86 results about "Cross correlation analysis" patented technology

The invention provides a multi-source data fusion modeling method and system in an aeration process, and is applied to the field of intelligent aeration control in sewage treatment. The method comprises the steps that multi-source time sequence data such as dissolved oxygen, turbidity, flow, temperature, power and pool bottom pressure pulsation signals are collected, dissolved oxygen response lag is calculated through cross-correlation analysis with power change as the reference, time sequence alignment is carried out, and a dissolved oxygen reference interval is predicted by utilizing calibration data in combination with a physical constraint LSTM model; performing spectral analysis on the pressure pulsation signal to extract a gas-liquid coupling characteristic value, and generating a cooperative regulation instruction of the frequency of the blower and the rotating speed of the stirrer based on the information; by means of the scheme, control oscillation caused by lag of the dissolved oxygen sensor can be effectively overcome, online monitoring of bubble form distribution is achieved, the gas-liquid mass transfer efficiency is improved, invalid aeration is avoided, and system energy consumption is remarkably reduced on the premise that stable effluent quality is guaranteed.

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 relates to the technical field of rock mechanical tests and safety monitoring, and discloses a rock fracture prediction method based on acoustic emission and surface strain cooperative monitoring, and the method comprises the steps: building a monitoring system comprising acoustic emission and an industrial camera array, and building a unified timestamp of each subsystem through a GPS time service module; through load triggering logic, a synchronous trigger is utilized to synchronously acquire an acoustic emission signal and a sample surface image sequence, and evolution characteristics of three-dimensional coordinates of an acoustic emission source and surface full-field strain data are solved. And on the basis, calculating a main strain field variation coefficient, and performing cross-correlation analysis on the resampled energy rate and strain rate. And finally, according to multi-parameter coupling criteria of acoustic emission time sequence parameters, positioning events and surface strain, identifying rock fracture precursor types, and predicting fracture moments and areas by combining a Voight model inverse velocity method and a seismic source projection technology.

The invention relates to the technical field of hydraulic engineering safety monitoring, and discloses a dam safety studying and judging method based on monitoring data multi-physics field simulation, which comprises the following steps: S1, multi-source data acquisition and time-space alignment; s2, dynamically updating the numerical model; s3, safety evaluation and early warning decision making; s4, performing multi-source risk coupling analysis; and S5, issuing the early warning information in a multi-mode manner. According to the method, time-space reference unification of multi-source monitoring data is achieved through feature point matching and sliding window cross-correlation analysis, a self-adaptive Kalman filtering algorithm is adopted to dynamically invert permeability coefficients and elastic modulus parameters, and boundary conditions of a finite element model are adjusted in combination with real-time water level changes; the dynamic simulation precision of a seepage field-displacement field-stress field coupling model is improved, the static evaluation limitation of a fixed threshold value method is broken through through a three-dimensional time-varying safety envelope surface and a Bayesian network grading early warning decision tree, and the risk prediction capability in the flood routing process is enhanced through multi-parameter joint probabilistic reasoning.

The invention discloses a pressure reconstruction method and system based on error correction and an adaptive grid, and a storage medium. The system comprises a PIV experiment test subsystem, a processing module (comprising a PIV post-processing unit and a pressure reconstruction unit) and the like. The method comprises the following steps: firstly, controlling a laser emitting sheet to irradiate tracer particles in a water tank through a synchronizer, and simultaneously triggering a high-speed camera to capture a particle image sequence at a microsecond-to-millisecond-level time interval; the PIV post-processing unit performs cross-correlation analysis on the image to generate a gridding velocity field; a pressure reconstruction unit solves a pressure Poisson equation based on the velocity field to obtain a preliminary pressure field, then iteratively corrects an error through an error correction mechanism and gradient correlation optimization, and triggers self-adaptive mesh refinement / coarsening according to a pressure / velocity gradient amplitude; and finally, outputting a high-precision pressure field on the optimized grid. The method can be applied to flow field tests and hydrodynamic performance analysis in ship and ocean engineering, especially pressure field reconstruction in a complex flow field, and has a wide application prospect.

