110 results about "Time series processing" patented technology

The system comprises a data acquisition layer, a data processing layer, a model layer and an application layer, the data acquisition layer is used for acquiring multivariate heterogeneous data in real time, the data processing layer is used for processing the acquired data, the model layer is used for training the processed data, and the application layer is used for applying the trained data to the data processing layer. And the application layer is used for performing dual drive by adopting a time sequence processing block and point prediction and probability prediction, the time sequence processing block is a double-branch parallel backbone network integrating a Decoder-Only Transform network and a multi-scale graph neural network, and the application layer is used for evaluating the running state of the electric energy metering device according to a predicted value and a predicted probability calculated by the model layer at a certain moment. According to the charging pile operation state prediction system based on time sequence analysis, the operation state of the electric energy metering device can be monitored more comprehensively and accurately, potential abnormity and fault hidden dangers can be found in time, early warning is carried out in advance, and economic losses caused by metering errors and equipment faults are effectively reduced.

The invention discloses an underwater sonar target recognition system based on multi-domain feature fusion and lightweight modeling, and the system comprises a Trifusion block, a novel lightweight attention residual network, a long and short time attention LSTM and a Mamba module which are connected in sequence, and achieves target recognition through multi-domain parallel extraction of fusion features, lightweight convolution and attention optimization, long and short time dependence capture and long sequence modeling. The method has the advantages that complex noise is comprehensively represented, the model efficiency and stability are improved, the bottleneck of time sequence processing is broken through, and the method has high performance, high robustness and wide adaptability.

The invention provides a crop planting suitability dynamic evaluation method and system and a storage medium, and the method comprises the steps: S1, carrying out the time sequence processing of collected NDVI, meteorological data and static data; s2, dividing the phenological period of the target crop into a plurality of stages; s3, constructing a seasonal suitability function for the NDVI and the meteorological data to calculate scores of the data in each stage of a phenological period; s4, determining the score of the static data in each stage according to a preset membership score and grade division; s5, according to the historical NDVI, the meteorological data and the static data, the correlation coefficient of each data and the historical target crop yield and the entropy weight of each data are calculated respectively, and the proportion of the data and the historical target crop yield is adjusted through empirical parameters to obtain the dynamic weight corresponding to each data; and S6, performing weighted summation on the data based on the scores obtained in the steps S3 and S4 in combination with corresponding dynamic weights to obtain a crop planting suitability dynamic index. Therefore, the phenological accuracy of the planting suitability evaluation result of the target crop is improved.

The invention relates to a robot-assisted surgery system, in particular to a force feedback monitoring method, medium and system for multi-sensor data fusion based on deep learning, and the method comprises the following steps: obtaining the magnitude and direction of the force applied by a tool, and the acceleration change during vibration and movement; preprocessing the data and performing feature extraction; inputting the extracted spatial features into a deep learning model for time sequence processing; judging a current stage state according to a result obtained by processing the deep learning model, and adjusting a transfer correction coefficient; and carrying out force feedback on the corrected force feedback result. Compared with the prior art, the problem that in the prior art, the touch sense of a doctor cannot be simulated during fine operation, so that surgical operation only depends on experience and visual speculation of the doctor is solved. According to the scheme, in combination with multi-sensor data, micro force changes in an operation are processed and analyzed in real time through a deep learning algorithm, and therefore the accuracy and safety of the system are improved.

The invention provides a loop current prediction method based on a CNN and a BiLSTM neural network. The loop current prediction method is used for improving the prediction precision of proton flux data in space weather. The method comprises the following implementation steps of: acquiring and preprocessing data; a CNN neural network model and a BiLSTM neural network model are constructed; training the model; and evaluating and visualizing the model. According to the method, the advantages of the CNN in the aspect of spatial feature extraction and the powerful time sequence processing capability of the BiLSTM neural network are utilized, and finally, the outputs of the CNN and the BiLSTM neural network are fused, so that the spatial-temporal dynamic distribution of the ring current proton flux can be more effectively captured. Experimental results show that the method has excellent performance in a loop current prediction task, is suitable for real-time detection and early warning of space weather, and has relatively high prediction precision and stability.

