96 results about "Correlational analysis" patented technology

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 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 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.

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.

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.

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 discloses a position information auditing method and device based on multi-modal information. Comprising the steps of converting a position description text and enterprise entity information into a predefined structured data format, and generating a system cue word matched with the position description text to be audited based on structured enterprise information and structured position information, the system cue word comprises an auditing rule used for performing association analysis between the structured position information and the structured enterprise information on N target position auditing dimensions; the N target position auditing dimensions at least comprise consistency verification between structured position information and enterprise qualification text information, enterprise credibility evaluation based on enterprise historical release position records, and compliance identification of position description texts; and inputting the system cue word into a pre-trained large language model to obtain an audit result for the position description text to be audited. According to the invention, the identification accuracy of false recruitment positions can be improved.

The invention provides a time sequence adaptive modeling-based hyperactivity electroencephalogram dynamic recognition method, which comprises the following steps of: acquiring a multi-channel electroencephalogram signal of a target subject through an electroencephalogram acquisition system, and preprocessing the multi-channel electroencephalogram signal; performing source positioning processing on electroencephalogram data by using a source positioning algorithm, projecting an electrode space signal to a brain source space, extracting an average time sequence signal of a brain region based on a brain region template, organizing into a unified input matrix according to a time window, inputting into a trained time sequence adaptive modeling module, and performing time-dependent learning and adaptive feature fusion to obtain a time sequence adaptive model; extracting fusion representation features of the reaction key time window and the brain region; classifying and identifying the fusion representation features, outputting the hyperactivity risk probability, and generating a visual map of the abnormal brain region and the key time window; and performing correlation analysis on the identification result and a clinical symptom scale to generate an individualized electroencephalogram abnormality report, and outputting an attention deficit hyperactivity disorder identification result according to a preset judgment rule. According to the method, high precision and high interpretability of hyperactivity recognition are realized.

This invention relates to a method for constructing an intelligent assessment model of county-level cigarette market order based on multi-source data fusion and feature optimization, belonging to the field of tobacco monopoly supervision and public governance technology. The method integrates heterogeneous data from multiple sources, including licensing management and market supervision, to construct a 5-dimensional, 31-item initial indicator set. Through Pearson correlation analysis, indicators significantly correlated with expert scores are selected. Combined with unsupervised learning (K-means, hierarchical clustering, GNN) weight analysis (using the F-value of variance analysis to measure feature discriminative power), six core features, including the licensed operation rate (K3) and market purification rate (K9), are ultimately selected. Based on these selected features, a classification model is constructed using supervised machine learning algorithms such as Random Forest (RF) to achieve intelligent judgment of market order as "good," "medium," or "poor." This invention overcomes the curse of dimensionality and overfitting risks, providing an automated and scientific assessment tool for tobacco supervision, and can be extended to the field of public governance.

The invention discloses a novel fuzzy and monotonous correlation image recognition and machine learning method, and belongs to the technical field of artificial intelligence of pattern recognition and machine learning. According to the method, the provided method is defined as FMMCA, the local fuzzy monotonic correlation is evaluated by comparing row vectors and column vectors of an image matrix pair by pair, then the local correlation is subjected to weighted accumulation, and finally the global fuzzy monotonic correlation between images is obtained. According to the method, fuzzy and monotonous correlation analysis is directly used for replacing classical correlation analysis for multi-view research, the problems existing in the classical correlation analysis cannot exist, the fuzzy and monotonous method features do not need to be subjected to static measurement through distances, dynamic measurement can be achieved through interval changes, therefore, some noise influences are counteracted, performance is improved, and the method is suitable for large-scale popularization and application. A novel fuzzy monotonic machine learning method is formed, optimization of a focusing loss function is not needed, parameters are few, robustness is good, and computing power is small.

The invention relates to the technical field of big data processing, in particular to a natural environment monitoring method and system based on big data analysis, and the method comprises the following steps: collecting multi-dimensional data to generate a meteorological thermal feature set, comparing with a physical reference to construct a three-dimensional concentration distribution tensor, and generating an effective transmission load sequence in combination with a wind speed projection and an industrial load. The method comprises the following steps: constructing a pollutant transmission lag parameter based on cross-correlation analysis, synthesizing a transfer matrix by combining real-time multiplying power evolution, and outputting environment prediction data, and comprises the following steps: comparing meteorological thermal characteristics with a physical reference to generate a correlation weight, carrying out three-dimensional grid filling on pollutant data, and reconstructing a three-dimensional distribution form of an atmospheric environment. Calculating a wind speed vector projection, establishing a transmission weight, generating an effective transmission sequence in combination with an industrial load, quantifying a directional driving mechanism of an emission source to a monitoring point, locking a transmission lag parameter by utilizing cross-correlation analysis, and realizing dynamic prediction of an environment state in combination with a real-time transmission multiplying power evolution state transition matrix.

