74 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.

Systems and methods for assessing fraud risk based on sequence transaction data using machine learning are disclosed. In some embodiments, a disclosed method includes: receiving, from a computing device, a risk assessment request regarding a user device; generating sequence data based on a time series of transactions associated with the user device; computing, using at least one machine learning model, risk score data of the user device based on the sequence data; and transmitting, in response to the risk assessment request, the risk score data of the user device to the computing device.

Implementations are described herein for utilizing the spectral-, spatial-, and temporal-information of a satellite image time series to facilitate crop control or monitoring. In various implementations, a plurality of training examples may be assembled for a generative model. Each training example of the plurality of training examples may include a respective high-resolution image capturing a respective region and a corresponding low-resolution satellite image time series capturing the respective region. The plurality of training examples can be used to train the generative model, to acquire a trained generative model. A high-resolution image depicting one or more agricultural conditions for a given region, can be received and processed using the trained generative model, to generate a synthetic low-resolution satellite image time series, where the synthetic low-resolution satellite image time series represent the one or more agricultural conditions.

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 text travel scene flow prediction method, device and equipment, a storage medium and a program product, and the method comprises the steps: generating a data set based on a normalized text travel time sequence which comprises tourist flow time sequence data, tourism consumption time sequence data, weather time sequence data and traffic time sequence data; through channel attention feature analysis, key time sequence features are obtained, multi-head attention analysis and time sequence dependency analysis are carried out on the key time sequence features respectively, global dependency features and time sequence dependency features are obtained, feature fusion is carried out on the global dependency features and the time sequence dependency features, and fusion features are input into a multi-layer perceptron for flow prediction. Key time sequence features in a data set are extracted through channel attention feature analysis, and the dependency relationship between different time scales and features is accurately captured in a dynamic complex travel scene by fusing global dependency features and time sequence dependency features, so that the accuracy and robustness of traffic prediction are 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 provides an ROP prediction method based on an LSTM and an ensemble learning algorithm, and the method comprises the steps: carrying out the clustering and denoising: carrying out the denoising of data through employing a DBSCAN clustering algorithm, and removing outliers; data interpolation: applying a Hermite interpolation method to missing data, filling missing values, and ensuring the smoothness of the data; time sequence generation: converting the data into a time sequence format suitable for LSTM, and segmenting the data by using a sliding window; lSTM training: using an LSTM model to train data, learning a time dependency relationship and performing ROP prediction; on the basis of data preprocessing and an LSTM model, regression prediction is carried out by using LightGBM and XGBoost, so that the accuracy of ROP prediction is improved; evaluating a prediction result: evaluating prediction precision on training and measurement data through mean square errors and R2 evaluation indexes; and model optimization: carrying out model optimization according to an evaluation result. According to the method, more accurate prediction can be provided for the drilling operation, and then the efficiency and safety of the drilling operation are optimized.

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 discloses a method and a system for optimizing power consumption of a producer and a consumer based on a load clustering algorithm, and the method comprises the steps: collecting power consumption data, load data and photovoltaic power generation data of the producer and the consumer in a preset time period, and generating load curve data according to a time sequence; performing feature extraction on the load curve data, and classifying load features of each producer based on a clustering algorithm to obtain load modes of different energy demand responses; based on the load modes obtained through classification, the user self-sufficiency rate and the power product total cost of the producer and the consumer under various scheduling models are calculated respectively, and the user self-sufficiency rate is used for representing the renewable energy source proportion in the user load; and calculating a comprehensive index value according to the self-sufficiency rate of the user and the total cost of the power product, and selecting the scheduling model with the highest comprehensive index value to optimize the power consumption of the producer and the consumer. The optimization method provided by the invention can be used for improving the utilization rate of the spontaneous energy by the disappearing people.

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.

The invention discloses a power system load data super-resolution reconstruction method and system, and relates to the technical field of power system data quality improvement, and the method comprises the steps: carrying out the weighting of weather influence factors under different weather conditions, calculating the correlation between load data and weather influence factor data, obtaining key influence factor data, and carrying out the calculation of the correlation between the load data and the weather influence factor data; performing dynamic range normalization processing by adopting a sliding window; carrying out super-resolution reconstruction on the normalized data by adopting a trained time sequence generation model to obtain high-resolution load data; the time sequence generation model adjusts the weight of a loss function according to the change of the current training loss during training, and after training is completed, super-resolution reconstruction is carried out by extracting combined features of normalized load data and key influence factor data. The defect of insufficient resolution of low-quality sampling data is overcome, the quality of reconstructed data is improved through meteorological factor constraints, and the problem that training or using requirements cannot be met due to the fact that the resolution of the sampling data is low is solved.

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 technical field of intelligent networking of power distribution communication networks, in particular to an intelligent routing decision-making system and method for a power distribution communication network and a storage medium. The real-time intelligent routing decision-making strategy is generated by combining the time sequence generation large model and the routing decision-making model. According to the method, the dynamic rule of the time sequence data can be fully learned, the future link state and the network load are accurately predicted through the historical data, a reliable routing scheme is generated, and the stability and the flexibility of the network are effectively improved.

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.

The invention discloses a power distribution system source load scene generation method. The method comprises the steps of calculating correlation between new energy output and load according to preprocessed new energy output historical data and load historical data of a power distribution system, and generating an extended adjacency matrix; based on a pre-constructed message passing neural network model and a time sequence generative adversarial network model, determining a power distribution system source load scene generation model; training a power distribution system source load scene generation model by using the new energy output historical data, the load historical data and the extended adjacency matrix; and generating a source load scene according to the trained power distribution system source load scene generation model. The method can adapt to the change of the topological structure caused by network reconstruction of the power distribution system, effectively captures the source load characteristics of the reconstructed network, and enhances the flexibility and adaptability of the model. And the space-time correlation between the new energy output and the load can be simulated more accurately, and the precision of power distribution system source load scene generation is improved.

A spatial-temporal predictive analytics system and method for time-series generation, forecasting, and predicting future resource needs across multiples forecast timescales and multiple physical locations. Examples of the system and method use a tiered integration approach to integrate one or more queuing structures with a generative artificial intelligence engine based on a given forecast timescale. The generative AI engine captures the highly complex, nonlinear spatial-temporal correlations often present in time-series data. Queuing network containing the queuing structures captures the underlying physical system dynamics and, at the various integration tiers, refines to varying degrees the generative AI engine with enhanced generation and prediction power, explainability, and interpolation. This tiered integration approach utilizes the strengths of both the generative AI engine and queuing theory and adapts predictions to ensure that the most relevant model drives the forecasting process at the various forecast timescales.

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