149 results about "Correlational analysis" patented technology

The invention relates to the technical field of environmental parameter measurement, in particular to an indoor environment quality intelligent evaluation method based on multi-dimensional data fusion, which comprises the following steps: step 1, performing time-space synchronous acquisition on multi-source data; 2, a dynamic correlation model is constructed, a parameter coupling matrix is generated based on inter-parameter time-delay cross-correlation analysis, the time-delay cross-correlation analysis is achieved through sequence translation in a sliding time window and correlation coefficient calculation, an environment state class is constructed according to a parameter mean vector, fluctuation intensity and abnormal event marks, and a dynamic correlation model is constructed; each environment state class is bound with an independent parameter contribution degree weight set; step 3, performing double-stage environment quality evaluation; and step 4, adaptive increment optimization: when the deviation between the comprehensive evaluation index and the subjective evaluation exceeds a threshold value, adjusting a parameter contribution degree weight set of an environment state class, and periodically reconstructing a parameter coupling matrix. The prediction capability is improved by considering the time-delay coupling between the parameters; the system has self-learning and optimization capabilities, and the stability of long-term operation of the system is improved.

The invention provides a data error compensation method for a biological net port flowmeter, and belongs to the technical field of ocean detection. A multi-dimensional sensor is arranged at a biological net port to collect fluid parameters, a deep learning model is used for recognizing multi-phase flow characteristics, and a corresponding compensation strategy is selected; based on a correlation analysis algorithm, compensation processing frequency is dynamically adjusted to achieve self-adaptive balance of precision and efficiency, the flow state change degree is monitored through the Euclidean distance, compensation parameters are hierarchically adjusted to ensure system stability, a parallel processing architecture is adopted to execute coarse compensation and fine compensation algorithms at the same time, and results are fused according to weights. Finally, dynamic and accurate compensation of the measurement error of the biological net port flowmeter in the complex multiphase flow environment is achieved, and the technical problem that in the prior art, the biological net port flowmeter cannot achieve dynamic and accurate error compensation in the complex multiphase flow environment is solved.

The invention discloses a lithium ion battery health state prediction method based on a double-branch feature fusion network, and the method comprises the steps: employing a unified feature analysis method to extract a plurality of health indexes based on NASA and CALCE battery data sets, and employing a Pearson correlation analysis method to select M health indexes highly related to the battery health state; based on the selected M health indexes, performing denoising processing on the health index data by adopting a variational mode decomposition method to obtain denoised health index data; constructing a dual-branch feature fusion network, and training by using the de-noised health index data to obtain a trained dual-branch feature fusion network; and utilizing the trained double-branch feature fusion network to predict a battery health state prediction value of the next round. According to the invention, by effectively integrating the multi-scale battery degradation characteristics, the accuracy, robustness and prediction precision of battery health state prediction are improved, and the adaptability and generalization ability of the network are enhanced.

A lake cyanobacterial bloom pixel level prediction method based on multi-source data fusion belongs to the technical field of algae prediction, and comprises the following steps: collecting lake pixel level multi-source basic data in a satellite image and carrying out preprocessing, calculating an algae index to generate a binary distribution product, carrying out space-time matching according to a zoning factor suitability parameter table, and carrying out prediction according to the zoning factor suitability parameter table. Inverting a blue-green algae proliferation rate and adjusting a factor weight; calculating a pixel comprehensive suitability degree; identifying a hysteresis effect factor through correlation analysis and causal test; screening a high impact factor through feature sorting, constructing a diffusion rule, extracting an initial water bloom pixel and determining a diffusion starting point; and constructing a neighborhood iterative diffusion model by using the space-time dynamic pixel-level suitability matrix, and iteratively simulating and outputting a pixel-level water bloom prediction map. According to the method, through multi-source pixel-level data standardization processing and partition threshold modeling, the coupling diffusion model is optimized in combination with the multi-source data, accurate water bloom prediction is achieved, and the space-time precision and practicability of pixel-level prediction are improved.

