58 results about "Time series generation" patented technology

The invention discloses a multi-elevator linkage dynamic response intelligent scheduling system and a scheduling method thereof. The scheduling method comprises the following steps: acquiring an operation rule of an elevator, constructing a historical operation database and updating the historical operation database in real time; capturing a field state in real time through a monitoring system, and generating a real-time data stream according to a time sequence; a digital twinborn model is constructed through a historical database and real-time data flow, and the elevator calling probability and the congestion risk of each floor are predicted; the elevator calling probability of each floor in the next five minutes is predicted based on a digital twinborn model, the congestion risk is calculated, a real-time demand thermodynamic diagram fusing the space-time sequence dimension is generated, and intelligent scheduling is conducted on the elevator through the thermodynamic diagram; by reducing the waiting time, the overall operation efficiency is improved, the user satisfaction is enhanced, and smoother vertical traffic experience is provided for high-rise buildings.

The invention provides a zero sample time sequence prediction method and system based on an adversarial neural network, and the method comprises the steps: constructing a time sequence generation module, training the generation module to stable convergence through obtained source domain data, and inputting collected random noise to generate a pseudo target sequence; constructing a time sequence prediction module, taking the pseudo sample sequence as a training set of the prediction module, and inputting the pseudo sample sequence into the prediction module for decomposition prediction; comparing the synthetic sequence with the output of the prediction module to obtain a prediction error, and feeding back the prediction error to the generation module to update generator parameters; through multiple rounds of generation-prediction-feedback closed-loop training, the obtained zero-sample linear architecture prediction model can effectively predict a corresponding future time sequence trend based on a synthetic pseudo sample under the condition that a target domain has no historical data completely. According to the method, data can be generated and self-training can be completed under the condition of no real data input, so that the quality of the generated data and the performance of the prediction model are synchronously improved.

The invention discloses a water conservancy project intelligent inspection system based on RFID, and relates to the technical field of intelligent inspection. According to the method, the time envelope atlas can be generated based on the RFID reading time sequence, so that accurate identification of the interruption position of the inspection path is realized; on the basis, a disturbance vector track is formed through multi-dimensional sensing parameter modeling, signal abnormity caused by environment shielding is effectively discriminated, and a data basis is provided for subsequent path correction; furthermore, by constructing a physical and logic dual index matrix, the dynamic number adjustment of the RFID nodes in the shielding section is realized, and the problems of node redundancy and number dislocation caused by the stable abnormality of the identification frequency are solved. And finally, on the basis of the path envelope extension direction and the conflict coefficient, constructing a path affiliation strategy, and effectively solving the cross conflict problem of the plurality of inspection paths in the shielded section.

The invention provides an industrial time sequence generation method based on a time sequence decomposition conditional diffusion model. The method comprises the steps that industrial time sequence data are collected and preprocessed to obtain a data set; introducing conditional variables to construct a conditional diffusion model, and injecting noise through forward diffusion; designing a time sequence decomposition and reconstruction UNet module, performing feature extraction on noisy data and conditional variables to obtain an intermediate state, performing time sequence decomposition on the intermediate state, and performing feature reconstruction by using a decoder; the Sinkhorn distance is used as a regularization term to be fused into conditional noise prediction loss to construct a loss function for training; and randomly generating pure Gaussian noise, inputting the pure Gaussian noise into the trained TDA-CDM model, obtaining predicted noise of the current time step, calculating noisy data of the next time step, performing cyclic operation until a time sequence without noise is obtained, and accelerating sampling by using a denoising diffusion implicit model in circulation. According to the method, coexisting multi-scale dynamic features in the complex industrial MTS can be carefully and effectively captured and restored, and the comprehensive quality of generated data is remarkably improved.

