2004 results about "Reconstruction error" patented technology

The embodiment of the invention provides a scene space model adaptive modeling method based on multi-source data fusion. Complete scene information is constructed by fusing a video sequence, point cloud data and IMU (Inertial Measurement Unit) data. A deep learning network is adopted to carry out scene analysis, a scene complexity score is calculated based on a scale coefficient, a shielding degree and a geometric deformation coefficient, and intelligent matching of a modeling strategy is realized. Meanwhile, a scene dynamic area is predicted by using an optical flow algorithm, a geometric deformation field and a camera pose change, the modeling quality is monitored through reconstruction errors, and self-adaptive switching of a modeling algorithm is realized. The method effectively solves the problems of unstable precision and poor adaptability of a traditional modeling scheme in a complex dynamic scene, and improves the quality and efficiency of three-dimensional scene reconstruction.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an intelligent anomaly recognition and intervention processing method, device, equipment and medium. The method comprises the following steps: carrying out feature fusion by using a gating fusion network and generating a preliminary abnormal score, determining a reconstruction error through an auto-encoder and triggering abnormal early warning, calculating a causal effect value of key features in combination with a causal graph model and anti-factual reasoning, and calibrating the abnormal score to generate a final abnormal score and an intervention instruction. And executing an intervention action and recording a result. According to the method, the multi-dimensional feature information and the causal reasoning mechanism are fused, the self-encoder reconstruction error is combined to carry out anomaly judgment, the intervention instruction is generated and executed, closed-loop control of anomaly detection, reasoning analysis and intervention execution is achieved, and the recognition accuracy of complex events and the system response capacity are improved.

Systems / techniques that facilitate data drift detection for unstructured texts via deep learning autoencoders are provided. In various embodiments, a system can access a pre-trained natural language model and a set of unstructured texts on which the model is to be executed. In various aspects, the system can determine, via execution of a deep learning autoencoder, how different a first set of reconstruction errors associated with the set of unstructured texts are from a second set of reconstruction errors associated with a set of training unstructured texts on which the pre-trained natural language model was trained. In various instances, the system can generate, in response to a determination that the first and second sets of reconstruction errors differ by more than a threshold margin, a first alert indicating that data drift has occurred and that the model is thereby unable to confidently analyze the set of unstructured texts.

The invention discloses a time series anomaly detection method based on reconstruction and prediction, and particularly relates to the field of time series data analysis of the Internet of Things, comprising the following steps: step 1, data preprocessing; 2, constructing a reconstruction network based on an auto-encoder architecture and a time convolution network; step 3, constructing a prediction network based on LSTM and an attention mechanism; and step 4, end-to-end model training is carried out, and anomaly detection performance is evaluated. According to the method, a reconstruction network based on an auto-encoder architecture is constructed, a time convolution network TCN is taken as a core, multi-scale features are extracted by using expansion causal convolution, and input data are reconstructed to obtain reconstruction errors. A prediction network is constructed to capture a long-term dependency relationship in time sequence data, and weights of different time steps are dynamically allocated, so that the modeling capability of a model to a complex mode is enhanced. By integrating the reconstruction error and the prediction error, an abnormal score is constructed, and a threshold value is automatically determined by using a POT algorithm, so that accurate discrimination of abnormal data points is realized.

The present disclosure relates to the technical field of videos. Disclosed are a video anomaly event detection method, apparatus and device, and a storage medium. The method comprises: acquiring the current video frame of an industrial site, and reconstructing, by means of an anomaly detection model, a current reconstructed frame corresponding to the current video frame, wherein the anomaly detection model is a model constructed on the basis of a two-stream autoencoder network, the two-stream autoencoder network being used for extracting a spatial feature and a temporal feature; calculating the current dynamic threshold of the current video frame, wherein the current dynamic threshold is used for performing anomaly detection on the current reconstructed frame; and on the basis of the current dynamic threshold and the current reconstructed frame, detecting abnormal events in the industrial site. In the present disclosure, whether abnormal events occur is detected by means of an anomaly detection model and a dynamic threshold value; therefore, the reconstruction capability of the model on the abnormal events can be suppressed, and an auto-encoder is encouraged to produce a higher reconstruction error for the abnormal events, thus reducing the problems of false detection or missed detection, and improving the accuracy of video anomaly event detection.