The invention relates to a food-borne microorganism detection method and system based on a spectrum technology. The method comprises the following steps: carrying out correlation analysis on the content sequence of each chemical substance in a plurality of blank matrix samples and the absorbance sequence of each wave band in spectral data of the plurality of blank matrix samples to obtain an aliasing coefficient of each wave band; analyzing the content of each strain in the plurality of strain samples and the absorbance of each wave band in the corresponding strain spectral data to obtain an absorbance influence coefficient between any two strains under each wave band; determining a flora representative coefficient of each wave band according to the number of the plurality of strains, the aliasing coefficient of each wave band and the absorbance influence coefficient of other strains on the first strain under the corresponding wave band; and according to the flora representative coefficient of each wave band and the absorbance sequence of the corresponding wave band in the spectral data of the plurality of target samples, screening a characteristic wave band from each wave band. According to the method, the analysis difficulty during coexistence of multiple flora in a complex matrix is simplified, and the generalization ability and the practical application value of the model are improved.

The invention relates to the field of fan spindle state monitoring, and aims at synchronously acquiring sound and vibration signals through multiple channels, realizing nanosecond time alignment by adopting a precision time protocol, and performing denoising and normalization preprocessing on the signals to improve data integration and signal fidelity. Furthermore, short-time Fourier transform and continuous wavelet transform are combined to extract multi-scale time-frequency features, and a high-dimensional combined feature vector is generated in combination with cross-correlation analysis. And mapping the feature vectors to a low-dimensional manifold space through a local linear embedding algorithm, and constructing a dynamic mode reference template. Indexes such as curvature, track length and direction entropy are monitored in real time, whether the spindle has an abnormal evolution trend or not is judged through a self-adaptive curvature threshold, and abnormity judgment is achieved in combination with track backtracking verification. According to the scheme, the abnormal starting boundary of the spindle state can be caught in a refined mode, and the early warning and operation and maintenance response capacity of fan operation is effectively improved.

The invention relates to the technical field of building energy conservation and green buildings, and discloses a design parameter optimization method for a tropical zero-carbon building integrated system. According to the method, an input correlation-output uncertainty-global sensitivity three-dimensional analysis framework is constructed, through high-fidelity simulation modeling, Latin hypercube sampling and Gaussian process proxy model construction and verification, and in combination with Pearson correlation analysis, Monte Carlo propagation and Sober sensitivity calculation, a dominant parameter set and key interaction items are accurately recognized, and the accuracy and the reliability of the system are improved. And finally, solving the optimal low-carbon parameter combination by adopting a sequential quadratic programming algorithm under the engineering constraint. The system comprises twelve functional units such as a meteorological acquisition unit, a parameter definition unit and a simulation modeling unit, and outputs a parameter list which can be directly used for enclosure construction, unit type selection and photovoltaic arrangement. According to the method, scientificity and implementability of parameterization design of the tropical zero-carbon building are improved, annual net carbon emission can be reduced through actual measurement, and intelligent decision support is provided for the building double-carbon target.

Example systems and methods for de novo sequencing of DNA or DNA-like sequences using Viterbi-like correlation analysis are described. A sequencing system receives the read data for multiple copies of a DNA strand from a sequence reader, such as a nanopore reader. The sequencing system generates a convolutional matrix based on one copy and a reference matrix based on another copy and uses them to generate a correlation matrix. A most likely path through the correlation matrix is determined to identify and correct errors between the two copies.