The invention discloses a time sequence generation method and system based on uncertainty quantization and frequency domain constraint, and belongs to the technical field of artificial intelligence and data mining. Outputting a mean value of the completion value and a variance representing the uncertainty of the completion result; an entropy weight adaptive mask generation mechanism is constructed, a soft mask matrix of continuous values is generated according to variance and local observation density, and high uncertainty corresponds to low mask weight; constructing a mixed loss function containing time domain weighted loss and frequency domain consistency loss; and training a diffusion model based on the soft mask matrix and a mixed loss function to generate a regularized high-fidelity time sequence. According to the method, the Bayesian uncertainty quantization and soft mask mechanism is introduced, so that the problems that a binary mask mechanism in the prior art is rigid and cannot sense completion errors are solved, and meanwhile, the periodic characteristics of generated data are ensured in combination with frequency domain constraints.

The invention discloses a riverway water quality prediction method based on generative data enhancement, and the method comprises the steps: (1) arranging automatic water quality monitoring stations on each monitoring section of a riverway, collecting multi-site and multi-index historical water quality data, combining with meteorological driving factors, sorting according to a time sequence, preprocessing, constructing a standardized multi-dimensional sequence, and obtaining an actual measurement sample; (2) establishing a water quality time sequence generation model based on a conditional variation auto-encoder, and generating a virtual sample with time-space consistency; (3) physical constraint and distribution consistency screening are applied to the virtual samples generated in the step (2), unreasonable data are removed, and high-quality virtual samples are obtained; (4) combining the high-quality virtual sample and the actual measurement sample to form an enhanced data set, and re-dividing the enhanced data set into a training set, a verification set and a test set; and (5) training a water quality prediction model based on the enhanced data set, and outputting a future multi-time-interval water quality index prediction result.

The invention provides an offshore energy island power toughness enhancing method based on data screening and ensemble learning. The method focuses on the problem of offshore energy island power supporting capacity under extreme conditions. The method comprises the following steps: firstly, constructing a redundancy suppression feature screening module, and screening out a feature combination which is highly related to wind-solar power and is low in redundancy based on a conditional mutual information and local dynamic correlation joint criterion; secondly, constructing a time sequence adversarial enhancement module, generating an adversarial network through an improved time sequence, and realizing small sample enhancement of power data under an extreme condition; and finally, constructing an integrated learning architecture which takes a convolutional neural network, a bidirectional long-short-term memory network and a gradient boosting decision tree as base learners and a lightweight gradient elevator as a meta learner, and enhancing the dynamic response and risk resistance of the system under extreme conditions by combining a multi-source joint compensation mechanism driven by a power gap. According to the method, accurate prediction, rapid compensation and toughness optimization scheduling of the power of the offshore energy island in an extreme environment are realized, and the power supply stability and economy of the system are remarkably improved.

A system for Time Series Generation (TSG) model selection. The system is configured to perform a method including: receiving a user prompt, the user prompt comprising a user input and a user-provided TSG dataset; providing the user input as input to a first machine learning model, to determine the user prompt as a TSG query; providing the user input and the user- provided TSG dataset as input to a second machine learning model, to select at least one shortlisted TSG model from a TSG database; and using the first machine learning model, providing the at least one shortlisted TSG model as a response to the TSG query.

The invention relates to a time sequence generation device. The present specification relates to an apparatus comprising a ring oscillator comprising a plurality of gates, each gate transmitting a fast clock signal. The first shift register includes successive first flip-flops, each of which is synchronized with a same first clock signal corresponding to one of the fast clock signals. The first shift register is looped back on itself and implements a second oscillator in which each first flip-flop transmits a slow clock signal. The second shift register includes successive second flip-flops, each of which is synchronized with a same second clock signal corresponding to one of the slow clock signals.

The invention discloses an actuarial calculation engine code compiler, an operation method and a related product. The actuarial calculation engine code compiler comprises an editing layer, an analysis layer and a mapping layer. The editing layer is configured to compile an actuarial process about the target actuarial business by adopting actuarial business terms and actuarial logic in response to the operation of the client; the analysis layer is configured to analyze the actuarial process and identify variables, formulas and time sequences in the actuarial process; generating a main syntax tree comprising target actuarial business semantics and a sub syntax tree comprising operation symbols and functions; and a mapping layer configured to parse the main syntax tree and the sub-syntax tree into machine executable code. In the embodiment of the invention, the actuarial process is edited through the actuarial business terms and the actuarial logic, and the actuarial process is converted into the machine executable code, so that the actuarial process adapts to high professionality and high suitability of professional evaluation of actuarial professionals.

