248 results about "Baseline drift" patented technology

The technical scheme of the invention relates to the technical field of gas detection, in particular to a hazardous chemical storage leakage positioning and tracing method based on a gas array. The method comprises the following steps: collecting gas concentration, components and environmental physical field parameters through a three-dimensional heterogeneous sensor array, and generating a multi-modal data set; and matching the noise mode feature library in the knowledge base by using a transfer learning algorithm, dynamically correcting the baseline drift of the sensor, and outputting calibrated data. A closed-loop adaptive mechanism adjusts sensor parameters and network hyper-parameters through meta reinforcement learning, neural architecture searches and optimizes a model structure, and a differential evolution algorithm updates fluid mechanics boundary conditions. Through cooperation of physical constraint and data driving, the problem of signal distortion in a complex environment is solved, the leakage source positioning precision and robustness are improved, and the method is suitable for the field of hazardous chemical substance storage safety monitoring.

The invention relates to an infrared spectrum baseline drift dynamic compensation method and system based on multi-parameter fusion, and the method comprises the steps: continuously obtaining spectrum data, current environment parameters and light source state data of a target oil full wave band, carrying out the feature extraction, and obtaining a baseline drift sensitive feature set and a coupling feature set; constructing a prediction model taking the baseline drift amount as output, and taking the baseline drift sensitive feature set and the coupling feature set as input; the reverse baseline drift amount based on the baseline offset of each wave band is superposed in the spectral data as compensation; according to the invention, through multi-source data acquisition and analysis, monitoring of spectrum baseline drift influence factors is realized; accurate correction of different spectral characteristics is realized through a dynamic compensation strategy special for a wave band; and through a continuous self-adaptive updating mechanism, stable tracking of the long-term drift trend is realized, and the effect of prolonging the equipment maintenance period is achieved.

The invention relates to the technical field of spectral analysis, and discloses a method and a system for detecting the concentration of trace metal ions. The method comprises the following steps: acquiring original spectral response data of an electronic smoke sample; carrying out signal separation by utilizing a frequency domain feature extraction technology to obtain an initial spectrum data set; performing background noise filtering and baseline drift correction on the initial data to obtain a refined spectrum data set; constructing a concentration-wavelength mapping relation through absorption peak positioning and characteristic wave band screening on the basis of the refined spectrum data set; carrying out continuous time sequence sampling on the samples based on the mapping relation to generate a concentration dynamic change data set; analyzing a concentration fluctuation rule and an abnormal point in the dynamic data, and generating concentration risk assessment data; and finally outputting a time sequence detection file. According to the method, high-precision dynamic monitoring and intelligent risk assessment of trace metal ion concentration are realized, interference is effectively inhibited, and sensitivity and early warning capability are improved.

The invention discloses a DC fault arc identification method and device based on a wavelet transform algorithm, and the method comprises the steps: collecting a DC line current signal through a high-bandwidth current sensor, carrying out the digitalization of a signal conditioning circuit and a high-speed ADC, and sequentially carrying out the preprocessing of the elimination of baseline drift through differential filtering and the suppression of specific frequency interference through adaptive notch; performing 3-8-layer multi-scale decomposition on the pure signal by adopting a Daubechies wavelet basis, preferably selecting high-frequency detail coefficients from the first layer to the third layer, and creatively fusing a multi-scale energy entropy representing energy distribution randomness and a time domain kurtosis representing transient impact to form a composite characteristic quantity; a threshold value (threshold value = mean value + adaptive coefficient * standard deviation) is dynamically generated based on sliding time window historical data, the adaptive coefficient is dynamically adjusted according to the load type, the resistive load is 1.8-2.2, and the inductive / capacitive load is 2.5-3.5; and when the composite characteristic quantity exceeds a threshold value and the sample proportion is greater than or equal to 85% within the duration of 0.5-5ms, a tripping execution mechanism is triggered.

