39 results about "Baseline wander" patented technology

The invention belongs to the technical field of coal quality detection, and particularly relates to a method for rapidly predicting coal quality parameters based on Raman spectrum in combination with machine learning. According to the method, a Raman spectrum technology and machine learning are combined, a Raman spectrum systematic preprocessing process is adopted, an adaptive iteration reweighted least square method Air-PLS is applied, spectral signals and baseline drift are accurately separated, Savitzky-Golay filtering is applied, peak shape features are reserved, random noise is effectively suppressed, and the method is suitable for large-scale popularization and application. Maximum-minimum normalization or Z-score standardization processing is provided, and the intensity difference between samples is eliminated; moreover, a data-driven Raman spectrum intelligent feature screening mechanism is introduced, and an optimal feature wavelength subset which is highly related to coal quality parameters and low in redundancy is screened out through automatic iteration; according to the method, the data quality in Raman spectrum coal quality detection is improved, the feature correlation is enhanced while the feature dimension is reduced, the model precision and generalization ability are improved, and rapid, lossless, high-precision and high-robustness coal quality parameter prediction is realized.

The invention relates to the technical field of digital medical treatment, and discloses an intelligent chronic disease screening method based on multi-source data fusion, which comprises the following steps: firstly, calculating a sensor credibility reference value based on the historical performance and real-time state of each sensor; performing quality evaluation on the physiological signal according to the credibility reference value, and identifying and marking a signal disturbance interval; then, performing frequency domain analysis and environment interference correlation verification on the marked disturbance interval, and dynamically adjusting the weight coefficient of each sensor; carrying out weighted fusion on the multi-source physiological signals by adopting the adjusted weight coefficient, and carrying out baseline drift correction on low-quality signals; and finally, extracting physiological feature parameters from the fusion signal, matching the physiological feature parameters with a health state pattern library, and outputting a chronic disease screening evaluation result. The problems that in a traditional monitoring system, data quality evaluation standards are not uniform, fusion strategies are rigid, and continuous optimization capacity is lacked are solved, and the accuracy and reliability of chronic disease screening are remarkably improved.

The invention discloses a heart rate measuring method based on a smart phone. The method comprises the following steps: (1) collecting fingertip pulse plethysmography (PPG) signals under the condition of preset camera parameters; (2) preprocessing the collected signals, removing baseline drift through a filtering method and the like, and suppressing noise; (3) evaluating the signal quality based on a template matching method, and when the signal quality score is lower than a preset threshold value, not outputting a heart rate result and giving an operation suggestion; and (4) carrying out time domain peak detection on the high-quality signal to obtain a heart rate estimation value. According to the invention, through systematic optimization in the links of signal acquisition, quality evaluation and peak value detection, the defects of low accuracy, easy interference and lack of reliability evaluation in a complex environment in the prior art are solved, and the heart rate measurement scheme is convenient, high in precision and high in robustness; and a more reliable and accurate mobile health monitoring solution is provided for the user.

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 present application belongs to the field of intelligent medical treatment and health monitoring technology, and specifically relates to a heart-source sudden death risk monitoring and early warning system and method based on multi-source signal collaborative analysis. The method aims to solve the technical problem of signal quality degradation caused by motion artifact interference in existing wearable electrocardio monitoring. It includes: synchronously collecting electrocardio signals and three-axis acceleration signals through a wearable device, performing adaptive motion artifact filtering based on acceleration reference, power frequency notch filtering and baseline drift correction; performing QRS wave detection and feature extraction on the preprocessed waveform, combining with the motion intensity index to mark the candidate risk event segment; transmitting the structured event data packet to a mobile terminal, outputting the risk arrhythmia confidence degree by using a lightweight time sequence neural network model, and performing comprehensive risk assessment by fusing the event duration and frequency, the user real-time activity state and the individualized historical baseline; and performing differentiated response strategies according to the risk level.

The invention discloses a sleep monitoring and intelligent early warning system and method based on a multi-modal time sequence signal, and the system comprises a multi-modal sensor module which is used for collecting an original heterogeneous time sequence signal of a subject in a sleep period; the time sequence data preprocessing module is used for performing timestamp alignment, band-pass filtering noise reduction, baseline drift correction and sliding window segmentation on the original heterogeneous time sequence signals and extracting multi-modal feature vectors; the sleep feature recognition module is used for receiving the multi-modal feature vector, performing deep feature learning and outputting sleep staging probability distribution and a sleep apnea event detection mark; the large language model module is used for performing joint coding on an output result, a historical sleep file of the subject and a clinical diagnosis text to generate a structured sleep quality assessment report and health risk association analysis; and the multi-level intelligent early warning and decision support module is used for triggering early warning signals of different levels and generating personalized sleep intervention suggestions.