The invention relates to the technical field of power equipment monitoring and fault diagnosis, and discloses a power transformer fault early warning method and system. The method comprises the following steps: acquiring a load current sequence, an environment temperature sequence and a top oil temperature sequence of a transformer; calculating an initial actually measured temperature rise based on the top oil temperature and the environment temperature, and determining thermal response lag time through cross-correlation analysis; carrying out translation correction on the load current according to the lag time, and constructing a time-aligned excitation input set and an actually measured temperature rise sequence based on a common effective time window; substituting the excitation input set into a thermal circuit model based on a thermal balance differential equation to calculate a theoretical reference temperature rise, calculating a residual error in combination with an actually measured temperature rise sequence, performing moving average filtering, and constructing a fault feature vector; and inputting the fault feature vector into a fault diagnosis classification model to determine a fault type and severity, and generating a grading early warning instruction according to the fault type and severity. According to the method, thermal hysteresis interference can be eliminated, and accurate quantitative early warning of the latent thermal fault of the transformer under the dynamic working condition is realized.

The invention relates to the technical field of optical fiber acoustic sensing, in particular to a method for monitoring invasion and damage along a coal conveying belt based on distributed optical fiber acoustic sensing. The method comprises the following steps of: acquiring acoustic signals when a coal conveying belt normally runs and is static, and establishing a reference model library of normal acoustic characteristics of each road section; a continuous online data acquisition module is utilized to receive and store optical fiber acoustic signals in real time according to a preset sampling rate and a time window, coherent noise reduction processing is performed on the received optical fiber acoustic signals by utilizing a sensing domain feature enhancement method based on dual-working-condition background field coherent modulation, and the optical fiber acoustic signals after coherent noise reduction are formed. According to the method, a normal acoustic feature reference model library under double working conditions (static and operation) is constructed, a sensing domain feature enhancement method based on double-working-condition background field coherent modulation is introduced, and coherent noise reduction enhancement is performed on real-time acoustic signals by using a cross-correlation analysis and adaptive filtering technology.

The invention relates to the technical field of radars, in particular to a weather radar distance resolution calibration method and system. The method comprises the following steps: collecting oversampling original baseband I / Q data of a point target in a plurality of continuous pulse periods; determining a pulse signal-to-noise ratio, and screening out effective pulses according to a threshold value; carrying out sub-sampling stage time alignment on the effective pulses based on cross-correlation analysis, and carrying out incoherent averaging on power module values of all the aligned pulses to generate an average pulse power envelope; converting the average pulse power envelope to a logarithm domain, and selecting discrete data points in a main lobe region; according to the discrete data points, a nonlinear least square method is adopted to carry out iterative fitting on the generalized Gaussian mathematical model, and a continuous analytic function describing the pulse envelope is obtained; according to the continuous analytic function, determining a pulse time width when the power is reduced to a peak value of-6dB; and determining the real distance resolution according to the pulse time width. The measurement robustness is improved, and sub-meter calibration precision is achieved.

The invention discloses an osmotic pressure analysis method and device fusing model optimization and physical inversion and a medium, and the method comprises the steps: calculating correlation coefficients of a specified reservoir water level and osmotic pressure under different time lags based on cross-correlation function analysis, and recognizing a differential nonlinear feature corresponding to the maximum correlation coefficient; aiming at the differentiated nonlinear characteristics of different dam types, constructing a plurality of corresponding diagnosis prediction models in parallel, and establishing a model selection decision rule to select an optimal diagnosis model; according to delay parameters obtained through cross-correlation analysis, a permeability coefficient is reversely deduced through a pore medium heat transfer diffusion theoretical formula; according to the regression slope and the theoretical value attenuation factor of the optimal model, a permeability coefficient is reversely deduced in combination with a correction diffusion equation, credibility evaluation and weighted fusion are performed on the permeability coefficient, and a comprehensive permeability coefficient is calculated; and diagnosing the soil type and the permeability characteristic grade. According to the method, the propagation delay characteristic of reservoir water level change in the pore medium can be accurately quantified, and the inversion precision of the permeability coefficient and the engineering applicability are improved.

