545 results about "Moving average" patented technology

The invention relates to the technical field of vibration monitoring, in particular to a slurry pump motion monitoring management system based on data analysis, which comprises a multi-source acquisition module, a period judgment module, a trend focusing module and a dynamic management and control module. According to the method, vibration data are collected through a three-axis acceleration sensor, a fundamental frequency amplitude is extracted through FFT, time-frequency characteristics and periodic distribution are fused in combination with an extreme value interval sequence, the data representation dimension is enhanced, the rising slope is processed through moving average, the periodic variation is calculated, random noise is restrained, and trend continuity is enhanced. Monotonicity test is combined with dynamic threshold judgment to improve anomaly recognition sensitivity; a sliding window is used for segmenting multi-cycle data; a sudden change interval is positioned based on a pressure amplification rate second derivative; the limitation of a frequency domain on transient feature recognition is broken through, a pressure amplification rate three-dimensional vector is constructed, cosine similarity is calculated, and a working condition difference degree is quantified to dynamically bind a maintenance instruction; and closed-loop feedback is formed to improve the early warning and decision matching degree.

The invention discloses a sparse dynamic graph convolution-based electroencephalogram signal decoding method and system. The method comprises the following steps of: acquiring a multi-channel electroencephalogram signal and preprocessing the multi-channel electroencephalogram signal; performing multi-band filtering on each channel signal, extracting statistical characteristics on each band signal, calculating a covariance matrix of a task electroencephalogram signal, constructing image electroencephalogram data by taking an electroencephalogram channel as an image node, the multi-band spliced statistical characteristics on the channel as a node feature vector, and the covariance matrix between the channels as an adjacent matrix; finally, a dynamic graph convolutional neural network model is constructed, the model constructs a graph convolutional neural network based on an autoregression moving average filter, graph electroencephalogram data is used as input, the category of electroencephalogram signals is used as output, an adjacency matrix is dynamically generated in combination with bilinear mapping, fuzzy label learning and sparse constraint are added to improve the decoding capacity of the model, and the dynamic graph convolutional neural network model is obtained. And the frequency domain response capability and robustness of the model to the graph structure are enhanced.

The invention discloses a computing power scheduling management method and system based on an algorithm. The method comprises the steps that an initial resource consumption data set is obtained, and the data set comprises the processor occupancy rate and the memory usage amount of a plurality of calculation tasks in different calculation stages; according to the initial resource consumption data set, a fixed time window is adopted to segment historical data, and a smooth resource consumption sequence is generated through moving average calculation; for the smooth resource consumption sequence, applying a trend decomposition method to separate long-term trend and periodic fluctuation features, and constructing a resource dynamic feature matrix; and according to the resource dynamic characteristic matrix, constructing a multi-layer long-short-term memory network model, and optimizing parameters through a gradient descent method to generate a resource demand prediction curve and the like. According to the method disclosed by the invention, the resource consumption data of the computing task is deeply analyzed and processed, so that efficient computing power resource allocation and scheduling are realized to cope with the change of the demand of the computing task on the processor and memory resources in different stages.

The invention belongs to the technical field of aero-engine state monitoring and intelligent fault diagnosis, and discloses a multi-modal information fusion bearing fault diagnosis method based on self-supervised learning. The method comprises the following steps: firstly, through mask reconstruction self-supervision pre-training, extracting stable feature representation insensitive to mask disturbance from an unlabeled multi-modal signal, and dynamically updating each modal feature reference point by using an index moving average algorithm; in a downstream fault diagnosis task, a multi-modal joint decision model comprising a pre-training encoder, a single-modal classifier and a fusion classifier is constructed, and adaptive weighted fusion of multi-modal decision is realized through contribution degree calculation based on a cooperative game Shapley value in combination with a deviation degree of modal features and a reference point. According to the method, the dependence of the deep neural network on fault labeling data is effectively reduced, the accuracy and robustness of the diagnosis system in a multi-modal signal diagnosis scene are improved through a dynamic fusion mechanism, and the method is suitable for industrial scenes with limited sample label resources.