This invention discloses a gas concentration detection method based on nonlinear fitting of second harmonic signals, comprising: 1. constructing the Voigt linear function of the second harmonic signal; 2. calculating initial fitting values, performing nonlinear fitting on the acquired second harmonic signals of the reference channel and the measurement channel to obtain the optimal fitting parameters; 3. calculating the fitted standard second harmonic signal and the peak value of the second harmonic signal based on the optimal fitting parameters, and using the peak value for concentration inversion. This invention, without increasing the hardware structure, solves the problems of signal baseline drift and signal deformation affecting measurement accuracy through a nonlinear fitting method of the second harmonic signal, thereby improving detection accuracy and ensuring the stability of the detection system.

The application relates to the technical field of magnetic variable measurement, in particular to a magnetic induction electric field dynamic compensation control method based on online disturbance response, which comprises the following steps: injecting a characteristic test signal to the induction axis of a magnetism sensor, extracting an amplitude-phase deflection response characteristic by using a digital lock-in amplifier to construct a dynamic mapping matrix, and online identifying the gain, time constant and pure lag measurement model parameters of a calibration coil loop; an error processing unit sets an error offset coefficient and generates a reverse compensation control instruction to drive a loop to generate an offset magnetic field; the intensity of a multi-node parasitic induction electric field is obtained as a dynamic weight, a Steiner-Weber algorithm is used to solve a space equivalent interference radiation center, and the test duty ratio and calibration weight of each node are adjusted. The application actively injects orthogonal signals to online decouple a measurement baseline drift, and adaptively cooperates with a multi-node measurement array, so that wide-band and extremely low dynamic residual measurement of a large-space alternating magnetic field is realized.

The present application relates to the technical field of Pickleball, and specifically relates to an intelligent wearable system for monitoring sports posture. The system synchronously collects multi-axis acceleration and angular velocity original signals before and after the player hits the ball through the inertial measurement unit built in the wearable device, generates time series after time window interception and normalization processing; removes noise and baseline drift by wavelet transform; fuses time domain and frequency domain features to construct a high-dimensional comprehensive feature vector; realizes preliminary classification of actions through a long short-term memory network, refines similar action types by combining a convolutional neural network, and finally outputs accurate classification results through a support vector machine; dynamically optimizes model parameters based on a real-time feedback mechanism, and enhances local feature representation through a channel attention mechanism. The system can accurately identify more than fifteen Pickleball special technical actions, has high robustness and self-adaptive ability, can provide real-time posture specification guidance for athletes, and helps to improve training efficiency and prevent sports injuries.

A method and an apparatus are provided in which an analog signal may be sampled by an analog-to-digital converter (ADC) to generate digital signal samples affected by baseline wander. Digital signal processing (DSP) may be performed on the digital signal samples to generate a recovered signal with corrected baseline wander. The DSP is performed using a first adaptable coefficient for a current digital signal sample and a second adaptable coefficient for a previous digital signal sample.

This invention relates to the field of signal data processing technology, specifically to an anti-interference connector for automotive electronic systems. This application first analyzes the characteristic behavior of the spectrum within the time period of arc flicker interference in the original signal, obtaining the arc flicker frequency band and arc flicker influence coefficient. Based on the arc flicker influence coefficient, and combined with baseline drift and signal fluctuation in the time series, the influence of electromagnetic interference, i.e., arc flicker interference, is more accurately quantified using the determined electromagnetic interference characteristic values. Furthermore, a sensitivity weight is determined to characterize the impact of the anti-interference connector's aging accumulation. Based on the sensitivity weights characterizing the interference components and the electromagnetic interference characteristic values, the simulated signal to be processed is corrected, making the obtained corrected simulated signal more accurate and improving the accuracy of anomaly monitoring in automotive electronic systems based on the corrected simulated signal.

