure is integrally formed, a video fusion feature sequence and current features are subjected to feature superposition and cross attention module fusion, a multi-modal prediction model is constructed, and the slump detection accuracy is improved.

The invention discloses a mechanical production well scaling state intelligent identification method and system. The method comprises the steps that abnormal values and noise information in a mechanical production well data set are filtered out; obtaining a reliable scaling label; influence factors related to the scaling height of the mechanical production well are screened out from the original data, and scaling related characteristics are determined; a neural network model based on CNN-LSTM is constructed, an SE attention mechanism is introduced to construct an SE-LSTM algorithm, and an Adam optimizer is adopted to optimize model parameters; according to the separable variables and the scaling correlation characteristics, inputting the separable variables and the scaling correlation characteristics into a CNN-LSTM-based neural network model for training, using multi-classification cross entropy as a loss function of training, and outputting a scaling type; and using the trained CNN-LSTM-based neural network model to predict scaling data of an unknown mechanical production well. By adopting the technical scheme, the scaling characteristics of the mechanical production well can be accurately identified.

The present application belongs to the field of computer vision, and provides an RGB-D saliency object detection method based on depth quality weighting, comprising the following steps: 1) obtaining an RGB-D dataset for training and testing the task, and defining the algorithm target of the present application; 2) constructing an RGB encoder for extracting RGB image features and a depth (Depth) image feature encoder; 3) constructing a cross-modal weighted fusion module, and guiding the weighted fusion of the extracted RGB image features and Depth image features through a depth quality evaluation mechanism guided by a weighting formula; 4) constructing a bidirectional scale correlation convolution mechanism for multi-scale feature extraction and fusion, so as to enhance the advanced semantic information of multi-modal features; 5) establishing a decoder to generate a saliency map P est ; 6) calculating the loss of the predicted saliency map P est and the manually labeled saliency object segmentation map P GT ; 7) testing the test dataset to generate a saliency map P est , and performing performance evaluation using evaluation indexes. The present application can effectively integrate complementary information from different modal images, and improve the accuracy of saliency object prediction in complex scenes.

The invention relates to the technical field of fault diagnosis, in particular to a big data-based display fault prediction system, which comprises a gray-scale synchronization module, a fluctuation identification module, a stage judgment module, a trajectory comparison module and a trend early warning module, analyzes gray-scale time sequence data output by a gray-scale optical acquisition instrument based on a display device, and determines whether a fault occurs or not by checking data integrity. And comparing the gray scale content of each frame with a driving chip synchronizing signal. According to the invention, by checking the synchronism and sequence integrity of the collected data, accurate arrangement of information on a multi-time sequence level, continuity analysis based on a change track and feature node extraction are realized, the difference expression of a key moment and a turning point of a focusing stage in a display process is clarified, and the brightness and a gray scale correlation trend are subjected to multi-dimensional comparison, so that the accuracy of the display process is improved. The dynamic state of the risk area is comprehensively assessed in combination with the trend continuity between the nodes, the capability of capturing display abnormal symptoms is improved, early sensing and timely early warning are realized, and the stability and risk prevention capability of fault prediction of the display device are effectively enhanced.

The invention relates to a lightweight stereo matching method based on weight sharing and channel attention. The left image and the right image pass through a weight sharing feature extractor to obtain a feature map, and an SE channel attention mechanism is inserted behind a residual block of the feature extractor for feature calibration; when a multi-scale correlation body is constructed, sparse indexing is carried out on parallax dimensions by adopting Hash coding based on space coordinates, and voxel sampling and trilinear interpolation with constant time complexity are realized; and obtaining a final disparity map through a variable-resolution iterative updating strategy. According to the method, the quantity of model parameters is effectively reduced, EPE and D1 indexes on a Middlebury data set are superior to those of an existing RAFT-Stereo method, and the method is suitable for real-time scenes such as automatic driving and robot navigation.

The application discloses a rock and outcrop cross-scale correlation method and system based on multi-modal data, belongs to the technical field of three-dimensional data platforms and geological learning, and comprises the following steps: S1, geological outcrop data acquisition and geological outcrop modeling are carried out, a three-dimensional geological outcrop model is obtained, multi-modal data are obtained by simultaneously collecting basic rock sample data, rock sample analysis and test data and rock sample explanation information; S2, the multi-modal data are managed by using a database and a file management mode, and the rock sample and the outcrop are correlated to obtain correlation data. Through the management of the multi-modal data, the deficiency of single expression mode is solved, and the fusion and integration of the multi-modal data are realized. Through the correlation mechanism of the rock and the outcrop, the deficiency of the existing system in the data correlation is solved.

This invention discloses a fault location method for power cables compatible with both high and low resistance, comprising: connecting to a test terminal to acquire voltage and current responses, determining the initial impedance state, and setting scanning excitation parameters; applying an exponentially increasing scanning excitation signal, calculating the complex impedance gradient characteristics, and determining the threshold energy level range; expanding a fractal excitation sequence within the threshold range, collecting propagation response data, and constructing a response matrix; normalizing the propagation response matrix data, calculating the cross-scale correlation strength, and forming a consistency curve; constructing a propagation consistency vector field, performing density clustering and topology analysis, and determining the fault stability region; and calculating the fault location based on the propagation time at the center of the stability region and the propagation velocity. This invention achieves stable identification and accurate location of faults in both high-resistance and low-resistance power cables through exponentially increasing scanning excitation and fractal energy spectrum multi-scale propagation analysis.

The present invention relates to the technical field of copper-containing waste treatment, and particularly to a method, system and equipment for dynamically detecting ion content in the copper extraction process. The method includes: obtaining in real time the time-series data of ion concentration in the reaction system, and extracting the mass transfer kinetics correlation features of the time-series data of ion concentration; constructing an ion content prediction model based on the mass transfer kinetics correlation features, and the ion content prediction model characterizes the mass transfer relationship at the reaction interface through a topological network; generating an initial extraction parameter set through parameter traversal, and performing process simulation based on the initial extraction parameter set using the ion content prediction model to obtain a dynamic prediction result; verifying the accuracy of the ion content prediction model by combining the dynamic prediction result with real-time detection data, and outputting a dynamic change curve of ion content. Through the present invention, the problems of large dynamic prediction deviation, lag in abnormal response and lack of cross-scale correlation in process optimization in the traditional copper extraction process are effectively solved.