455 results about "Predictive analytics" patented technology

The present invention relates to a system and method for cross-domain predictive modeling using Bedrock-based foundation models and blockchain-anchored data. The invention integrates large-scale foundation model reasoning with distributed ledger-based data provenance to enable verifiable, secure, and explainable predictive analytics across heterogeneous domains such as finance, healthcare, logistics, and environmental systems. The system comprises a data ingestion unit for receiving and normalizing multi-domain datasets, a blockchain anchoring unit for generating cryptographic hashes and recording data provenance into a distributed ledger, a cross-domain harmonization processor for aligning heterogeneous feature representations into a unified latent space, a foundation model processor configured to execute Bedrock-based predictive inference with adaptive domain contextualization, a verification processor for validating predictions against blockchain-anchored ground truths, and a governance processor for maintaining immutable audit trails of model evolution.

The embodiment of the invention provides an elastic fragment routing and cold and hot data optimization method based on multi-dimensional feature prediction and related equipment, and belongs to the technical field of distributed database storage optimization. The method comprises the following steps: receiving a data stream from Internet of Things equipment; performing analysis and feature extraction on the data stream to obtain multi-dimensional feature data; according to the multi-dimensional feature data and the time sequence prediction model, performing prediction analysis on a fragmentation strategy to obtain an optimal fragmentation strategy; analyzing the routing decision according to the data priority of the data stream and a dual-mode routing engine to obtain routing decision result data; generating a target index name and creating a target index according to the optimal fragmentation strategy and the routing decision result data; according to the target index name, the data stream is routed to the target index according to the fragmentation key for batch writing; the fragmentation keys are from multi-dimensional feature data. According to the embodiment of the invention, intelligent prediction of the fragment number and automatic hierarchical storage of data can be realized, and the problems of resource elastic scaling and cost efficiency optimization are fundamentally solved.

The invention discloses an intelligent manufacturing real-time decision-making method and system based on digital twinning, and relates to the technical field of intelligent manufacturing. Comprising the following steps: S1, collecting multi-source operation data in a physical production system in real time; s2, dynamically constructing and updating a digital twinborn model of the physical production system based on the multi-source operation data acquired in real time; and S3, based on the digital twin model, monitoring the operation state of the physical production system in real time. According to the method, the multi-source operation data of the physical production system are collected in real time, and the digital twin model is dynamically updated, so that no lag deviation between the model and the physical system is ensured; when a dynamic disturbance event is identified, event-driven real-time simulation and predictive analysis are started immediately, delay caused by traditional batch processing is avoided, and disturbance influence can be accurately evaluated in combination with historical data and expert experience; and then an optimal decision scheme is generated and screened in real time through a multi-objective optimization algorithm.

The invention discloses a new energy electricity price accurate prediction method and system based on virtual power plant aggregation regulation and control, and relates to the technical field of industrial data processing. The method comprises the steps: S1, carrying out the preprocessing of meteorological environment data, output equipment data and battery power grid operation data; s2, constructing a variation dynamics analysis and mutation depth discrimination mechanism based on the meteorological environment data, and entering an output prediction and optimization process and a scheduling load response process in a layered manner; s3, executing energy storage collaborative optimization scheduling and parameter correction according to a meteorological output regression prediction analysis result; and S4, performing electricity price time sequence multi-source driving analysis, and reconstructing an electricity price fluctuation response strategy. The problems that new energy output violently fluctuates within a minute level due to sudden weather change, and electricity price prediction lags due to the fact that an existing prediction model depends on too low weather data updating frequency and cannot capture the rapid change in time are solved.

The invention discloses a new energy automobile charging strategy optimization method based on deep learning, and the method comprises the following steps: collecting multi-source operation data of a new energy automobile, and carrying out the preprocessing; inputting a multi-scale space-time diagram attention network, and performing time sequence feature extraction, spatial dependence modeling and space-time feature fusion processing; an improved Meta-SAC method is adopted to carry out charging power distribution decision making, return calculation and strategy network updating; performing predictive analysis on the real-time multi-source operation data based on the optimized strategy network; monitoring the running state deviation, and calculating and outputting a charging strategy correction result based on the updated state vector; executing a charging strategy correction result, calculating a difference with a charging execution result, and updating network parameters; and a self-adaptive charging strategy data set is generated after parameters are optimized for multiple times. According to the method, deep learning and meta reinforcement learning technologies are fused, intelligent optimization of the new energy automobile charging strategy is realized, and the method has the advantages of high adaptability, high response efficiency and excellent stability.