This invention discloses a polarity testing method for metering devices suitable for high-noise environments, belonging to the field of metering device testing technology. The method includes: generating a square wave excitation signal with preset parameters based on algorithm control and applying it to the primary side of the metering device; acquiring the secondary side induced response signal, which is then synchronously transmitted after filtering, baseline drift correction, and amplitude scaling preprocessing; integrating the excitation and preprocessed signals, extracting phase difference, delay time, and amplitude characteristic parameters, and sequentially performing polarity, phase sequence, and on / off determination to obtain a preliminary comprehensive result; based on the preliminary result and characteristic parameters, outputting the determination result and confidence level through an improved CNN model, and obtaining the final polarity conclusion through weighted fusion; and visually outputting the final conclusion. This invention, through the collaborative design of precise excitation, anti-interference preprocessing, multi-dimensional preliminary determination, and AI-fused final determination, effectively suppresses noise interference, improves the accuracy and stability of polarity determination, and is suitable for high-noise scenarios such as industrial sites and substations.

The invention discloses a period-phase optimized robot tactile signal segmentation and alignment method, which can quickly extract effective signal segments when a robot contacts an object, and solves the problems of unstable tactile signal form, baseline drift, uncertain period length and the like. The method provides a joint optimization normal form: firstly, estimating the average period of a tactile signal through self-correlation analysis; then, unified modeling is carried out on the segmentation tangency points and the fragment duration under structural constraints to form variables to be optimized, and an aggregation similarity objective function with normalized mutual measurement standards is constructed; and finally, iteratively solving the optimal segmentation point and phase of the time sequence signal in the contact process by maximizing the objective function. Compared with a traditional non-global optimization method, the method can be suitable for non-fixed-length quasi-periodic signals with unstable forms, has high robustness for amplitude drift and periodic jitter, is higher in segmentation precision and universality, and can provide accurate contact segment effective signals for robot touch recognition.

The application provides an arrhythmia signal detection method and system based on expert knowledge, wherein the method comprises the following steps: performing signal filtering on an acquired electrocardio signal to filter out high-frequency noise and baseline drift in the electrocardio signal; performing P-QRS-T positioning detection on the signal-filtered electrocardio signal to obtain a QRS complex and P / T wave; performing expert-knowledge-based feature mapping on the obtained QRS complex and P / T wave to obtain a category feature of the electrocardio signal; comparing the obtained category feature of the electrocardio signal with a preset threshold to determine an arrhythmia signal, thereby completing detection of the arrhythmia signal. Meanwhile, a corresponding terminal and medium are provided. The application improves the detection accuracy; fully considers the morphology of an abnormal heartbeat and the timing of positioning the P / T wave, can effectively avoid the case that the P-QRS-T positioning is inaccurate due to the abnormal heartbeat, has high robustness, and has low computational complexity.

The application relates to the technical field of data collection, and relates to a sports movement data collection system based on a wearable device, which comprises a signal synchronization fusion module, a reference modeling and calibration module, a signal enhancement and segmentation module, a feature matching and extraction module, a deviation quantization module and a comprehensive scoring module. In the progress process of a body measurement project, original inertia signals of a tested person are acquired; waveform features of standard actions are analyzed, baseline drift compensation is carried out on the analyzed reference waveforms, and measurement reference data are obtained; principal component signal reconstruction is carried out on the original inertia signals, and action stage signal segments of the tested person are segmented; the action stage signal segments are mapped to the measurement reference data, and key kinematic parameters are obtained; the key kinematic parameters are quantized, and action deviation degrees are obtained; comprehensive movement evaluation is carried out on the action deviation degrees, and movement performance data are obtained; and the application can improve the efficiency of sports movement data collection based on a wearable device.

The invention discloses a cross-modal silent voice reconstruction method and system based on ear canal air pressure micro-motion sensing, and belongs to the technical field of man-machine interaction and wearable computing. The method comprises the following steps: acquiring a non-acoustic air pressure sequence caused by vocal organ movement by using a micro air pressure sensing unit arranged in the in-ear earphone; constructing a robust feature space through adaptive baseline drift suppression and rhythm perception data enhancement processing; and further mapping the TPVS into a high-fidelity acoustic Mel spectrum by using an end-to-end deep neural network including domain adversarial adaptation, cross-modal semantic alignment, coarse-grained Mel spectrum generation and residual detail correction. The technical bottleneck that low-frequency mechanical signals lack high-frequency acoustic features is effectively broken through, and high-precision silent voice instruction analysis in a mobile and noise scene is achieved; and coupling quality evaluation gating and trigger type start / stop control are introduced at a reasoning end, so that invalid reasoning is inhibited and power consumption is reduced under the condition of poor wearing or no trigger.

