62 results about "Predictive systems" patented technology

Systems, methods, and computer-readable storage media for aggregating media (and commentary on that media) into a topology. To do so, the system receives first content (and associated metadata) as well as second content (and associated metadata). The system then generates a topology based on a relationship between the first content and the second content, where the topology has a number of dimensions based on the metadata of the different pieces of content. The system then compares the topology and the metadata to previously stored topologies and / or metadata and, based on that comparison, executes a machine learning algorithm. The output of that machine learning algorithm includes predicted future changes to the topology, which the system uses to reduce the number of dimensions within the topology.

The invention relates to the technical field of social network information spreading prediction, and discloses an information spreading prediction method fusing public opinion field effect and a heterogeneous hypergraph. In order to solve the problems that in the prior art, only pairwise user relations are relied on, multi-user group influences cannot be described, different information is subjected to cascade independent processing, and multi-topic competition is not considered, the invention provides a prediction scheme fusing public opinion field effects and heterogeneous hypergraph learning. A heterogeneous hypergraph is constructed to obtain user multivariate relation representation, then a public opinion field effect is utilized to quantify attraction energy of different information topics to a user, attention competition among multiple topics is modeled, and a more real user propagation tendency is obtained; and finally, realizing joint prediction of user interest features and social influence features through an interactive fusion mechanism. The method can be used for scenes of information propagation trend analysis, public opinion monitoring, marketing recommendation, false information early warning and the like.

A machine learning based (ML-based) method and system for forecasting cash flow, is disclosed. Initially, the data associated with business units are obtained from data sources. The data are pre-processed to generate pre-processed data. Features associated with financial information are determined for horizons based on the pre-processed data using a plurality of input AI models. Feature combinations are generated by integrating the features associated with the financial information, for each horizon of the horizons. Forecasts are generated for each horizon for a pre-determined time interval, using a stacked AI model including forecasting models. The generated forecasts for the cash flow of the business units, are provided as an output, to the users on user interfaces associated with electronic devices associated with the users.

The invention relates to the field of manufacturing execution systems and intelligent manufacturing, in particular to a manufacturing execution system management method oriented to production data, which comprises the following steps: a multi-source data acquisition step: acquiring physical sensing state data, manual interaction time data and system scheduling data of a manufacturing execution system, and integrating to generate multi-source heterogeneous production data; a trust quantitative evaluation step of quantifying the distortion degree of the execution state based on the multi-source heterogeneous production data to obtain an execution trust entropy; a cross validation prediction step: comparing the physical sensing state data with the man-machine interaction time data to calculate execution trust entropy, and predicting system failure risk; a dynamic scheduling decision step: setting a scheduling degradation adjustment mechanism based on execution trust entropy, and outputting a target scheduling instruction to adjust the running state of the system; according to the method, the dynamic balance between pursuit of efficiency and maintenance of authenticity of underlying data is realized, and the vulnerability resistance of the complex manufacturing network is effectively improved.

This invention provides a system and method for verifying system interfaces based on artificial intelligence, relating to the field of interface verification technology. The method includes: acquiring historical concurrent user counts, historical system performance data, and historical load impact data; acquiring system interface architecture data and historical system interface architecture data; acquiring real-time load impact data; determining a concurrent user count relationship function based on historical concurrent user counts and historical load impact data; determining the predicted concurrent user count for a prediction period based on the real-time load impact data and the concurrent user count relationship function; obtaining a trained system performance prediction model; processing the predicted concurrent user counts and system interface architecture data according to the trained system performance prediction model to obtain predicted system performance coefficients; and generating a system interface verification report based on the predicted system performance coefficients. According to this invention, the accuracy of system interface verification can be improved.

A system and method for measuring, forecasting, and modulating aqueous outflow is described here. The system and method operate on an ocular microphysiological system that reproduces the trabecular-meshwork-membrane-Schlemm's canal interface under a defined hydrodynamic program. The signals may include TEER resistance, pressure-flow measurements, OCT / OCTA images, and Raman spectra. The Ocular MPS system may include encoding the multimodal signals into a device-agnostic feature representation that includes descriptors of junction continuity, belt thickness, tortuosity, pathway activity, and outflow-resistance proxies. The Ocular MPS system may execute a physics-informed graph state-space model that fuses the device-agnostic feature representation into inferred parameters. The inferred parameters may be inferred from barrier integrity, permeability, and outflow facility, subject to monotonic constraints between structural and hydraulic variables. The Ocular MPS system may quantify uncertainty of inferred parameters, generating calibrated forecasts of aqueous outflow performance. The Ocular MPS system may control a drive actuator according to the calibrated forecasts.

