213 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 discloses an inland water body turbidity satellite remote sensing method based on optical classification and spectral simulation, which comprises the following steps: S1, acquiring water body spectral data, water body turbidity, satellite remote sensing images and meteorological data of a to-be-detected area, and calculating water body remote sensing reflectivity; s2, preprocessing the satellite remote sensing image to obtain a water body area; performing equivalent calculation on the remote sensing reflectivity data of the water body actual measurement spectrum to obtain the remote sensing reflectivity of each channel of the satellite sensor; performing water body optical classification by combining the actually measured water body spectral shape and turbidity distribution characteristics of the research area; s3, performing correlation analysis on different optical types of the actually measured spectrum according to the actually measured turbidity and the remote sensing reflectivity of multiple channels of the satellite, selecting channel remote sensing reflectivity data with high turbidity correlation as a sample, and constructing a turbidity remote sensing model; the turbidity remote sensing model is trained and tested, and the optimal turbidity remote sensing model is screened out. The method can improve the precision of remote sensing inversion of the turbidity of the inland water body.

The invention discloses an extremely-short-term ship motion attitude prediction method and system based on data enhancement, and relates to the field of ship motion attitude prediction. The method is used for solving the problem that data quality and model generalization performance are affected due to insufficient completeness of input data of a ship motion attitude extremely-short-term prediction model. The method comprises the following steps: performing data expansion on original observation ship attitude data by adopting a Slim-based generative adversarial filling network to obtain an expanded data set; performing data splicing and feature zooming on the original observation ship attitude data and the extended data set; performing time delay correlation analysis on the data after feature scaling to determine optimal sample data, then dividing the optimal sample data into a training set and a test set according to a proportion, and inputting the training set and the test set into a prediction model for training; optimizing parameters of the prediction model according to the prediction value of the training set; and outputting a predicted value by adopting the parameter-optimized prediction model, and carrying out reverse normalization processing to obtain a final prediction result. The method is suitable for extremely short-term ship motion attitude prediction.

The invention relates to the technical field of landslide monitoring, in particular to a landslide monitoring data prediction method fusing time-lag reconstruction and an attention mechanism, which takes landslide displacement historical monitoring data and external induction factors as modeling objects, and aims at nonlinear and time-lag characteristics of a landslide deformation process. Constructing a time-delay driving feature set through time-delay cross correlation analysis, and identifying a guiding-following causal relationship between landslide displacement and an induction factor; and a multi-source time-lag feature expression fused with an attention mechanism is constructed, and dynamic prediction modeling is performed on the landslide displacement through a gating recurrent neural network, so that accurate prediction of the landslide displacement trend and periodic change is realized. Experimental data prove that the method is superior to an existing traditional method in the aspects of landslide deformation prediction precision and robustness, the prediction precision, the feature utilization rate and the model interpretability are effectively improved, and the method has good engineering practicability and popularization prospects.

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.

An information processing device according to an aspect of the present disclosure includes an acquisition unit that acquires a fluorescence spectrum derived from a biological sample and position information of the biological sample from a sample including the biological sample, an identification unit (133b) that identifies, from the fluorescence spectrum, information regarding a plurality of different biomarkers of the biological sample associated with the position information of the biological sample, and a correlation analysis unit (133d) that performs matrix decomposition processing corresponding to a combination of the plurality of biomarkers on the information regarding the plurality of biomarkers and outputs a correlation of the information regarding the plurality of biomarkers.

The invention discloses a transformer area current characteristic identification method based on a correlation peak judgment method, and the method comprises the steps: collecting the synchronous phase information of a power grid, and generating and injecting a unique phase coding characteristic signal; current signals are collected at key nodes of the transformer area, and phase information is extracted; constructing a dynamic self-adaptive phase relation network, and extracting a feature vector with topology invariance; correlation analysis is carried out on the extracted feature vectors and a standard template, and an identification result is determined through adaptive correlation peak judgment; and dynamically adjusting system parameters according to an identification result. The method of combining phase coding and topology invariant features is adopted, and the method has the advantages of being high in anti-interference capacity, good in environment adaptability and high in recognition precision.