The invention relates to the technical field of self-supervised learning, in particular to a target detection pre-training method based on Transform. A CL-MAE (Control Learning-Masked Autocoder) self-supervised pre-training method is designed, an original image and an enhanced image are processed by adopting a double-branch architecture, branch parameters of the original image are frozen, enhanced branch parameters are updated by using index moving average, and multi-view contrast learning is introduced, so that the problem of'lazy 'of an encoder is effectively prevented, namely, a reconstruction task is completed by depending on a decoder. The method comprises the following steps of: pre-training a target detection network based on a PVT (Pyramid Vision Transfer), transferring the weight of a Vision Transfer) encoder to the target detection network based on the PVT after the pre-training is completed, and realizing the conversion from self-supervised pre-training to target detection in cooperation with FPN (Feature Pyramid Networks) feature fusion and a special detection head. According to the method, the problem that a traditional target detection model depends on annotation data is solved, and meanwhile, the problem of lazy during self-supervision pre-training of a mask auto-encoder is avoided. Compared with a non-pre-training model, the method achieves better target detection precision and convergence speed.

The invention discloses a horizontal wind field inversion method and device based on the radial velocity of a wind profile radar, and relates to the technical field of wind profile radars, and the method comprises the steps: reading the radial velocity data of a wind profile radar in each direction wave beam, carrying out the rearrangement and splicing arrangement of the radial velocity data of each direction wave beam according to a preset sequence, and carrying out the reconstruction of the radial velocity data of each direction wave beam; forming a beam radial velocity profile; performing data identification, one-dimensional median filtering and one-dimensional moving average filtering on the beam radial speed profile in sequence so as to perform consistency processing on the data of the beam radial speed profile; carrying out time-space continuity processing, and then carrying out time consistency average processing; and obtaining an inverted horizontal wind field according to the processed radial speed data of the beams in all directions based on a space geometrical relationship between the wind field and the radial speed data of the beams in all directions. According to the invention, the problem of low accuracy during horizontal wind field inversion in the prior art is solved.

The invention provides a driving risk early warning method based on long-term and short-term driving style features. The method comprises the following steps: constructing long-term and short-term driving feature vectors according to driving behavior data; based on the standardized long-term and short-term driving feature vectors, a clustering method is adopted to determine long-term style feature vectors and long-term and short-term driving risk score labels; constructing a multilayer driving risk assessment model considering long and short term driving styles, and performing fitting training on the assessment model based on the long and short term driving style sample set to obtain an optimal multilayer driving risk assessment model; determining a long-term driving style category according to a long-term driving feature vector collected in real time, inputting the long-term driving style category and a short-term driving feature vector collected in real time into an optimal multilayer driving risk assessment model, and outputting a moving average trend of a scoring time sequence and a fluctuation trend of a driving behavior in a time window according to a predicted driving risk score output in real time. And formulating a two-stage early warning strategy. According to the invention, highly personalized and adaptive risk early warning can be provided.

The invention discloses a filter intelligent backwashing control system based on a pressure sensor, and relates to the technical field of intelligent equipment, and the filter intelligent backwashing control system comprises the following steps: S1, the system firstly executes pressure difference reference initialization, and determines a pressure difference reference value; s2, constructing a corrected differential pressure offset model, and determining the current differential pressure offset of the system; s3, constructing a blockage risk assessment model, and pre-judging whether the system needs to start backwashing or not; s4, carrying out segmented pulse backwashing control; and S5, constructing a closed-loop judgment model, and evaluating the actual backwashing effect of each stage. According to the method, a high-credibility differential pressure reference is obtained through a reference calculation model; the blocking trend is tracked in real time through the moving average pressure difference offset; constructing a blockage risk coefficient fusing the offset, the offset rate and the duration, and judging the flushing opportunity in multiple dimensions; a three-section flow is adopted to distribute flushing strength and duration according to needs; and after each section of pulse is finished, the backwashing effect is evaluated in a closed-loop manner through the closed-loop judgment model.