Time Sequence Generation Device. This description relates to a device (200) comprising a ring oscillator (RO) with a plurality of gates (I1, INf), each providing a fast clock signal (CKf1, CkfNf). A first shift register (OSC) comprises a series of first flip-flops (FFs1, FFsNS), each synchronized to the same first clock signal (CK1) corresponding to one of the fast clock signals (CKf1, CkfNf). The first shift register (OSC) is fed back into itself and implements a second oscillator where each first flip-flop (FFs1, FFsNs) provides a slow clock signal (CKs1, CKsNs). A second shift register (SR) comprises a series of second flip-flops (FF1, FFNd1), each synchronized to the same second clock signal (CKs) corresponding to one of the slow clock signals (CKs1, CKsNs). Figure for the summary: Fig. 2

The present disclosure relates to a user learning data prediction method, device, medium, equipment and program product, comprising: obtaining a learning data time sequence, the learning data time sequence comprising actual data of a target analysis dimension at a plurality of historical time nodes; generating an interval grey number sequence according to the learning data time sequence, the interval grey number sequence comprising interval grey numbers of the target analysis dimension at the plurality of historical time nodes, the interval grey numbers being determined according to corresponding actual data; and predicting the target analysis dimension of a future time node according to the interval grey number sequence to obtain a prediction interval of the target analysis dimension. The present disclosure represents the historical learning data of a user as a learning data time sequence, represents the uncertainty variable of the target analysis dimension by an interval grey number to obtain an interval grey number sequence, and thus predicts the target analysis dimension of a future time node, so that the prediction interval of the target analysis dimension is more in line with the small sample and uncertainty characteristics of individual learning data.

One variation of a method for deriving and storing emotional conditions of humans includes: writing timeseries biosignal data, output by a set of biosensors in a local device coupled to a user, to a rolling buffer spanning a look-back duration; in response to a trigger event at a first time, retrieving a set of biosignal data, spanning a first period of time preceding the first time, from the rolling buffer; transforming the set of biosignal data into a timeseries of emotions exhibited by the user during the first period of time; generating a visualization of the timeseries of emotions; and rendering the visualization of the timeseries of emotions on a display.

The invention relates to the field of time synchronization information security, and provides a time synchronization information security examination method and device based on dual-scale feature collaboration, equipment and a medium, and the method comprises the steps: generating a large-scale sample set based on a historical time sequence; utilizing the large-scale sample set to train and generate a large-scale branch model; generating a small-scale sample set based on a predicted value output by the large-scale branch model; using the small-scale sample set to train and generate a small-scale branch model; generating a final predicted value which comprises predicted values of the large-scale branch model and the small-scale branch model; and performing security review on the real-time time synchronization information based on the final predicted value. According to the method, the dual-scale branch neural network model with a cooperative training mechanism and a dual optimization mechanism are utilized to realize complementary enhancement of long and short scale feature extraction of time synchronization information, and cooperative consideration of the model on long-term security situation stable monitoring and short-term abnormal feature sensitive identification is completed; and the accuracy and the reliability of time synchronization information security examination are obviously improved.

A method and system for generating synthetic data is provided, in which longitudinal time series data is retrieved and a neural network is trained to generate synthetic time series data that meets a privacy metric based on the longitudinal time series data, where the longitudinal time series data is unlabeled and univariate.

The invention relates to the field of enterprise operation data analysis, in particular to an enterprise operation data detection and visual analysis system, which comprises a data acquisition module used for acquiring basic operation data and determining a complete acquisition period and actual starting and ending time; the time sequence generation module is used for sorting, caching and storing various basic operation data; the thermodynamic diagram generation module is used for generating various enterprise operation data thermodynamic diagrams; the operation judgment module is used for obtaining an enterprise operation judgment result based on logic judgment; and the mark supplementing module is used for mapping the judgment result into a graphic symbol and marking the graphic symbol in the thermodynamic diagram. According to the invention, a manager can accurately grasp the operation state of an enterprise, the provided data processing flow can be combined with an enterprise management strategy in a closed-loop manner, the judgment of post configuration, production plans and incentive measures is assisted, and the overall operation efficiency and decision-making rapidness of the enterprise are improved.

