116 results about "Time horizon" patented technology

A deep learning system for time series prediction comprising a preprocessor that receives time series input sequences, truncates them by removing terminal values, and appends padding values to maintain the original sequence length. An encoder compresses these padded sequences into latent space representations, while a decoder reconstructs predicted sequences matching the original length, specifically trained to reconstruct values matching the removed terminal values in positions corresponding to the padding values. A training system optimizes the encoder and decoder by minimizing differences between original sequences and predicted sequences. The system can process multiple time horizons simultaneously while maintaining statistical properties and providing uncertainty quantification through confidence intervals. This approach enables accurate short-term forecasting while preserving both temporal patterns and statistical relationships in the predicted sequences.

An AI-driven financial planning system for real-time market adjustment, consisting of: a neural inference coprocessor configured to execute deep financial forecasting models, including recurrent neural networks and attention-based encoders, on the device, and wherein the processor dynamically updates portfolio parameters in response to market signals exhibiting volatility differences above a statistical threshold calculated using an exponentially weighted moving standard deviation; a financial data acquisition module configured to continuously receive and analyze heterogeneous data streams, including market indices, interest rates, stock and bond price fluctuations, economic indicators, regulatory updates, and financial news sentiment feeds; a behavioral analytics engine configured to create a dynamically evolving user-specific financial behavior profile based on real-time analysis of transaction history, income-expenditure cycles, psychometric test results, and temporal lifestyle patterns using supervised and unsupervised machine learning algorithms; A goal optimization module configured to transform high-level, user-defined financial goals into quantitatively tracked multi-level goals. It uses a reinforcement learning framework that predicts optimal asset allocations across multiple time horizons. a real-time strategy simulation engine configured to perform Monte Carlo simulations and deep Q-learning-based assessments to simulate the resilience of proposed financial strategies under different macroeconomic regimes and trigger redistribution events based on predefined confidence thresholds; a compliance-aware execution interface configured to interact with financial institutions through encrypted API channels, ensuring policy enforcement using a smart contract validator and a hardware-enabled secure transaction signing unit; and a recommendation display unit configured to render dynamic dashboards for visualizing investments, reallocation warnings, confidence intervals, and sensitivity sliders, and where user interaction with the unit flows back into the behavioral model for real-time learning.

An electronic device for multimodal temporal-axis fusion artificial intelligence models is proposed. The electronic device comprises a storage unit, and a processor, wherein the processor may obtain a plurality of first visual features respectively corresponding to a plurality of different time points or time periods from a video, obtain a plurality of text features respectively corresponding to the plurality of time points or time periods from text, obtain a plurality of first local fusion features, respectively corresponding to the plurality of time points or time periods, from the plurality of first visual features and the plurality of text features by fusing the first visual features and the text features, which correspond to a same time point or time period, and obtain at least one global fusion feature from the plurality of first local fusion features.

The invention discloses a method for preventing main steam pressure fluctuation on the basis of controlling the coal combustion amount. The method comprises the following steps: acquiring running state data of the coal-fired boiler in real time; based on the operation state data and a preset boiler dynamic prediction model, predicting a steam pressure sequence of the boiler in a future preset time range, and judging whether to trigger a control action or not in combination with an event triggering strategy obtained by learning of a reinforcement learning agent; if it is judged that the control action is triggered, the reinforcement learning agent outputs an adjustment instruction of the total coal feeding amount according to the current operation state data; and controlling the total coal feeding amount of the boiler according to the adjusting instruction. According to the method, the timing and the force of control intervention can be intelligently determined according to the real-time state and future trend prediction of the boiler, and the accuracy and the foresight of main steam pressure control are improved.

The invention discloses a method for automatically screening session flood based on actually-measured daily average flow. The method specifically comprises the following steps that 1, a peak threshold value is set from actually-measured daily average flow data, and a flood maximum value is searched for to determine a flood peak value; 2, judging the independence of the peak values by setting time interval limitation between two adjacent peak values and flow minimum value limitation of a peak value interval; 3, determining the starting and ending time of the session flood by setting a session flood starting and ending flow threshold value and a time interval limit of a corresponding flood peak; and 4, when the starting and ending time ranges of the flood process corresponding to the two independent peak values are partially overlapped, the session flood is segmented. According to the method provided by the invention, the flood peak position can be accurately identified, and the screening efficiency is improved.