The invention discloses an electrocardiosignal preprocessing system and method based on filtering and deep learning, and relates to the field of electrocardiosignal data processing. The multi-stage adaptive filtering module comprises a baseline drift elimination unit, a power frequency interference suppression unit and a myoelectricity noise removal unit, and all the units are connected in sequence to form pipelined parallel processing; the deep learning fusion module is used for performing deep feature extraction and noise classification on the output signal and feeding back a classification result to the multi-stage adaptive filtering module; the signal quality evaluation module carries out quality evaluation on the electrocardiosignals subjected to multi-stage adaptive filtering and deep learning fusion and judges whether the signal quality is qualified or not; a feature enhancement and standardization module; the technical effects of improving the self-adaptability and robustness, improving the calculation efficiency of heart disease classification, the signal fidelity and the diagnosis reliability, and enhancing the signal quality evaluability and the self-adaptive ability are achieved.

The invention is suitable for the field of traditional Chinese medicine quality control and analysis technology improvement, and provides a traditional Chinese medicine fingerprint spectrum intelligent decision-making system and analysis method based on artificial intelligence, and the method comprises the steps: S1, obtaining one-dimensional fingerprint spectrum data information of a large number of traditional Chinese medicine samples through a plurality of analysis technologies; s2, converting the acquired one-dimensional fingerprint spectrum data information into a two-dimensional time-frequency matrix by using adaptive noise complete set empirical mode decomposition-Hilbert-Huang transform; s3, inputting the obtained two-dimensional time-frequency matrix into a pre-trained multi-mode deep learning model, dynamically optimizing model parameters through a model agnostic element learning framework, and outputting traditional Chinese medicine quality evaluation, authenticity identification and origin traceability results; and S4, writing the decision result into a block chain evidence storage system to generate a non-tampering quality tracing record. The deep learning model can effectively process complex data, identify subtle modes and manage baseline drift, peak overlapping and other problems, so that more reliable fingerprint analysis is realized.

The invention provides a data processing method and system for an aerosol analyzer, and belongs to the technical field of data processing of aerosol analyzers, and the method comprises the following steps: collecting light absorption signals of aerosol particles according to a light source system and a detector, and generating an original electric signal; preprocessing the original electric signal to obtain preprocessed data; extracting an absorption coefficient, a multiband light attenuation characteristic and a mass concentration parameter of the black carbon aerosol from the preprocessed data; analyzing the features based on a machine learning model, outputting a real-time prediction result of the concentration of the black carbon aerosol, and preprocessing the original electric signal to obtain preprocessed data, including the step of noise removal. According to the method, baseline drift is dynamically corrected through a polynomial fitting algorithm, wavelet transform denoising and a machine learning model are combined, the accuracy and real-time performance of black carbon aerosol concentration detection are remarkably improved, and the problems that in a traditional method, manual intervention is much, and the model generalization ability is poor are solved.

The invention discloses a transformer insulating oil chromatographic data abnormity identification method, and relates to the technical field of data identification, and the method comprises the following steps: carrying out dissolved gas analysis on an insulating oil sample through a gas chromatograph to obtain a chromatographic original response curve; performing baseline correction and de-noising processing on the chromatographic original response curve, positioning each gas peak based on a first-order derivative method and a second-order derivative method, and extracting chromatographic peak height and chromatographic peak area parameters; according to the extracted chromatographic peak height and chromatographic peak area, three Duval fault ratios are calculated, and the ratio of the total soluble gas concentration to each gas concentration threshold value is calculated; combining into a multi-dimensional diagnosis feature vector; according to the method, the chromatographic peak shape of the insulating oil sample is subjected to multi-stage baseline correction and de-noising treatment, and the gas peak position is accurately positioned based on the first-order and second-order derivative methods, so that the high precision of chromatographic peak height and chromatographic peak area extraction is ensured, the accuracy of dissolved gas concentration calculation is improved, and the misjudgment risk caused by noise or baseline drift is reduced.