The invention discloses a DC power system fault detection method, system and device, and a storage medium, and relates to the technical field of fault detection. The method comprises the following steps: collecting node data in a direct-current power system, and generating a column vector according to a time sequence; performing standardization processing on the column vector to obtain a standard non-Hermitian matrix; calculating a standard matrix product and a corresponding characteristic value of the standard non-Hermitian matrix, and determining characteristic value distribution of the standard matrix product according to a single-ring theorem; the characteristic value distribution is limited by the inner ring radius and the outer ring radius; constructing a random variable based on the characteristic value distribution and the average spectral radius; when the random variable is smaller than the radius of the inner ring, judging that the system breaks down; and when the random variable is greater than or equal to the radius of the inner ring, determining that the system has no fault. According to the method, the defects of the traditional method in quickness, anti-interference performance and multi-end system adaptability can be overcome by quantifying the linear change trend of the fault correlation characteristics.

The application provides a high-reliability anti-interference engine rotating speed measurement data processing method, including the following steps: generating a square wave interval time sequence based on a square wave time sequence, generating an interval time sequence multiset based on the square wave interval time sequence, and obtaining a calculated rotating speed through the interval time sequence multiset; performing abnormal value checking and replacement on the calculated rotating speed to obtain an optimized rotating speed; and performing smoothing filtering processing on the optimized rotating speed to obtain an output rotating speed. The method can reasonably divide and classify the time intervals collected by the rotating speed sensor, reduces effective data loss, improves rotating speed measurement accuracy, suppresses rotating speed jump, and calculates a more reasonable and reliable real-time rotating speed under a large amount of high-frequency noise, random interference, intermittent or continuous time sampling abnormality and the like.

The invention discloses a synchronous positioning method and system based on multi-channel sound source and panoramic image fusion, and particularly relates to the technical field of target positioning, and the method comprises the following steps: collecting an audio and a panoramic image, embedding a unified time stamp, calculating the credible parameter value of each frame of sound source and image, and calculating the credible parameter value of each frame of sound source and image; if the frame is lower than the threshold value, searching an adjacent frame and estimating or marking the frame as a failure frame; effective frames are input into a fusion proportion control model, registration and weighted synthesis are executed to obtain positioning coordinates, and a movement track is generated according to a time sequence; and finally, calculating a failure ratio, setting a grade identifier, and correspondingly executing acquisition restart, threshold adjustment or model replacement strategies. According to the method, modal alignment is realized through audio and image signal time stamps, and the fusion positioning accuracy is improved; a low-credibility frame is stably processed by adopting an interpolation strategy based on bidirectional search, and the positioning continuity is enhanced; and realizing stable operation and dynamic adjustment capability of the system by fusing proportion self-adaptive adjustment and combining a failure frame proportion setting grade strategy.

This invention discloses a method and apparatus for generating long-term series data of photovoltaic power generation from a photovoltaic power plant. The method includes: inputting pre-prepared time-series data of power generation from a target photovoltaic power plant under different weather conditions and random time-series data of the same dimension into a pre-constructed generative adversarial network model for generating typical daily power curves of the photovoltaic power plant under different weather conditions, thereby generating typical daily power time-series data of the target photovoltaic power plant under different weather conditions; constructing a daily weather type time-series generation model based on implicit Markov and Monte Carlo simulations; and sorting the typical daily power time-series data of the target photovoltaic power plant under different weather conditions using the daily weather type time-series generation model to generate long-term series data of power generation from the target photovoltaic power plant.

