23 results about "Normalized Time" patented technology

The invention discloses a deep learning-based safety state evaluation method for a concrete-filled steel tube node. The method comprises the following steps of: 1, constructing a comprehensive safety evaluation index of the concrete-filled steel tube node; 2, collecting dynamic performance data of the nodes, and performing denoising, normalization and feature extraction processing to form multi-dimensional normalized time sequence data; 3, calculating the weight of each index, and optimizing weight distribution; 4, inputting the multi-dimensional normalized time sequence data into a deep learning model, carrying out model training, verification and optimization in combination with optimized weight distribution, and outputting a preliminary comprehensive safety score and a long-term performance prediction result of the node; and 5, further comprehensively evaluating the safety state of the node, determining a final comprehensive safety score, and grading to formulate corresponding grading maintenance suggestions. And 6, dynamically predicting the future performance change trend of the node, and quantifying the fatigue life, the crack growth rate and the material degradation condition of the node. The method is suitable for dynamic safety evaluation and long-term performance prediction of the concrete-filled steel tube node.

The invention belongs to the technical field of satellite orbit prediction, and provides a low-orbit satellite orbit state prediction method based on artificial intelligence, which comprises the following steps of: dividing step lengths of a predicted orbit according to a time window, calculating a resultant acceleration of each step length, performing stress input on an initial position and speed, and obtaining a nominal orbit state sequence; forming an augmented state vector with aerodynamic synthesis parameters; inputting a time sequence neural network model, and performing rolling updating on the augmented state according to the output; obtaining an original standard deviation through mapping by using uncertainty measurement, calculating a standardized residual error, solving a right side weighted quantile according to a normalized time weight, comparing the right side weighted quantile with a preset threshold value, and carrying out adaptive deviation adjustment; and calculating risk measurement by using the acquired data, comparing the risk measurement with a set threshold value and a priority rule, automatically executing a judgment action on the risk measurement, recording a trigger reason and the judgment action, and updating parameters in the time sequence neural network model.

The invention relates to the technical field of multivariable time series data prediction, in particular to a heat supply data prediction method, system and device for adaptive hysteresis relation modeling. The method comprises the following specific steps: carrying out data preprocessing on an industrial data set to obtain a normalized time sequence, and segmenting the normalized time sequence to obtain a true value of time sequence data; lagging relation calculation is carried out on the true value of the time series data to obtain lagging relation representation; performing auxiliary prediction on the lagging relationship representation to obtain a predicted value of the time series data; according to the true value of the time series data and the predicted value of the time series data, calculating hysteresis relation loss and a mean square error, and according to the hysteresis relation loss and the mean square error, performing joint training to obtain a heat supply data prediction model; and inputting the to-be-predicted data into the heat supply data prediction model to obtain a heat supply data prediction result. According to the method, the problems of instability and low robustness of heat supply system data prediction are solved, and the adaptability and robustness of real-time heat supply system data prediction are improved.

The invention provides a motor defect acoustic feature enhancement and visualization method and system based on normal sample spectrum normalization, and a storage medium. The method comprises the following steps: step 1, establishing a standard qualified sample database and calculating a normalization vector; the method comprises the following steps: collecting audio signals of qualified motors of the same model when running under a standard working condition, and performing statistical analysis after Fourier transform to obtain a frequency domain intensity normalized vector; 2, processing a to-be-measured motor signal and generating a normalized time-frequency diagram; the method comprises the following steps: acquiring an audio signal of a to-be-detected motor with the same model during operation under a standard working condition, carrying out Fourier transform processing to obtain an original time-frequency diagram, and then carrying out spectrum normalization processing to generate a one-dimensional frequency domain intensity normalization vector; and step 3, generating a visual time-frequency grey-scale map and a color map. The method has the beneficial effects that the convenience and accuracy of labeling work are improved, better-quality input data are provided for a subsequent AI model, and the development efficiency and universality of a detection scheme are remarkably improved.