The invention discloses a photovoltaic power generation capacity prediction method and system applied to a photovoltaic power station, and relates to the field of photovoltaic power generation prediction, and the method comprises the steps: carrying out the Pearson correlation analysis and Spearman correlation analysis, and determining a key covariable; performing multivariate singular spectrum signal decomposition processing to obtain reconstructed sequence data; training the multi-scale covariable interaction model by adopting the reconstructed sequence data to obtain a local model; based on a multi-source-domain cooperative training framework, uploading model parameters of the local models corresponding to all the photovoltaic power stations for global training to obtain a trained global model; and carrying out fine tuning on the trained global model by adopting a transfer learning fine tuning strategy to obtain a photovoltaic generating capacity prediction model. According to the method, the generalization ability of the model used in the photovoltaic power generation prediction process can be improved, high-precision and high-stability prediction of the photovoltaic power generation is realized, and the method is particularly suitable for application scenes with poor data quality.

The invention discloses a communication monitoring method and a communication system. The system comprises a monitoring module, a feature extraction module, a dynamic identification module, a behavior analysis module, an adaptive optimization module and a visual interface module. The monitoring module collects communication data flow through a mirror image port, the feature extraction module extracts packet length, time interval, direction sequence and encryption features by using a multi-dimensional feature fusion algorithm, and the dynamic recognition module realizes multi-protocol type recognition based on an improved self-attention convolutional neural network. The behavior analysis module constructs a communication relation graph and performs abnormal communication tracing; and the adaptive optimization module realizes model self-learning and parameter optimization through reinforcement learning. According to the method, through sliding window sampling, self-correlation analysis, entropy detection and GNN map modeling, accurate identification and behavior restoration of implicit traffic in a complex network environment are realized. According to the method, high-precision identification can be kept under the conditions of multi-protocol mixing and encrypted communication, and the characteristics of intelligence, self-adaption and expandability are achieved.

The invention discloses a data processing method and device, electronic equipment, medical equipment and a storage medium, and the method comprises the steps: obtaining a first brain magnetic resonance image set of a first type of object and a second brain magnetic resonance image set of a second type of object, imaging parameters of the first brain magnetic resonance image and imaging parameters of the second brain magnetic resonance image are the same, and bone mineral density value change degrees of the first type object and the second type object are different; determining candidate brain partitions according to the volume parameter difference of the first brain magnetic resonance image set and the second brain magnetic resonance image set in the same brain partition; and for each candidate brain partition, performing correlation analysis on the basis of the grey matter volume parameter, the associated bone mineral density value and the scale data of the candidate brain partition, and determining the target brain partition associated with the bone mineral density change according to the correlation analysis result, so that the correlation analysis of the bone mineral density change and the brain partition is realized, and the correlation relationship between the bone mineral density change and the brain partition is accurately determined.

A method for target detection based on spatial phase correlation analysis of acoustic vortices comprises the steps of generating a vortex-like excitation acoustic field, receiving the scattered acoustic pressure information after the incidence of the acoustic vortex, extracting the correlation coefficient between the spatial phase of the scattered acoustic field and the reference phase matrix, and predicting the presence, size, and spatial orientation of the target to achieve the target detection function. The method breaks through the diffraction limit to detect small targets and determine their spatial positions. Using acoustic vortices as information carriers, the method provides new ideas and technical solutions for the field of target detection.

The application relates to the field of alloy defect detection, and specifically discloses a praseodymium-neodymium alloy nondestructive detection method and system based on acoustic feature analysis, which comprehensively captures defect information contained in an original probe signal from two complementary physical perspectives of instantaneous dynamic characteristics and frequency band energy distribution by simultaneously adopting Hilbert-Huang transform and wavelet packet transform. Further, the scheme discards simple feature splicing, and instead utilizes canonical correlation analysis as an information decoupling tool to online decompose two groups of original feature vectors into a shared part describing defect commonality and unique information parts respectively representing the unique resolution capabilities of HHT and wavelet packet. Finally, the three decoupled components are structurally recombined to form a fusion feature vector which can effectively eliminate redundancy, amplify differences and has higher information density, thereby providing a clear structure and highly refined input for a subsequent classification model.