The invention provides a structure health early warning method and system based on space-time correlation characteristics and digital twinning, and relates to the technical field of data processing. The method comprises the following steps: acquiring multi-source heterogeneous data of a target structure; performing dynamic sampling alignment and wavelet packet decomposition on the multi-source heterogeneous data to extract energy features to obtain synchronous data, and performing abnormal data filtering on the synchronous data to obtain cleaned fusion data; performing wavelet decomposition on a high-frequency vibration signal in the fused data to obtain a damage impact feature, performing time sequence processing on low-frequency temperature data in the fused data to obtain a temperature time feature, and performing dynamic graph convolutional network processing on strain data in the fused data to obtain a spatial correlation feature; constructing an input vector; calculating a damage degree index; and according to the damage degree indexes, early warning grades are divided, and corresponding control instructions are triggered for different early warning grades. By implementing the technical scheme provided by the invention, the accuracy of structural health early warning can be improved.

The invention discloses a method and system for constructing a city information model, and belongs to the field of city intelligent management, and the method comprises the steps: carrying out the spatial registration and semantic alignment of future multi-source heterogeneous data through a unified coordinate reference, and constructing a multi-dimensional incidence relation between a spatial entity and the attribute of the spatial entity through a graph database; performing time serialization processing on the city information model based on the real-time sensing data, and performing real-time correction on the geometric state and the attribute of the model by adopting an incremental modeling algorithm; performing automatic calibration on the model data in combination with rule reasoning and probability correction methods; predictive calculation is carried out on multiple scenes, and parameterized optimization is carried out on the city information model according to a calculation result; and carrying out adaptive hierarchical abstraction on the city information model, and automatically generating model subsets with different precision levels. According to the method, spatial registration and semantic integration are performed on the data, so that the global consistency of the city information model is realized, and the problems of data inconsistency, repetition and redundancy in a traditional method are solved.

The invention relates to a teaching quality dynamic evaluation system based on big data, and the system comprises a data collection module which is used for obtaining an original teaching time series data flow containing student scores, classroom performance and homework quality in real time; the time sequence processing module is used for adding a time stamp to the original teaching time sequence data stream and verifying the time stamp to generate a teaching time mark data set, performing time sequence analysis on the data set to obtain a teaching fluctuation feature set, and performing multi-dimensional decomposition on the feature set to extract a teaching quality feature set; and the real-time monitoring module is used for constructing a teaching monitoring model based on the teaching target parameters, calculating a multi-dimensional deviation between the current teaching quality feature set and the historical teaching quality feature set by using the model, and generating a multi-dimensional teaching early warning signal when the deviation exceeds a preset multi-dimensional joint early warning threshold. According to the invention, real-time dynamic monitoring and risk early warning of teaching quality are realized, and evaluation efficiency and accuracy are improved.

The invention discloses a power transmission tower deformation monitoring method and device and a computer program product, and the method comprises the steps: obtaining a multi-angle collection image of a random speckle pattern on the surface of a power transmission tower; constructing an image sequence based on the multi-angle acquired images, reconstructing a three-dimensional displacement field on the surface of the power transmission tower by using a stereoscopic vision reconstruction algorithm, and exporting a strain tensor field; performing time sequence processing on the strain tensor field under a plurality of time nodes through a strain evolution analysis model of a continuous time sequence, and extracting a strain change trend of each preset structure part; carrying out space consistency mapping and weighted interpolation processing according to the strain change trends of the plurality of preset structure parts, and calculating to obtain the overall structural dependent variable of the power transmission tower; and S5, in combination with historical strain data and the current overall structural dependent variable, identifying an abnormal deformation area of the power transmission tower by comparing the spatio-temporal evolution mode of the strain tensor field. The method has the effect of improving the accuracy of deformation monitoring of the power transmission tower.

The invention discloses an automatic control method and system for transformer substation maintenance, operation and maintenance supervision based on data analysis, and belongs to the technical field of transformer substation maintenance, and the method comprises the steps: collecting multi-source monitoring data of a transformer substation, including voltage, current, transformer oil temperature, partial discharge signals, GIS equipment vibration frequency and insulating oil chromatographic data; performing time sequence processing on the multi-source monitoring data to generate time sequence monitoring data, performing filtering processing on power frequency noise in the time sequence monitoring data by adopting a Daubechies wavelet basis function, and performing feature extraction to obtain time sequence processing data; inputting the time sequence processing data into a pre-constructed long short-term memory network model for fault prediction to obtain an equipment fault probability prediction value; when the equipment fault probability prediction value exceeds a preset threshold value, triggering a multi-stage early warning signal and executing an automatic control operation; and according to the maintenance record data, calculating a fault recovery rate and an operation stability index, and generating a maintenance evaluation report.