The invention discloses an orbit-driven atmospheric density estimation method. The method comprises the following steps: acquiring historical orbit element data and screening satellite numbers in a required time range; based on the obtained historical orbit element data and the screened satellite number, calculating a propagation orbit of the satellite in a future time period through an orbit propagation model; defining historical orbit element data as a real orbit, and calculating a propagation error at the same time point according to the real orbit and the propagation orbit; dividing the propagation errors into a regular class and an irregular class, and selecting satellite orbit parameters of the regular class; constructing an atmospheric density prediction model, and inputting the selected rule-class satellite orbit parameters into the atmospheric density prediction model for training and evaluation to obtain an optimized atmospheric density prediction model; and inputting the orbit parameter, the state vector, the space environment index and the error of the front and back orbit propagation in the previous time period into the optimized atmospheric density prediction model to obtain the atmospheric density at the future moment.

The invention relates to a time-phased electricity consumption curve prediction method based on a CNN + LSTM + AM depth model. The prediction method comprises the following steps: data collection and arrangement; extracting features; model training; inputting new data into the trained model, and predicting the electricity consumption in the future time range; according to the method, the convolutional neural network CNN in deep learning is used for feature extraction, the long-term and short-term memory network LSTM is used for capturing the long-term dependency relationship of time sequence data, and the attention mechanism AM is used for enhancing the attention of the model to key information, so that the accurate prediction of the electricity consumption curve is realized.

A method performed by a network node for an adaptive prediction of a time horizon for at least one key performance indicator, KPI, in a wireless network is provided. The method includes transmitting towards a user equipment, UE, a machine learning, ML, model to predict the at least one KPI; and receiving from the UE a first message including the predicted at least one KPI and a predicted time horizon for the predicted at least one KPI. A method performed by a UE is also provided.

A multi-tiered optimization and machine learning framework is provided in which operational decisions as far as driver capacity allocation are taken at three different levels or stages, namely, long-term, short-term, and real-time. While decisions at each stage have individual importance for the operations at different time horizons, this framework utilizes decisions upstream to inform better results downstream. This synergy helps improve allocations when compared to solutions that assign driver capacity for each stage individually.

The invention discloses a renewable energy source-oriented power system planning method. The method comprises the following steps of S1, collecting load data of a power system; s2, according to the historical renewable resource power generation power, the historical meteorological data and the predicted meteorological data, predicting renewable resource power generation power within a preset time range; s3, predicting a power load demand in a preset future time range by adopting LSTM according to the collected load data and historical load data; s4, according to the predicted power generation power, the predicted power load demand, a preset constraint condition and a preset optimization target, a power system planning model is constructed, and the optimization target comprises the minimum power generation cost and the maximum renewable energy utilization rate; and S5, solving the power system planning model by adopting a genetic algorithm to obtain an optimal power system planning scheme. Through the optimal power system planning scheme, the power generation cost is reduced, and the utilization rate of renewable energy sources is improved.

The invention discloses a service resource scheduling method, device and equipment, a storage medium and a product, and is applied to the technical field of artificial intelligence. The method comprises the following steps: for any slave node, when a corresponding application service under the slave node meets a preset resource prediction condition, acquiring historical resource use data of the corresponding application service within a preset historical time range; determining predicted resource use data of the corresponding application service in a preset future time range according to the historical resource use data of the corresponding application service; determining to-be-allocated resource data of the corresponding application service according to the predicted resource use data of the corresponding application service; and performing service resource scheduling on each application service according to the to-be-allocated resource data corresponding to each application service. According to the technical scheme of the embodiment, automatic scheduling of the application service resources is realized, and the resource scheduling efficiency is improved.

The invention discloses a dynamic environment and multi-event collaborative awareness method based on a multi-modal neural network. According to the method, multi-modal data is obtained through a multi-source sensing device, and a multi-modal data sequence under a unified reference coordinate is formed through time synchronization and space registration. And inputting the data sequence into a multi-modal deep neural network, and adaptively correcting each modal feature weight according to a confidence coefficient change rate between time slices to obtain corrected feature information. And performing external target identification and internal event identification on the feature information to generate a candidate event set, establishing an event priority relationship, and outputting a collaborative event result including an event tag, a spatial position and a time range. And finally, the risk level is determined according to a collaborative event result, and a corresponding early warning or control instruction is generated, so that intelligent identification and collaborative decision-making of multiple events in a complex dynamic environment are realized, and the real-time performance and accuracy of the system in multi-source data fusion and safety monitoring are improved.