The application discloses a forest structure and intelligent coupling analysis and modeling method for absorbing photosynthetically active radiation, and belongs to the technical field of forest ecology remote sensing, structure analysis and computer simulation. The method focuses on fusing airborne and handheld laser radar information, accurately inverting forest structure indexes, covering single-tree level attributes, neighborhood spatial structure indexes, gap structure, stand density SD and slope SL; three-dimensional radiation transmission simulation is carried out; through the construction of a single-tree three-dimensional model library and a digital sample plot, high-precision three-dimensional forest reconstruction and hierarchical APAR simulation are realized, and photosynthetic spatial heterogeneity parameters are extracted; Spearman correlation analysis is carried out based on the structure and APAR parameters, and PCA dimension reduction and KMeans clustering are combined to identify the dominant structure mode and spatial type; based on the clustering result, a typed structure-APAR coupling integrated learning model is constructed, and the regulation mechanism of single-tree attributes and neighborhood structure on APAR is revealed.

The present application relates to the field of quantum bit reading, in particular to a superconducting quantum bit quantum state reading method, device, equipment and medium, a reading driving signal is sent to a superconducting quantum chip; The reading driving signal is a variable frequency signal comprising the |0> state resonance frequency and the |1> state resonance frequency of the superconducting quantum bit; The reading feedback signal is collected from the superconducting quantum chip; According to the reading feedback signal, the pre-stored |0> state response signal and |1> state response signal corresponding to the reading driving signal, the quantum state of the superconducting quantum bit is determined by correlation analysis. The present application uses a variable frequency signal as the reading driving signal, which can reduce the sensitivity of the system to frequency-independent noise, improve the signal-to-noise ratio of the signal, and the correlation calculation does not need to be converted to the IQ plane, reducing the calculation steps, and the correlation analysis algorithm can usually use hardware acceleration, reducing the resource consumption of the solution, improving the operation efficiency.

The application discloses a new correlation image recognition and machine learning method based on fuzzy monotony, and belongs to the technical field of artificial intelligence of pattern recognition and machine learning; the method is defined as FMMCA, which evaluates local fuzzy monotone correlation by comparing row vectors and column vectors of an image matrix pair by pair, then the local correlations are weighted and accumulated, and finally the global fuzzy monotone correlation between images is obtained. The application directly uses fuzzy monotone correlation analysis to replace classical correlation analysis for multi-view research, so that the problems existing in classical correlation analysis do not exist, and the fuzzy monotone method feature does not need to be measured statically by distance, but can be measured dynamically by interval change, so that the influence of some noise is offset, the performance is improved, a new fuzzy monotone machine learning method is formed, the optimization of a focus loss function is not needed, the parameters are few, the robustness is good, and the computing power is small.

The invention relates to the technical field of roadbed cavity detection, in particular to a roadbed cavity intelligent detection method based on multi-feature fusion and deep learning and related equipment. The method comprises the following steps: acquiring road-base pulse signal data and preprocessing to form a preprocessed signal sequence; multi-dimensional features such as amplitude attenuation and frequency dispersion change are extracted based on a cavity physical effect model, and a high-discrimination fusion feature vector is generated through feature fusion and dimension reduction; and training a deep learning cavity recognition model by using the fusion features to realize automatic recognition of the cavity existence probability of the to-be-detected road section. And finally, spatial positioning and risk assessment are performed by combining cross-correlation analysis and frequency dispersion curve inversion, so that high-precision detection and reliable judgment of the roadbed cavity are realized. According to the invention, the accuracy, efficiency and intelligent degree of roadbed cavity detection are improved.

