184 results about "Cross correlation coefficient" patented technology

The invention belongs to the technical field of production monitoring systems, and discloses a numerical control machine tool dynamic fault diagnosis method and system. The method comprises the following steps: generating a global time reference signal through a main shaft encoder and a clock synchronization protocol; the method comprises the following steps: collecting vibration data of a main shaft bearing in each unit time, current data of an electric cabinet and process parameters, and generating a preprocessed data sequence through transmission delay compensation and multi-rate frequency raising processing; inputting the vibration data and the current data into a preset mechanical-electrical transfer function model, and calculating a time delay parameter; performing phase alignment on the preprocessed data sequence based on the time delay parameter to generate an aligned data sequence; inputting the aligned data sequence into a time sequence neural network, and outputting a fusion feature vector; calculating a cross correlation coefficient of the fusion feature vector, and generating a fault diagnosis result based on a preset cross correlation threshold value; the problem of failure of fault feature extraction caused by data asynchronization in the prior art is solved.

The invention relates to the field of ventilation equipment early warning, in particular to a ventilation equipment early warning method and system for a smart laboratory, and the method comprises the steps: building a window with a target moment as a center, and obtaining a multi-dimensional state parameter sequence of ventilation equipment in the window; calculating the maximum cross correlation coefficient of the target moment, obtaining the optimal lag, weighting the optimal lag to obtain a final lag coefficient, and obtaining the cross correlation difference significance according to the maximum cross correlation coefficient of each sampling moment in the window; and aligning the state parameter sequences of different dimensions in the window based on the final lag coefficient, obtaining the correlation of the state parameter sequences of any two dimensions, calculating a risk value of the target moment, and correcting the risk value to obtain a risk correction value. According to the technical scheme, the early warning precision of the ventilation equipment can be improved, and the influence of hysteresis on early warning is reduced.

The invention provides an exoskeleton man-machine motion intention cooperative control method and device and a storage medium, and relates to the technical field of robot motion control signal processing. According to the method, signal phase differences are acquired through energy buttons at human joints, and an original phase difference sequence is formed and preprocessed; calculating a cross correlation coefficient of the target and the associated joint based on the preprocessed sequence, and constructing a motion feature set in combination with the joint speed and acceleration; constructing a prediction model by using a long-short-term memory network, and outputting motion intention and joint position prediction information through multi-feature fusion training; and analyzing the prediction information by adopting a second-order linear impedance model, generating a driving torque instruction and sending the driving torque instruction to the servo motor for execution. According to the method, signal phase difference analysis, LSTM prediction and impedance control are fused, the problems of response lag and poor adaptability of a traditional exoskeleton are solved, the control real-time performance, accuracy and cooperative fluency are improved, and the use requirements of different users in different scenes are met.

The invention relates to the technical field of safety monitoring, and provides an oil and gas pipeline leakage monitoring method and system, a medium and equipment, and the method comprises the steps: obtaining a leakage vibration signal of each measurement point of an oil and gas pipeline, and extracting a time domain feature, a frequency domain feature, a spatial-temporal feature and an initial distance from a leakage point to a starting end of an optical fiber; performing cross-correlation analysis on the leakage vibration signals of the adjacent measuring points to obtain a propagation time difference and a cross-correlation coefficient, and calculating a correction distance from the leakage point to the starting end of the optical fiber; if the leakage point is in the spiral section, calibrating the correction distance to obtain a calibration distance; calculating the difference value between the initial distance from the leakage point to the optical fiber starting end and the corrected distance to obtain a preliminary positioning error value; and based on the time domain feature, the frequency domain feature and the spatial-temporal feature, in combination with a cross correlation coefficient, a calibration distance, an amplitude enhancement coefficient and a preliminary positioning error value, obtaining a leakage medium type through a medium identification model. The method gives consideration to the positioning efficiency and precision, and greatly improves the accuracy and robustness of a leakage medium type recognition result.