The invention provides an electric power spot market price prediction system and method based on artificial intelligence. The system comprises a market boundary prediction analysis module, a spot market price prediction module, an agent electricity purchase decision optimization module and a prediction result redisk analysis module. According to the system, a deep learning model fused with a CNN-MLP-Attention algorithm is adopted, kernel density estimation is combined to carry out electricity price interval prediction, meteorological data weighting processing, feature engineering, rolling training and multi-day prediction functions are integrated, and 1-7-day high-precision determinacy and uncertainty prediction of the electricity price of the electric power spot market is achieved. The method solves the problems of low electricity price prediction precision, large uncertainty, lack of scientific support of electricity purchasing strategies and the like in the existing electric power spot market, can effectively improve the decision scientificity of power grid enterprises in the spot market, reduces the transaction risk, and improves the efficiency of the power grid enterprises. And full-process data support and strategy simulation capability are provided for a power grid enterprise to participate in an agent power purchase transaction mode.

An intelligent dosing platform incorporating comprehensive predictive analytics and forecasting capabilities for injectable medication administration. The platform includes predictive analytics modules configured to generate forecasts for financial costs, medication demand patterns, and adverse event probabilities using machine learning algorithms and statistical modeling techniques. The system features financial modeling modules that analyze cost patterns associated with medication procurement, administration infrastructure, and insurance reimbursements, while demand forecasting modules predict medication needs based on seasonal trends, patient population changes, and treatment protocol modifications. Adverse event prediction modules identify risk factors using patient risk profiles and clinical outcome histories. The platform incorporates machine learning capabilities that continuously improve prediction accuracy using patient outcome data and administration patterns. Comprehensive reporting modules generate customized predictive reports for healthcare administrators, pharmaceutical suppliers, and insurance providers, while optimization modules recommend actions for cost reduction and patient safety improvement based on predictive insights.

The invention belongs to the technical field of power dispatching and operation, and particularly provides a negative electricity price scene-oriented source-grid-load-storage collaborative interaction and marginal cost optimization system and method, and the method comprises the steps: monitoring the state of a power market through a data collection module, predicting the supply and demand conditions of the system, and judging that there is a negative electricity price risk; calculating real-time marginal cost of the schedulable resources, wherein the real-time marginal cost comprises power generation resource marginal cost, energy storage resource marginal cost and load regulation marginal cost; establishing an optimization model by taking the minimization of the total marginal cost of the system as a target, and solving to obtain an optimal scheduling scheme of each resource; converting the optimal scheduling scheme into a specific control instruction, issuing the specific control instruction to each resource main body of a source, a network, a load and a storage, and executing power generation adjustment, charging and discharging or load interruption operation; and monitoring the execution effect of each resource instruction and the actual clearing electricity price of the market in real time, calculating the execution deviation, and carrying out rolling correction in the next optimization period. According to the invention, early warning of the negative electricity price risk can be realized, and global optimization scheduling is carried out on the source network load storage resources.

The invention relates to a parking lot pricing prediction system and method based on artificial intelligence and big data, and belongs to the technical field of big data analysis. The system comprises a three-layer state sensing and coding module, wherein a graph loop fusion network, an attention scoring model and a user clustering model are arranged in the three-layer state sensing and coding module; the graph loop fusion network is used for encoding the network game layer data into graph embedding vectors; the attention scoring model is used for carrying out dynamic weight distribution and fusion according to the importance of the influence of the current moment on the parking demand; the user clustering model is used for clustering individual heterogeneous layer data into a plurality of typical user groups, and extracting group features to obtain user heterogeneous state vectors. According to the method, a'three-layer perception-collaborative game 'framework is constructed, a regional Pareto improved pricing strategy is generated by utilizing a multi-agent dynamic game, meanwhile, the method has real-time response and decision interpretation capabilities, and social benefits and collaborative commercial and public benefits are embedded in a reward function of the method.

The invention provides a method and device for creating an intelligent decision data set based on log data, and relates to the technical field of cloud computing, cluster management, big data processing and artificial intelligence, and the method comprises the steps: collecting original log data from a server and a cluster, and achieving the collection and preprocessing of the log data; key information is extracted from the preprocessed log data, and log event analysis and structured processing are realized based on a self-attention mechanism; high-quality features are extracted and constructed from structured log data, and feature engineering and data enhancement are achieved; and combining the enhanced feature set with a predefined decision target to generate an intelligent decision data set for model training and evaluation, thereby realizing construction and evaluation of the intelligent decision data set. According to the method, high-quality data support is provided for the intelligent decision model, the system resource demand, the task execution time and the failure risk can be effectively predicted, the resource allocation efficiency is greatly optimized, and a solid foundation is provided for large-scale computing system resource management and scheduling.