The invention relates to the technical field of laboratory environment control, and discloses an environment parameter control method and system for a complex laboratory, and the method comprises the steps: obtaining environment index data, and carrying out the time sequence processing, and obtaining a real-time change trend; according to the real-time change trend, predicting a future offset by using a prediction model, and performing data fusion when the offset exceeds a threshold value to obtain an accurate environment state estimated value; according to the accurate environmental state estimation value, quantifying the mutual influence among the environmental indexes to obtain the mutual influence relationship of the environmental indexes; determining an environment index priority sequence according to the mutual influence relationship; generating and executing a control instruction according to the priority sequence; and when continuous deviation exists, the prediction model and the mutual influence relation are updated through feedback circulation. According to the method, the dynamic mutual influence among the environmental indexes can be quantified, and accurate and stable cooperative control of multiple parameters in a complex dynamic environment is realized.

The invention provides a time sequence scene-oriented chip architecture level power consumption evaluation method, which comprises the following steps of: S100, generating an original data set by utilizing micro-architecture design parameters of a chip to be designed and adding simulation excitation; removing redundant data in the original data set, merging to obtain a preprocessed data set, and training a preset LSTM-based power consumption prediction model; and adjusting the micro-architecture design parameters and the ESL model, regenerating input features, and inputting the input features into the trained LSTM-based power consumption prediction model to obtain a power consumption prediction result. According to the method, the multi-dimensional features are introduced, the influence of different workloads and architecture configurations on the power consumption is comprehensively considered, and the time sequence processing capability of the LSTM model is utilized, so that the time dependency relationship in power consumption fluctuation can be effectively captured, and the power consumption prediction precision is remarkably improved, especially in a dynamically changing time sequence scene.

The invention relates to the technical field of laboratory instrument management and control, in particular to a laboratory instrument management system based on artificial intelligence, which comprises the following steps: acquiring instrument operation data and artificial use data in a target laboratory, constructing a use behavior sequence in combination with time sequence processing, and generating an abnormal residual vector through a residual generation network; according to the method, tiny deviation characteristics in operation of an instrument are depicted, abnormal judgment is carried out in combination with a contrast learning neural network, and an abnormal event probability value is output, so that early recognition of a potential fault trend is realized, and the problem that the fault is detected after being serialized is avoided. And the alarm generation module dynamically generates alarm information according to the abnormal event probability value, and performs alarm processing according to a preset strategy. The problems that in an existing laboratory instrument management system, fault response lags behind, the monitoring dimension is single, and manual operation behaviors are ignored are effectively solved.

The invention discloses a concrete dam crack opening degree prediction method based on feature optimization recognition. Comprising the following steps: S1, data preprocessing; S2, saliency feature extraction of a two-channel point riding attention mechanism module; S3, convolutional neural network feature extraction and fusion; S4, time sequence feature learning of a long and short term memory network; the beneficial effects of the invention are that deep learning and cooperative training are combined; a two-channel point riding attention mechanism is introduced to enhance the representation of key features, and the feature extraction capability of the convolutional neural network and the time sequence processing capability of the long and short term memory network are fused; self-adaptive weighted learning of multivariate environment characteristic information in crack opening monitoring data is realized, and the opening response of the crack under the influence of a complex environment is accurately predicted; the prediction capability of the concrete dam crack opening effect can be improved, and an innovative calculation scheme can be provided for concrete dam crack monitoring, early warning, prevention and control.

The invention relates to the technical field of IT support, and provides a time sequence processing method, device and equipment adopting a quantum pulse neural network, and the method comprises the steps: obtaining time sequence data which comprises network alarm data, network equipment performance index data and network operation and maintenance work order data; encoding the time sequence data into a first quantum state by using a quantum preprocessing layer, inputting the first quantum state into a pulse neural network layer, converting the first quantum state into a time sequence pulse sequence, and processing the time sequence pulse sequence to obtain an output result; by utilizing a quantum attention enhancement mechanism, calculating attention weight of an output result in a quantum state space, and weighting to obtain a second quantum state; and decoding the second quantum state by using the hybrid decoding layer to obtain a final prediction result. The final prediction result is used for realizing fault root cause positioning, abnormal work order identification or network service quality prediction. According to the method, the parallelism of quantum calculation and the superposition characteristic of the quantum state are utilized, and the calculation efficiency can be improved when large-scale time sequence data are processed.