Embodiments herein relate to techniques for baseline wander (BLW) compensation. The technique may include identifying a data stream that is to be modulated by a ring modulator of an optical transmitter, wherein the data stream has a frequency operable to cause thermal-based BLW of an optical output of the optical transmitter. The technique may further include adjusting a time-varying direct current (DC) voltage bias of the ring modulator based on the frequency of the data stream. Other embodiments may be described and / or claimed.

The present disclosure provides an ultrasonic bolt pretightening force detection method and system based on improved CEEMDAN-wavelet threshold, relating to the technical field of bolt pretightening force detection, comprising: obtaining ultrasonic echo original signals under different loads; performing improved CEEMDAN decomposition on the ultrasonic echo original signals to obtain IMFs (Intrinsic Mode Functions) without modal aliasing; calculating MPE values of each IMF, classifying the components through a double-threshold quantization method to obtain to-be-de-noised components, effective reserved components and baseline drift components; introducing an improved threshold function with smoothing characteristics, accurately de-noising the to-be-de-noised components through the improved threshold function, and reconstructing to obtain de-noised IMFs; fusing the de-noised IMFs and the effective reserved components, reconstructing a pure ultrasonic echo signal and extracting a time-of-flight feature; calculating the acoustic time difference between a reference signal and a measurement signal using a cross-correlation method, and finally obtaining an accurate bolt pretightening force. The present disclosure ensures high-precision dynamic monitoring of bolt pretightening force in harsh engineering environments.

The application discloses a flexible tactile perception system with a geometric strain compensation function and a signal processing method thereof, and belongs to the technical field of flexible electronics and intelligent perception. The flexible tactile perception system comprises a flexible adaptive conformal layer, a flexible sensing array layer and a packaging protective layer which are sequentially stacked from bottom to top. The flexible sensing array layer comprises a plurality of asymmetric micro-dome units which are discretely distributed, and the elastic modulus of the flexible adaptive conformal layer is lower than that of the flexible sensing array layer. The signal processing method comprises the following steps: obtaining the curvature radius of the attached position; calculating the initial resistance value under the current curvature based on an electromechanical mapping function and setting the initial resistance value as an electrical zero point; and solving a contact pressure signal by using a compensation model related to the curvature. The system and the signal processing method are used to eliminate the baseline drift and sensitivity heterogeneity caused by the surface attachment through a physical structure cooperative algorithm compensation, so that the tactile perception accuracy and reliability are improved.

The application belongs to the technical field of medical signal processing, and in particular to a method for detecting coronary heart disease based on time-frequency fusion of heart sound and electrocardiogram, comprising the following steps: S1: data preprocessing of PCG and ECG original signals, including noise reduction, baseline drift elimination, power line interference elimination, etc., and normalization processing of the PCG and ECG signals, generation of power spectrum signals of heart sound and electrocardiogram by using the Welch method, and segmentation of the time domain signals and power spectrum signals of PCG and ECG into equal-length segments. In order to effectively solve the problem of inaccurate diagnosis caused by poor quality of single-mode data, a heart sound and electrocardiogram dynamic fusion module is constructed based on the time-frequency fusion results of the two signals, dynamic weight distribution is performed, and a suitable classifier is selected for classification, the frequency domain information is supplemented, and the weights are distributed according to the contributions of the heart sound and electrocardiogram signals, so that the detection and classification results of coronary heart disease are more accurate.

The invention belongs to the field of driver emotion monitoring, and discloses a driver emotion fluctuation monitoring method based on a biological radar and a related system. The problems of discomfort and poor use compliance caused by the fact that a traditional contact sensor needs to be worn or attached to the human body are solved, the emotion monitoring process does not perceive or interfere with a driver, and the method is more suitable for being applied to long-term and continuous driving scenes. According to the invention, by preprocessing the biological radar signal, baseline drift is removed and environmental noise is suppressed, so that the collected vital sign signal can still maintain stability and reliability in complex driving environments such as vehicle vibration and airflow change; therefore, the problem that monitoring is unstable due to the fact that a visual emotion monitoring scheme is prone to being influenced by illumination changes, shielding and posture changes is solved.