The invention discloses an intelligent disturbance and analysis method and system for hemodialysis data, and relates to the field of hemodialysis data analys.The intelligent disturbance and analysis method comprises the steps that in the hemodialysis process, disturbance signals are superposed through a blood input pump, so that blood parameters input into a dialyzer generate preset configuration changes, and therefore input disturbance signals are formed; in the disturbance applying period, blood parameters of the blood output end of the dialyzer are collected in real time, and corresponding output response signals are formed; performing cross-correlation analysis on the input disturbance signal and the output response signal, and calculating a time delay parameter between the input disturbance signal and the output response signal; periodically and repeatedly detecting, carrying out regression analysis on the time delay parameter, and when the time delay parameter is continuously increased along with time, judging that the dialyzer has a blockage trend and outputting early warning information. According to the method, early detection on the dialyzer blockage is realized through the dynamic time-lag characteristics under the condition of not increasing a sensor, and the safety of a hemodialysis system is improved.

The present application relates to the technical field of noise diagnosis, in particular to a method and device for identifying abnormal sound source applied to a vehicle transmission system, the method comprising: acquiring abnormal sound noise data, and confirming an abnormal sound generation period according to the abnormal sound noise data; acquiring transmission system vibration data, and performing time-frequency domain analysis on the transmission system vibration data and the abnormal sound noise data according to the abnormal sound generation period to obtain abnormal sound time domain data; the transmission system vibration data is obtained by vibration detection of the vehicle transmission system at a previous time; performing cross-correlation identification of the abnormal sound source according to the abnormal sound time domain data to obtain an abnormal sound source identification result; the abnormal sound source identification result is used to represent that the abnormal sound source is a gearbox and / or a drive axle. By considering the state of strong coupling connection between the gearbox and the drive axle in the vehicle transmission system, cross-correlation analysis is performed on the abnormal sound time domain data to identify the abnormal sound source, thereby improving the accuracy of identifying the abnormal sound source of the vehicle identification system.

The invention relates to the technical field of heart sound signal processing, and particularly discloses a heart sound signal respiration interference suppression method and system based on multi-source sensing fusion, and the method comprises the steps: constructing a multi-source synchronous signal set through synchronously collecting heart sound signals and respiration monitoring signals; separating a heart sound principal component, a breathing interference component and a noise component by utilizing an adaptive modal decomposition and multi-dimensional feature clustering technology; constructing a time-frequency domain interference template through multi-scale cross-correlation analysis and phase registration by taking a respiration monitoring signal as a reference; based on the template, adopting time-varying gain control and an iterative optimization strategy to realize accurate suppression of respiratory interference, and generating a pure heart sound signal; and through multi-resolution feature enhancement and multi-index quantitative evaluation, generating a purity report containing a signal quality grade and a clinical applicability scheme.

The application discloses a kind of gear engagement loop resistance measurement based on gear face lubrication state judging method, belong to gear face lubrication technical field.This method includes: installation angle sensor identifies the engagement start-stop point of each gear tooth;Constant current is applied to collect loop resistance signal, and is divided into the engagement period resistance waveform of each gear tooth;The similarity matrix between waveform and the discrete degree value of its row mean are calculated, compared with the reference value under initial good lubrication state to obtain relative deviation degree;According to relative deviation degree, elastohydrodynamic lubrication, mixed lubrication or boundary lubrication state is judged, and the gear tooth number of similarity abnormality is output simultaneously.The application can also extract oil film extrusion depth coefficient and recovery residual coefficient, and identify lubricating film dynamic instability through inter-circle cross-correlation analysis.The method realizes gear tooth level lubrication state evaluation and abnormal positioning, is robust to working condition change, and is convenient for online monitoring and predictive maintenance.

The invention relates to the technical field of power system automation and relay protection, and discloses a low-current grounding system fault line selection and closed-loop control method and device, and the method comprises the steps: applying preset parameter disturbance to an arc suppression coil in a system after the system is detected to have a grounding fault; in the process of applying the parameter disturbance, synchronously collecting zero-sequence current of each outgoing line as a response signal; analyzing the response signal, and determining a fault line and a fault property; and executing a corresponding closed-loop control strategy according to the determined fault line and the fault property. After a fault is detected, an arc suppression coil is utilized to inject a high-frequency pseudo-random binary sequence as a disturbance signal, and a cross-correlation analysis algorithm is adopted to process the zero-sequence current response of each line, so that a fault feature component related to a disturbance source can be accurately extracted from strong background noise; therefore, high-precision selection of the fault line is realized.