The invention relates to an instant denoising method for electroencephalogram signals of students in a classroom scene. The method comprises the following steps: based on electroencephalogram acquisition equipment and a Daisy expansion board, acquiring electroencephalogram signal data of a student through a dry electrode device; and carrying out sliding mean filtering on the electroencephalogram signal data to remove baseline drift, eliminating noise by adopting a band-pass filter to obtain preprocessed electroencephalogram signal data, and inputting the data into a lightweight neural network model to carry out channel-by-channel denoising processing to obtain a denoised electroencephalogram signal. The model comprises a multi-scale feature extraction layer, a time sequence modeling module and a residual connection structure. According to the method, through lightweight neural network design such as multi-scale feature extraction, time sequence modeling and a residual connection structure, the denoising effect is improved, the real-time performance and high efficiency of electroencephalogram denoising processing are guaranteed, and an electroencephalogram data basis with a high signal-to-noise ratio is provided for subsequent teaching quality evaluation.

The invention relates to the field of crystal detection, and discloses a multifunctional comprehensive scintillation crystal and semiconductor material energy spectrum test system, which comprises a crystal adaptation module for judging the type and size of a placed crystal through an optical identification and parameter matching mechanism, and a signal acquisition module for amplifying, filtering and digitally processing an acquired signal, the signal correction module is used for correcting waveform distortion and baseline drift through a dynamic gain adjustment algorithm of a low-noise pre-amplifier and a multi-channel pulse analyzer; the data analysis module is used for generating an energy spectrum curve according to input signals and calculating energy resolution, counting rate, light attenuation time or carrier mobility, and the environment calibration module is used for dynamically evaluating shading degree, environment light, temperature and electromagnetic interference through an optical sensor and a temperature and humidity detector in the camera obscura and outputting updated calibration parameters. The method has the advantages of improving the detection efficiency, precision and consistency.

The invention discloses a lie detection system and method based on AC excitation multi-frequency skin electric signals, and belongs to the technical field of psychological and physiological detection and signal processing. According to the system, a multi-frequency AC excitation signal is applied to human skin, skin complex impedance is measured by adopting a four-electrode method, and the problems of electrode polarization and baseline drift of direct current excitation are solved. A high-dimensional feature vector is formed by extracting the amplitude, phase and differential ratio of multi-frequency impedance and Cole-Cole relaxation model parameters, and an individualized baseline is established. Feature changes are analyzed through a machine learning algorithm, and lie judgment is achieved. The method has the advantages of strong anti-interference capability, abundant information dimensions, high measurement accuracy and good safety.

The invention relates to the technical field of pulse electric field measurement, and discloses a time drift compensation method of a pulse electric field measurement system based on model parameterization. According to the method, a parameterized model is constructed in simulation software, and the parameterized model is additionally provided with two servo zeroing compensation circuits on the basis of an original measurement system, and the two servo zeroing compensation circuits are connected to the output ends of two in-phase operational amplification circuits respectively to form a negative feedback loop used for dynamically suppressing low-frequency baseline drift; acquiring electric field time domain waveform data of the output end of the third in-phase operational amplifier circuit; calculating a transfer function of a pulsed electric field measurement system in the parameterized model, converting the transfer function from a frequency domain state to a time domain state through inverse Fourier transform, and then performing deconvolution on the transfer function of the time domain state and electric field time domain waveform data to obtain real electric field intensity; according to the invention, time drift caused by operational amplifier input imbalance, power supply fluctuation and temperature change is automatically compensated in real time, and the stability and precision of system measurement are improved.

The invention provides a ballastless track joint performance degradation monitoring method and system, and relates to the technical field of track joint performance monitoring, and the method comprises the steps: knocking a ballastless track plate at a track joint through a force hammer to generate excitation, collecting a vertical vibration acceleration response signal through an acceleration sensor, and synchronously recording the knocking time and static parameters. The vertical vibration acceleration response signal is preprocessed, abnormal pulses and baseline drift are removed, a vibration event sample is formed, and joint mechanics characteristic parameters including a vibration frequency parameter reflecting rigidity degradation and a vibration attenuation parameter reflecting damping are extracted. According to the method, the joint performance evaluation index is calculated in combination with the static parameters, the degradation grade is evaluated after the evaluation index is compared with the preset threshold value, an early warning signal is triggered, the joint degradation grade and position information are provided, and monitoring and evaluation of the joint performance are achieved.