The invention relates to a semi-supervised remote sensing image classification method based on hierarchical crossing, and belongs to the technical field of remote sensing image processing and computer vision. The method comprises the following steps: firstly, constructing a representation learning module, adopting a hierarchical cross pseudo-tag generation mechanism, randomly combining different hierarchical features in a main model and an index moving average (EMA) model to construct a cross model, and generating a high-quality pseudo-tag for a weakly enhanced image; secondly, a self-adaptive weight mechanism is introduced, the label loss-free contribution is dynamically adjusted in combination with the training progress and the pseudo label utilization rate, and the self-adaptability of the model in different training stages is enhanced; and finally, designing a label alignment strategy based on a training stage, adjusting pseudo label category distribution through a time decline function, guiding the model to pay more attention to minority categories at the initial stage, and improving small sample category performance. Through hierarchical cross random combination, an adaptive weighting mechanism and a label alignment module, pseudo label quality, training stability and minority class performance are improved.

The invention discloses a cross-view image geographic positioning method based on dynamic threshold pseudo tag self-training learning, and the method specifically comprises the following steps: introducing a difficult sample feature mining method, dynamically adjusting the loss weight of a sample according to the change of similarity, and building a dynamic difficult sample triple loss model; the method comprises the following steps: dynamically adjusting a confidence threshold value of a sample by adopting an index moving average weighting method, iteratively training and screening an unlabeled sample, namely a pseudo label, establishing a pseudo label self-training mechanism of a dynamic threshold value, mining and utilizing non-paired data, and solving the problem of high manual labeling cost; a reference image most similar to a query image is found through image retrieval, and the offset of a query position is predicted. Experiments on CVUSA and CVACT data sets show that as the distance threshold increases, the accuracy of the cross-view image geographic positioning method based on dynamic threshold pseudo tag self-training learning presents a stable rising trend, and the cross-view image geographic positioning method based on dynamic threshold pseudo tag self-training learning is superior to other methods under the same threshold condition.

The invention provides a bedding structure rock finite element model reconstruction method based on a rock core digital image, and belongs to the technical field of digital rock cores, and the method comprises the following steps: S1, obtaining a gray level image of a rock sample; s2, calculating a transverse mean value of the analysis area to obtain a longitudinal gray level distribution curve; s3, applying moving average filtering and smooth filtering to obtain a smooth gray curve; s4, first-order difference is carried out on the smooth gray level curve, the position with the absolute difference value larger than a threshold value is taken as an initial stripe boundary candidate, and starting and stopping pixels, the width and the average gray level of each stripe are determined; s5, mapping each clustering label interval into a physical interval based on FEM grid partition; and S6, constructing a sample finite element model, and generating a reconstructed finite element model with real partition information. According to the method, the macrostructure features of the rock can be efficiently recognized and extracted by directly utilizing the pictures and combining gray profile analysis, and efficient and low-cost reconstruction of the finite element model of the bedding structure rock is achieved.

A computing device may perform training of a set of machine learning models on a first data set associated with a first domain. In some examples, the training may include, for each machine learning model of the set of machine learning models, inputting, as values for a set of parameters of the respective sets of parameters and for an iteration of a set of iterations, a moving average of the set of parameters calculated over a threshold number of previous iterations. The computing device may select a set of model states that are generated during the training of the plurality of machine learning models based on a validation performance of the set of model states performed during the training. The computing device may then generate an ensembled machine learning model by aggregating the set of machine learning models corresponding to the set of selected model states.