The invention relates to the technical field of data processing, in particular to a data management method for automatically detecting data abnormal values based on deep learning, which comprises the following steps of: based on an input data set, performing synchronization and missing value filling on a plurality of data sources, and performing standardization and normalization processing on data to obtain a preprocessed data set; according to the method, the data preprocessing is executed, the consistency and quality of data input are optimized, feature extraction and model layer-by-layer training are utilized, the recognition capability of the model for abnormal data is enhanced, the precision and efficiency of data anomaly detection are improved, calculation of residual errors and reconstruction errors is combined, the accuracy of abnormal data scoring is improved, and the accuracy of data anomaly detection is improved. The detection threshold value is dynamically adjusted by utilizing data characteristics, the false alarm rate is reduced, and the reliability and the stability of the enhanced data processing flow are ensured by combining time sequence analysis, evaluating the abnormal development trend, performing comparative analysis on abnormal data and normal data modes and performing data correction.

The invention belongs to the technical field of power batteries, and particularly relates to a power battery attenuation prediction method based on a depth auto-encoder, which comprises the following steps: firstly, collecting new energy automobile power battery sample data, pre-processing, and constructing a depth auto-encoder model; adjusting the parameters of the depth auto-encoder model according to the gradient information of the loss function by adopting an optimization algorithm until the training of the depth auto-encoder model is completed; then collecting real-time operation data of a current target new energy automobile as original data, inputting the original data into the depth auto-encoder model, outputting reconstruction data, calculating reconstruction errors, associating the reconstruction errors with the health state of the power battery, and representing the reconstruction errors as a health index of the power battery; and finally, based on the time sequence data of the power battery health index, predicting the decline trend and the residual life of the power battery by adopting a bidirectional LSTM + Attention mechanism. The problem of low accuracy of a power battery life prediction technology in the prior art can be solved.

The invention discloses a traffic accident identification method based on unsupervised pre-training and a multi-modal large model, and the method comprises the following steps: S1, collecting and sorting video data in a traffic monitoring scene, selecting a video clip without a traffic accident as a data sample, extracting a video key frame, carrying out the unsupervised pre-training of a mask auto-encoder, and carrying out the unsupervised pre-training of the mask auto-encoder; reconstructing a video clip which is shielded randomly; s2, calculating a reconstruction error between the original video and the reconstructed video to evaluate the reconstruction quality, and screening out key frame video clips with poor reconstruction quality; according to the invention, through unsupervised training of the mask auto-encoder, even under the condition of limited annotation data, more traffic accident-free segments can be filtered, video segments in which traffic anomalies possibly exist can be rapidly screened out, and by constructing fine-tuning training of multi-modal traffic data on a multi-modal large model, the real-time performance of the model is improved. The method can improve the recognition accuracy and efficiency of a traffic accident in a complex scene, provides real-time alarm and detailed analysis, and takes countermeasures in time.

The invention relates to a network flow anomaly detection strategy generation method based on machine learning, and belongs to the technical field of machine learning. The method comprises the following steps: firstly, collecting network traffic data in a preset time window, and extracting feature vectors containing traffic, a time sequence and a protocol type; and inputting the feature vector into a long short-term memory auto-encoder model, and calculating a reconstruction error to judge whether the network flow is abnormal or not. Aiming at the abnormal feature vector, adopting a multi-agent depth deterministic strategy gradient algorithm to construct a plurality of cooperative agents, and independently generating a candidate abnormal detection strategy by each agent; through a cross-agent strategy evaluation mechanism, the difference between a joint strategy and a single-agent strategy in the aspect of anomaly detection accuracy is compared, cooperation gain is calculated, strategy exploration parameters of all agents are adjusted according to the cooperation gain, and a global optimal anomaly detection strategy is optimized and determined in real time. According to the method, high-precision and low-missing-report network traffic anomaly detection can be realized, and the method has good self-adaptability and real-time performance.

The invention discloses an industrial equipment fault early warning analysis method and system based on a graph neural network, and relates to the technical field of intelligent fault diagnosis and equipment state monitoring, and the method comprises the steps: constructing a time slice data tensor, and forming a graph neural network input structure; on the basis of the graph neural network input structure, feature learning and correlation analysis of the graph neural network are executed, and node-level embedding representation is obtained; and based on the node-level embedding representation, calculating fault relevance response distribution of the nodes, evaluating propagation risks and positioning equipment fault sources. According to the method, the problem that a traditional method cannot effectively express a spatial structure or a cooperative relationship between measuring points in an early modeling stage is solved, the spatial perception capability of overall modeling is improved, so that a data foundation is laid for subsequent feature extraction and anomaly analysis, and the reconstruction accuracy is improved by comparing an embedded reconstruction value with an original observation value and calculating a node-level reconstruction error. Fault influence range evaluation and traceability positioning are realized, and quantitative support is provided for early warning response and maintenance decision.