The invention belongs to the technical field of information safety and signal processing, and particularly relates to a time sequence dislocation analysis-based multi-mode anomaly detection method and system, and the method comprises the steps: synchronously collecting a multi-mode time sequence signal, standardizing a timestamp, and detecting or injecting a time sequence anchor point event. And setting a multi-time scale window, calculating time sequence dislocation feature vectors between modes by taking the anchor point as a reference, aggregating to form a multi-scale feature set, and generating a comprehensive consistency score. And calculating a stability evaluation score based on the historical feature vector sequence, and constructing a time sequence dislocation manifold model by using normal samples through manifold learning. And mapping the multi-scale feature vector to a model embedding space, calculating a distance with a statistical boundary, fusing the multi-scale distance, the comprehensive consistency score and the stability score, and judging abnormity according to a preset rule. Normal time sequence fingerprints of each device and scene combination can be self-learned, so that inherent normal offset and abnormal offset can be distinguished, and content camouflage and misleading are not easily caused.

The invention discloses a multi-bridge monitoring data anomaly identification method and system based on multi-modal feature fusion, and relates to the field of data anomaly identification, and the method comprises the following steps: 1, collecting SHM time series data of a plurality of bridges, and carrying out the normalization processing and segmentation; 2, visualizing the normalized time series data, extracting statistical characteristics in the data, and manually labeling the images and the statistical characteristics to construct a data set; 3, establishing a multi-modal convolutional neural network model based on the input of the grayscale images and the statistical features, and inputting the grayscale images and the corresponding statistical features into the network model at the same time for training by using a data set from a plurality of bridges, and 4, performing anomaly recognition on the SHM data of the target bridge by using the trained convolutional neural network model. According to the invention, automatic identification of the bridge SHM data abnormity is realized, the identification precision of the abnormal data is improved under the condition that the types of the bridge monitoring data set are unbalanced and the data volume is limited, and a basis is provided for evaluation and early warning of the bridge operation state.

The invention provides a kinematics equation linearization method of normalized time, which converts a time variable under an absolute scale into a relative scale, thereby facilitating derivation and numerical calculation of an optimal control problem. Converting free time into time with boundary conditions by using normalized time, and converting time serving as a differential term in the motion equation into a multiplication term, so as to realize transformation of the motion equation; and according to the motion equation after time normalization, the time after normalization is optimized, and equivalent solution of an optimal control problem is realized. Equivalent solution of an original optimal control problem is realized, and derivation and numerical calculation of the optimal control problem are facilitated.

The invention provides a multi-dimensional feature extraction and attention fusion edge resource calculation method and device, and the method comprises the steps: obtaining current scheduling data and time sequence data under a current historical time window when a current scheduling moment arrives; performing data preprocessing on the current scheduling data and the time sequence data under the current historical time window to obtain normalized time sequence data and scheduling data tensor; inputting the normalized time sequence data and the scheduling data tensor into a resource scheduling model, so that the resource scheduling model outputs a resource state prediction result of each host at the next moment and a task scheduling scheme at the next moment according to the normalized time sequence data and the scheduling data tensor; and updating a resource scheduling strategy according to the resource state prediction result of each host at the next moment and the task scheduling scheme at the next moment. According to the scheme of the invention, efficient fault prediction and fault-tolerant scheduling optimization are realized, and an efficient and adaptive solution is provided for dynamic fault tolerance in an edge computing environment.

The invention relates to the technical field of image processing, in particular to a compensation dispersion transfer entropy-based causal analysis method, and aims to accurately capture an instantaneous causal relationship and improve the accuracy of a constructed brain network. The method comprises the following steps: acquiring an original time sequence, and normalizing the original time sequence by adopting a normal cumulative distribution function to obtain a normalized time sequence; and performing dispersion processing on the normalized time sequence, and endowing each normalized data point with an integer in a preset integer range through a linear algorithm to obtain a dispersion time sequence. And performing information transmission quantitative analysis on the dispersive time sequence based on the information entropy, and evaluating the uncertainty degree of information flow transmission from the source system to the target system and the uncertainty degree of information flow transmission from the source system to the target system under the constraint of a preset condition. And introducing a compensation mechanism to optimize the uncertainty evaluation result.