The invention belongs to the technical field of medical signal processing, and particularly relates to a coronary heart disease detection system for heart sound and electrocardio correlation analysis, which can perform data preprocessing, including noise reduction, baseline drift elimination, power line interference and the like, on PCG and ECG original signals, perform normalization processing on the PCG and ECG signals, and improve the accuracy of data processing. Performing wavelet transform on the processed heart sound and electrocardiosignals to generate a two-dimensional time-frequency image, performing feature extraction on the processed signals and the time-frequency image by using a 1D CNN and a 1D CNN, performing feature splicing on 1D CNN features and 2D CNN features in the same mode through global pooling, generating fusion features with discriminant lines by using a discriminant correlation analysis method, and performing feature fusion on the fusion features with discriminant lines. Performing multi-classification on the diseases through a support vector machine; in order to effectively utilize the correlation between different modal signals, a discriminant correlation analysis method is adopted to maximize the correlation between modal features, eliminate the inter-class correlation and retain the intra-class correlation, the inter-modal correlation features are enhanced, and multi-classification of the coronary heart disease is realized.

The present application belongs to the technical field of fault diagnosis, and relates to a GIS partial discharge fault-oriented SF6 decomposition product multi-component spectrum detection method and system. The method comprises the following steps: firstly, obtaining initial spectrum data of SF6 decomposition mixed gas in a GIS three-phase gas chamber, and obtaining corrected spectrum data through support vector machine baseline correction; obtaining independent spectrum data through independent component analysis algorithm blind source separation; obtaining enhanced spectrum data through baseline drift calculation, partial least squares concentration inversion reconstructed spectrum and residual determination; extracting absorption peak characteristic value and completing matching verification to determine the actual concentration of each characteristic gas; obtaining the screening concentration of the target characteristic gas through diagnostic contribution degree threshold screening, inputting the random forest model to calculate the diagnostic weight data; finally, combining the weight and the concentration to calculate the insulation life decay amount, and outputting the GIS equipment fault state judgment result. The present method can adapt to complex operation conditions of GIS on site, can eliminate multi-component spectrum cross interference, and can realize accurate quantification.

The invention discloses a person respiration non-contact detection method based on single-reflection schlieren. The method comprises the steps that a schlieren video stream of a mouth and nose area of a subject is collected through a single-reflection schlieren imaging device; the schlieren optical system is configured to enable a light spot of the light source, which is gathered back through the reflector, to fall on the edge of an entrance pupil diaphragm of the imaging lens, and the physical edge of the entrance pupil diaphragm is utilized to partially shield the gathered light spot so as to form a virtual knife edge effect; performing frame-by-frame processing on the schlieren video stream, and in a region of interest (ROI) below the mouth and the nose, calculating a characteristic statistic representing airflow disturbance intensity, namely a respiratory airflow intensity signal; performing baseline drift removal and frequency domain filtering processing on the respiratory airflow intensity signal to obtain a standardized respiratory waveform; on the basis of the breathing waveform, multi-dimensional breathing parameters including the real-time breathing frequency, the relative breathing depth and the inspiration and expiration ratio are calculated through a self-adaptive waveform feature extraction algorithm; and analyzing transient characteristics and periodic laws of the respiratory waveform, and identifying apnea, cough or sneezing events.

The application relates to the technical field of pattern recognition, in particular to a cardiovascular physiological signal feature extraction method applying a pattern recognition algorithm, which comprises the following steps: collecting original signals through a sensor array and converting the original signals into digital signal streams; synchronously filtering out baseline drift and electromagnetic interference by applying a composite filtering algorithm; determining a key frequency interval by using a discrete orthogonal transform algorithm for multi-scale pattern deconstruction; accurately positioning ECG and pulse wave feature key points by applying a pattern recognition model based on a deep residual network, and marking coordinate sequences; and calculating a multi-dimensional feature vector including a nonlinear dynamics index according to key point attributes. The application improves extraction precision in a complex environment, enhances the generalization ability for different individuals and pathological states, realizes deep quantification of physiological features, and is suitable for mobile monitoring and clinical real-time monitoring.