The invention discloses a bearing electrostatic signal noise reduction method based on VMD-OMP. In order to solve the problem that early fault features are difficult to extract due to the fact that bearing electrostatic signals are easily affected by power frequency interference and environmental noise, a noise reduction technology based on combination of VMD (variational mode decomposition) and OMP (orthogonal matching pursuit) is provided. The method comprises the following steps of: firstly, performing automatic optimization on a modal number and a secondary penalty factor of VMD decomposition by taking an envelope entropy as a fitness function through a grey wolf optimization algorithm to realize adaptive decomposition; and then, screening effective modal components according to the cross correlation coefficient and the kurtosis, determining the optimal sparseness by adopting a saturation value method, respectively reconstructing the components through an OMP algorithm, and finally, directly adding reconstructed signals to obtain a noise reduction result. According to the method, power frequency noise and broadband interference in the bearing electrostatic signal can be effectively suppressed, and the feature definition and the fault recognition capability of the electrostatic signal are improved.

The invention discloses an integrated pediatric vital sign intelligent monitoring and early warning method and system, and relates to the technical field of biomedical signal processing. The method is used for solving the problems of large monitoring interference and early warning lag in a dynamic environment. The method comprises the following steps: firstly, synchronously collecting a multi-modal signal to generate a motion intensity envelope signal and an R wave peak value interval sequence, carrying out orthogonal projection denoising on a photoelectric volume pulse wave signal by utilizing motion envelope based on a recursive least square algorithm, and outputting a reconstructed blood oxygen waveform; then, calculating a cross correlation coefficient of a heart rate trend and a motion envelope, quantifying a physiological coupling degree to generate an independent heart rate anomaly index, and accurately identifying a non-motion related risk; finally, the real-time blood oxygen value is analyzed, the index is used for dynamically increasing the low oxygen threshold value so as to tighten the alarm boundary, physiological parameter linkage grading early warning is achieved, and the recognition sensitivity of the compensation period risk is improved while interference resistance is guaranteed.

The invention provides a water chiller predictive maintenance method and system based on digital twinning, and the method comprises the steps: obtaining the evaporation pressure, suction temperature, refrigerant flow and outlet water temperature parameters during the operation of a main module of a water chiller, and carrying out the synchronous collection through multiple sensors, thereby forming a time series data set; according to the time sequence data set, a sliding window method is adopted to extract the mean shift amount, variance volatility and cross correlation coefficient of evaporation pressure and suction temperature, and a collaborative drift mode reflecting micro leakage is obtained; according to the corrected drift rate and cross correlation coefficient, generating a feature vector of a collaborative change direction among the evaporation pressure, the suction temperature and the refrigerant flow; and by calculating the Euclidean distance between the feature vector of the cooperative change direction and the health baseline, whether the micro leakage reaches the early warning triggering degree is judged, and the strength of an early warning signal is obtained.

The invention relates to the technical field of bridge deformation monitoring, in particular to an online monitoring method, system and equipment for bridge structure deformation, and the method comprises the steps: analyzing the similarity degree of a spectrogram between each transverse component and a reference component thereof, and determining the transverse feature value of each transverse component in each GPS receiver, so as to obtain a reconstructed transverse displacement signal; determining a vertical characteristic value of each vertical component in each GPS receiver by analyzing a correlation time fitting result of all the vertical components and combining the cross correlation coefficient so as to obtain a reconstructed vertical displacement signal; and respectively analyzing extreme value distribution of the reconstructed transverse displacement signal, the longitudinal displacement signal and the reconstructed vertical displacement signal, and determining a deformation monitoring result of the bridge floor of the to-be-detected bridge. The interference of external wind power and traffic load on bridge structure deformation monitoring is solved, and the accuracy of bridge structure deformation monitoring is improved.