The invention discloses a gas concentration detection circuit and method based on differential measurement and reference compensation, and the circuit comprises at least one active gas sensitive element, the electrical parameters of which change along with the concentration of a target gas; the sensitivity of the reference gas sensitive element to the target gas is at least lower than that of the active gas sensitive element by a preset percentage threshold value; the active gas sensitive element and the reference gas sensitive element are connected in a differential pair form to form a differential signal generation unit; the differential amplification circuit is used for amplifying the differential signal output by the differential signal generation unit; the output signal of the differential amplification circuit is used for representing the concentration information of the target gas. According to the invention, environmental interference and baseline drift are directly and effectively suppressed from the hardware level, the stability and selectivity of gas concentration measurement are improved, and meanwhile, the holding circuit is simple in structure, low in cost and convenient for large-scale application.

The invention relates to an overlapped chromatographic peak positioning and splitting method and system based on a numerical stable function. The method comprises the following steps: de-noising chromatographic data and correcting a baseline; calculating a second derivative by using SG filtering, grouping continuous points according to a negative threshold value, and determining an initial position of each peak by using a second-order minimum value; establishing a peak function of bidirectional exponential correction by taking a peak position as an initial value, setting parameters and boundaries such as area, center, width, trailing and the like, constructing a residual error minimization target, and performing robust fitting by adopting a trust region reflection algorithm; and indexes such as suitability, output peak area, center, broadening, separation degree, theoretical plate number and the like are verified by residual errors. According to the method, valley point or prior component information is not needed, the method is insensitive to noise and baseline drift, serious overlapping and trailing / leading edge peaks of multiple components can be effectively processed, convergence is high, the method is insensitive to initial values, parameters can be expanded to different peak shapes, and the method is suitable for qualitative and quantitative analysis of automatic and high-flux chromatographic data.

The invention relates to the technical field of traditional Chinese medicine decoction piece screening, and discloses an intelligent traditional Chinese medicine decoction piece screening method based on multi-modal data fusion. The method comprises the following steps: synchronously acquiring visible light image data, near infrared spectrum data and smell volatile matter data of a target traditional Chinese medicine decoction piece through a multi-source sensor array; respectively performing morphological cleaning, baseline drift correction and environmental interference elimination operations on the three types of data; extracting a form feature set, a chemical component feature set and a volatile substance feature set of the decoction pieces from the preprocessed data; calculating morphological stability factors, component stability factors and volatilization stability factors corresponding to the three types of feature sets; constructing a three-dimensional feature fusion space, and mapping the three types of feature sets into fusion feature vectors; executing centralized search in the space by taking the fusion feature vector as a starting point, and determining an optimal screening decision vector; therefore, more accurate grade classification and quality screening are realized, and the screening efficiency of the traditional Chinese medicine decoction pieces can be remarkably improved.

The invention relates to the technical field of spectral measurement, in particular to a portable human body rubidium microelement detector and a spectral detection method. Converting the spectral data to obtain an absorbance curve; carrying out data segmentation on the absorbance curve, and calculating to obtain a baseline drift degree of the absorbance curve based on a segmentation result; performing baseline correction on the absorbance curve based on the baseline drift degree in combination with an iterative adaptive baseline correction algorithm to obtain an absorbance curve after baseline correction; and comparing the absorbance curve after the baseline correction with a standard absorbance-concentration curve to obtain the concentration of the rubidium element. According to the method, for the phenomenon that the content of the rubidium element is relatively low, the baseline drift degree of the spectral data is analyzed in a targeted manner, and the baseline drift degree is set as an iteration termination condition of an iteration self-adaptive baseline correction algorithm, so that a baseline drift part in the spectral data is separated, and the accurate content of the rubidium element in a sample is obtained on the basis of conversion.