The application discloses a multi-bridge monitoring data anomaly identification method and system based on multi-modal feature fusion, relates to the field of data anomaly identification, and comprises the following steps: one, collecting SHM time series data of multiple bridges, performing normalization processing and segmentation; two, visualizing the normalized time series data, simultaneously extracting statistical features in the data, and manually marking labels for the image and the statistical features to construct a data set; three, establishing a multi-modal convolutional neural network model based on the input of the gray image and the statistical features, using the data set from the multiple bridges, simultaneously inputting the gray image and the corresponding statistical features into the network model for training, four, using the trained convolutional neural network model to perform anomaly identification on target bridge SHM data. The application realizes automatic identification of bridge SHM data anomalies, improves the identification precision of abnormal data under the condition that the categories of the bridge monitoring data set are unbalanced and the data volume is limited, and provides a basis for bridge operation state evaluation and early warning.

The invention discloses a fire-fighting system alarm method based on multi-sensor coupling data support utility index prediction, and relates to the technical field of fire-fighting system alarm, and the method comprises the steps: synchronously collecting real-time measurement data, and forming respective time sequences; carrying out normalization processing on various measurement data by adopting a minimum-maximum normalization method to obtain a normalized time sequence; an autoregressive integrated moving average model algorithm is adopted for the normalized time sequence, and normalized values of various parameters at the next moment in the future are predicted; and calculating a multi-sensor coupling data support utility index according to the predicted normalized values of various parameters at the next moment in the future, evaluating the coupling utility of the sensors, and triggering a fire behavior judgment strategy. According to the method, comprehensive identification of early characteristics of the fire is realized, so that the system can process abnormal data in a complex environment more stably, technical support is provided for upgrading of an intelligent fire-fighting system, and the method has engineering application value and popularization significance.

Embodiments of the present invention relate to the field of change detection technology, and specifically disclose a method for change detection of an anti-hallucination multi-modal large model of an autonomous land management agent. Embodiments of the present invention receive remote sensing images at two different time points, capture the differences between the two remote sensing images to generate a normalized time difference map, import it into a preset large language model for processing to generate a rough change map, construct an iterative input based on the normalized time difference map, text markers, and the previous calibration mask, perform iterative update and convergence processing to obtain a change detection map, receive multiple change detection tasks, generate multiple inference paths, and perform prediction aggregation to generate a final prediction. It can improve the generalization ability of the model in complex and dynamic environments, gradually optimize the detection results, significantly reduce the hallucination problem, and improve the accuracy and reliability of change detection, ensuring the robustness and consistency of the detection results, and making it applicable to complex and variable remote sensing scenarios.

The invention provides a turnout zone wheel-rail coupling dynamic response analysis method, device and equipment and a medium, and relates to the technical field of rail traffic wheel rails. Defining a time sequence, and sampling the orbit parameters on the defined time sequence to obtain time sequence data reflecting each orbit parameter; performing normalization processing on the time series data of each track parameter, analyzing the normalized time series data based on a random process theory, and extracting statistical characteristics of the time series data; performing standardization processing on the statistical characteristics of the time series data to obtain an overall sampling sequence; numerical simulation analysis is conducted on the overall sampling sequence, simulation results of spatial dynamic changes of the wheel-rail contact relation at different time points are obtained, the spatial dynamic changes of the wheel-rail contact relation are predicted through numerical simulation, and technical support is provided for design, maintenance and management of the railway turnout area.

This invention belongs to the field of satellite orbit prediction technology and provides an artificial intelligence-based method for predicting the orbital state of low-Earth orbit satellites. The method includes: dividing the predicted orbit into time windows, calculating the composite acceleration for each step, inputting the initial position and velocity with forces to obtain a nominal orbital state sequence, and constructing an augmented state vector with aerodynamic composite parameters; inputting the vector into a time-series neural network model, and updating the augmented state based on the output; obtaining the original standard deviation through mapping using an uncertainty metric, calculating the standardized residual, obtaining the right-hand weighted quantile value according to normalized time weights, comparing it with a preset threshold, and performing adaptive bias adjustment; using the acquired data to calculate a risk metric, comparing it with a set threshold and priority rules, automatically performing a judgment action on the risk metric, recording the triggering cause and judgment action, and updating the parameters in the time-series neural network model.