The invention relates to the technical field of physiological parameter monitoring, in particular to a construction method of a cardiology rehabilitation evaluation model, which comprises the following steps: monitoring a systolic pressure peak value, a diastolic pressure valley value and duration time of a postoperative patient, constructing a waveform parameter set, calculating a peak sequence amplitude change rate through a moving average method, and calculating a peak sequence amplitude change rate. Marking a form abnormal period and a structure drift region; dividing an active phase and a passive phase to extract wave crest characteristics; analyzing time sequence difference to generate a three-dimensional rhythm vector; constructing a regression model; according to the method, heart pressure change is continuously monitored, a waveform structure is identified, compression period change is dynamically captured, a structure drift area is determined and positioned by combining an amplitude change rate and a difference value, multi-dimensional heart rhythm characteristics are extracted, day and night differences are analyzed, and multi-day deviation values are integrated to generate a scoring system; the abnormal recognition, rhythm quantification and trend prediction capabilities are improved, and a high-adaptability quantification basis is provided for rehabilitation evaluation.

The invention discloses an electro-hydrogen system safety domain dynamic regulation and control method based on digital twinning-reinforcement learning, and belongs to the field of power grid safety operation, and the method comprises the following steps: S1, constructing a multi-energy flow coupling model comprising new energy power generation, an electrolytic cell and a hydrogen storage tank; s2, decomposing a new energy original output sequence by adopting a method of combining continuous wavelet transform and an autoregressive moving average model to form time-frequency characteristics; s3, state space modeling considering space-time coupling; s4, generating a dynamic security domain based on digital twinning; s5, constructing a multi-target reward function, and solving by taking the dynamic security domain as a constraint; and S6, performing closed-loop verification and model evolution. By adopting the electric hydrogen system safety domain dynamic regulation and control method based on digital twinborn-reinforcement learning, safety domain dynamic adaptation and intelligent control of the electric hydrogen system under new energy fluctuation are realized, and the operation safety and economy of the system are remarkably improved.

The invention provides a neighbor projection type gradient coordination compression method and device and a federated learning system, and relates to the technical field of federated learning. According to the method, the uploading gradient of the client side is obtained, direction normalization is carried out, direction embedding updating of the client side is carried out through index moving average, then the similarity between the client sides is calculated, a neighbor set is generated, a similar graph is constructed, and then conflict detection and weight setting are executed. And performing weighted orthogonal projection in the frozen neighbor direction of each client to correct the gradient, and finally performing weighted aggregation on the gradient and updating global model parameters. According to the method, gradient conflicts are efficiently detected and corrected in a local range by constructing the client similar graph and combining a neighbor projection mechanism, the problems of high complexity and excessive information reduction caused by global processing are avoided, the convergence speed, stability and precision of a global model are improved under the condition that additional calculation and communication overhead of the client is not increased, and the user experience is improved. The method is suitable for large-scale non-independent identically distributed data scenes.

The invention relates to the field of gas concentration detection, and particularly discloses a real-time feedback and adaptive learning method for natural gas infrared spectrum measurement, which comprises the following steps: S1, acquiring infrared spectrum information of a natural gas body by using an infrared spectrometer, and constructing a historical sample set; s2, preprocessing the spectral data of the historical sample set; s3, selecting an optimal algorithm and a hyper-parameter by adopting XGBoost and Bayesian optimization; s4, constructing a qualitative model to identify gas types and match data; s5, calculating the similarity between a field sample and a historical sample through a Siamese network, and setting a threshold value to screen local data; s6, improving the KNN to construct a local dynamic quantitative model to predict the concentration; s7, processing low-similarity abnormal data by the global dynamic model, and improving the reliability by combining moving average and abnormal calibration; and S8, introducing reinforcement learning and online gradient descent to adjust parameters in real time to optimize the precision. According to the technical scheme, high-accuracy natural gas detection can be carried out in a complex environment.

The invention relates to the field of intelligent control of electromagnetic appliances, and provides a control method and system for online fault diagnosis of a magnetic latching relay, and the method comprises the steps: responding to an external control instruction, collecting real-time bus voltage and real-time environment temperature data, constructing environment state characteristics through a magnetothermal coupling algorithm, and generating an initial PWM drive duty ratio; pulse modulation voltage is loaded, a current sampling sequence is collected, a current change rate sequence is obtained through moving average filtering and electromechanical coupling decoupling, extreme value searching is executed to extract counter electromotive force distortion characteristics, and a dynamic current sequential sequence is generated; a fusion input tensor is constructed, armature state probability distribution is output through a gating circulation unit, and a real-time energy compensation instruction is generated in a displacement interval before closing; and according to the real-time energy compensation instruction, respectively generating an enhanced driving waveform, a delayed driving waveform or a vibration driving waveform. According to the method, multi-mode physical characteristic perception and depth time sequence deduction are fused, and online fault diagnosis and active intervention closed loop of the magnetic latching relay are realized.

