14 results about "Time series representation" patented technology

The invention discloses an AI modeling data cleaning and obtaining method and system based on a probability strategy, and relates to the technical field of industrial time series data processing and artificial intelligence modeling, and the method comprises the steps: obtaining target time series data; constructing a time sequence statistical model containing a trend term; performing parameter estimation on the time sequence statistical model based on a probability strategy, and calculating a probability weight corresponding to each data point; performing weighted modeling on the time sequence statistical model to obtain cleaned time sequence representation; based on the cleaned time sequence representation, trend characteristic parameters reflecting the system operation state corresponding to the target time sequence data are extracted; according to the trend characteristic parameters, the dynamic state and the steady state of the target time sequence data are judged, cleaned dynamic data and cleaned steady data are obtained, and classified acquisition of the dynamic data and the steady data of the cleaned data is achieved. The precision, efficiency and adaptability of data processing can be improved.

The invention relates to a time sequence anomaly detection method based on dynamic memory perception. The method comprises the following steps: (1) carrying out data preprocessing on a photovoltaic power generation multivariate time sequence; (2) obtaining the preprocessed standardized multivariate time sequence data from the step (1); (3) obtaining a multivariate time sequence expression Se embedded in the step (2); a U-Net model is constructed, the U-Net model is processed by an encoder-decoder based on Transform and a dynamic adaptive memory module, and an original multivariate time sequence S is reconstructed. (4) constructing a photovoltaic power generation anomaly detection model based on dynamic memory perception, and training the model by using a photovoltaic power generation multivariate time sequence; and (5) evaluating the performance of the model based on three evaluation indexes including the accuracy rate, the recall rate and the F1 score. According to the scheme, the accuracy of photovoltaic power generation anomaly detection is improved.

The application discloses a kind of based on equipment operation memory curve Building mechanical and electrical abnormality prediction method and system, comprising: first, collect and preprocess equipment historical multi-source operation data, utilize time series representation learning to generate the benchmark memory curve of representation health state.Succeed in constructing space-time memory network model, learn the prototype of health operation mode by comparison learning strategy training model and form semantic representation space.Furthermore, the real-time operation data sequence is input into the model, and the deviation degree of the real-time operation data sequence and the health mode in the semantic representation space is calculated.Based on dynamic threshold and multi-scale prediction framework, the abnormal state, predictive warning level and confidence of the equipment are output according to the deviation degree.Finally, the benchmark memory curve and model memory bank are dynamically updated through online learning mechanism, and the interpretable abnormal report is generated by using causal reasoning model.The application realizes early and accurate prediction and root cause tracing of equipment abnormality, and improves the intelligent level of building mechanical and electrical system operation and maintenance.

The application belongs to the technical field of Internet, and particularly relates to an industrial Internet cross-domain metadata trusted fusion method and system, comprising the following steps: collecting structured metadata, time series metadata and relationship metadata of each industrial domain node, and constructing an intra-domain metadata graph based on field attributes and call relationships; performing semantic coding and time series coding on the intra-domain metadata graph, obtaining corresponding semantic representations and time series representations, and calculating inter-field consistency; identifying stable field clusters, drift field clusters and abnormal nodes according to the inter-field consistency in each industrial domain node; and performing segmented aggregation on the stable field clusters and the drift field clusters by each industrial domain node to generate anchor field summaries and drift trajectory summaries. The application combines metadata graph coding, field clustering and federal dynamic weighting, and realizes industrial cross-domain metadata private trusted fusion through abnormal compensation correction.

Software for the analysis of accumulation phenomena (1A) in an environment to be monitored (10); the software for the analysis of accumulation phenomena (1A) being storable in and executable by electronic processing resources (2), and designed to cause, when executed, these electronic processing resources (2) to become configured to receive a time sequence of representations of the environment to be monitored (10); where each representation of this time sequence represents the environment to be monitored (10) at a different time instant. In addition, the software for the analysis of accumulation phenomena (1A) is designed to cause, when executed, these electronic processing resources (2) to become configured to implement a segmentation model (6) configured to locate, or segment, one or more target areas occupied by one or more substances deposited on, or covering, a surface locatable in an input representation. The software for the analysis of accumulation phenomena (1A) is designed to cause, when executed, these electronic processing resources (2) to become configured to compute (block 8) an evolutionary scenario of a deposition, accumulation, or coverage phenomenon, based on the time sequence of representations of the environment to be monitored (10) and based on the segmentation model (6) implemented. The evolutionary scenario of the phenomenon is a time sequence of representations, of different future time instants compared to the current time instant, representing one or more substances deposited on, or covering, a surface present in an environment to be monitored (10). In addition, the accumulation phenomena analysis software (1A) is designed so that, when executed, these electronic processing resources (2) become configured to provide an output (9) based on this evolutionary scenario of the phenomenon.