Adaptive Forecasting System This description concerns a control system configured to supply and / or receive, in a buffer tank, a quantity of a resource destined for or received from a target system (102). The control system comprises: - at least one sensor (106) configured to periodically measure, within a first time interval, quantities of the resource supplied and / or received; - a processing device (110) configured to perform a processing operation on the measurements taken by the at least one sensor. This processing operation corresponds to the generation of a first time series whose values ​​are sums of the quantities measured at several time instants. The processing device further comprises a neural network (202) to generate at least one quantity estimate based on the first time series and to control an actuator (108). Figure for the abstract: Fig. 1A

The invention discloses a TTAO-LSTM less-data container cloud resource load prediction method, and relates to the technical field of deep learning. The method comprises the following steps: firstly, carrying out normalization processing on original container cloud resource load data, and dividing time sequence data into a plurality of fixed-length sample sequences through a sliding window mechanism; carrying out data enhancement on small sample load data by utilizing an improved TimeGAN structure so as to improve the training data volume and the model generalization ability; a TTAO algorithm is adopted to search and optimize key hyper-parameters of the LSTM network, and an optimal load prediction model is constructed; and finally, inputting the sample after data enhancement into the trained TAO-LSTM model to obtain a prediction result. According to the TTAO-LSTM less-data container cloud resource load prediction method provided by the invention, data enhancement is performed on small sample data through time sequence generation, and the generated data is sent to the TTAO optimized LSTM model, so that prediction of a future resource load trend is completed, a high-precision result is output, and accurate prediction of a less-sample resource load task is realized.

Provided is a technique that makes it possible, as evaluation of a control plan with respect to a control target, to evaluate a plan sequence while taking into consideration long-term influence. An information processing apparatus includes: an acquisition section that acquires state information and a plan sequence, the state information indicating a state of at least one of a control target and an environment, and the plan sequence being a time series of control plans with respect to the control target; a generation section that generates a state sequence using output obtained by inputting the state information and the plan sequence into a learned model, the state sequence being a time series of pieces of state information each indicating a predicted future state; and a calculation section that calculates, using the state sequence, a success probability of the plan sequence which has been acquired by the acquisition section.

The invention discloses an adaptive game difficulty design method, device and equipment based on dynamic time warping and a computer readable storage medium, and the method comprises the steps: obtaining a player skeleton point sequence corresponding to real-time actions of a player, and carrying out dynamic time warping algorithm comparison with a standard action template to generate a real-time action score; performing statistical analysis on the real-time action score by applying a preset sliding window to generate score trend data; judging the score trend data by applying a preset difficulty adjustment rule to generate a preliminary game difficulty adjustment instruction; generating a growth curve model representing the long-term ability change of the player based on the stored time sequence of the historical action scores of the player; based on the growth curve model, difficulty adjustment sensitivity parameters are determined; the initial game difficulty adjusting instruction and the difficulty adjusting sensitivity parameter are combined to generate the final game difficulty parameter, and the method has the advantage of being high in difficulty adjusting sensitivity.

The invention relates to the technical field of intelligent decision making, can be applied to business system platforms such as financial science and technology and medical health, and discloses a time sequence generation method, device and equipment and a medium, and the method comprises the steps: obtaining sequence description information inputted by a target user, recognizing the field category of the sequence description information, and obtaining a field matrix corresponding to the field category; semantic features of the sequence description information are extracted, feature mapping is conducted on the semantic features through the domain matrix, and semantic vectors of domain categories are obtained; calculating the similarity between the semantic vector and each semantic tag vector in a semantic prototype library, and calculating a weighting coefficient of the semantic tag vector according to the similarity; based on the semantic vector and the weighting coefficient, utilizing a conditional diffusion model to perform reverse denoising to generate an initial time sequence of the sequence description information; and correcting the initial time sequence according to a constraint strategy corresponding to the field category to obtain a time sequence of the sequence description information. Through the method, the time sequence generation accuracy can be improved.