The invention relates to the technical field of distribution network unmanned aerial vehicle autonomous tour-inspection navigation, in particular to a distribution network unmanned aerial vehicle tour-inspection autonomous navigation method based on GPS information and visual information, which comprises the following steps: acquiring a GPS and smoothing projection coordinates, constructing a Thiessen region random index, optimizing a route and acquiring a flight image, evaluating a risk and adjusting exposure parameters, and generating a light environment value. According to the method, an environment model is constructed by fusing GPS information, Gaussian projection is adopted to eliminate earth curvature errors, moving average filtering is adopted to suppress signal noise, Thiessen polygon is utilized to quantify routing inspection point and obstacle distribution, a tornado optimization algorithm is combined to generate an optimal route, the shortest path without repetition and obstacle avoidance are realized, and obstacles are identified based on a YOLOv12 model. A risk avoiding strategy is made according to the risk value, a route is dynamically optimized, a light environment fingerprint database is constructed, illumination change is predicted in combination with LSTM, aperture shutter sensitivity and white balance are adjusted in a self-adaptive mode, image brightness and definition are optimized, and inspection efficiency and data reliability are improved.

The invention discloses a deep neural network model freezing training optimization method based on tensor similarity. According to the method, a reference model generation module regularly generates a single-layer reference model to support real-time tensor similarity evaluation; the tensor similarity calculation module generates a normalized Gram matrix by using the activation output, and accurately evaluates the stability of the active layer; the freezing decision module can smooth instantaneous fluctuation in an evaluation process based on a moving average value of tensor similarity so as to make a steady freezing decision; the tensor I / O module caches the forward propagation result of the freezing layer, and repeated calculation is avoided. According to the method, the calculation amount of back propagation and forward propagation in the deep neural network training process can be effectively reduced, and the calculation resource utilization rate in the training process is improved. The method is remarkably superior to an existing freezing method in deep neural network model training tasks such as image classification, target detection and image segmentation, and the training stability and efficiency are improved while the final precision of the model is not sacrificed.

The invention discloses a semi-supervised flotation working condition recognition method based on spatial-temporal feature fusion, and belongs to the field of industrial process monitoring. The method comprises the steps that flotation froth videos are collected and preprocessed to obtain a data set of a preset proportion; constructing a student model fused with spatial-temporal characteristics to predict each working condition category; a teacher model is constructed, and index moving average of student model parameters is used for updating so as to maintain stability and guide learning of the student model; and calculating supervision loss based on the labeled training set, calculating consistency loss based on the unlabeled training set, and performing semi-supervised training on the model by minimizing the total loss. According to the method, a spatial-temporal characteristic representation model is embedded into a semi-supervised learning framework, foam state evolution characteristics are fully represented, and high-precision working condition recognition is realized by using limited labeled samples; through the spatio-temporal feature fusion method of the cross additive attention mechanism, mutual enhancement of spatio-temporal features is realized, the time complexity is reduced, and the calculation efficiency is improved.

The invention provides a gait monitoring system based on multi-modal sensing fusion, which comprises the following steps of: establishing an initial base line to form a personalized gait base line model; baseline updating is adopted, and after a system obtains new gait data, a weighted moving average algorithm is adopted for self-learning updating, so that the historical data weight is enhanced, and the baseline stability is improved. The technical problems of insufficient individual difference adaptation, lack of dynamic threshold adjustment, poor real-time performance and the like in the prior art are solved.