The application discloses a kind of multi-element coupling under coastal area estuary water level prediction method, comprising: the time series representation of short-term dynamics and long-term rhythm is established, and spatial hydrological dependence information transmission mechanism is established by time series representation, to output estuary response prediction;The application models the spatial topology and physical coupling relationship between monitoring stations through "heterogeneous correlation body network structure", realizes the multi-level hydrological dependence expression from precipitation driving to water level response.This mechanism significantly improves the explainability and propagation accuracy of spatial characteristics, enabling the model to truly reproduce the transmission and diffusion behavior of hydrological quantities in natural basins.The proposed "periodic spectrum mapping mechanism" projects precipitation and water level time series from time domain to frequency spectrum space, and through principal frequency energy decomposition and feature reconstruction, realizes the extraction of periodic hydrological law.

The application belongs to the technical field of artificial intelligence and high-performance computing, and specifically discloses a financial time sequence representation method fusing dynamic sparse topology and parallel computing power optimization. Through the application, a multi-dimensional pattern capturer is used to identify typical market nonlinear states; an asymmetric dynamic sparse feature fusion unit based on content perception is used to generate self time sequence dependent feature tensors, and high-order hidden layer interconnection representation tensors are generated in combination with coarse-grained correlation feature tensors intercepted; a multi-objective decoding based model is used to generate financial sequence prediction results of target assets. Through the above method, the multi-dimensional pattern capturer is introduced, the feature tensors are strictly aggregated, the endogenous sparse self-attention is used to construct fine-grained time sequence dependence, the exogenous sparse cross-attention is used to perform coarse-grained feature dimension reduction and key information filtering, and the joint optimization model trained by the multi-objective joint optimization functional is introduced for prediction, so that the accuracy of representing financial time sequences can be effectively improved.

The invention discloses a space-time fusion photovoltaic power generation prediction method, system and device and a medium, and relates to the technical field of power systems, the method comprises the following steps: carrying out space-time and time conversion on obtained photovoltaic power generation data, generating a fusion embedding representation, and carrying out block processing to obtain a block embedding representation; inputting the block embedding representation into a space-time attention layer for transformation to obtain a target representation vector of each node, and outputting an initial power generation prediction result; calculating the Euclidean distance between each target node and each class cluster center represented by the fusion space-time embedding and carrying out dynamic tuning to determine the class cluster center corresponding to each target node; aggregating all nodes in each cluster center to obtain a class cluster time sequence representation; in cooperation with the class cluster time sequence representation and the time sequence representation vector, distributing weights for the prediction experts so as to output a target power generation prediction result; the optimal power generation plan of the target power system is determined according to the target power generation prediction result, and the scheduling reliability is improved.

The application discloses a kind of crop precision irrigation method and system based on algorithm fusion, it is related to intelligent agriculture and precision irrigation control technical field, method includes: the time series data of meteorology, soil, crop physiology and irrigation process are collected, carry out exception elimination, missing completion and standardization processing and construct derivative feature;The historical characteristic sequence is input to the bidirectional time series network with attention to obtain time series representation, input neural fuzzy inference network to output future water demand prediction value and corresponding prediction interval, obtain confidence from prediction interval;Water demand prediction value, confidence and crop stress are input to the first layer fuzzy inference to obtain basic irrigation duration;Soil water potential and post-irrigation feedback are input to the second layer fuzzy inference to obtain valve fine tuning amount;Fusion generates irrigation instruction and drives execution equipment.By outputting water demand prediction interval and constructing confidence, the prediction reliability is improved, and by double-layer fuzzy inference, soil water potential and post-irrigation feedback are combined to realize executable and safety protection of irrigation instruction.