The invention relates to the technical field of cable cover plate detection, and discloses a cable cover plate anomaly detection method and system based on a variational auto-encoder, and the method comprises the steps: carrying out the denoising processing of the original data of a cable cover plate collected by a sensor, obtaining a denoised signal, carrying out the dynamic resampling of the denoised signal according to a local change rate, and carrying out the detection of the anomaly of the cable cover plate. Resampling data is obtained; inputting the resampling data into a pre-established data reconstruction model to obtain reconstructed data, the data reconstruction model being constructed based on a variational auto-encoder and being obtained by training based on a dynamic learning rate adjustment strategy and a data enhancement strategy; and performing anomaly detection according to the reconstruction error change rate of the reconstruction data to obtain abnormal data, and generating a corresponding alarm prompt according to the abnormal data. According to the method, the integrity and reliability of data can be improved, the flexibility and accuracy of anomaly detection are improved, the change of the operation state of the cable cover plate can be responded in time, and the real-time performance and effectiveness of anomaly recognition are improved.

The invention relates to the technical field of spatial-temporal trajectory data anomaly detection, in particular to a trajectory anomaly route detection method and system based on self-supervised trajectory representation learning. The method comprises the following steps: preprocessing acquired trajectory data; track double-view comparison representation learning is carried out based on a double-view-angle synchronous mask strategy; coding time dynamic based on a space-time fusion mechanism and fusing the time dynamic with spatial features; learning essential representation from the fused spatio-temporal features to perform trajectory reconstruction; and error checking is carried out based on the reconstructed trajectory, and an abnormal trajectory is judged through the reconstructed error. The invention provides a brand new trajectory anomaly detection model. According to the model, a GPS track and a grid-based track feature are fused, so that track representation is enriched; and meanwhile, a double-view synchronous mask mechanism is designed, so that the model can sense local disturbance of space and time dimensions at the same time in a training stage, and thus the sensitivity to local anomaly is improved.

The invention relates to the technical field of anomaly detection, and discloses an anomaly analysis method and system based on blasting data, and the method comprises the steps: collecting the blasting data of each monitoring node, dividing the monitoring node into a plurality of regions, carrying out the statistics of the anomaly density of each region in a preset historical period, and calculating the anomaly density of each region; according to the abnormal density, dynamically setting a region weight of an auto-encoder training sample; reconstructing the current round of data by using the weighted trained auto-encoder, calculating the residual error of each node, and extracting the activation path information of the node of which the residual error is higher than a reconstruction error preliminary screening threshold value; constructing a graph structure model between the monitoring nodes, and calculating an abnormal propagation path according to the correlation between the nodes; and matching the activation path with the propagation path, and determining the abnormal state of the target node. And the accuracy, the stability and the intelligent feedback capability of blasting data anomaly analysis are remarkably improved.

The present invention relates to the technical field of image processing, and in particular to a hyperspectral remote sensing image anomaly detection method based on deep learning. The method comprises: performing normalization preprocessing on a hyperspectral remote sensing image, so as to obtain an original hyperspectral image; inputting the original hyperspectral image into a low-rank background feature extraction network, so as to generate a low-rank background feature; inputting the low-rank background feature into a backbone feature extraction network, so as to obtain a backbone feature; inputting the backbone feature into a feature convolutional network, so as to obtain a convolution feature; inputting the convolution feature into an up-sampling network, so as to obtain an up-sampling feature; inputting the up-sampling feature into a feature convolutional sub-network, so as to obtain a reconstructed background feature map; and subtracting the reconstructed background feature map from the original hyperspectral image, so as to obtain a reconstructed error map, and using an adaptive weighted loss function to suppress an abnormal reconstruction weight. The present invention aims to solve the problem whereby low-rank characteristics of a background are not fully utilized in existing hyperspectral anomaly detection, and the problem whereby existing algorithms still face challenges in separating the background and an anomaly.