The invention discloses a multi-scale adaptive time sequence prediction method and system based on multi-expert cooperation, and belongs to the technical field of time sequence data analysis. The method comprises the following steps: firstly, dividing a time sequence data sample into a plurality of normalized time sequence blocks with equal length; secondly, obtaining a time sequence prediction result for the normalized time sequence block by using a hybrid multi-expert self-adaptive predictor; and finally, reconstructing the hybrid multi-expert adaptive predictor as an auto-regression iterative residual network architecture, so as to extract complementary multi-scale information in the time sequence representation to improve the time sequence prediction performance. The method is realized through a multi-scale adaptive time sequence prediction system, and comprises a normalized time sequence partitioning module, a mixed multi-expert adaptive prediction module and an iterative residual error autoregression learning module. According to the method, the limitation of a fixed-length prediction head can be broken through through iterative residual error autoregression learning, the problem that a shared linear prediction layer is insufficient in capturing a heterogeneous time sequence mode is solved through hybrid expert prediction, and a high-precision and high-flexibility cold load adaptive time sequence prediction solution is provided for realizing intelligent environmental control and energy-saving operation.

The invention relates to an intelligent monitoring system based on heat flow multi-dimensional characteristics and a cross-scene application method, and belongs to the technical field of intelligent sensing and monitoring. The system comprises a heat flow detection unit which adopts a distributed sensor array to collect multi-point heat flow data in a contact or radiation mode; the intelligent heat flow transmitter converts a weak signal into a high-reliability digital or analog signal through an integrated filtering circuit, a signal following circuit and a differential amplification circuit; and the dynamic risk assessment module is used for calculating normalized time domain dynamic characteristics, steady state characteristics and spatial distribution characteristics in real time, fusing weights of the three characteristics based on a risk index model and outputting a comprehensive risk index. The technical effects of the invention lie in that reliable transmission and fidelity of heat flow signals are realized, multi-dimensional feature values are deeply mined, and the cross-scene adaptive regulation and control capability is improved.

The invention relates to an analytical machine arrangement (100). A method comprises obtaining (A10) N time domain measurement samples at a time T (n), where n traverses the number of samples N, and the distance between adjacent samples in the time domain is T. The method further comprises determining (A12) updated, normalized time-domain measurement samples having a timing Tadapt (n) for each sample based on the associated T (n) and T adjustment, further based on an actual frequency factual (n) of the machine arrangement (100), and further based on an undesired behavior and / or error-related frequency F. The method further comprises determining (A12) the updated, normalized time-domain measurement samples having the timing Tadapt (n) for each sample based on the associated T (n) and T adjustment, further based on the actual frequency factual (n) of the machine arrangement (100). The method further comprises determining (A14) M reconstructed, normalized time domain measurement samples having a constant sampling period T2 at a time T (m), where m traverses the number M of samples. Furthermore, the method comprises performing (A16) a frequency analysis on the reconstructed time-domain measurement samples.

The present invention relates to the field of atmospheric pollutant prediction technology, and specifically discloses a PM2.5 prediction method and system based on spatiotemporal features. The method comprises: obtaining atmospheric pollutant data and meteorological data from a target site and surrounding sites; processing the data from the surrounding sites and then concatenating it with the data from the target site to obtain data to be analyzed; normalizing the data to be analyzed, decomposing the normalized time series data into trend features and seasonal features, respectively subjecting them to feature enhancement mapping and linear prediction, and adding them together to obtain a temporal prediction result; constructing a graph structure based on the normalized time series data using a mutual information method, and obtaining a spatial prediction result through a graph attention neural network; and dynamically fusing the two prediction results through a gating network to obtain a predicted value of PM2.5 concentration at the target site. The present invention significantly improves the accuracy of PM2.5 prediction tasks at the hourly level.