The invention discloses a tobacco primary processing technology regulation and control method based on a large model, and the method comprises the steps: carrying out the feature extraction at an edge layer according to technological parameters, raw material detection data and environmental parameters collected in a historical time interval, and obtaining a plurality of feature vectors; according to the multiple feature vectors, a quality index is obtained through prediction in an edge layer through a preset prediction model, and according to the quality index, process parameters are adjusted for execution; and according to the quality index acquired after execution and the quality index obtained by prediction, obtaining a prediction residual moving average at a decision-making layer, and when the prediction residual moving average is continuously greater than an error threshold for multiple times, triggering model re-calibration and synchronizing to an edge layer. According to the method, the multi-modal features are fused in the edge layer, closed-loop control is realized through the real-time prediction model, and the prediction accuracy and the response speed are improved. In combination with a prediction residual moving average dynamic calibration mechanism, the problem of model failure caused by parameter mutation is effectively solved, and production continuity is guaranteed.

The invention relates to a red light therapeutic instrument real-time calibration method based on multi-modal data fusion, which comprises the following steps: firstly, collecting data such as optical power, target surface temperature, internal temperature, reflection spectrum and light source-target surface distance in a time window, and unifying time base alignment; performing anomaly elimination, de-noising and normalization on each modal data, and constructing a standardized data matrix; extracting multi-modal features through power spectral density, wavelet packet decomposition, exponential moving average and Kalman filtering, inputting the multi-modal features into a fusion network containing a modal special encoder and a cross-modal attention unit, and generating a red light treatment state vector; the driving current, the pulse width, the duty ratio, the emission angle and the aging compensation factor are solved in real time through constrained least square optimization and are issued to the driving control module, and the red light source is smoothly adjusted in a soft start mode; the system monitors the output power, calibration is completed if the output power meets a threshold value, and otherwise, the next iteration is started. According to the method, power closed-loop correction can be realized, and dose consistency and thermal safety are improved.

The invention discloses a landslide image instance segmentation method based on a dual adaptation mechanism, relates to the technical field of image processing, and ensures that a model can have a better effect and numerical stability on data from different sources through a complete process from multi-source data collection to data normalization processing. The designed model is based on a multi-scale state alignment mechanism, in the forward process of the model, exponential moving average fusion is carried out on feature information in the same level, transmission fusion is carried out on feature information of different levels, feature robustness is enhanced, and error accumulation caused by local deviation is reduced. A meta-context incremental learning mechanism is designed, and input data are dynamically converted into a series of key value vector sequences. In the reasoning process of the model, data distribution different from a training domain is dynamically recognized, a gradient descent process is implicitly executed according to the characteristics of data, and model parameters are finely adjusted, so that the characterization capability is greatly improved, and efficient and robust instance segmentation is realized.

The invention provides a linkage control method and system for computer auxiliary equipment, and the method comprises the steps: generating an equipment linkage control instruction sequence through prediction probability distribution, managing a multi-equipment concurrency control request through a priority queue, and dynamically adjusting an instruction execution sequence according to the equipment response time and the resource occupation condition, if the response time of the equipment exceeds a preset delay threshold value, reducing the linkage priority of the equipment and activating standby equipment; and adjusting equipment linkage strategy parameters according to a model updating result, adopting an adaptive threshold mechanism to optimize a prediction trigger condition, calculating a user satisfaction index through a moving average algorithm, and if the satisfaction is lower than a reference value, backtracking and analyzing operation sequence features and recalibrating personalized operation habit model parameters.

The invention discloses a supplied land idle risk assessment method and system based on spatio-temporal data, and belongs to the technical field of geographic information, and the method comprises the steps: screening out a first land parcel from supplied land parcels according to the static data of each supplied land parcel; calculating an idle risk assessment index corresponding to the first land parcel according to the static data; extracting historical construction time-space sequence data from the space remote sensing data of the first land parcel from the time when the first land parcel is supplied to now; extracting space-time trend change characteristic data through a time window moving average method by adopting a long-short-term memory network model and taking the historical construction space-time sequence data as input, and predicting a development progress corresponding to the first land parcel according to the space-time trend change characteristic data; and when the development progress and the idle risk assessment index do not meet the preset conditions, determining that the first plot has an idle risk. Through the method and the device, the problem of low timeliness and accuracy of supplied land idle risk assessment can be solved.