The invention discloses a high-precision fast reflecting mirror calibration method based on cross-correlation analysis, which belongs to the technical field of optical measurement, and is characterized by comprising the following steps: S1, original data acquisition, S2, data preprocessing, S3, cross-correlation calculation, S4, signal interpolation refinement, S5, time alignment adjustment, S6, batch processing of multiple groups of data, and S5, calibration of a high-precision fast reflecting mirror. Step S7, precision determination, step S8, calibration error synthesis, and a specific calibration method formed through the steps S1-S8. According to the method, automatic time migration estimation is achieved by introducing cross-correlation analysis, sampling frequency limitation is broken through in combination with high-order interpolation, meanwhile, statistical analysis is conducted on multiple sets of data, the problems that starting is asynchronous, the resolution ratio is insufficient and errors cannot be quantified are solved, and the calibration precision and stability of the fast steering mirror are improved.

The application relates to the technical field of medical care, and discloses a personalized nursing optimization method for rapid recovery after gastrointestinal surgery, which takes an infusion pump driving reference signal as a noise source input, performs adaptive filtering on an original acoustic signal to output purified borborygmus; a physiological-acoustic coupling index is calculated in combination with body surface electrocardio characteristics to quantize the synchronization degree of mechanical peristalsis and nerve innervation; when the coupling index confidence is insufficient, a pseudo-random fluid pulse excitation is applied for system identification, nerve reflex sensitivity and reflection response time delay parameters are extracted through cross-correlation analysis; a multi-dimensional state vector containing the above index and parameters is constructed, and a gain scheduling control algorithm is used to dynamically adjust the infusion flow rate. Through source reference adaptive filtering and active fluid pulse identification, the application accurately evaluates the nerve innervation state, and realizes closed-loop flow rate control adapting to individual recovery processes by using a gain scheduling algorithm.

The application discloses a method and system for evaluating CAR-T treatment related neurotoxicity based on behavior characteristics, relates to the technical field of medical behavior analysis, and provides the following scheme, which comprises the following steps: synchronously collecting angular velocity time series data of double upper limbs of a patient and electromyographic signals corresponding to muscle surfaces of the double upper limbs; performing frequency band optimization filtering on the angular velocity time series data of the double upper limbs respectively; generating angular velocity waveforms; taking a wave peak time point of the angular velocity waveform as a reference; extending forward for a preset time length; and calculating an energy integral curve of the electromyographic signals. The angular velocity time series data of the double upper limbs is processed through frequency band optimization to generate angular velocity waveforms, the energy integral curve of the electromyographic signals is extracted forward based on the wave peak time point of the angular velocity, candidate time points are obtained, and local cross-correlation analysis is enabled to locate and intercept points when the wave peak value is lower than a preset threshold, so that the problem of missing weak tremor signals is solved, effective detection and reliable interception of low-amplitude tremors are realized, and weak signal features are avoided from being lost.

The invention provides a flood propagation time calibration method and device based on cross-correlation analysis. According to the cross-correlation analysis-based flood propagation time calibration method provided by the embodiment of the invention, automatic and objective calibration of flood propagation time can be realized, and the adaptability and calculation precision of complex flood forms such as complex peaks and flat peaks can be remarkably improved.

A photographing apparatus (100) includes: a data acquisition unit (20) that acquires first partial binning data (p1) based on a first pixel group that is constituted by at least one pixel by binning in a unit pixel group constituted by a plurality of pixels that are grouped; an analysis unit (30) that analyzes a frequency characteristic of an image signal of a region constituted by the unit pixel group based on cross-correlation between the first partial binning data (p1) and second partial binning data (p2) based on a second pixel group constituted by pixels other than the first pixel group in the unit pixel group; and a moire removal unit (40) that removes a moire generated in the region constituted by the unit pixel group based on an analysis result.