The invention discloses a rock mass fracture early warning method for a hydraulic tunnel, belongs to the technical field of tunnel safety monitoring, and can solve the problem that an existing method cannot specifically recognize isolated impact noise and rock mass real fracture signals in the construction period of the hydraulic tunnel under the background of strong noise. The method comprises the following steps: S1, acquiring an original micro-seismic signal synchronously acquired by a sensor array in a hydraulic tunnel; s2, detecting a micro-seismic event from the original micro-seismic signal to obtain an event signal set, and determining a reference channel signal according to the event signal set; s3, extracting cross correlation coefficient peak values of event signals of each channel in the event signal set and reference channel signals, and counting the number of channels of which the cross correlation coefficient peak values exceed a cross correlation coefficient threshold value as the number of coherent channels; and S4, calculating a frequency band energy ratio of the reference channel signal, and generating a rock mass fracture early warning signal when the number of coherent channels and the frequency band energy ratio meet preset conditions. The method is used for rock mass fracture early warning of the hydraulic tunnel.

The invention relates to the technical field of mechanical equipment fault diagnosis, and discloses a fault diagnosis method and system for an automatic tensioning control valve of a scraper conveyor. The method comprises the following steps: acquiring pressure and flow data of a main flow channel and an auxiliary flow channel through a sensor array, and extracting frequency domain characteristics to obtain a standardized frequency spectrum; detecting parameter coupling abnormity based on the spectrum cross correlation coefficient and generating an abnormal index; environment interference and medium viscosity data are fused, and non-linear feature distribution and correction deviation representing the mechanical state are obtained through classification and filtering analysis; matching the correction deviation with a preset fault mode, determining a fault type, and generating a fault positioning coordinate in a three-dimensional space by using an optimization algorithm; and finally, performing fluid dynamic simulation through the high-fidelity virtual model to verify the coordinate stability, and outputting accurate early warning when conditions are met. Through cooperation of multi-source information fusion and an intelligent algorithm, the accuracy of early-stage internal fault diagnosis of the complex double-flow-channel valve is improved.

The invention discloses a high arch dam operation modal parameter automatic identification method and system based on discharge excitation. The method comprises the following steps: 1) obtaining a vibration displacement response signal: determining a penalty factor and an optimal decomposition layer number based on an adaptive multivariate variational mode decomposition algorithm to obtain an optimal IMF component of each sensor channel signal, and performing IMF component screening and signal reconstruction through a frequency domain cross correlation coefficient to realize adaptive noise reduction of the signal; 2) establishing a Monte Carlo three-dimensional stability diagram based on a random subspace recognition algorithm driven by a covariance matrix; and 3) modal parameter automatic identification based on an intelligent clustering algorithm. According to the method, the noise is suppressed by automatically optimizing the modal component reconstruction signal of the multi-sensor vibration signal; a Monde-Carlo three-dimensional stability diagram is established in combination with a Monde-Carlo theory and a covariance driven random subspace method to determine a model order, and automatic interpretation of the stability diagram is realized by applying improved fuzzy clustering, so that operation modal parameters of the high arch dam are accurately identified.

The invention discloses a parameter selection method for adaptive characteristic mode decomposition based on three criteria, and belongs to the technical field of mechanical fault diagnosis and signal processing analysis. Firstly, a mechanical vibration signal is preprocessed and reconstructed by adopting an iterative envelope spectrum method, and the periodicity of the signal is enhanced; and then the reconstructed signal is input into adaptive characteristic mode decomposition for processing. Similarity modals and modals containing high noise are removed by setting triple criteria of correlation kurtosis, modal cross correlation coefficient and envelope harmonic signal-to-noise ratio. Information corresponding to the maximum value of the WEHNR is used as parameter output of adaptive characteristic mode decomposition, envelope spectrum analysis is carried out on an optimal decomposition mode, fault characteristic frequency is extracted, and finally faults are detected and fault positions are identified. According to the method, adaptive decomposition of the fault signal can be realized, over-decomposition and under-decomposition can be avoided, impact and periodicity of mechanical fault response can be considered, and the method has relatively good robustness and practicability.