This invention discloses a port communication system and method for a power router, relating to the field of power communication technology. The system includes a data acquisition module, an intelligent communication decision engine, a communication execution module, and a storage module. This invention utilizes an intelligent communication decision engine that integrates artificial intelligence technology to perform deep analysis and learning of data, predicting system operation trends and communication needs. This solves the problem that traditional fixed-rule decision-making cannot adapt to dynamic system changes, achieving accurate prediction of communication needs. When an anomaly is detected, the intelligent communication decision engine automatically generates the optimal communication decision scheme, which is then executed by the communication execution module. This addresses the problems of delayed response and unreasonable allocation of communication resources in existing technologies when dealing with faults or anomalies, enabling rapid handling of abnormal situations. Ultimately, this improves the intelligence and adaptability of the power router port communication system, ensures timely transmission and processing of critical data, and improves the operating efficiency of the power system.

The disclosed system provides advanced mission control predictive systems for low earth orbit semi-autonomous satellites. In operation, the system may include a satellite transit behavior prediction module configured to train a neural network based on current and past satellite orbital transit paths so as to predict future transit paths. Knowing precise future satellite transit paths enables a ground-based satellite control system to more efficiently (and with greater accuracy) control certain operations of the satellites. For example, a ground-based satellite control system can prepare for communications to commence at a particular time. Legacy systems can only predict one or two days in advance However, using a neural network that not only takes into consideration historical transit data, but also learned details such as solar wind, cloud patterns, etc., the neural network predicts future satellite transit paths with statistically-certain accuracy leading to a much narrower cone of uncertainty.

The invention relates to the technical field of cloud edge computing, and particularly provides a cloud task migration decision-making method based on deep reinforcement learning, which comprises the following steps of: constructing a target function by taking maximization of a cloud task success rate as a target; a deep reinforcement learning framework is constructed, and an LSTM network model is adopted to observe and predict the current state of the system based on the system history; describing a decision process under the condition that the system environment is uncertain based on a partially observable Markov decision process; and taking the current state of the system as the input of the improved deep reinforcement learning model, and obtaining a migration strategy of the cloud task based on the improved deep learning model. According to the method, a proper service migration mechanism is designed aiming at the cloud edge calculation problem in a mobile state, so that the low delay of the service is ensured, and modeling is carried out on the delay and reliability of the service migration process; and a DCPPOSM algorithm based on deep reinforcement learning is designed for effective migration decision making with an optimization objective of maximizing a task execution success rate.

Aspects of the present disclosure relate to a demand forecasting system. The demand forecasting system may include components for developing forecasting models, generating demand forecasts, and handling outputs of demand forecasting models. In some embodiments, the demand forecasting system may include a model training system and one or more components that can be used by the model training system to improve model performance.

This invention discloses a physically constrained dual-tower interactive prediction system and method for carbon nanotube synthesis, comprising a physical feature engineering module, a yield prediction tower, a mass prediction tower, a deep bridging module, and a training control module. The physical feature engineering module transforms the original experimental parameters into physical feature parameters. The yield prediction tower, connected to this module, implements mass conservation constraints through null space projection and outputs predicted yield values. The deep bridging module unidirectionally transmits yield information to the mass prediction tower through an information bottleneck mechanism. The mass prediction tower integrates physical features and bridging information, and outputs predicted mass features using an environment modulation gating unit. The training control module is used for overall training optimization. This invention addresses the problems of lack of physical consistency, negative transfer across multiple tasks, and insufficient generalization ability under small sample conditions in prior art purely data-driven models.

An information handling system including a processor and a memory coupled to the processor. The processor may be configured to monitor the information handling system to detect whether an information handling system is in a low power state. If the information handling system is in the low power state, then power consumption of the information handling system may be monitored. If the power consumption of the information handling system deviates from an expected power consumption of the information handling system, then a diagnostic routine may be performed.