The invention belongs to the field of photovoltaic power prediction, and discloses a TimeGAN-based photovoltaic power prediction method in consideration of turning weather, and the method comprises the steps: detecting an abnormal value through a reconstruction error, carrying out the filling of missing data through a bidirectional K-nearest neighbor algorithm of meteorological similarity weighting, and selecting high-correlation meteorological features; aiming at the condition of insufficient turning weather samples, expanding a turning weather data set through a time sequence generative adversarial network; and outputting a photovoltaic output feature sequence through a CEEMDAN-VMD double decomposition algorithm, and inputting the photovoltaic output feature sequence and the high-correlation meteorological feature sequence into an SSA-LSSVM model for photovoltaic output prediction. According to the method, the photovoltaic output and meteorological characteristic sequence is fully extracted through correlation analysis and double decomposition, the prediction model is jointly input, parameter selection is optimized, the influence of meteorological driving force on the system uncertainty in the turning weather is enhanced, and the accuracy and stability of photovoltaic output prediction in the turning weather are improved.

The application discloses a kind of wetland water quality monitoring pollution source intelligent tracking method based on deep learning, to solve the problem that wetland hydrodynamic boundary is complex, parameter is difficult to obtain, leading to the position of pollution source is difficult to be stably deduced from monitoring result, large positioning error, the application is by obtaining wetland water system spatial data and water quality monitoring data and preprocessing to construct observation water quality time series and working condition time series, construct the initial map of wetland water system including monitoring point and candidate water inlet and candidate water inlet set;Time-varying adjacency matrix sequence, direction constraint matrix sequence and condition vector sequence are generated from working condition time series;Train the forward prediction model including the space-time graph Transformer network limited by direction-constrained attention and the conditional neural operator network modulated by condition vector;In candidate water inlet set, the pollution source parameter is iteratively updated with differentiable inversion optimization, and the maximum weight corresponding water inlet number and coordinate are output, realize the stable tracking positioning of pollution source water inlet and coordinate under the condition of dam scheduling, tide and other working conditions, improve inversion stability and reduce positioning error.

The invention discloses a high-resolution photosynthetically active radiation absorption ratio time sequence generation method based on random forest iterative interpolation, and the method comprises the steps: building a Gaussian process regression model with Sentinel-2 surface reflectance as input and GLASS FAPAR as output, carrying out the downscaling of the GLASS FAPAR, and obtaining a FAPAR result of a 10-meter clear sky pixel; carrying out pre-filling on the missing value by utilizing GLASS FAPAR, establishing a random forest model, and carrying out second filling on the missing FAPAR; updating the pre-filled FAPAR value by using the result of the second filling, re-establishing the random forest model, and performing third filling on the missing FAPAR; and applying the final model to the tinel-2 reflection data of the target area until the precision reaches a target threshold, and generating a high-resolution space-time complete FAPAR product of the target area. According to the method, the FAPAR time sequence with the resolution ratio of 10 meters can be accurately generated, and important data support is provided for applications such as vegetation monitoring, ecological system evaluation and carbon cycle research.

The invention relates to a method for processing time series of noisy images of a same area, the method comprising: generating a set of time series of images from an input image time series by combining by first linear combinations each pixel of each image of the input image time series with selected neighboring pixels in the image and in an adjacent image of the input image time series; applying filtering operations in cascade to the set, each filtering operation combining each pixel of each image of each time series of the set by second linear combinations with selected neighboring pixels in the image and in an adjacent image in each time series of the set; performing an image combination operation to reduce each time series of the set to a single image; introducing a model image of the area as a filtered image in the set; and combining each image in the set into an output image, by third linear combinations.

This invention proposes a vector data processing method for drainage systems oriented towards SWMM modeling, relating to the technical fields of urban flood simulation and vector data processing. The method involves reading and unifying the basic vector data of the modeling area, and reading time series files. Based on the pipe network topology, outlet nodes are identified through set operations, and outlet attribute vector data conforming to SWMM model requirements is constructed. Based on the outlet name mapping relationship, the attributes and topology of manholes, pipes, and sub-catchments are updated. Rain gauges are deployed and their attributes configured. Two types of time series are processed to generate standardized time series files conforming to SWMM format requirements. After data preparation, the data is converted into an SWMM model file. This invention achieves standardized conversion from raw input data to model input files, offering advantages such as clear logic, simple calling, and strong operability. It provides an efficient means for preliminary data preparation in urban flood simulation and improves the reliability of flood simulation results.