The invention discloses a method for detecting tea saponin in a tea extract, and relates to the technical field of chemical analysis and computer science, and the method comprises the following steps: eliminating high-frequency noise of tea extract data by using a Gaussian filtering algorithm, eliminating baseline drift by using a baseline correction algorithm, and generating a tea extract sample data stream; the method comprises the following steps: performing dynamic gradient separation through a chromatographic signal simulation algorithm on the basis of a tea extract sample data stream to generate a virtual chromatographic peak sequence, calculating a peak area through a sliding window integration algorithm on the basis of the virtual chromatographic peak sequence, and generating chromatographic signal data by using a wavelet transform threshold denoising algorithm, the chromatographic signal data are input into the deep convolutional neural model, the feature pyramid network is obtained through the multi-scale feature fusion algorithm, the feature information of the chromatographic peaks is extracted from different scales by constructing the deep convolutional neural model and combining the multi-scale feature fusion algorithm, and therefore the recognition precision of the feature peaks is improved.

The invention relates to the technical field of hydrogen detection, in particular to a hydrogen detection and measured value correction method based on a hydrogen sensor, and the method comprises the following steps: analyzing the change trend of voltage data in an active unit at each moment and in a preset duration before the moment, and the correlation of the voltage data between the active unit and a reference unit; determining hydrogen purity responsivity; the hydrogen compensation confidence coefficient is determined by analyzing the change trend of the hydrogen purity responsivity at each moment and all moments in a previous preset time period and the average distribution and dispersion degree of the hydrogen purity responsivity, so that the hydrogen concentration at each moment is corrected. According to the invention, the influence of cross interference of other combustible gases except hydrogen and baseline drift of the hydrogen sensor on hydrogen detection and concentration measurement is solved, and the long-term stability of hydrogen concentration monitoring and the accuracy and reliability of a measurement result are improved.

The invention requests to protect a motor imagery electroencephalogram signal enhancement method based on graph attention. The method comprises the following steps: firstly, carrying out multi-stage preprocessing on an original electroencephalogram signal, including band-pass filtering and baseline drifting, and removing artifacts in combination with independent component analysis, then, extracting time information of each channel by utilizing a one-dimensional convolutional network so as to capture time sequence characteristics in a motor imagery process, on the basis, constructing a graph attention network, taking the electroencephalogram signal channels as nodes, and taking the electroencephalogram signal channels as the nodes; the method comprises the following steps of: dynamically modeling and optimizing the internal relation between channels, strengthening motor imagery related channels such as C3, C4 and Cz by combining priori knowledge, further introducing a time attention network to identify and enhance key time slices rich in discrimination information, and finally, forming a high-dimensional feature matrix by fusing space-enhanced and time-enhanced electroencephalogram features, so as to realize the recognition and enhancement of the motor imagery related channels. And inputting into a classifier for pattern recognition.

The invention relates to the technical field of artificial intelligence, and discloses a self-supervised learning-based electrocardiosignal anomaly detection system, which comprises an electrocardiosignal acquisition module, a data preprocessing module, a feature coding module, a self-supervised comparative learning module, an anomaly score calculation module and an anomaly judgment module which are in communication connection, the electrocardiosignal acquisition module is used for acquiring an original electrocardiosignal and transmitting the original electrocardiosignal to the data preprocessing module; the data preprocessing module is used for conducting denoising, baseline drift correction and standardization processing on original electrocardiosignals and transmitting the processed signals to the feature coding module. According to the invention, through a dynamic negative sample selection unit in the self-supervised contrast learning module, depending on technologies such as feature queue maintenance, feature distance calculation, clustering analysis and the like, samples which have significant difference from positive sample feature distribution and are different in category are screened as negative samples, so that the model can accurately learn real similarity between similar electrocardiosignal samples; and feature learning confusion is avoided.