The application discloses a time series data prediction method and device, equipment and a storage medium. The method comprises the following steps: obtaining a time series to be processed; the time series to be processed is used for representing running data of a to-be-predicted object at a plurality of detection times; performing feature extraction processing on the time series to be processed through a prediction model to obtain local feature data, global feature data and fusion feature data of the time series to be processed; performing feature coding processing on the local feature data, the global feature data and the fusion feature data by using the prediction model to obtain a coding result, and performing classification prediction on the coding result to obtain a prediction result of the to-be-predicted object. The scheme can capture short-range periodic information and long-range periodic information of the time series to be processed in a more fine-grained manner, thereby combining more comprehensive time series representation information to perform classification prediction on a downstream task, and greatly improving the prediction accuracy.

The invention provides a cross-modal fusion time sequence prediction method, device and equipment based on a large language model, and relates to the technical field of time sequence prediction. The method comprises the following steps: acquiring numerical time sequence data in a historical time window and associated text information; respectively processing the numerical time sequence data and the text information through parallel processing modal preprocessing branches to obtain a time sequence feature representation, a semantic reasoning prediction result generated by a large language model and a text semantic feature representation; performing hierarchical interactive fusion on the time sequence feature representation, the semantic reasoning prediction result and the text semantic feature representation through a multi-stage cross-modal attention fusion network to obtain enhanced time sequence representation; generating an adaptive fusion weight for each time step in the prediction time window based on the enhanced time sequence representation; and carrying out weighted fusion on a numerical prediction result generated based on the time sequence feature representation and a semantic reasoning prediction result by using the adaptive fusion weight to obtain a final time sequence prediction result.

The invention relates to the technical field of computers, can be applied to the fields of medical health, financial science and technology and the like, and discloses a cloud native system fault analysis method, device and equipment and a storage medium, and the method comprises the steps: obtaining multi-modal observation data of a plurality of service entities in a cloud native system; performing feature fusion on the multi-modal observation data through a multi-modal data fusion tool to generate time sequence representation; based on the time sequence representation, a root cause entity causing the fault is positioned through a root cause positioning tool; based on the root cause entity, determining a fault type of the root cause entity through a fault classification tool; integrating the root cause entity, the fault type and pre-stored system context information into a text prompt according to a preset format; and inputting the text prompt into a big language model expert agent, and generating a root cause analysis report. By means of the method, the data size input into the large language model is reduced, the analysis accuracy is improved, reasoning illusion is effectively avoided, and the reliability of the positioning result is guaranteed.

The invention discloses a user behavior anomaly detection method. The method comprises the steps of obtaining behavior counting sequences of a target user under multiple time granularities to form multivariable time sequence input data; learning time correlation in the TFT model to obtain time sequence representation, starting Bayesian inference to carry out multiple forward inference to obtain a prediction mean value and a prediction standard deviation at the next moment, and calculating a prediction confidence interval; a real behavior count of a target user at a current moment is obtained, a current prediction error is calculated through a TFT model deployed at an edge side, a standardized deviation index Z-score is calculated based on a prediction standard deviation, a cumulative offset is constructed based on the prediction error, a CUSUM statistical magnitude is calculated, the Z-score and the CUSUM statistical magnitude are fused to obtain a combined abnormal variable, and the combined abnormal variable is calculated. And comparing with two risk thresholds, and judging three risk levels of normal transmission, probe exposure and direct interception. According to the method, millisecond-level prediction of user behaviors is realized, abnormity is dynamically judged, and abnormal user behaviors are intercepted before bidding.

The invention discloses a high-dimensional hierarchical time sequence modeling method for predicting the residual life of industrial rotating equipment, which comprises the following steps of: constructing global representation based on information bottleneck constraint by using an information bottleneck theory, and fusing and mapping multi-source time sequence data to a low-dimensional feature space so as to capture low-dimensional global representation with rich information; constructing a block interaction mechanism to extract local features and perform hierarchical feature fusion to realize global-local collaborative enhancement representation fusion; time sequence representation based on manifold learning non-diagonal covariance priori constraint is constructed, and time sequence degradation features are embedded in global representation. The method solves the key problem that the existing method is difficult to capture the degradation trend in the residual service life prediction task under the high-dimensional data challenge, obviously reduces the residual service life prediction error and parameter quantity under the high-dimensional multi-source data, and provides accurate and lightweight technical support for the application of the equipment residual service life prediction.