The invention discloses a dexterous hand adaptive force control system based on an improved iTransform neural network. The dexterous hand adaptive force control system comprises a sensing module, a prediction module and a control module. The sensing module collects contact data in real time through a multi-channel capacitive sensor, and the contact data is input into the prediction module after high-dimensional time sequence processing. And the prediction module analyzes the stress time sequence characteristics by using an improved iTransform network, and generates a contact force prediction model. And the control module combines task planning, parameter optimization and real-time feedback through an efficient architecture of a microcontroller and a processor, and dynamically calibrates the grabbing force. High-dimensional time sequence processing is carried out on multi-channel sensor data, efficient processing and high-speed transmission are carried out on the data through a brain-like architecture of a system microcontroller unit and a high-performance processor, and a dexterous hand is operated in real time to grab a task.

The invention discloses an intelligent assessment and transformation management system for scientific and technological achievements, and belongs to the technical field, and in the intelligent assessment and transformation management system, the combination of DNN and RNN provides powerful feature learning and representation capabilities for the system. The DNN can extract high-level features from complex data of scientific and technological achievements, and the RNN can capture a long-term dependency relationship in time sequence data. And meanwhile, the XGBoost model algorithm can utilize the features extracted by the DNN and the RNN and the original features, and through constructing a plurality of decision trees and carrying out gradient lifting, the accuracy and reliability of prediction are improved, the nonlinear relationship between the features is effectively processed, the model deviation is reduced, and the evaluation result of the scientific and technological achievements is more accurate. The combination of the three algorithms enables the advantages of each model to be complementary, and the deep feature extraction of the DNN, the time sequence processing capability of the RNN and the integrated learning mechanism of the XGBoost act together, so that the over-fitting risk can be effectively reduced. The diversity enables the model to have better generalization ability when facing unknown data.

The invention discloses a clothing demand dynamic prediction method and system based on multi-source data fusion and machine learning, and the method comprises the steps: collecting and preprocessing multi-source heterogeneous data, and obtaining basic time sequence features, static attribute features and external situation features containing fashion trend quantification features; inputting the basic time sequence and the static attribute characteristics into a time sequence processing network to obtain a reference trend prediction value; inputting the external situation features into a situation feature processing network to obtain a situation influence vector; generating a dynamic adjustment coefficient by the vector through a gating unit; and finally, performing fusion calculation according to the reference trend prediction value and the dynamic adjustment coefficient to obtain a final demand prediction quantity. According to the invention, through a double-flow network structure and a gating fusion mechanism, effective modeling is carried out on an internal sales law and external situation impact, the prediction accuracy and the response speed to market changes are significantly improved, and accurate and dynamic decision support is provided for a clothing supply chain.

The invention discloses a distributed power supply cooperative control method based on transient dynamic characteristic adaptive driving, which adopts a multi-dimensional transient performance coupling evaluation function, unifies multiple targets including frequency, voltage, power angle and operation economy into a global optimization target, and fundamentally solves the defect of single target in the prior art. A collaborative strategy network based on dynamic context awareness is adopted, and a time sequence processing technology based on an attention mechanism is utilized, so that an intelligent agent can extract key transient dynamic characteristics from a local observation sequence, and the limitation of no memory and non-self-adaption in the prior art is broken through; according to the method, a cooperative control flow of a cooperative control strategy is provided, a high-performance learning type strategy is combined with a high-reliability safety monitor, the problem that safety and performance are difficult to be compatible is solved, fundamental transformation of distributed power supply control from passive, local and static rule-based modes is achieved, and the transient stability margin of a novel power system is remarkably improved.

The embodiment of the invention discloses a database dynamic query optimization and resource scheduling method and device and a medium, belongs to the technical field of databases, and solves the problems that the mode of manually adjusting PostgreSQL resource parameters is low in efficiency and causes resource waste. Obtaining query feature data and resource state data corresponding to the database, and performing time sequence processing on the resource state data to obtain a resource prediction result; inputting the query feature data and the resource state data into a preset AI strategy optimization model to output an optimization execution plan and optimization resource allocation parameters; inputting the optimization execution plan into a dynamic injection module; dynamically adjusting the operation parameters of the PostgreSQL based on the optimized resource allocation parameters and the resource prediction result; and in response to the plan injection instruction, selecting a required execution plan in the dynamic injection module so as to execute the optimized query task based on the adjusted operation parameters and the required execution plan.