The invention discloses a method for detecting heart rate, respiration and oxyhemoglobin saturation based on micro-vibration images, and belongs to the technical field of physiological detection and image processing. A camera collects facial micro-vibration videos of a testee in real time, uses an ROI algorithm to lock a facial key area, extracts RGB micro-vibration change signals to replace red light and near-red light signals, and determines whether the micro-vibration videos are abnormal or not; performing wavelet transform five-layer decomposition, three rounds of peak valley screening and abnormal value elimination on the RGB microvibration time sequence signal, performing fine tuning training by using a yov11 model, and calculating to obtain heart rate, respiration and blood oxygen physiological indexes. According to the invention, non-contact detection is realized through video acquisition and signal processing of micro-vibration of a face area, and the comfort level and the use convenience of a testee are improved; interference caused by motion artifacts, baseline drift and expression changes is eliminated through multi-scale wavelet denoising; the three-wheel peak-valley screening algorithm deeply excavates the features of the face micro-vibration signals, and the recognition accuracy of the signal peak-valley pairs is improved; and an abnormity elimination and self-feedback parameter adjustment mechanism improves the calculation precision.

The invention relates to the technical field of liquid chromatographic analysis, and discloses a data acquisition method and system of a liquid chromatograph. The method comprises the following steps: extracting chromatograms without solvent residues and with known solvent residues from a historical database, and training by using a self-encoding neural network and a convolutional neural network to generate a baseline reference model and a ghost peak early warning model; obtaining solvent parameters of the previous analysis method, and calculating the residual solvent concentration through residual solvent information; inputting the residual solvent concentration into the baseline reference model and the ghost peak early warning model, and calculating to obtain a baseline drift prediction correction value and a ghost peak prediction risk area; pre-correcting the chromatographic signal acquired in real time to obtain a purified chromatographic signal; performing qualitative and quantitative analysis on the purification signal according to dynamic allocation resources of the ghost peak risk area to generate final chromatographic data; the data interference of the mobile phase residue is effectively eliminated, and the accuracy of data acquisition is improved.

The invention belongs to the technical field of Raman spectrum detection, and relates to a mixture qualitative and quantitative analysis method and system based on Raman spectrum. The method comprises the following steps: taking spectral information of a mixture and a qualitative dictionary as input, adopting a sparseness-based adaptive step-by-step orthogonal matching pursuit method for analysis, preliminarily determining various pure substance components in the mixture and corresponding spectrums and weights thereof, and adding constraint conditions to the pure substance components to obtain a final qualitative analysis result. And finally, taking the mixture spectrum, the qualitative component list and the quantitative dictionary as input of an orthogonal matching pursuit algorithm, and carrying out iterative loop according to qualitative analysis steps to obtain a quantitative analysis result. The components and the concentration of the mixture can be quickly and accurately judged through an orthogonal matching pursuit algorithm; meanwhile, by adding constraint conditions, the problem that analysis results are affected by factors such as baseline drift and noise interference due to the reasons of spectrums is effectively avoided, and the accuracy and efficiency of the analysis results are effectively improved.

The present invention relates to the field of spectral detection technology, and more specifically to an intelligent blood smear quality detection method for a laboratory. The method comprises: obtaining overall spectral data of a blood smear and spectral signals of various detection positions in different regions of the blood smear; determining the baseline drift fluctuation segment and normal fluctuation segment of the spectral signal at each detection position; performing a primary correction on the spectral signal at the detection position and the overall spectral data to obtain a corrected spectral signal and first corrected overall spectral data; performing a secondary correction on the first corrected overall spectral data using the peak values of the baseline drift fluctuation segments immediately before and after the current baseline drift fluctuation segment and the peak value of the current baseline drift fluctuation segment in the corrected spectral signals at each detection position to obtain final second corrected overall spectral data; and detecting the quality of the blood smear using the second corrected overall spectral data. Thus, the present invention improves the detection accuracy and reliability of blood smear quality detection.

The invention relates to a low-quality data identification method for strong vibration records, and belongs to the technical field of earthquake monitoring data processing. The method solves the problems of low manual identification efficiency and high omission ratio. According to the technical scheme, the method comprises the steps of firstly obtaining three-component acceleration time history data of strong vibration records; then constructing a multi-dimensional dynamic quality evaluation index system including data integrity, a signal-to-noise ratio dynamic range, a waveform anomaly index, a frequency response offset degree and a polarity consistency coefficient; performing real-time quality scoring on the data based on the system, and marking the data as low-quality data when the score is lower than a dynamic threshold value; and finally, adopting a self-adaptive classification algorithm to identify the abnormal type of the marked data, wherein the abnormal type comprises at least one of waveform distortion, baseline drift, polarity reversal and field response abnormity. The method is mainly used for improving the reliability and engineering application value of strong vibration observation data.