The invention relates to the technical field of roller cooling, and discloses a roller cooling temperature control method and system.The method comprises the steps that the length of a roller is obtained, and the roller is equally divided into N temperature monitoring areas according to the length of the roller; n pieces of detection temperature value data are obtained and preprocessed; inputting the detected temperature value data into a pre-trained state model for comparison, and obtaining a temperature prediction value; the optimal cooling water flow is calculated according to the temperature prediction value through an MPC controller; acquiring historical data of the roller, and inputting the historical data into the auto-encoder for reconstruction; inputting detection temperature value data collected in real time into the auto-encoder, and calculating a reconstruction error value; and determining a current cooling water flow control state based on the reconstruction error value, and compensating the cooling water flow to the MPC controller. The roller cooling efficiency is improved.

The invention provides an intelligent cold chain transportation information management method, system and device and a storage medium, and the method comprises the steps: recognizing the type of transported goods and environment demands through a task recognition module, and determining and configuring a target sensor of a cold chain vehicle through a sensor configuration module; the target sensor collects environment data, the vehicle-mounted GPS module collects position information and a driving state, the RFID tag records a cargo loading state, and all data are sent to the central management platform. The platform constructs a multi-dimensional feature vector, the multi-dimensional feature vector is compressed into a low-dimensional feature representation through an encoder, a reconstruction data vector is obtained through reconstruction of a decoder, and a reconstruction error is calculated. And by analyzing the reconstruction error, the platform generates an analysis result, and the environment control equipment in the vehicle is automatically adjusted according to the result, so that the stability of cargo transportation is ensured.

The invention relates to a multi-mode AIGC cold-chain logistics path optimization decision-making method and a multi-mode AIGC cold-chain logistics path optimization decision-making system. The method comprises the following steps: inputting position data, temperature data, road condition data and weather data in a cold-chain logistics system into an AIGC model of a stack auto-encoder for feature extraction and reconstruction error calculation to obtain a feature index set; performing space-time interval division and similarity matching on the cold chain distribution path to obtain an interval reference path library; inputting a hierarchical control model for stage processing to obtain an initial control parameter; an objective function including temperature constraint, time window constraint and vehicle dynamics constraint is constructed for solving, and a path optimization scheme is obtained; performing road condition random disturbance and temperature fluctuation influence analysis to obtain a path risk scoring index; and performing feedback correction and iterative optimization on the initial control parameter to obtain a target control parameter. According to the invention, the temperature control precision is ensured, and the distribution efficiency and the system reliability are effectively improved.

The invention discloses a power battery fault detection method based on a bidirectional Mama architecture, and the method comprises the steps: firstly collecting the voltage, current, temperature and internal resistance of a power battery as original data, and carrying out the preprocessing of the original data, and generating a data set; secondly, the bidirectional Mama feature extraction network and the weighted reconstruction network are connected in series to construct a bidirectional Mama network model, bidirectional representation modeling of the time dimension and the feature dimension is carried out on the power battery data in the data set, and reconstruction output is carried out through the weighted reconstruction network. And finally, taking the time sequence data of the power battery under the normal working condition as the input of the bidirectional Mamba network for training. And according to a reconstruction error between the output data and the input data of the weighted reconstruction network, a fault threshold is set, and fault detection is realized. The key information in the time sequence data of the power battery is accurately extracted, potential faults in the power battery are efficiently and comprehensively detected, and the safety of the power battery is improved.

The invention discloses an adaptive mask medical image segmentation method based on a self-supervised mask and deep reinforcement learning, and the method comprises the steps: employing a classic encoder-decoder architecture for a self-supervised mask reconstruction network, fusing a Swin Transform encoder, and carrying out the feature fusion of local image blocks through a self-attention mechanism; according to the self-adaptive mask model, a PPO deep reinforcement learning algorithm is adopted, a strategy network and a value network are constructed, mask actions are dynamically regulated and controlled, reconstruction errors are gradually reduced, a mask strategy is continuously optimized in multiple times of strategy updating for self-adaptive optimization, and high-quality reconstruction of a medical image influenced by missing information is achieved; according to the method, high-quality feature representation can be obtained in an unlabeled data environment, and relatively high precision and accuracy are presented on a public data set.

The invention discloses an industrial image anomaly detection method based on deep learning, and particularly relates to the technical field of industrial visual detection. The problems of high false alarm rate, fuzzy fine defect positioning, insufficient real-time response capability, difficulty in model increment updating and the like caused by data distribution drift in an industrial scene are solved. According to the method, robust features are extracted through a multi-scale feature fusion auto-encoder, and a dynamic memory bank is constructed to update a normal sample prototype online; a dual-path detection mechanism is adopted to cooperate with a pixel-level reconstruction error and attention weighted feature matching deviation; efficient edge reasoning is realized in combination with block parallel processing and model compiling optimization; and designing an elastic incremental learning framework to prevent disastrous forgetting. And finally, false alarms caused by environmental changes are reduced, accurate positioning of pixel-level defects is realized, millisecond-level detection requirements of high-resolution images are met, safe and efficient model online evolution is supported, and adaptability and reliability of an industrial quality inspection system are comprehensively improved.