The invention discloses a triple scene graph video understanding method fused with a time sequence perception moving window, which comprises the following five core steps of: inputting a video clip, acquiring a frame sequence through an encoder, carrying out space division on each frame and adding time embedding; executing multi-head self-attention calculation of space-time offset enhancement in each sliding window, and integrating space structure modeling and time sequence processing; constructing a cross-space-time sliding window based on continuous frames to extract a token subset; compressing the window output into a fixed-length video token (triple scene graph) through a sliding query converter; and inputting the triple scene graph of the video content and a user question into the multi-modal large model to realize semantic analysis and content understanding. According to the method, the video understanding precision is remarkably improved while the space-time relation modeling capability is enhanced, the average recall rate of behavior recognition is improved by 10%, a high-precision video understanding benchmark covering 25 object classes and 26 relation classes is constructed, the labeling error rate is lower than 3%, and the shielding problem and the long tail deviation problem are effectively solved.

The invention relates to the technical field related to pollution, in particular to an industry pollution emission contribution pre-judgment method and device based on a multi-modal model. The method comprises the following steps: collecting original data; wherein the original data comprises related data of PM2.5 (Particulate Matter 2.5); the SwinLSTM model is used, the Swin Transform multi-scale feature extraction capability and the LSTM time sequence processing capability are fused, and the original data are preprocessed to obtain training data; constructing a multi-modal optimization deep learning model composed of a convolutional neural network and a graph attention network; training and optimizing the multi-modal optimization deep learning model based on the training data; obtaining to-be-predicted target industry information; and performing prediction based on the target industry information by using the trained multi-modal optimization deep learning model to obtain pollution emission pre-judgment after the target industry is located in a certain section.

The invention discloses an MEMS inertial sensor reliability analysis method based on long and short term memory deep learning, and belongs to the technical field of microelectronic reliability evaluation, and the method comprises the steps: collecting an original output signal and environmental parameter data of an MEMS inertial sensor in a target scene, and recording a collection timestamp; then preprocessing is carried out to obtain a time sequence processing data set; performing feature extraction on the time sequence processing data set to form a multi-dimensional feature vector; carrying out correlation analysis on the multi-dimensional feature vectors, and screening out a key feature set; constructing an LSTM model based on the key feature set, and performing training and parameter optimization on the model to enable the model to capture a time sequence dependency relationship of sensor performance degradation; inputting the real-time key feature set subjected to preprocessing and feature extraction into the trained LSTM model, and outputting probability distribution of the current health state of the sensor; and then reliability evaluation is carried out and an early warning signal is output through the system terminal to prompt maintenance personnel to carry out timely intervention.

The invention discloses a power equipment inspection device fault detection and diagnosis method based on deep learning, and the method comprises the following steps: collecting multi-modal data, and generating an original data set; performing time calibration normalization on the original data set to generate a preprocessed data packet; performing anomaly detection on the preprocessed data packet, outputting an anomaly candidate region set and generating a feature slice set; executing group convolution operation on the feature slice set, and outputting structure preserving representation; performing classification and calibration on the structure preserving representation, and outputting a fault type and a confidence coefficient vector; performing risk assessment on the fault type, and outputting a risk level code; performing time sequence processing on the structure retention representation, and outputting a fault trend curve and a degradation rate; and sorting the abnormal candidate region set, archiving the risk level code, marking the fault trend curve and the degradation rate, and generating diagnosis report data. According to the invention, fault detection and diagnosis of the power equipment inspection device are realized.

The invention provides a line fault position prediction method, and belongs to the technical field of fault detection, and the method comprises the steps: carrying out the modeling of a transmission line, and carrying out the preprocessing of a feature signal; analyzing the frequency domain of the discrete signal in combination with discrete Fourier transform and fast Fourier transform; and constructing a dynamic shaping fusion model, and predicting a line fault position by using the dynamic shaping fusion model. According to the method, a dynamic shaping model is introduced, the feature extraction capability of CNN, the time sequence processing advantage of LSTM and the complex mode learning capability of ANN are combined, and an efficient algorithm framework suitable for transmission line multi-fault positioning is provided. On the basis of deep learning, the problems that an existing fault positioning method is inaccurate in fault positioning, low in actual use reliability, weak in fault signal detectability and weak in characterization capacity are solved, and accurate fault positioning of the energy transmission line is more effectively achieved.