The invention discloses an intranet penetration traceability method for multi-scale heterogeneous log behavior analysis, and relates to the technical field of network security, and the method comprises the steps: carrying out the standardization processing of an original log stream of a heterogeneous log source, and generating a unified semantic event stream; performing multi-scale time window division on the unified semantic event stream, calculating an event density characteristic index of each window, and identifying a short-time dense subsequence based on the event density characteristic indexes; clustering analysis is carried out on the identified short-time dense subsequences based on semantic weighted entropy, and aggressive dense segments and normal high-load segments are distinguished; based on the semantic relevance of the event, extracting long-time sparse clues from the unified semantic event stream; a long-time sparse clue is used as a time anchor point, an aggressive dense section adjacent to the time anchor point is positioned in an extended time range of the long-time sparse clue, and an intermediate interpolation event sequence is generated through a conditional time sequence model to reconstruct a complete attack chain.

The invention relates to the technical field of simulation modeling, in particular to a BIM-based building embedded pipeline simulation system, which comprises a pipeline path optimization module, a space conflict detection module, a construction sequence planning module, a dynamic adjustment generation module and a resource utilization rate evaluation module. According to the method, by automatically extracting the spatial distribution information of the pipelines and analyzing the geometrical characteristics and the connection relation of the pipelines, accurate marking of potential conflict positions can be achieved, the comprehensiveness and the accuracy of spatial conflict recognition are effectively improved, and further based on extraction of the construction sequence relation and logic dependency analysis, the construction efficiency is improved. The method can achieve the intelligent judgment of the construction conflict state, achieves the dynamic adjustment of a layout path and a time range, assists in forming a targeted wiring strategy in a conflict region, optimizes the resource space matching degree, automatically recognizes a low-efficiency time period, carries out the real-time identification, improves the response capability of a construction process to the resource utilization efficiency, and improves the construction efficiency. And the precision of path adjustment and the coordination of construction organizations are improved.

The invention belongs to the technical field of water supply network leakage monitoring, and particularly relates to a water supply network leakage node and time window combined positioning method based on a hybrid neural network, which realizes spatial positioning of a water supply network leakage node and time positioning of a leakage time window. Comprising the following steps of 1, collecting node pressure and flow time sequence data of the water supply network in different scenes, and recording leakage nodes and leakage starting and ending time labels; 2, carrying out normalization preprocessing on the data, and constructing a standardized time sequence data set containing a multi-leakage scene mark; step 3, a dual-branch hybrid neural network model TCN-Transform-KAN is built, and a dual-branch hybrid neural network model TCN-Transform-KAN is built; 4, constructing a joint loss function of node classification loss and time regression loss; and step 5, training through a joint loss function constraint model, and outputting a leakage node and a center position and a continuous width of a leakage time window. According to the method, joint precise identification of the leakage nodes and the time range is realized, and technical support of time-space two-dimensional information is provided for intelligent operation and maintenance of a water supply pipe network.

The invention belongs to the technical field of traffic, and discloses a service traffic analysis method and device. The method comprises the following steps: acquiring business logs of a plurality of businesses, and constructing a plurality of levels of time windows corresponding to the plurality of businesses; for any service, according to the system load index, adjusting the window levels of the time windows of the multiple levels; for any service, determining the fluctuation characteristics of the traffic of the service according to the service log, and adjusting the window levels of the multiple levels of time windows according to the fluctuation characteristics; for the time window of any level, determining a service basic index and a service composite index in the time range of the previous time window according to the service log; and for any service, determining whether the service is abnormal or not according to the service basic index and the service composite index of the time window of each level, and if so, outputting an abnormal alarm. By adopting the method, the real-time performance of service detection can be improved by dynamically adjusting and analyzing the size of the time window.

The invention relates to a navigation method wherein a navigation device calculates a navigation route (20) from a starting point to a destination point. The navigation method according to the invention is characterized in that - a computing unit obtains information about at least one event that varies in time and space (1); - the computing unit estimates a temporal and spatial progression of the event (1) for a future time horizon and stores it as a dynamic area of ​​interest (2); - the navigation device specifies a starting location and a starting time, receives the dynamic area of ​​interest (2) and calculates the navigation route (20) from the starting location to the destination location, where the destination location is within the dynamic area of ​​interest (2) or an observation point (3) is set as the destination location from which the event (1) can be observed.