The invention discloses a VSG stability analysis method and system based on power grid intensity disturbance, and belongs to the technical field of virtual synchronous generator control. According to the method, a small signal model of VSG power grid intensity disturbance is established, the small signal model of power grid intensity change is introduced into a closed-loop control model of a VSG system, and a coupling transfer function between the power grid intensity disturbance and active power and reactive power is deduced; further constructing an oscillation transfer function model, quantitatively analyzing the influence of the power grid intensity disturbance on the electrical quantity of the system, and judging the stability and oscillation transfer effect of the system under the disturbance through the amplitude of a transfer function; according to the method, the blank that a traditional small-signal model does not consider the influence of power grid strength change is filled, the dynamic behavior of the system under power grid parameter perturbation can be accurately predicted, the precision and accuracy of stability analysis are improved, and a theoretical basis and technical support are provided for VSG system control parameter optimization and robustness improvement.

The invention discloses an EMB motor control method based on CNN-LSTM-MPC, and belongs to the field of EMB motor control, and the method comprises the following steps: S1, generating preprocessed time sequence data; s2, predicting an EMB system state; s3, determining a group of optimal current control instructions based on the predicted EMB system state and the actual EMB system state; and S4, executing the optimal current control instruction. By the adoption of the EMB motor control method based on CNN-LSTM-MPC, accurate prediction of nonlinear dynamic characteristics of the EMB system through the deep learning model is combined with rolling optimization decision of model prediction control, high-precision and fast response to motor control is achieved under complex working conditions, meanwhile, through working condition self-adaptive parameter adjustment, the control precision of the motor is improved, and the control precision of the motor is improved. And multi-target requirements of braking distance, vehicle stability, heat fading inhibition and the like are considered, so that the control performance of the EMB system and the braking safety of the whole vehicle are comprehensively improved.

The invention provides a dairy cow ketosis regulatory gene prediction system based on multi-omics analysis and machine learning, which belongs to the technical field of molecular breeding and disease prevention and control, and comprises a data acquisition and preprocessing module, a feature set establishment module, a machine learning model establishment module and a result evaluation module, the method comprises the following steps: integrating dairy cow genome, transcriptome and metabolome data, screening candidate regulatory genes through whole genome association analysis, gene differential expression analysis, cis-eQTL positioning and co-positioning analysis, and constructing a gene expression feature set; core regulation genes are screened through L1 regularization penalty by means of a Lasso model, weights are distributed, model parameters are optimized in combination with grid search and cross validation, and model performance is evaluated through an ROC curve and an AUC value. According to the invention, an integrated technical system from gene screening to risk prediction is constructed, efficient screening of the core regulation gene and accurate prediction of ketosis risk are realized, and the economic loss of breeding is effectively reduced.

The invention relates to the technical field of computer operation and maintenance, in particular to an artificial intelligence-based multi-source information fusion dynamic alarm decision-making method, which comprises the following steps of: acquiring an original alarm signal of a to-be-predicted system and extracting corresponding multi-source data; performing semantic extraction and time sequence analysis on the multi-source data, and associating knowledge graph information as extraction features; identifying a service scene according to the multi-source data, and determining a dynamic weight of the extraction feature of each category according to the service scene; and performing prediction according to the dynamic weight and the extracted features to obtain a decision action and outputting the decision action. In order to solve the problem that a server early warning scheme in the prior art depends on a fixed rule and is not accurate enough, multi-source data is collected, semantic extraction, time sequence analysis and knowledge graph information association are carried out respectively, so that judgment on a service scene where a server is actually located currently is achieved, the importance degree of each index is determined and predicted, and the accuracy of the server early warning scheme is improved. A more accurate matching process for the risk rules is realized, and the accuracy of risk prediction is improved.

In an example embodiment, a predictive system maintenance module is created based on machine learning. The predictive system maintenance module achieves an improvement in predicting condition-based maintenance decision-making through a cloud-based approach, using a wide variety of information. Factors that influence downtime loss are identified and a generalized loss function, known as the downtime loss function, is defined. A prediction model is then built based on a multi-step forecasting time series model. The prediction model is then used to forecast a window that minimizes downtime loss. The predictive maintenance module uses historical data to foresee when and how to implement the seamless upgrading at a proper time so that it could have minimum downtime loss on the customer.

The application discloses a kind of distributed photovoltaic water electrolysis hydrogen production control methods based on artificial intelligence optimization, it is related to new energy and hydrogen energy technical field, the method first real-time acquisition photovoltaic array and electrolytic cell and so on operating data and carry out pretreatment;Then the double-layer optimization model including upper mixed integer programming model and lower deep learning model is established, wherein the upper model is with system total operating cost or unit hydrogen production cost minimum as target, lower model is LSTM time series prediction model, for predicting system efficiency;Utilize historical data to train lower model and update online;In each control cycle, the predicted information of lower model is input into upper model, and the optimal control sequence is solved by rolling horizon optimization strategy and is executed down.This application also considers electrolytic cell attenuation cost, multiple safety constraints and fault-tolerant control, suitable for the distributed hydrogen production system of multiple electrolytic cells in parallel, can realize system overall efficiency optimization.