The invention provides an individualized TMS target positioning method and system for spastic cerebral palsy children, and aims to realize TMS precise nerve regulation and control for different types of SCP children. According to the method, the structural features of the healthy child are obtained, and after image processing, accurate division of the SCP child brain region is achieved, the defects of a current brain injury child brain region spectrogram are overcome, and a basis is provided for seed point selection of different SCP symptoms; multi-modal image parameter features are further scanned, whole-brain voxel correlation analysis is performed based on seed points to construct functional connection diagrams, the crossed and overlapped functional connection diagrams are searched to serve as a liability focus network of different symptoms, a voxel set close to the scalp surface serves as a TMS target, and SCP child treatment targets of different clinical symptoms are accurately positioned. Based on a focus network mapping technology, functional connection characteristics related to different symptoms or symptom complexes are better positioned, a specific brain network is identified, and an accurate intervention target is provided for TMS treatment.

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 technical field of exploration and development of water flooded layers, in particular to a sandy conglomerate water flooded layer porosity determination method based on an XRD (X-Ray Diffraction) logging technology, which comprises the following steps of: performing correlation analysis on the content of each mineral in mineral content data and the actually measured porosity of the same group; screening out mineral types of which the determination coefficient with the actually measured porosity is greater than a determination coefficient set value; performing multiple linear regression fitting on the content of each mineral type obtained by screening and the corresponding actually measured porosity of the same group to obtain an optimal multiple linear regression model; and for the rock debris sample of the non-coring section of the reservoir stratum of the glutenite water flooded layer, obtaining the porosity calculated value of the rock debris sample through the optimal multiple linear regression model. The porosity data of the rock debris sample of the non-coring section of the reservoir stratum of the glutenite water-flooded layer can be obtained, and the porosity data of the oil reservoir water-flooded layer of the whole well can be obtained by combining the porosity data of the coring section of the rock debris sample, so that the porosity data of the whole oil reservoir water-flooded layer can be obtained.

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 fruit wine quality prediction and improvement method based on machine learning, and relates to the field of fruit wine and machine learning. According to the method, a multi-fractal detrending fluctuation cross correlation analysis (MF-DCCA) and a transfer entropy (TE) method are combined, a nonlinear causal relationship between physicochemical properties and quality scores of the fruit wine is analyzed, and key features are extracted; then, key characteristics of the fruit wine are adjusted within a reasonable range, and the quality of the adjusted fruit wine is predicted through a gradient lifting regression model. By analyzing the prediction result, data support can be provided for optimization and improvement of new fruit wine products. The controllability of the quality of the fruit wine can be improved, the research and development period is shortened, and material waste is reduced.

The invention discloses application of beta-alanine in preparation of a medicine for preventing and / or treating uric acid metabolic disorder related diseases. The invention provides a theoretical source of beta-alanine for reducing the uric acid level. In the earlier stage, the applicant of the invention adopts non-target metabolome and targeted metabolome data to construct a hyperuricemia prediction model, and Rmcorr correlation analysis shows that high beta-alanine level and low uric acid level have strong correlation. On the other hand, beta-alanine acid is adopted to intervene in a hyperuricemia mouse, it is proved that beta-alanine can reduce the blood uric acid level of the hyperuricemia mouse and promote urine uric acid excretion, and beta-alanine can be used for preventing and / or treating uric acid metabolic disorder related diseases.

The invention provides an EMD (empirical mode decomposition) and correlation analysis-based double-probe vortex shedding flowmeter anti-interference method, which relates to the technical field of signal processing, and is characterized in that a vortex shedding sensor signal sequence containing periodic vibration interference is decomposed into multi-scale IMF components through self-adaptive characteristics of an EMD method, and spectrum components of a flow signal and vibration noise are accurately separated. According to the method, aiming at the non-stable and multi-band coupling characteristics of double-probe signals, the vibration noise interference under the complex vibration interference of an industrial field is effectively solved, and the environmental adaptability of noise suppression is remarkably improved. According to the method, on the basis that the advantages of self-adaptive decomposition are reserved, the IMF component series is dynamically controlled, that is, only the IMF components of the first four stages are taken, redundant calculation caused by over-decomposition is avoided, and the real-time performance of the system is guaranteed. An upper probe signal is introduced to serve as a reference channel, interference components are removed through a correlation analysis method, the complexity of a blind source separation algorithm is reduced, and meanwhile the method has higher detection precision and stability.