An electric power system (10) is supplied at least in part by renewable energy sources (12). A method for managing storage of energy in such a system includes obtaining a stochastic model (16) that models probabilistic variability (22) in weather (20) across a sequence of time periods (P-1 . . . P-N) within a time horizon (18), each time period (P-n) being at least one day in duration. The method further includes determining, using the stochastic model (16), one or more values (24V) for one or more design or operational parameters (24) of the electric power system (10) that optimize a level of energy (L-1 . . . . L-N) stored by the electric power system (10) at each time period (P-1 . . . P-N) by minimizing an expected impact of renewable energy production variation occurring over the time horizon (18) due to the modeled probabilistic variability (22) in weather (20).

Systems and methods are provided for providing an equitable availability distribution real-time calculator. More particularly, systems and methods reduce the potential for telecommunications outages following upgrades and allow markets better control over availability metrics. To do so, an outage indication is initially received that identifies a plurality of cell sites affected by an outage and a timeframe for an update corresponding to the outage to be deployed to each of the plurality of cell sites. A selection of a portion of the plurality of cell sites to deploy the update at a particular time within the timeframe is received. Real-time data is received from each cell site of the portion of the plurality of cell sites corresponding to the particular time within the timeframe. Using the real-time data, the impact of deploying the update for the portion of the plurality of cell sites at the particular time is determined.

A method may include receiving historical data indicating a demand for a product during a plurality of weeks within a previous year; determining optimal fitting gamma distribution per week based on the historical data; performing a first simulation of the gamma distribution over a short-term time range to generate a first estimated demand for the product over the short-term time range; performing a second simulation of the gamma distribution over a long period of time to generate a second estimated demand for the product over the long period of time; determining a first inventory amount of the product required to meet the first estimated demand at a first confidence level; determining a second inventory amount of the product required to meet the second estimated demand at a second confidence level; and ordering a third stock amount, wherein the third stock amount comprises a minimum value of the first stock amount and the second stock amount.

Devices, systems, methods, and processes for generating and sharing a verifiable Zero Knowledge (ZK) proof are described herein. A device may utilize one or more non-reversible aggregation techniques to receive, normalize, and aggregate one or more Carbon Footprint Metrics (CFMs) of the device corresponding to a timeframe. The device can generate a ZK attestation and the verifiable ZK proof based on an aggregated CFM or a sum of normalized CFMs, and a carbon footprint threshold for the timeframe. The device may further transmit the verifiable ZK proof to an auditing device. The auditing device can receive the verifiable ZK proof and verify, in a trustworthy manner, that the CFMs of the device corresponding to the timeframe are in compliance with the carbon footprint threshold. The device may hence prove the compliance with the carbon footprint threshold to the auditing device without actually sharing the CFMs with the auditing device.

A method performed by a network node for an adaptive prediction of a time horizon for at least one key performance indicator, KPI, in a wireless network is provided. The method includes transmitting towards a user equipment, UE, a machine learning, ML, model to predict the at least one KPI; and receiving from the UE a first message including the predicted at least one KPI and a predicted time horizon for the predicted at least one KPI. A method performed by a UE is also provided.

The predictive controller determines, using the deep generative decoder model, a conditional probabilistic distribution of the latent representations of the disturbance conditioned on the partial observations of the disturbance, and samples the conditional probabilistic distribution of the latent representations to produce a latent sample of the time-series values of the disturbance affecting the mechanical system over the time horizon. The predictive controller decodes the latent sample with the deep generative decoder model to produce predicted values of the disturbance acting on the system within the time horizon with a probability of the latent sample on the conditional probabilistic distribution of the latent representations and controls the mechanical system using a predictive controller that determines control commands changing a state of the operation of the mechanical system using the probability of at least some of the predicted values of the disturbance.

A computer-implemented method, computer program product, and computer system for automated model predictive control. The computer system trains multiple step look-ahead regression models, using historical states and historical actions for a to-be-optimized system, for each timestep of a past time horizon. Regression models may be either linear or nonlinear in order to capture process dynamics and nonlinearity. The computer system generates optimization constraints for each timestep of a future time horizon. The computer system generates optimization variables, based on the multiple step look-ahead regression models, for each timestep of the future time horizon. The computer system constructs a mixed integer linear programming based optimization model that includes an objective function, the optimization constraints, and the optimization variables. Nonlinear regression models are converted into piecewise linear approximation functions. The computer system solves the optimization model to produce actions for the to-be-optimized system, over the future time horizon, and recommend commitment-look-ahead actions.