The invention belongs to the field of network security of smart grids, and discloses a method for detecting an antagonistic network attack in a smart grid based on an iterative algorithm, and the method comprises the steps: generating an antagonistic attack sample through iteration, so as to avoid the protection of a detection system; performing feature extraction and reconstruction of input data by using the constructed automatic encoder network model, and capturing complex time sequence features through convolution, pooling and up-sampling operations; a dynamic parameter adjustment strategy is adopted to adaptively optimize the training optimization momentum coefficient and the training optimization target reconstruction error in the training process, and the system robustness is improved; and finally, verifying the concealment and stability of the resistance attack sample under different scaling conditions through multi-scale evaluation so as to optimize the automatic encoder network model. According to the invention, the security protection level of the smart grid system in a complex network attack scene is improved, and stable operation of the power system is ensured.

The invention provides an anomaly detection method and device based on adaptive multi-scale feature modeling, and relates to the technical field of data anomaly detection. The method combines time component decomposition and a gating network, and allocates suitable expert networks for data on different time scales. In an expert layer, a deep convolutional network and a cross-time-slice attention mechanism are utilized to mine mutual relations in different time periods and among the different time periods in a time sequence. Then, combining a graph structure, and modeling spatial interaction characteristics among variables; and finally, learning the potential representation of the data to generate reconstruction data, and calculating a reconstruction error and the KL divergence of a potential space to carry out model training, thereby carrying out efficient anomaly detection under an unsupervised condition. According to the method, the precision of anomaly detection in the industrial control system is improved, a more flexible and accurate anomaly detection tool is provided for practical application, and the method adapts to complex dynamic changes of time sequence data in the industrial control system under the condition that no label data exists.

The invention provides a gas turbine exhaust gas temperature prediction and fault early warning system and method, and relates to the technical field of gas turbine state monitoring and fault diagnosis. The system comprises a data acquisition and preprocessing module used for acquiring and preprocessing data; the dynamic graph construction module is used for constructing graph structure data; the graph neural network module is used for extracting spatio-temporal features through learning; the improved auto-encoder module is used for realizing feature reconstruction by learning normal working condition data and then outputting reconstruction errors to the fault early warning module; the temperature prediction module is used for predicting a future exhaust temperature value; the fault early warning module is used for performing fault judgment and decision; and the result display and output module is used for summarizing analysis results. According to the system, the prediction precision of the exhaust temperature is remarkably improved, the fault detection and early warning capabilities are enhanced, the system has better feature representation capability and adaptability to complex working conditions, and close integration and mutual enhancement of prediction and early warning are realized.

The invention relates to the technical field of structural damage identification, in particular to an unsupervised structural damage identification method based on CAE-LSTM. The method comprises the following steps: training a CAE-LSTM model by using a training set to obtain a trained model, and reconstructing unknown data including health data and damage data by using the trained model to obtain a reconstruction error of the health data and a reconstruction error of the damage data; determining a damage sensitivity factor of the acceleration response signal of each batch by combining a probability density function of health data, a probability density function of damage data and a reconstruction error in the acceleration response signal of each batch of the undamaged structure so as to determine a damage threshold and screen a damage position; acquiring a damage factor according to the health state data and the damage state data of the damage position sensor; and judging the damage degree based on the size of the damage factor. According to the invention, the accuracy and reliability of the structural damage identification result are improved.

A computer implemented method for defect detection comprises obtaining an imaging dataset of a wafer, and verifying a defect criterion in a subset of the imaging dataset of the wafer. The defect criterion comprises an observation representation of the subset of the imaging dataset with respect to a number of characteristic elements derived from reference images of semiconductor structures. The observation representation and the characteristic elements define a reconstruction of minimal reconstruction error, and a tolerance statistic on defect-free representations of subsets of defect-free observed imaging datasets. Each of the defect-free representations and the characteristic elements define a reconstruction of minimal reconstruction error of a subset of the defect-free imaging datasets. The computer implemented method further comprises generating defect information.