The invention discloses a prediction system for predicting a fault point of a leveler based on a leveling result, and the system comprises the steps: collecting the leveling result in real time through image collection equipment, and obtaining the positions where waves, bends and damages are possibly generated, and the three-dimensional coordinates of lines through image processing and calculation; carrying out abnormity judgment on the working related parameters of the leveler and judging whether the three-dimensional coordinates of the lines are bent and overrun or not, and comparing whether the two judgment results are consistent or not; and the problem of missed judgment of a fault point caused by errors generated in acquisition of related working parameters of the leveler is prevented. And meanwhile, a Transform and RF fusion model is adopted to predict the line three-dimensional coordinate change trend of the leveling result, so that whether the roll gap and the gradient of the leveling roll are suitable for the leveling effect of the current leveling plate or not is judged according to the comparison of the prediction result, and then the function and the effect of judging and predicting the fault point of the leveling machine according to the leveling result are realized.

The invention relates to the technical field of traditional Chinese medicine informatics and artificial intelligence, in particular to an automatic screening method and system for traditional Chinese medicine efficacy substances based on a large model. According to the method, target efficacy attributes are determined, related targets are obtained, semantic comprehension and knowledge reasoning ability of a large model are combined, target related active compounds and compound data of target traditional Chinese medicinal materials or prescriptions are automatically collected and integrated, and after structure and attribute standardization processing, a training set and a prediction set are constructed. And performing model training and optimization by using a large model driven deep neural network, constructing a drug effect substance screening model, and accurately predicting specific drug effect substances in the traditional Chinese medicine. The system comprises a data automatic acquisition module, an intelligent standardization module, a large model knowledge enhancement training module, a prediction reasoning module, a visual output module and the like, the automation level, efficiency and accuracy of drug effect substance screening can be remarkably improved, blindness of traditional experiments is reduced, and intelligent technical support is provided for discovery of active ingredients of traditional Chinese medicine and research and development of new drugs.

Methods and systems for managing data processing systems are disclosed. A data processing system may include and depend on the operation of hardware and / or software components. Inference models may be implemented to predict future system infrastructure outcomes (e.g., component failures) using information recorded in logs that reflect the operation of the components. However, the models may be complex “black boxes” and may generate critical outcome predictions for downstream consumers without explanations of how the predictions are determined, resulting in downstream consumers having low confidence in the predictions. Therefore, hidden knowledge (e.g., structured knowledge attributes) of the models may be extracted and / or used to understand the underlying processes that the models use to predict the system infrastructure outcomes. The hidden knowledge may be utilized interactively (e.g., using AI chatbots) to provide users with failure prediction responses that allow users to better remediate failures of the data processing systems.

Apparatus and methods present a content item and question from an inquirer to a group of users in a first feedback session with a requirement for the users to provide predictions of how a member of a distinct target group will respond to the content item, receiving, during the first feedback session, the first group's prediction of the target group's responses, presenting, during a second feedback session, the same content item and the same question to the target group with a requirement for the target group to provide responses directed to their own responses, constructing a predictive model of the target group based on responses received from the target group during the second feedback session, measuring accuracy of the first group's predictions using the target group predictive model and providing the inquirer access to an ordered visual representation of the first group users arranged as a function of accuracy.

Aspects of the present disclosure relate to a demand forecasting system. The demand forecasting system may include components for developing forecasting models, generating demand forecasts, and handling outputs of demand forecasting models. In some embodiments, the demand forecasting system may include a model training system and one or more components that can be used by the model training system to improve model performance.

A predictive system may access a set of physical structures corresponding to a physical system. Each physical structure representative of a configuration. The predictive system may encode the accessed physical structures to produce a set of encoded physical structures by encoding, for each accessed physical structure, a position of each constituent unit of the physical system within the accessed physical structure. The predictive system may train a machine-learned model using the encoded physical structures. The predictive system may retrain the machine-learned model by iteratively: accessing a set of two or more candidate physical structures, determining a first energy difference among the set of candidate physical structures, obtaining a second energy difference between a set of physical structures corresponding to the set of candidate physical structures using a method to calculate reference energy values.