The invention relates to the technical field of fatigue crack nondestructive testing, in particular to an orthotropic steel bridge deck fatigue crack Lamb wave signal fusion processing method which comprises the steps that theoretical analysis and numerical simulation are conducted on an orthotropic steel bridge deck ultrasonic Lamb wave signal, and Lamb wave detection basic parameters are obtained; based on Lamb wave detection basic parameters and original signals, baseline correction is carried out on the Lamb wave signals, signal baseline drift caused by the load effect is eliminated, and to-be-separated signals without baseline interference are obtained; for a to-be-separated signal without baseline interference, performing multi-mode separation on the Lamb wave signal after baseline correction, extracting a low-order symmetric vibration mode signal and a low-order anti-symmetric vibration mode signal of the Lamb wave, and obtaining a single-mode original signal; and processing the separated single-mode signal, and correcting the frequency dispersion compensation process by combining with the thickness change caused by the fatigue crack damage of the orthotropic steel bridge deck to obtain a single-mode non-frequency dispersion signal. And a high-quality detection signal can still be obtained in a non-ideal environment.

The invention discloses a method for suppressing aviation electromagnetic motion noise based on DBVMD. Aiming at a baseline drift problem caused by motion noise in aviation transient electromagnetic measured data, a modal isolation frequency is dynamically set based on an emission fundamental frequency and an original time sequence data spectrum, and is used for presetting a search boundary of a modal center frequency before iteration; and dynamically and adaptively adjusting the bandwidth of each mode through a penalty factor, reconstructing the effective signal mode, and obtaining a reconstructed transient electromagnetic signal. According to the invention, the signal-to-noise ratio of the transient electromagnetic signal in a complex motion environment is obviously improved, and high-quality basic data is provided for subsequent deep inversion.

The application discloses a flexible electrode for integrated sensing of electrocardio and heart sound. The dry electrode is used for noninvasive collection of human body surface Laplace electrocardio signals. The dry electrode and a collection system thereof are composed of three parts. The first part is a multilayer bioelectric dry electrode based on silk screen printing, which is composed of a. an electrocardio signal collection electrode (right limb RA), b. an electrocardio signal collection electrode (left limb LA) and c. an electrocardio signal collection electrode (right leg RL). The second part is d. a front-end signal conditioning circuit, which is composed of a filter circuit and a power follower. The filter circuit is used for filtering high-frequency noise such as electromyographic noise and part of low-frequency baseline drift signals. The power follower is used for inhibiting signal attenuation, providing low output impedance, enhancing signal anti-interference ability, improving system signal-to-noise ratio and ensuring signal integrity. The third part is e. a system processing and transmission circuit, which is composed of a filter, a main control chip and a Bluetooth module. The main control chip part completes processing of the Laplace signal, and obtains a Laplace electrocardiogram.

The application discloses a kind of periodic characteristic magnetic field signal real-time filtering system and method based on FPGA, for replacing the iteration post-processing of host computer offline software.System includes data acquisition interface and FPGA master unit, and pipeline processing module is constructed inside FPGA: power frequency suppression, low-pass filtering and Savitzky-Golay shape smoothing are carried out to input digital signal;Based on smoothed signal, differential absolute value is constructed, and synchronous trigger pulse is generated through adaptive threshold and refractory mechanism;Under trigger reference, single-cycle template is maintained using dual-port Block RAM, and displacement recursive weighting is used to realize recursive synchronous average and output filtered enhanced magnetic field waveform.The scheme can effectively suppress non-periodic baseline drift and reduce storage and computing overhead, and is suitable for online real-time magnetic field signal processing of low-cost FPGA.

The invention discloses a digital measuring tape based on 90-degree orthogonal photoelectric stripes in the technical field of photoelectric displacement measurement, and aims to solve the technical problems that an existing digital measuring tape is easily influenced by baseline drift and amplitude change, has the risks of miscounting, jitter and step loss, depends on a high-speed analog comparator and is complex in hardware structure. The measuring tape comprises a measuring tape main body, an optical detection module, an analog conditioning module, a microcontroller module and a display communication module, the optical detection module comprises a shared infrared transmitter and two photoelectric receivers, and the two receivers are arranged in a 1 / 4 period space phase difference mode in the stripe movement direction and output 90-degree orthogonal analog signals. The method does not need a high-speed analog comparator, is compact in hardware structure, is high in anti-noise and anti-shake capability, improves the measurement precision and stability, and is suitable for various complex use scenes. In embodiments where an ADC is employed to sample orthogonal analog signals, phase interpolation subdivision may also be selected to further improve measurement resolution.