This application discloses a statistical method for the delivery timeliness of a remote sensing satellite ground system. The method includes: identifying each stage of the ground system delivery process and constructing a ground system delivery timeliness model. The ground system delivery timeliness model characterizes the delivery time value of the ground system, which is obtained by weighting the time consumption and stability coefficient of each stage. A time sequence is obtained based on the recorded time values ​​for processing each task at each stage, and the time sequence for each stage is normalized to obtain a normalized time sequence. The time consumption and stability coefficient of each stage are calculated based on the normalized time sequence, and the time consumption and stability coefficient of each stage are substituted into the ground system delivery timeliness model to calculate the delivery time value of the ground system. This application solves the technical problem of the lack of a statistical scheme for ground system delivery timeliness capability in the prior art.

The invention provides a power system load prediction method and device based on multi-scale time-frequency domain analysis, and relates to the technical field of power system data processing. The method comprises the steps of obtaining a historical time sequence of a power system load; performing normalization processing on the historical time sequence to obtain normalized time sequence data; extracting periodic features and non-periodic features of a multi-time-scale time-frequency domain in the normalized time series data; for each time scale in the multiple time scales, predicting a prediction sequence of the time scale according to the periodic feature and the non-periodic feature of the time-frequency domain of the time scale; and carrying out adaptive integration on the multi-time scale prediction sequence, and carrying out inverse normalization processing to obtain a power system load prediction result. According to the embodiment of the invention, high-precision prediction is realized by cooperatively extracting periodic features and non-periodic features of different time scales (such as a day period and a week period) in the time sequence data.

This application provides a time-sensitive diffusion model weight calibration method and content generation method, including: calculating the sensitivity index of each layer of the diffusion model at different time steps using several calibration data; normalizing the sensitivity of all time steps to obtain a normalized time-series importance score; modeling the weight quantization problem as a weighted least squares problem based on the normalized time-series importance score; and solving the weighted least squares problem to obtain the calibrated quantized weights. This application employs time-series importance assessment and normalization techniques, enabling accurate positioning and weighting of key stages in the generated trajectory, solving the problem in traditional mean calibration methods where the fitting requirements of key time steps are diluted by non-key time steps. The quantized diffusion model calibrated using this method, when deployed on resource-constrained devices, can ensure the generation of semantically accurate and structurally stable virtual scenes at key stages, effectively improving the reliability and real-time performance of the device.

Techniques for detecting impactful performance anomalies in storage systems. The techniques include obtaining, for each performance metric of a storage system's workload, a training set of series diffs based on a threshold. Each diff represents a difference between an observed value from an observed set of time series values for the performance metric and a normalized value from a corresponding set of normalized time series values. The techniques include applying the training set of series diffs for each performance metric to an unsupervised anomaly detection algorithm and running the algorithm to identify potentially impactful anomalies in a multi-dimensional search space. The techniques include identifying impactful anomalies from among the potentially impactful anomalies that exceed an anomaly score. In this way, impactful anomalies having a causal effect on multiple performance metrics of the storage system's workload can be identified in a manner less complex and less costly than prior multivariate approaches.

The present application belongs to the field of information security and signal processing technology, and particularly relates to a multi-modal anomaly detection method and system based on time sequence misplacement analysis. The method synchronously collects multi-modal time sequence signals and standardizes time stamps, detects or injects time sequence anchor point events. Multi-time scale windows are set, time sequence misplacement feature vectors between modes are calculated based on the anchor points, multi-scale feature sets are aggregated to generate comprehensive consistency scores. Stability evaluation scores are calculated based on historical feature vector sequences, and a time sequence misplacement manifold model is constructed through manifold learning using normal samples. Multi-scale feature vectors are mapped to model embedding space and the distance from the statistical boundary is calculated, multi-scale distances, comprehensive consistency scores and stability scores are fused, and anomalies are determined according to preset rules. The normal time sequence fingerprint of each device and scene combination can be learned, so that inherent normal deviation and abnormal deviation can be distinguished, and content camouflage is not easy to mislead.