The invention relates to the technical field of hydrometeorological monitoring, in particular to a rainstorm flood water line early warning and monitoring method. According to the invention, the consistency and timeliness of real-time water level data from different data sources are ensured through a multi-source hydrometeorological data acquisition and time synchronization technology. Through accurate time synchronization processing, deviation caused by data time lag is avoided, accurate butt joint and fusion of various water level data are guaranteed, and subsequent flood risk assessment and early warning are more reliable. Particularly, under sudden weather conditions such as rainstorm and the like, quick response can be realized, and accurate risk assessment can be provided; according to the method, the noise in the water level data of each data source is effectively removed by combining a combined preprocessing algorithm of moving average filtering and Kalman filtering, and weights of different data sources are weighted according to prediction errors, so that comprehensive accurate water level data is obtained. According to the fusion method, the smoothness and accuracy of the data are improved, and high-quality input data are provided for subsequent risk assessment.

The invention discloses an entropy-difference-guided source-domain-free transfer learning image target detection method, and belongs to the technical field of image processing and computer vision. The method comprises the following steps: initializing a teacher model and a student model, enhancing a target domain data set, screening teacher model output by using a fixed confidence threshold to generate a pseudo tag, calculating an image entropy difference and an intersection-to-union ratio of the same category of detection frames, screening the pseudo tag based on entropy difference guidance, training the models and updating the teacher model. According to the method, the finally screened pseudo tag is used as supervision information to train the student model, and the teacher model is updated through index moving average to complete model migration, so that the problem of insufficient reliability of the pseudo tag in a cross-domain scene is effectively solved, and high-performance target detection under the condition of passive domain data is realized.

The invention provides a comprehensive noise reduction performance evaluation method for a nonlinear ultrasonic detection signal noise reduction algorithm, and the method comprises the steps: firstly constructing a multi-working-condition noise reduction performance pre-screening mechanism based on error band analysis based on early-stage experimental data, and then introducing a radar map as an evaluation tool for the comprehensive noise reduction effect of the signal noise reduction algorithm. The noise reduction effects of a moving average method (MA), a spectral subtraction method (SS), a short-time Fourier transform method (STFT), a wavelet transform method (WT) and an orthogonal matching pursuit algorithm (OMP) are compared, and finally a signal noise reduction algorithm with the optimal comprehensive efficiency is screened out. On the basis of algorithm optimization, quantitative mapping rules between microcrack three-dimensional geometric parameters and relative nonlinear coefficients are analyzed through regression modeling, and the significant level of the correlation degree of the relative nonlinear coefficients and microcrack size parameters is effectively improved; and a high-confidence theoretical support is provided for quantitative nondestructive detection of the microcracks in engineering practice.

The invention belongs to the technical field of energy storage system state monitoring, and provides a lithium ion energy storage system abnormal state identification method and system based on impedance spectrum analysis, and the method comprises the steps: carrying out the decoupling of voltage response data and current response data through employing an excitation-response decoupling algorithm under a dynamic working condition, and obtaining an impedance spectrum; obtaining a voltage response component and a current response component; obtaining the total impedance spectrum of the lithium ion energy storage system under the current working condition; constructing a system-level impedance cloud picture; calculating a difference value between the current impedance spectrum and the weighted moving average baseline spectrum based on the system-level impedance cloud atlas to obtain an enhanced difference cloud atlas; and performing non-uniform segmentation and feature extraction on the enhanced difference cloud picture by using a visual Transform model oriented to electrochemical prior to obtain a multi-dimensional abnormal probability vector, and obtaining an abnormal state type of the lithium ion energy storage system based on the multi-dimensional abnormal probability vector. According to the technical scheme, the accuracy, timeliness and reliability of abnormal state recognition of the energy storage system are remarkably improved.