The invention discloses an individual care product process optimization method and system based on user behavior data, and relates to the field of intelligent data processing, and the method comprises the steps: collecting a historical user behavior data set in a historical time range after a target product is put into the market through multiple channels, and carrying out the data preprocessing and product improvement correlation feature extraction, obtaining a historical product improvement associated feature set; determining an optimal process adjustment time period by combining a predicted product demand growth rate and a predicted product demand fluctuation coefficient within a preset time range; according to the user behavior data set in the optimal process adjustment time period, determining a product improvement index according to the product quality associated feature set; and obtaining an optimal process adjustment scheme by taking approximation to a product improvement index as an optimization target, and carrying out production process iterative optimization within a preset time range. According to the method and the device, the problems of poor user experience, low optimization strategy scene adaptability and inaccurate process optimization direction in the prior art are solved.

The invention belongs to the technical field of computers, and discloses a query method for a closely associated transaction account group, which comprises the following steps of: 1, extracting all connected k-trus communities in a global time interval of an account transaction network, and calculating a real time interval; step 2, carrying out forward evolution refinement; step 3, carrying out backward evolution refinement; step 4, constructing a DAG index structure of the time evolution community; step 5, querying the maximum connected k-trus community of the given account in a specified time interval by using the index; and step 6, returning a query result. According to the method, by constructing the account community structure diagram evolved in the time interval, the repeatedly stored community information is effectively compressed, and the expression ability and query efficiency of account community evolution in a cross-time range are improved. Through recursive stripping and sharing of edge sets among communities, minimum redundant community representation across multiple time windows is realized, and the storage overhead is remarkably reduced while the historical integrity of the communities is ensured.

A system and a method for stabilizing hydrogen flow to a downstream process in a facility determining a hydrogen density and pressure profiles in the hydrogen storage unit for different target net hydrogen flows at different time intervals of a time horizon of a renewable power availability profile, determining an operating target net hydrogen flow of a hydrogen feed to the downstream process, determining a target direct hydrogen flow of a hydrogen feed and a target stored hydrogen flow of a hydrogen feed to the downstream process, and controlling the operation of the downstream process based on the operating target hydrogen flows.

The invention discloses a hotel reservation management method and device, computer equipment and a storage medium, and relates to the technical field of hotel reservation, and the method comprises the steps: generating and publishing a hotel future option product based on the inventory prediction data of a hotel supplier and a preset price strategy; in response to a purchase request, sent by a user terminal, for future option products of the hotel, receiving earnest money paid by the user, creating an option holding record uniquely associated with the user, and generating an encrypted digital option certificate at the same time; before the option expires or when an agreed performance triggering condition is met, a hotel inventory management interface is automatically called, available hotel room resource information conforming to the check-in time range, the applicable geographic area and the room type level is acquired, and the room type level of the hotel is determined based on a preset multi-factor weighted matching algorithm. And determining a target hotel for the user from the available hotel room source information to complete performance. According to the method, the contradiction between hotel income uncertainty and user travel variability is solved.

The invention discloses a data processing method and system for weather management engineering, and the method comprises the steps: carrying out the standardized processing of weather forecast data, and building a weather forecast data reference line; determining an influence domain, wherein the influence domain comprises a space range and a time range; the form of an objective function and the weather element input type of the influence domain are determined, the objective function is # imgabs0 #, and T is larger than 0; and setting a basic equation, and obtaining an analytical solution # imgabs2 # by using the time of # imgabs1 # to carry out efficiency evaluation and economic evaluation. According to the method, the technical problem of insufficient weather forecast accuracy can be solved to a certain extent, the use value of weather forecast data is improved, and the method has the advantages of universality, portability and convenience in practical application.

The invention discloses a detection method and device for a customer service system in the financial field and electronic equipment. Relates to the financial science and technology field or other fields, and comprises the following steps: obtaining business information of a customer service system in the financial field in a target time range of a target date, and obtaining target business information; according to the date type of the target date, determining N reference dates of which the date type is the same as that of the target date from the plurality of historical dates; based on the service information in a reference time range of the N reference dates, reference information is determined, the reference time range and a target time range are the same time range under different dates, and the reference information comprises at least one of a first reference threshold value of the number of incoming lines of a user and a second reference threshold value of the online rate of a plurality of customer service accounts; and determining a detection result based on the reference information and the target service information. Through the method and the device, the problem that the detection accuracy is low when whether the customer service system in the financial field is abnormal or not is detected in related technologies is solved.