The invention provides a multi-station radar passive time difference positioning accurate pulse matching method and system based on signal correlation, and aims at realizing accurate matching of different signal pulses in a complex battlefield situation and improving the accuracy and anti-noise capability of passive positioning. The method belongs to the technical field of passive time difference positioning. According to the method, signal waveform cross-correlation analysis is introduced, the matching reliability of pulse pairs is dynamically verified through time domain waveform similarity calculation of signals received by the main station and the auxiliary station, and the pulse matching method which does not need to carry out signal sorting firstly under the dense pulse condition is innovatively provided. Specifically, when parameters such as time difference and frequency difference of two paths of pulses fall into a preset threshold range, cross-correlation operation is further performed on the two paths of signals, and a cross-correlation function peak value and corresponding time delay are extracted to eliminate false correlation caused by noise interference or asynchronous signal aliasing. The invention provides a solution with robustness and practicability for a passive positioning system in the fields of electronic reconnaissance and the like.

The application relates to the technical field of urban underground pipe network safety monitoring, and discloses a wave corrugation compensator leakage positioning method and system based on an Internet of Things, which is applied to an Internet of Things system comprising a cloud server and a plurality of sensing nodes, and the sensing nodes are installed in the interlayer space of the wave corrugation compensator. The method comprises the following steps: collecting pressure and temperature data of the interlayer space by the nodes at a low frequency, triggering an adjacent node to enter a high-frequency acoustic sampling mode when the pressure change rate is greater than a dynamic threshold value; collecting an acoustic emission signal, filtering mechanical noise through a noise reduction model, and extracting fluid jet noise characteristics; performing cross-correlation analysis on the acoustic signals between the nodes to calculate the actual sound wave conduction distance, and verifying whether real leakage occurs by comparing with a physical topological distance; and finally evaluating the leakage grade according to the steady-state peak value of the pressure data and the acoustic frequency energy data, and outputting device coordinate information. The application has both ultra-low power consumption and strong anti-interference capability, and realizes accurate element-level leakage positioning.

The application discloses an autism child social interaction behavior video image analysis method and system, belongs to the technical field of computer vision and medical image analysis, and comprises the following steps: a video acquisition step, wherein video images of a child and an assessor performing a structured social interaction task are acquired through a multi-angle camera; a human body posture estimation step, wherein two-person skeleton key point detection is performed and posture parameter sequences are calculated; a social signal detection step, wherein key social behavior events such as eye contact, social smile, directional gesture and body orientation response are identified; an interaction synchronism analysis step, wherein the time coupling degree of posture changes of both parties is calculated through cross-correlation analysis; and an evaluation report generation step, wherein a social interaction ability evaluation report is generated in the form of a radar chart by comprehensively considering multi-dimensional features. The application realizes objective quantitative evaluation of the social interaction ability of an autism child and improves the consistency and repeatability of evaluation.

The invention relates to the technical field of ultrasonic testing, in particular to a nondestructive testing method and device for inclusions in a titanium alloy bar. The method comprises the following steps: synchronously acquiring ultrasonic A scanning signals before and after multi-moment thermal imaging and thermal excitation by applying thermal pulse excitation; calculating a delay cooling factor based on the pixel temperature time sequence change, determining a hot spot existence rate in combination with the spatial distribution of the delay cooling factor in a multi-direction neighborhood, and screening a thermal abnormal region; in the region, a peak redundancy coefficient is determined by using time sequence volatility of ultrasonic signal amplitude, A scanning waveforms before and after thermal excitation are analyzed through window sliding cross-correlation to obtain a distortion degree, the two are fused to obtain an ultrasonic abnormal degree, and whether inclusions exist or not is judged according to the ultrasonic abnormal degree. According to the method, ultrasonic scattering noise caused by a titanium alloy coarse grain structure is effectively avoided, and the reliability of tiny inclusion detection is improved.

The application discloses a kind of multi-mode data fusion central venous catheter related thrombus early warning method and system.The method uses infusion pump flow rate step event as excitation signal, and the original sequence of pump end pressure and central venous pressure is synchronously collected;Through cross-correlation analysis, the pressure transmission time difference is calculated and time domain difference compensation is performed, and the net pressure difference sequence reflecting the characteristics of catheter body is generated.The core is to use the inherent mechanical peristalsis of pump as detection signal, decouple the net pressure difference sequence into average perfusion resistance value representing the macroscopic patency of catheter, and extract resistance normalized asymmetry representing the viscoelasticity of soft tissue at the end of catheter by period folding reconstruction.The system constructs two-dimensional orthogonal decision logic, realizes the accurate distinction and hierarchical early warning of safe patency, rigid mechanical obstruction and flexible valve fault state.The application can actively identify early occult thrombus valve characteristics without interrupting infusion or manual backflow.