The invention relates to an industrial Internet of Things anomaly detection method based on time sequence and text joint modeling, and belongs to the technical field of industrial Internet of Things anomaly detection. The method comprises the following steps: constructing text prompt information based on collected industrial Internet of Things time sequence data, and respectively taking the text prompt information as inputs of a time sequence channel and a text prompt channel; a sensor association graph is constructed by using a multi-hop GCN, and on the basis of the association graph, time feature modeling from local to global is completed by using multi-scale expansion convolution and combining a differential attention mechanism; performing word segmentation processing on the text prompt information through a word segmentation device, and encoding the text prompt information into vector representation; and calculating attention weight between time sequence embedding and text prompt embedding, fusing to obtain joint embedding representation, enhancing the joint embedding representation, inputting the enhanced joint embedding representation into MLP for reconstruction, calculating an abnormal score through a reconstruction error, and carrying out industrial Internet of Things anomaly detection according to the abnormal score. The method is high in anomaly detection accuracy, and can improve the equipment anomaly perception and risk early warning capability.

The invention discloses a battery abnormal data prediction and state evaluation method, and relates to the technical field of battery state prediction, and the method mainly comprises the steps: carrying out the preprocessing of an experiment data set, and obtaining multi-dimensional time series data; a combined feature encoder, a pre-response encoder and a memory analysis module are constructed to realize a battery abnormal data fault prediction model; training the model by using the multi-dimensional time sequence data to obtain a trained model, and predicting the to-be-predicted data to obtain a prediction result; and calculating a reconstruction error between a prediction result and original data, constructing an AUROC evaluation model, and evaluating the battery abnormal data fault prediction model. By implementing the battery abnormal data prediction and state evaluation method provided by the invention, the feature extraction efficiency, the abnormal recognition precision, the detection stability and the generalization ability can be improved.

The invention discloses a power distribution cabinet bus temperature change analysis and early warning method and system, and relates to the technical field of power distribution cabinets, and the method comprises the steps: collecting the temperature data of a three-phase bus of a power distribution cabinet, dividing a temperature sequence into time-space units, and calculating the spatial characteristics and time characteristics of each time-space unit; performing frequency domain analysis on the temperature data of the space-time unit, decomposing the temperature data to obtain a low-frequency component, an intermediate-frequency component and a high-frequency component, extracting sub-components of different time scales, and calculating a time coupling feature, a space coupling feature and a physical coupling feature; fault-free data is collected to establish a normal mode library, historical fault data is collected to establish a fault mode library, the deviation degree of features and a baseline is calculated in real time, and marking and feedback updating are carried out on potential new faults; on the basis of distributed monitoring, temperature abnormal points are positioned, reconstruction errors of all scales are calculated, the abnormal origin scale is determined, and graded early warning is triggered, so that the problem that a traditional monitoring method is insufficient in fault recognition capability is solved.

The invention discloses an operation data management system and method based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the following steps: 1, collecting multi-source data in real time through an intelligent sensor and industrial equipment, achieving time synchronization, and ensuring data alignment; step 2, cleaning, labeling and standardizing the collected data, extracting feature vectors of the data, and generating multi-modal feature vectors through dynamic weighted fusion of an attention mechanism; step 3, calculating a reconstruction error based on an auto-encoder to identify an abnormal state, quantifying the contribution degree of each feature to the abnormity in combination with an SHAP algorithm, and positioning key influence factors; and step 4, dynamically adjusting the detection threshold by adopting reinforcement learning to reduce the false alarm and missing alarm rate, and evaluating the prediction confidence by quantifying the model error and the data error to realize the dynamic adjustment of the early warning priority. According to the invention, the condition that abnormal data and false alarm of the abnormal data are difficult to effectively warn in the prior art can be improved.

The invention belongs to the technical field of time series prediction, and more specifically relates to a time series data prediction method based on a deep integrated learning model and high and low frequency separation. The method comprises the following steps: collecting time sequence data in a system, and preprocessing the obtained data; vMD variational mode decomposition is carried out on original time sequence data, and a time sequence is decomposed into K mode components with limited bandwidth; combining a maximum information coefficient method and a reconstruction error analysis method to determine an optimal decomposition mode number K; dividing high and low frequency components for all decomposed modes by using a zero-crossing rate and a center frequency; appropriate prediction models are established for the high-frequency component and the low-frequency component respectively; and superposing all modal prediction results to obtain a final time sequence prediction result. According to the method, multiple deep learning models are combined, so that the prediction method has higher flexibility and adaptability, and proper models can be selected for components with different characteristics for prediction, thereby improving the overall performance.