The invention relates to the technical field of gene detection, and discloses a mycobacterium tuberculosis drug resistance gene detection data analysis system which comprises a data import module, a signal correction module, a feature extraction module, a multi-stage interpretation module, a result fusion module and a report generation module. The system performs baseline drift correction, abnormal point detection and nonlinear normalization on a fluorescence intensity value output by a detector, extracts multi-dimensional features such as peak intensity, a mutation ratio, a consistency coefficient and an in-batch difference index, and constructs a dynamic threshold function to realize wild type and mutation type layered judgment. A drug resistance risk conclusion is generated through drug resistance characteristic matrix mapping, a standardized detection report is output, and intelligent analysis of a detection result and automatic report generation are achieved. Through multi-stage signal correction, multi-dimensional feature extraction and dynamic threshold interpretation, intelligent analysis and standardized report output of a mycobacterium tuberculosis drug resistance gene detection result are realized, and data judgment accuracy and clinical application efficiency are improved.

The invention provides a multi-parameter environment sensor chip data fusion method based on artificial intelligence, and belongs to the technical field of agricultural product growth environment monitoring. A continuous data sequence is constructed through a multi-parameter environment sensor chip, and the data quality is evaluated and preprocessed by using an abnormal jump matrix and an interference abnormal matrix; a multi-dimensional fusion algorithm is constructed to calculate a convergence index, a stability index and a disorder index to quantify a data fusion state, and a self-adaptive calibration algorithm is combined with a jump compensation matrix and an abnormal compensation matrix to carry out real-time dynamic compensation on sensor baseline drift and sensitivity attenuation phenomena. The number of attention approximation features is dynamically adjusted through a feature adjustment function, finally classification prediction and quality evaluation of the agricultural product growth environment are achieved, and the technical problem that in the long-term operation process of a multi-parameter environment sensor, baseline drift and sensitivity attenuation cause data fusion precision to be remarkably reduced along with time lapse is solved.

The invention discloses a calibration and correction method of a current transformer, and belongs to the technical field of current measurement. The method comprises the following steps: acquiring a voltage signal output by a current transformer, and obtaining a sampling waveform signal; performing baseline drift detection on the acquired sampling waveform signal; if it is detected that baseline drift exists, dynamic baseline calibration is carried out in a high-altitude proportion scene; performing baseline correction on the sampling waveform signal; calculating the increment of pulse attenuation compensation based on inertial filtering; performing linear interpolation compensation on the positive pulse width in the sampling waveform signal; and outputting the corrected detected current signal. According to the calibration and correction method of the current transformer provided by the invention, the current measurement precision of the current transformer in a high duty ratio scene is improved by monitoring the baseline position and the linear interpolation compensation dynamic correction signal in real time.

The invention relates to the technical field of heating and ventilation fault recognition, in particular to a heating and ventilation system fault recognition method based on operation state offset modeling and a control system. The method comprises the following steps: acquiring steady-state operation data of the heating and ventilation system and extracting actual parameters of equipment; inputting the environment and load parameters into the lightweight integrated tree base line model, and outputting an equipment operation parameter reference value; performing baseline drift correction on the reference value in combination with the accumulated operation duration of the equipment, calculating a dynamic difference value between an actual value and a calibration reference value, and constructing an operation state offset characteristic matrix; and inputting the matrix into a preset fault diagnosis rule base, matching a hidden fault in combination with the dynamic tolerance interval and the multi-dimensional Boolean logic, and giving an alarm. Performing reference compensation by using the accumulated operation time length in combination with a linear or exponential progressive attenuation model; on the premise of not consuming high computing power to re-train an underlying algorithm model, the interference of natural physical aging of the equipment on residual calculation is eliminated.