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 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 invention relates to a lithium ion battery health state estimation method based on ultrasonic characteristics, and belongs to the technical field of battery detection and diagnosis. The method comprises the following steps: applying pulse ultrasonic excitation to a to-be-detected battery and collecting an ultrasonic echo signal formed by multi-interface reflection; carrying out a standardized accelerated aging test on the battery under the conditions of constant temperature and fixed multiplying power, obtaining current, voltage and capacity data, and calculating the SOH of the battery at different aging stages; preprocessing the ultrasonic echo signals, extracting ultrasonic time of flight (TOF) features corresponding to main echoes by adopting a cross-correlation analysis method, and performing time synchronization; and constructing an ultrasonic health factor based on the TOF features and the SOC, and introducing a BP neural network model to realize online estimation of the SOH of the battery. According to the method, through fusion of ultrasonic nondestructive testing and an intelligent algorithm, rapid and accurate evaluation of the health state of the lithium ion battery under non-intrusive and non-charging and non-discharging working conditions is realized, and the method has the advantages of high detection speed, high applicability and high engineering popularization value.

The present application relates to the technical fields of artificial intelligence and equipment running state detection, and particularly relates to a kind of distillation column running state intelligent monitoring and fault detection method, as follows: multiple sensors are deployed at key positions of the distillation column to collect data and construct a dataset, and the data is manually annotated, while the physical constraint function is derived based on the physical law of the distillation column;Based on the physical constraint function, the physical scaling factor is calculated, and the data samples in the training set are normalized to obtain the standardized value;The time delay compensation eigenvalue is calculated by cross-correlation analysis;The time delay compensation eigenvalue is extracted by a double-channel wavelet fusion mechanism to extract high-frequency and low-frequency features;A distillation column running state detection model is constructed and trained to obtain a trained model;The newly collected data from the sensors is preprocessed and input into the trained model, and the final result is output.The present application can improve the timeliness of equipment fault detection and enhance the accuracy of detection results.

The invention discloses a cancer rehabilitation period patient and caregiver physiological health collaborative monitoring and intervention system, which comprises a multi-source physiological data acquisition module, a physiological interaction modeling module, a collaborative health intervention module and a closed-loop feedback regulation and control module, the multi-source physiological data acquisition module synchronously acquires heart rate variability, electrodermal response and body movement acceleration data of a patient and a caregiver through a wearable device and outputs a physiological stress index, a physiological load index, a physiological synchronization index and a rhythm matching index, and the physiological interaction modeling module constructs a double-person physiological state interaction model based on cross-correlation analysis. Key intervention nodes are recognized, the collaborative health intervention module determines the optimal intensity ratio and time sequence cooperation parameters of intervention of the two parties through a collaborative optimization algorithm, the closed-loop feedback regulation and control module executes intervention and monitors the effect, and when deviation exceeds a threshold value, a feedback signal is transmitted back to trigger scheme adjustment; a complete technical link of two-person physiological data collaborative acquisition, interactive modeling, collaborative intervention and closed-loop feedback is realized.

This invention discloses a method, apparatus, and storage medium for analyzing the time-delay response of downstream water temperature to air temperature changes. The method includes: first, collecting flow and water temperature data from upstream and downstream hydrological stations, as well as air temperature data from a nearby meteorological station, to establish a basic meteorological and hydrological dataset; second, establishing a heat balance equation for the downstream hydrological control section as a physical constraint condition for a TFT water temperature model; and finally, constructing a TFT water temperature model for the downstream hydrological control section, utilizing the multi-head attention mechanism of the TFT model to analyze the contribution weight of air temperature to the downstream water temperature simulation at different times, and analyzing the daily dynamic process of the time-delay response of downstream water temperature to air temperature changes. This invention overcomes the limitation of traditional cross-correlation analysis, which can only output a single static time delay, and can output daily, dynamically changing time delays, precisely depicting the dynamic evolution of the time delay of downstream water temperature response to air temperature changes over time.