The application discloses a kind of variable working condition diagnosis methods of rotary reducer based on multi-view transfer, belong to fault diagnosis technical field.The method is first to the multiple visual vibration data collected is preprocessed and constructs variable working condition data set;Through fourier transform, extract each visual spectrum feature;Typical correlation analysis is used to construct embedding class discriminant transferable feature objective function, combined with maximum mean difference technique reduces the distribution difference between training and testing field, and introduces multi-view consistency constraint;Through generalized feature decomposition, solve common subspace projection, extract multi-view features with discriminant and transferability;Finally, nearest distance classifier is used to realize the fault diagnosis under variable working condition.The application effectively solves the problem that rotary reducer has poor generalization ability due to data distribution difference under variable working condition, improves the accuracy and reliability of fault diagnosis.

The present application relates to the field of intelligent agriculture and automation control technology, in particular to a rice field precision irrigation system based on a microcontroller, comprising: reconstruction of hydrodynamic benchmarks, collection of water level and meteorological data, construction of an ideal water level curve reflecting an undisturbed state; injection of interference simulation, generation of wind wave interference and siltation drift factors, synthesis of a simulated interference curve; double-track differential extraction, construction of parallel path calculation real residual vector and theoretical residual vector; correlation decision steps: perform cross-correlation analysis to distinguish between real water shortage and environmental noise, execute irrigation or maintenance instructions by the microcontroller; the present application realizes decoupling of the topological characteristics of the water level signal, effectively filters out wind wave and siltation interference, avoids false start and stop of the water pump, and reduces equipment wear and energy consumption.

The application discloses a photovoltaic power generation amount prediction method and system applied to a photovoltaic power station, relates to the photovoltaic power generation prediction field, and comprises the following steps: performing Pearson correlation analysis and Spearman correlation analysis to determine key covariants; performing multivariate singular spectrum signal decomposition processing to obtain reconstructed sequence data; training a multi-scale covariant interaction model by using the reconstructed sequence data to obtain a local model; based on a multi-source domain collaborative training framework, uploading model parameters of the local model corresponding to all photovoltaic power stations to perform global training to obtain a trained global model; and adopting a transfer learning fine-tuning strategy to fine-tune the trained global model to obtain a photovoltaic power generation amount prediction model. The application can improve the generalization ability of the model used in the photovoltaic power generation amount prediction process, realizes high-precision and high-stability prediction of the photovoltaic power generation amount, and is especially suitable for application scenarios with poor data quality.

The invention relates to the field of computer vision, artificial intelligence and medical image analysis, in particular to a disease prediction method for supervising multi-omics tensor fusion, and the method comprises the steps: obtaining a tensor covariance among multi-omics data according to the processed multi-omics data, and defining a target function of tensor canonical correlation analysis; according to the tensor covariance and the target function, introducing structured sparse constraint and disease supervision information, and constructing a multi-modal image correlation analysis model based on tensor; solving the multi-modal image correlation analysis model by using an alternating iteration method to obtain a typical weight of each omics data; and predicting the disease according to the typical weight of each piece of omics data. According to the supervised multi-omics data fusion method based on the tensor, high-order related information can be effectively mined, the classification accuracy of chronic diseases is improved, and powerful technical support is provided for clinical diagnosis.

The invention discloses an intelligent coupling analysis and modeling method for a forest structure and absorbed photosynthetically active radiation, and belongs to the technical field of forest ecological remote sensing, structural analysis and computer simulation. Focuses on fusion of airborne and handheld laser radar information, forest structure indexes are accurately inverted, and individual tree level attributes, neighborhood space structure indexes, forest gap structures, forest stand density SD and gradient SL are covered; three-dimensional radiation transmission simulation is carried out, high-precision three-dimensional forest reconstruction and layered APAR simulation are realized by constructing a single-tree three-dimensional model library and a digital sample plot, and photosynthetic spatial heterogeneity parameters are extracted; spearman correlation analysis is carried out based on the structure and APAR parameters, and a dominant structure mode and a space type are identified in combination with PCA dimension reduction and KMeans clustering; and constructing a typing structure-APAR coupling ensemble learning model based on a clustering result, and revealing a regulation and control mechanism of individual tree attributes and a neighborhood structure on APAR.