The invention relates to the technical field of vision measurement, and discloses a three-dimensional vision measurement system based on multi-view structured light, and the system comprises a data collection module which obtains image data, system data and point cloud data through a sensor and a system; the synchronous link module is used for generating a synchronous pulse interval based on the system data; calculating a cross correlation coefficient to judge interference and finish construction of a synchronous link; the three-dimensional measurement module is used for adjusting and calibrating by utilizing a composite structure, adjusting brightness through double modulation, constructing a target section data representation model for iterative optimization, analyzing to obtain an absolute phase value, constructing an energy function, obtaining a matching point pair based on image data and calculating a depth error; the data architecture module is used for dividing point cloud data, carrying out gridding discretization on an integral region, carrying out parallel calculation on energy values, summarizing the energy values, and dynamically adjusting the order of multi-pole expansion; and the enhanced anti-interference module is used for carrying out anti-interference processing on the image data, adjusting measurement parameters and adjusting the projection light intensity based on the predicted light intensity.

The invention provides a millimeter wave radar miscellaneous point removing method and device based on frequency spectrum matching, a medium and a program product, and the method comprises the steps: obtaining a multi-channel echo signal of a millimeter wave radar array, and constructing an actual measurement angle spectrum reflecting the energy distribution in each direction through beam forming and angle traversal processing; carrying out peak value search on the actually measured angle spectrum, and determining an effective target angle corresponding to each power peak; according to geometric structure parameters and effective target angles of the millimeter-wave radar array, performing weighted stacking on each target response based on a theoretical echo signal model to obtain a theoretical angle spectrum; performing spectrum matching analysis on the theoretical angle spectrum and the actually measured angle spectrum, and calculating cross correlation coefficient distribution of the theoretical angle spectrum and the actually measured angle spectrum under different angle deviations; and when the cross-correlation obtains a maximum value at the zero offset and is greater than a threshold value, judging that the corresponding target is an effective target, otherwise, regarding the target as a miscellaneous point and removing the miscellaneous point. According to the method and the device, accurate identification of a real target is realized by utilizing the change characteristics of the cross correlation coefficient under different angle deviations.

The invention relates to a time-lapse seismic data time difference correction method and device, electronic equipment and a medium, and relates to the field of seismic data processing, and the method comprises the steps: carrying out the preprocessing of two groups of time-lapse seismic data collected at a preset time interval; based on a first constraint of a preset time window length and a second constraint of a preset time-shifting maximum value, performing cross-correlation matching calculation on each sampling point in the two groups of time-shifting seismic data after preprocessing to obtain a cross-correlation coefficient; according to a preset time window length, the distance between the sampling points in the two groups of time-lapse seismic data and the cross correlation coefficient, generating a matching error function between the sampling points in the two groups of time-lapse seismic data; performing dynamic planning according to the matching error function, and determining an optimal matching path for connecting corresponding matching sampling points in the two groups of time-lapse seismic data; and carrying out time difference correction on the corresponding matched sampling points according to the optimal matching path. The time difference correction of the time-lapse seismic data can be realized more accurately, and the time consistency of the time-lapse seismic data is ensured.

The invention discloses a multi-temporal optical remote sensing image robust registration method based on multi-scale joint similarity measurement, and the method comprises the steps: 1, inputting a dual-temporal image, and obtaining uniformly distributed control point pairs through SIFT extraction and interactive manual auditing; 2, constructing a global dense displacement field by using a thin-plate spline, completing coarse registration and partitioning according to an overlapping strategy; 3, performing three-stage coarse-to-fine template matching on each image block, inheriting an initial displacement value step by step, and realizing radiation difference and geometric deformation synchronous self-adaption by taking a product of normalized mutual information and a normalized cross correlation coefficient as joint similarity measurement; and 4, performing sub-pixel-level deformation correction on the reliable displacement field by using a local thin plate spline, and outputting a registration image and metadata. According to the method, a global sparse-local dense framework and a multi-scale progressive + joint measurement + quality constraint mechanism are coupled, and a high-robustness and high-precision registration solution is provided for quantitative remote sensing application such as change detection and disaster assessment.