A memory network method based on automatic addressing and recursive information integration. The method is based on a memory neural network framework integrating automatic addressing and recursive information, and is an efficient and lightweight memory network method. A memory is read and written by means of an automatic addressing operation having low time and space complexity, and memory information is effectively utilized by a novel computing unit. The whole framework has the characteristics of high efficiency, high speed and high universality. The method is suitable for various time sequence processing tasks, and shows the performance superior to that of the conventional LSTM and the previous memory network.

The invention discloses a multi-source collaborative dynamic time sequence TCP prediction method and system, a medium and a program product, and the method comprises the steps: collecting multi-source heterogeneous data, including DVH data, CT data, CPP data and longitudinal follow-up data of a plurality of time nodes, of a patient in a whole radiotherapy period; performing hierarchical preprocessing and data format unification on the multi-source heterogeneous data; constructing a dynamic time sequence feature fusion matrix containing a time dependency relationship; constructing a deep learning model integrating a time sequence processing unit and a cross-modal fusion unit, wherein the model supports an incremental learning iteration updating mechanism; using the model to output TCP prediction results of a new patient at different time nodes; and if the new patient is a special case, calling the clinical rule adaptation model, and correcting the TCP prediction result by adopting a mixed correction strategy combining rule matching and doctor experience weight. According to the method, multi-source heterogeneous data can be deeply fused, time dynamic association is captured, modal feature contribution degree is quantified, continuous iterative updating is supported, and individual differences are adapted.

The invention provides a corrosion life prediction method, system and equipment for a high-cleanliness chemical supply system pipeline and a storage medium. The corrosion life prediction method comprises the following steps: obtaining target parameters related to corrosion in a high-cleanliness chemical supply system pipeline, wherein the target parameters comprise a pipeline corrosion state parameter, a medium characteristic parameter, an operation environment parameter and historical failure data; inputting the target parameter into a pre-constructed corrosion life prediction model to enable the corrosion life prediction model to extract a potential corrosion characteristic sequence based on the target parameter, and performing time sequence processing on the potential corrosion characteristic sequence to generate a corrosion life prediction value; and grading the corrosion risk of the high-cleanliness chemical supply system pipeline according to the corrosion life prediction value. According to the technical scheme, quantitative evaluation and real-time prediction of the corrosion state of the pipeline of the high-cleanliness chemical supply system can be achieved.

A system includes a machine-learning model implemented on a data processing apparatus, which features a parallel processor with a memory hierarchy. The machine-learning model is a recurrent neural network (RNN) with a multi-head architecture, comprising multiple sub- vectors that process parallel data streams. The RNN's weight matrix is structured as a block-diagonal matrix, allowing for parallel processing of the sub-vectors. A fused computational kernel executes an entire time-series processing loop for the multi-head RNN, maintaining the weight matrix blocks in on-chip memory and performing matrix multiplications and element-wise operations for each sub-vector in a single kernel execution.

The invention relates to a leaf area index time sequence processing method, which comprises the following steps of: inputting a time sequence remote sensing image and suburb forest and economic forest classification data of the same region; calculating a normalized differential vegetation index of the research area and carrying out time sequence sorting; calculating vegetation coverage data of the research area and carrying out time sequence sorting; synthesizing to obtain monthly FVC time sequence data of the suburb forest region and the economic forest region; calculating a suburb forest leaf area index LAI in the research area and performing time sequence sorting; calculating the economic forest leaf area index LAI of the research area and performing time sequence sorting; performing seasonal decomposition on the suburb forest time sequence data after adaptive filtering; performing seasonal decomposition on the economic forest time series data after adaptive filtering; merging the suburb forest time sequence data and the economic forest time sequence data; and outputting to obtain final optimized data. The invention further relates to a leaf area index time sequence processing system. According to the invention, a purification LAI time sequence product which clearly represents long-term trend, mutation and gradual change signals of respective vegetation canopy structures can be output.