The invention provides a visual temperature sensitive area identification method and system for a numerical control machine tool, and the method comprises the steps: carrying out the measurement of temperature field data, and employing a peak lag correlation algorithm to identify the temperature gradient change caused by the high-frequency component of a temperature signal; identifying a temperature gradient change caused by a low-frequency component of the temperature signal by adopting a self-adaptive inflection point time sequence detection algorithm; acquiring temperature field data and adjusting and identifying a data structure; and analyzing and verifying the visual temperature sensitive area by adopting hierarchical clustering-wavelet correlation, and outputting a visual result. According to the method, the arrangement position of the temperature sensor can be determined under the condition that the thermal error of the machine tool is not measured, so that the arrangement position of the temperature sensor is visualized, complex installation and debugging of multiple sensors are avoided, and the arranged sensors have high correlation coefficients while the time and the use cost of the instrument are saved; and establishing a thermal error mapping model under the condition of changing working conditions so as to carry out thermal error mapping under different operation conditions.

The invention discloses a partially ordered data classification method and system based on order relevancy, and belongs to the technical field of partially ordered data classification research. Aiming at how to accurately recognize ordered features from a complex data set and solve the classification problem of ordered data, interference of other features in the ordered feature recognition process can be effectively removed on the basis of a Spearman correlation coefficient in combination with partial correlation analysis, and compared with other recognition methods, the method has the advantages that the recognition efficiency is improved, and the recognition efficiency is improved. According to the method, real ordered and unordered features in part of the ordered data set can be accurately distinguished; on the basis of a traditional monotonic neural network, a scheme of disordered feature weighting is utilized to design a partially-ordered neural network, so that the classification problem of partially-ordered data can be solved, and the problem that monotonic constraint is applied to ordered features on a partially-ordered data set while the influence of the disordered features is fully considered is solved; the method is superior to other existing algorithms in performance.

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 geographic weighted correlation analysis-based carbon emission fine scale comparison method and system. The method comprises the following steps of: acquiring carbon emission data released by different mechanisms in the same region under the same scale; carrying out attribute calculation on the carbon emission data released by different mechanisms to generate space panel data of different mechanisms; based on the generated space panel data, calculating correlation among the carbon emission data released by different mechanisms from a global perspective by adopting a Pearson's correlation coefficient; when the correlation calculated from the global perspective meets a first threshold value, the correlation coefficient between the carbon emission data released by different mechanisms is calculated from the local perspective through a geographical weighted correlation analysis method, and the first threshold value is a preset numerical value or a numerical value range. According to the method, the geographical weighted correlation coefficients among the carbon emission data issued by different institutions are compared, the correlation strength is judged from a fine scale, and reference is provided for selecting proper carbon emission data and more efficiently implementing a carbon emission reduction policy.

The invention relates to the technical field of model processing, and particularly discloses a multi-model fusion processing and automatic switching method. Comprising the steps of obtaining input data of a user, performing data preprocessing on the input data, inputting the processed data into a plurality of different types of pre-training models, performing statistics on output results of all the models, performing fusion processing on the collected results, and obtaining a fusion result; the method comprises the steps that firstly, a deep learning model, a traditional machine learning model and a statistical model are integrated, then performance parameters of all the models are monitored in real time based on performance monitoring indexes, and the current model is automatically switched according to the performance parameters of the real-time monitored models. The advantages of the models in different data types and task scenes can be fully exerted, image feature extraction can be processed through the deep learning model, rule mining of structured numerical data can be coped with through the traditional machine learning model, data distribution correlation analysis can be carried out through the statistical model, and the accuracy of industrial detection results can be improved.

The invention discloses a transformer electric-thermal-vibration signal prediction and anomaly detection method, which comprises the following steps of S1, accurately quantifying delay time among transformer electric-thermal-vibration signals by using a cross-correlation analysis method, re-calibrating a data set according to the delay time, aligning different signals on a time axis, and normalizing data; s2, establishing a transformer data prediction model based on a Transform-KAN network, taking the aligned transformer current, load rate, oil temperature, environment temperature and vibration fundamental frequency amplitude data as model input, and predicting transformer operation data; s3, sharing an encoder in the prediction model, learning a normal data rule, and reconstructing a transformer operation signal; and S4, detecting anomalies from two aspects of reconstruction errors and prediction errors by using a joint optimization transformer anomaly detection method. According to the method, the delay time among the electric-thermal-vibration signals can be accurately quantified, the prediction precision of the state data of the transformer is improved, and the reliability of abnormal detection of the transformer is enhanced.

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.