A multi-parameter feedback-based intelligent control method and system for aquaculture wastewater, relating to the field of wastewater treatment and control technology, includes: acquiring key water quality monitoring indicators for each treatment subsequence; real-time monitoring of each treatment subsequence; critical change analysis of historical monitoring periods marked as abnormal for the treatment subsequences, obtaining critical feature sets for historical monitoring periods, comparing significant differences in the critical feature sets, and constructing a critical correlation feature network of abnormal indicators for abnormal historical monitoring periods; pseudo-correlation analysis of the critical correlation feature network, obtaining pseudo-correlation nodes and interference nodes, and constructing a critical directed acyclic graph and critical interference link graph of abnormal indicators; constructing a multi-device collaborative control model based on the critical directed acyclic graph and critical interference link graph of several abnormal indicators, outputting specific control parameters of the associated devices for each treatment subsequence and performing preventive control to achieve efficient, economical, and stable treatment of aquaculture wastewater.

The application discloses a kind of data mining methods, systems, terminals and media of electric pumping station operation rule, it is related to electric pumping station data analysis technical field, its technical scheme main point is: to the cumulative rainfall in pump station operation record sequence and pump station opening state are carried out sliding correlation analysis, obtain the time offset scale of correlation reaching peak value;Determine the perspective time scale not less than time offset scale, and according to the original electric pumping station data is carried out data perspective according to perspective time scale, obtain data perspective result;According to data perspective result, the correlation analysis is carried out to cumulative rainfall and pump station state, and with the rainfall of the highest correlation of cumulative rainfall corresponding as the rainfall response value of pump station opening state.The application can greatly improve the correctness of electric pumping station operation rule compared with original water level start boundary, improve the matching degree of electric pumping station model simulation and actual operation condition, provide better basis for hydrology and hydrodynamic matching of later model.

The application is suitable for the technical field of artificial intelligence and medical health, and provides a postpartum depression intelligent screening and early intervention system and method, which comprises a whole life cycle management module for managing continuous data flow and state transition; a physical entity layer for collecting physiological data of puerpera, mother-infant behavior interaction data and environmental semantic data; a digital twin layer for constructing a dynamic virtual mapping model, the model comprising a causal graph model based on a structural causal model and a time sequence evolution mechanism; an application service layer for generating and pushing a risk early warning, an attribution analysis and a self-adaptive intervention scheme based on causal reasoning in stages; through causal reasoning instead of correlation analysis, the early warning accuracy is improved, the digital twin layer constructs a puerpera-baby binary group dynamic mapping based on a structural causal model, and through a directed acyclic graph to quantify the causal relationship between variables and combine Monte Carlo tree search to deduce the future risk probability, compared with a traditional scale, high-risk events such as self-harm thoughts can be early warned.

The application provides a method for identifying potato drought-resistant genes using hyperspectral, and relates to the technical field of crop drought-resistant gene identification, and comprises the following steps: setting a drought group and a control group, synchronously acquiring plant canopy hyperspectral images and transcriptome data and establishing a correlation data set; extracting spectral features, screening drought response candidate genes; calculating the correlation coefficient of each candidate gene and the spectral features based on Spearman correlation analysis, and determining the primary gene set according to whether the average value of a plurality of positions after the absolute value is sorted exceeds a threshold value; constructing a random forest regression model with the spectral features as input and the expression amount of the primary gene as output, predicting the expression amount through a verification sample, and screening target drought-resistant genes according to the coefficient of determination and the prediction error; constructing a target gene overexpression plant, measuring the relative change rate of physiological indexes and the change multiple of gene expression amount under drought, calculating a comprehensive function verification index, and determining the gene as a drought-resistant gene if the index exceeds a threshold value.