The invention relates to the technical field of wind and light power generation prediction, in particular to a solar photovoltaic and wind power generation prediction method based on DTCN-FFT and a related device. Comprising the following steps: acquiring photovoltaic and wind power generation data of solar energy, and preprocessing the photovoltaic and wind power generation data of the solar energy to obtain processed data; spearman correlation analysis is carried out on the processed data, and input characteristics with high correlation with the generating capacity are screened out; inputting the input features into a pre-constructed DTCN-FFT model, and outputting prediction results of solar photovoltaic power generation and wind power generation; according to the method, frequency domain information and time domain information are fused, original time sequence data are converted into a frequency domain through fast Fourier transform, global features such as periodic components, trend terms and high-frequency noise are extracted, and the defect that a traditional model is insufficient in long-range dependence capture is overcome.

A kind of biological chip data feature engineering method based on statistical machine learning, comprising the following steps: generating data matrix;Carrying out z-score standardization;Value is calculated, and large-value gene data is screened;Correlation coefficient matrix is generated;Screening gene pair;Complex correlation coefficient is calculated;Mark gene complex correlation coefficient change.The present application is beneficial to analyze the correlation between data in a large number of biological chip data, and select a certain number of genes reflecting the difference between data groups according to the demand by using the method of feature selection.The present application uses correlation analysis statistics correlation coefficient, partial correlation coefficient and complex correlation coefficient for feature selection, which is beneficial to further reduce the data dimension, and is beneficial to predict the correlation change between two genes under different experimental treatment conditions.

The application discloses a kind of industry chain quality analysis methods based on big data, it is related to artificial intelligence technical field, the method is in the process of multi-vehicle mixed line production in automobile industry chain, based on order release cycle time, logistics arrival interval time and station actual operation cycle time Construction rhythm alignment dataset, and utilize cross correlation analysis to calculate rhythm phase shift Pm, combined with line edge work-in-process residence time and station beat adjustment frequency Construction rhythm disharmony effectiveness determination condition set, to convert the time mismatch relationship between upstream supply rhythm and downstream assembly rhythm into calculable, comparable quantitative index.Compared with prior art which only relies on result quality indicators such as pass rate and rework rate, the present application can identify the rhythm disharmony state of the industry chain in advance before the quality problem is explicit, significantly improving the foresight and sensitivity of the industry chain quality risk identification.

The application provides an electroencephalogram adaptive recognition method and system, a storage medium and an electronic device, comprising: preprocessing SSVEP electroencephalogram signals to obtain effective SSVEP electroencephalogram signals; based on a filter bank canonical correlation analysis algorithm, replacing each sub-band corresponding filter with M candidate filters, and based on the candidate filters, extracting a correlation coefficient group of each sub-band of the effective SSVEP electroencephalogram signals for each stimulation target; obtaining a normalized correlation coefficient group; selecting a sub-filter of each sub-band based on the normalized correlation coefficient group; calculating a sub-band correlation coefficient of the SSVEP electroencephalogram signals for each stimulation target, and selecting a stimulation target corresponding to a maximum value of the sub-band correlation coefficient as a recognition result. The electroencephalogram adaptive recognition method and system, the storage medium and the electronic device can better adapt to the recognition of different types of electroencephalogram signals by dynamically adjusting filter bank parameters.

The invention relates to the technical field of pierced billet production anomaly analysis, in particular to a pierced billet anomaly tracing method and device, electronic equipment and a storage medium. Then, for each first monitoring data queue, determining a first anomaly indication value representing the anomaly degree of the first monitoring data queue in a mode of performing slip comparison with a plurality of second monitoring data queues; constructing the plurality of first monitoring data queues into a first matrix, and determining a plurality of target items from a plurality of production parameter items through the first matrix and a co-correlation analysis method; and finally, according to the plurality of first abnormal indication values and the plurality of target items, determining abnormal production parameter items. According to the method, the production parameter items causing the pierced billet abnormity are determined through the slip comparison and parameter item redundancy screening method, the reason causing the pierced billet abnormity can be rapidly and accurately determined, and the yield of the pierced billet can be improved.

The invention discloses a rapid bacterial identification method based on thermally driven death-melting curve analysis, and belongs to the technical field of microbiological detection. According to the method, a death-melting curve of bacterium / strain specificity is constructed by utilizing membrane thermal stability difference of bacteria and genome dsDNA sequence difference, and Pearson correlation analysis is performed on the death-melting curves of different bacteria by using an SPSSPRO data analysis platform, so that bacteria variety differentiation and rapid identification of the bacteria are realized. The identification method provided by the invention has the characteristics of rapid and efficient detection, simple operation, low cost, high identification accuracy, high specificity and strong universality.