The application discloses a double-lead finger clip electrode biological wave detection method and device, and belongs to the technical field of biomedical engineering, and comprises a mechanical contact terminal, an intelligent adaptive module and a display terminal; the mechanical contact terminal comprises left and right two finger clip electrodes and is used for receiving biological wave signals in real time; the intelligent adaptive module is used for pre-processing the received biological wave signals, dividing the biological wave signals into low, medium and high frequency bands to extract key features, calculating cross-correlation coefficients and baseline drift amplitudes, constructing a classification model, identifying a user age range and automatically adjusting mode parameters, checking contact stability through a fast resampling mechanism and a dynamic threshold, and performing impedance compensation; and the display terminal is used for decomposing biological wave signals, extracting multi-domain features, modeling through a space-time attention LSTM network, calculating a physiological age and a health risk index, and generating an intervention measure recommendation list.

The application discloses a Parkinson gait evaluation method and system based on a millimeter wave radar signal. The method comprises the following steps: acquiring a millimeter wave radio frequency signal in an environment, performing data processing on the signal to obtain a distance spectrum of position change of a Parkinson patient's body and a Doppler spectrum reflecting the degree of change of the Parkinson patient's body; extracting a peak value at each moment from the distance spectrum through a filtering method, calculating a cross-correlation coefficient of the Doppler spectrum as an index of gait pattern matching, and dividing the Doppler spectrum according to each step; calculating gait features according to the filtered distance spectrum peak value and the divided Doppler spectrum; inputting the normalized gait features into a trained machine learning model for feature evaluation to obtain a UPDRS-III score of the patient. The application realizes non-invasive measurement and evaluation of Parkinson gait features in a low-cost manner, is convenient to operate, and is accurate in evaluation effect, thereby providing quantitative data and diagnosis reference for doctors.

The application provides a millimeter wave radar clutter point removal method and device based on spectrum matching, a medium and a program product. The method comprises the following steps: acquiring a multi-channel echo signal, constructing a measured angle spectrum reflecting the energy distribution of each direction through beam forming and angle traversal processing; determining each effective target angle of the measured angle spectrum; based on the geometric structure parameters of the millimeter wave radar array and the effective target angle, weighting and superimposing each target response based on a theoretical echo signal model to obtain a theoretical angle spectrum; performing spectrum matching analysis on the theoretical angle spectrum and the measured angle spectrum, performing point-by-point translation processing on the theoretical angle spectrum along the angle axis, and calculating the cross-correlation coefficient distribution of the two at different angle offsets; when the cross-correlation reaches a maximum value at zero angle offset and is greater than a threshold value, it is determined that the corresponding measured angle spectrum is effective, otherwise it is regarded as a clutter point and removed. The application realizes accurate identification of real targets by using the change characteristics of the cross-correlation coefficient at different angle offsets.

The application provides a land interlayer multiple wave suppression method and device, and relates to the technical field of seismic data processing. The technical scheme is as follows: a land interlayer multiple wave suppression method comprises the following steps: acquiring seismic data which needs to be subjected to land interlayer multiple wave suppression; performing far-offset noise cutting and random noise suppression on the seismic data to obtain denoised data; based on the denoised data, fitting an amplitude curve by using middle and far offset distance data in multiple wave removal, and approximately calculating theoretical seismic data which does not contain multiple waves according to the amplitude curve; calculating a normalized cross-correlation coefficient of the theoretical seismic data and actual data; and performing data reconstruction according to the normalized cross-correlation coefficient to obtain seismic data after land interlayer multiple wave suppression. The application has the beneficial effect of suppressing land seismic near-trace interlayer multiple waves by using AVA fitting and data reconstruction based on a normalized cross-correlation coefficient.

The invention relates to the technical field of radar detection and application thereof, and provides a data processing method for ground penetrating radar data, which comprises the following steps: firstly, carrying out preprocessing and gain recovery on original radar data, then carrying out mixed denoising by adopting empirical mode decomposition and Wiener filtering, screening and reconstructing effective signals by utilizing cross correlation coefficients, and finally, carrying out data processing on the effective signals. The method comprises the following steps: firstly, constructing a filter in a frequency wave number domain to remove random noise and low-frequency interference, then constructing a fan-shaped filter in the frequency wave number domain, introducing transition band processing to suppress linear coherent interference, then carrying out wave velocity estimation based on an image entropy minimization criterion to obtain an optimal imaging wave velocity, and finally executing Stolt migration imaging by using the optimal wave velocity and outputting a result. According to the method, edge details of effective signals are effectively reserved through multi-scale decomposition, the problem of imaging defocusing is solved through self-adaptive wave velocity search, and the signal-to-noise ratio, the resolution ratio and the focusing precision of ground penetrating radar data in a complex and strong-interference environment are remarkably improved.