The invention discloses a motor robust control method based on bus voltage adjustment, and relates to the field of motor position servo systems, and the method comprises the following steps: 1, building a simulation model based on MATLAB / Simulink simulation software, and carrying out the analysis of the bus voltage in the aspects of steady-state rotation speed precision, current waveform, load adaptability and the like; 2, implementing a robust control method based on a large-range load change matching technical scheme of bus voltage adjustment; and step 3, performing simulation verification based on a large-range load change matching technical scheme of bus voltage adjustment. A structure of a rotating speed outer ring, a current inner ring and a bus voltage parallel ring is constructed; the invention provides a method for determining two variables with the highest correlation degree based on simulation and correlation analysis technologies, further provides a method for integrating the variables by adopting a weighting method so as to simplify engineering implementation, and finally provides a method for introducing the variables into Boost circuit control, iq * is adopted as a basis for variable bus voltage regulation in an example, and the variable bus voltage regulation accuracy is improved. And the performance of the system is effectively improved.

This invention discloses a method and system for synchronous monitoring of bridge structural displacement based on wireless intelligent vision, belonging to the field of bridge health monitoring technology. The method involves acquiring images of the bridge monitoring area and performing format standardization processing; synchronizing the clock and frame acquisition timing of multiple monitoring nodes, achieving microsecond-level synchronization through linear drift compensation, hardware-triggered synchronization, and frame buffering technology; calculating sub-pixel displacement data using either target-independent or improved marker tracking modes, the former based on frequency domain phase correlation analysis, and the latter using a constrained attitude decomposition algorithm to improve accuracy; performing localized visual calculations on the displacement data and outputting displacement time-series data; and wirelessly transmitting the data to achieve real-time visualization and data interaction with downstream systems. The system includes modules for image acquisition, node synchronization, displacement calculation, local calculation, wireless transmission, and data interaction. This invention achieves non-contact, high-precision displacement monitoring, improves multi-node synchronization and data real-time performance, and is adaptable to various inspection scenarios.

The invention provides an unmarked photoacoustic microscopy blood flow direction and speed combined measurement method based on space-time cross correlation, which comprises the following steps: acquiring a two-dimensional structure image of a target tissue through a photoacoustic microscopy imaging platform, and carrying out image preprocessing and blood vessel enhancement on the two-dimensional structure image to obtain an enhanced tubular region; based on the enhanced tubular region, performing blood vessel segmentation, morphological optimization, center line extraction and direction calculation to obtain blood vessel direction information; path planning, scanning point sequence optimization and time sequence synchronization are carried out based on the blood vessel direction information, and a directional scanning path is obtained; a photoacoustic signal sequence is collected in the blood vessel direction, and space-time cross-correlation analysis is carried out; the blood flow direction and the blood flow speed are quantitatively calculated and visually displayed. According to the method, structural information guidance can be combined, rapid scanning in the blood vessel direction can be achieved, directional blood flow analysis is achieved through space-time cross-correlation, and the measuring speed and precision are remarkably improved.

The invention belongs to the technical field of tungsten-copper alloy control processes, and provides a control process for preparing hard tungsten-copper alloy, which comprises the following steps: in the process of preparing the tungsten-copper alloy, collecting powder apparent density data and environment humidity data in real time, and analyzing the data through a set first sliding window to obtain an abnormal coefficient; if the abnormal coefficient exceeds an abnormal coefficient limit value, triggering an association judgment process of the powder apparent density and the environment humidity; according to the correlation judgment process, the apparent density lag order and the correlation degree coefficient influenced by the environment humidity are determined through cross-correlation analysis; if the correlation judgment result judges that the apparent density abnormity of the powder is caused by the environment humidity, a drying compensation process is executed. Therefore, the abnormal apparent density of the powder caused by humidity can be identified in time, the humidity interference is accurately compensated, the forming pressure is stabilized, the preparation quality of the tungsten-copper alloy is effectively improved, and the problem of unstable alloy performance caused by the fluctuation of the apparent density is reduced.