This improves the degradation of the cross-correlation coefficient caused by the error between the set frequency and the actual frequency of the light source, and makes it possible to improve the degradation of the measurement accuracy of strain and temperature. [Solution] The light source unit has a frequency-stabilized wavelength-tunable laser that generates continuous light, and a first optical coupler splits the continuous light generated by the light source unit into two, sending one to a second optical coupler and the other to an optical frequency measurement unit. The optical frequency measurement unit measures the frequency of the continuous light generated by the light source unit, and the second optical coupler further splits the continuous light split by the first optical coupler into two, sending one as probe light to the optical pulse generation unit and the other as local light to a detection unit. The detection unit measures the frequency change Δf by means of coherent time domain reflectometry and a means for heterodyne detection of the Rayleigh scattered light and the local light resulting from Rayleigh scattering of pulsed light in the fiber under test. max and means for acquiring the

The present invention discloses a furniture panel structural performance detection system and detection method, comprising: a multi-source acquisition module: collecting panel structural data and environment-related data, extracting a first correlation parameter from the collected panel structural parameters, and extracting a second correlation parameter from the related environment parameters; an association acquisition module: performing cross-modal fusion analysis on the first correlation parameter and the second correlation parameter to obtain an optimized correlation index, and obtaining trend data from the optimized correlation index; a data correction module: collecting panel characteristic data, and optimizing the mutual correlation coefficient between the panel characteristic data and the trend data, performing correlation analysis based on the optimized mutual correlation coefficient to determine an attenuation mode, performing attenuation compensation on the collected real-time panel characteristic data based on the attenuation mode, obtaining corrected panel characteristic data, and ensuring that the corrected strength data is closer to the actual performance under the actual environment.

This invention belongs to the field of data processing technology, specifically relating to an IoT data edge computing processing method for ship-shore collaborative management. The method includes: acquiring real-time operating data of the ship's engine room equipment; loading a physical association weight matrix, calculating the local spatiotemporal coupling strength index of each sensor by calculating the cross-correlation coefficient and time lag between each sensor within the current window; extracting the transient change characteristics of the data at the current moment, calculating the prediction residual of the current observation using a prediction model, and calculating the adaptive transmission priority weight of each sensor's data based on the local spatiotemporal coupling strength index; and dividing the data stream into different levels according to the adaptive transmission priority weight, and executing a dynamic queue scheduling strategy for each level. This invention can improve the accuracy of fault diagnosis while reducing redundant data uploaded by the system.

The invention provides a multi-output channel Wiener filtering implementation method with low operand. The method comprises the following steps: (1) respectively calculating a reference signal cross correlation coefficient and a secondary path cross correlation coefficient; (2) the central part of the reference signal cross correlation coefficient is subjected to convolution of the secondary path cross correlation coefficient, and an obtained result is the central part of the filtered reference signal cross correlation coefficient; (3) performing secondary path filtering on the error signal, performing summation, and performing convolution with the reference signal to calculate a cross correlation coefficient between the filtered reference signal and the error signal; and (4) solving the coefficient of the filter by using a Levinson-Durbin algorithm. According to the method, solving of the standard Wiener filter is achieved with low calculation amount, the noise reduction amount is high, stability is good, the structure is simple, and the noise reduction amount and the convergence speed of an active noise reduction system can be improved.

The application discloses a kind of star-based ADS-B overlap signal time of arrival estimation method and system, signal is received by uniform array antenna, based on FastICA algorithm decomposition obtains two ADS-B signals;Since there is uncertainty in signal decomposition by FastICA algorithm, therefore, two kinds of mixed signals are designed for each time delay, that is, any signal may be ahead of another signal;By cross-correlation coefficient signal processing method, the designed mixed signal is calculated with the original mixed signal;Compare two cross-correlation coefficients, find the larger value, which corresponds to the correct order of each signal in the mixed signal, the maximum cross-correlation coefficient corresponds to the relative time delay of two signals;Based on the relative time delay and the order of the mixed signal before separation obtained, the signal is effectively separated, the signal error rate is low, and the arrival time of ADS-B signal is accurately estimated.

The invention relates to the technical field of fire detection, and discloses a multi-sensor pyrolysis particle fire detection control system and device, and the system comprises a power supply and signal coupling module, a dielectrophoresis modulation detection cavity module, a multi-dimensional environment sensing module, a central processing unit and an execution module. The power supply and signal coupling module extracts a high-frequency conducted interference signal of a power supply circuit and generates a high-voltage driving signal and a reference signal respectively; the dielectrophoresis modulation detection cavity module is used for establishing a non-uniform electric field by utilizing a high-voltage driving signal and carrying out dielectrophoresis modulation on aerosol particles passing through a detection area; the central processing unit synchronously collects the photoelectric detection signal and the reference signal, and determines the fire state by calculating a cross correlation coefficient. The device integrates the modules in a shell. According to the invention, physical and signal correlation between power supply noise and particle concentration change is established, electrical fire pyrolysis particles and environmental dust are effectively distinguished, and high-sensitivity early detection of electrical fire is realized.

This invention provides a method for optimizing energy storage configuration in distribution networks based on renewable energy consumption, belonging to the field of energy storage optimization configuration. The method involves: a synchronization feature module for time-domain synchronization processing to generate feature vectors; a frequency mapping modeling module to construct an equivalent feature matrix including inter-axis cross terms; an admittance reconstruction module to inject this into the static topology to generate a real-state dynamic admittance model and solve for the impedance matrix; a sensitivity evaluation module to map and generate real-state voltage sensitivity and determine the impedance offset index; a configuration module to perform nonlinear hedging to generate configuration data; and a calibration module to perform recursive calibration. The system also generates source-grid attribution weights based on normalized cross-correlation coefficients, enabling accurate determination and differentiated correction of causes on the equipment side and grid side, and introduces a self-evolving evolutionary flow for closed-loop optimization. This invention effectively reduces asynchronous interference from heterogeneous data, improves the accuracy of risk attribution and control, and ensures the stability of the distribution network.

The present application relates to the technical field of bridge deformation monitoring, and specifically to a method, system, and device for online monitoring of bridge structure deformation. The method comprises: analyzing the similarity of the frequency spectrum between each lateral component and its reference component, determining the lateral eigenvalue of each lateral component in each GPS receiver, and obtaining a reconstructed lateral displacement signal; determining the vertical eigenvalue of each vertical component in each GPS receiver by analyzing the correlation time fitting results of all vertical components and combining the cross-correlation coefficient to obtain a reconstructed vertical displacement signal; and separately analyzing the extreme value distribution of the reconstructed lateral displacement signal, the longitudinal displacement signal, and the reconstructed vertical displacement signal to determine the deformation monitoring results of the bridge deck to be tested. The present application solves the interference of external wind and vehicle loads on bridge structure deformation monitoring, and improves the accuracy of bridge structure deformation monitoring.

The application relates to a paint mist stable state change judgment method based on image sequence cross correlation, and relates to the technical field of image data processing. In order to solve the problems that the prior art can only calculate a fog cone angle through a single frame static image, cannot reflect dynamic changes of paint mist, is easily interfered by accidental factors and lacks a stable reference, the method comprises the following steps: step one, paint mist image acquisition; step two, establishment of a reference image; step three, image preprocessing; step four, calculation of a fog cone angle; step five, calculation of a fog cone angle relative error; step six, calculation of a cross correlation coefficient; and step seven, comprehensive judgment of paint mist stable state changes. The application replaces the traditional single frame image detection mode by collecting a paint mist image sequence and generating a plurality of average reference images, effectively avoids accidental interference of single frame data, establishes a stable and reliable paint mist standard reference state, and greatly improves the anti-interference ability and result reliability of detection.