The invention relates to an application that utilizes artificial intelligence to enhance project management in construction and repair industries. The system includes a user computing device, an application server, and a database server. The application server processes project data, including blueprints, to generate materials lists, integrates with online marketplaces to facilitate purchasing, and employs machine learning for product recommendations. The system also uses predictive analytics to forecast project deliveries and prevent backorders, while optimizing resource allocation through efficient supplier and material selection. The software application features an AI-powered chat, job planner, marketplace, and cart, providing a comprehensive platform for real-time collaboration among contractors, suppliers, and manufacturers. This innovation streamlines project workflows, reduces errors, and improves overall efficiency in managing construction and repair projects.

The invention relates to the technical field of aviation safety, in particular to a fixed-wing aircraft natural icing prediction system based on a prediction model and big data analysis. Comprising a multi-source data acquisition module, a data processing module, a feature extraction module, an icing case database, a prediction model module, a prediction analysis module and an early warning result output module. The data processing module is used for preprocessing and cleaning the collected original data; the feature extraction module is used for extracting key feature variables having significant influence on natural icing of the aircraft from the processed data; the prediction model module is used for taking the extracted key feature variables as input and combining big data analysis to construct a prediction model; and the prediction analysis module is used for inputting the currently collected multi-source data into the trained prediction model for analysis, and by adopting the mode, the icing risk is predicted in advance, the operation fault-tolerant space is greatly increased, and the accurate prediction requirement in the flight scene can be met.

Dynamic valuation of virtual spaces in a virtual environment includes receiving telemetry data describing current user interactions with a virtual environment; performing predictive analysis on the telemetry data to predict future user interactions with a plurality of virtual spaces in the virtual environment; and continually adjusting values of the virtual spaces based on the predicted future user interactions without considering virtual objects in the plurality of virtual spaces.

The invention relates to a cerebral hemorrhage hematoma enlargement prediction method, system and device and a medium. The method comprises the following steps: acquiring baseline NCCT and CTA images of a patient; hematoma segmentation and three-dimensional reconstruction are carried out, and a key quantitative feature surface regularity index (SRI) and a density variation coefficient (DCV) are calculated; and inputting the SRI, the DCV and the clinical variables into the prediction model, and outputting the hematoma expansion risk probability. The prediction model is subjected to variable screening and strict verification, and risk visualization is realized through a column graph. The system comprises an image processing module, a feature calculation module, a prediction analysis module and a visualization module. According to the method, SRI and DCV are innovatively introduced to accurately quantify the hematoma morphology and density characteristics, and clinical variables are combined, so that the prediction accuracy and stability are remarkably improved; the efficiency is improved through AI-assisted segmentation; and the column graph enhances the model interpretability and the clinical decision support capability.

A computing system includes a machine learning algorithm executing a machine learning model to predict a probability of a fracture driven interaction associated with a hydrocarbon well. The machine learning algorithm trains the machine learning model using well treatment pumping data, offset well production data, and well stage data. Feature extraction is performed on the pumping data, production data, and well stage data to produce a machine learning model that is used to predict the probability of a fracture driven interaction. The resulting machine learning model can be deployed for use in ongoing hydraulic fracturing operations to predict and reduce real-time fracture driven interactions.

The invention discloses a cloud service cost optimization and prediction analysis system based on artificial intelligence, and relates to the technical field of cloud service cost optimization, and the system comprises a cost feature deep analysis module which accesses a cloud service full life cycle standardized cost data set, and generates a cost efficiency evaluation matrix and an abnormal attribution report; the multi-modal prediction engine module is used for constructing a multi-scene cost prediction model; the dynamic optimization decision module is used for generating a resource dynamic scheduling strategy, a service type selection optimization scheme and a cost budget dynamic allocation plan, and calculating an expected cost saving rate and a risk coefficient of each scheme; and the closed-loop iteration upgrading module tracks the implementation effect of the optimization scheme in real time and updates the three-dimensional characteristic spectrum and prediction model parameters. According to the invention, through a three-dimensional characteristic spectrum, multi-algorithm fusion prediction, SLA constraint verification and a closed loop iteration mechanism, and by matching with an intelligent interaction visualization and early warning module, intelligent transformation of the cloud service cost from passive accounting to active prediction and from experience optimization to scientific decision is realized.

A system for real-time generation of therapeutic plans based on predictive analytics of patient profile data including a processor of a therapeutic plan server (TPS) node configured to host a machine learning (ML) module and connected to at least one patient-entity node over a network and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: receive the patient profile data including patient nutrients intake data and medications intake data from the at least one patient-entity node; parse the patient profile data to derive a plurality of key classifying features; query a local database to retrieve local historical patients-related data based on the plurality of key classifying features; generate at least one classifier feature vector based on the plurality of key classifying features and the local historical patients-related data; provide the at least one feature vector to the ML module coupled to an Artificial Neural Network (ANN); receive a plurality of nutrients-medications correlation parameters from a therapeutic plan predictive model generated by the ML module using outputs of the ANN based on the at least one feature vector; and generate a therapeutic plan for the at least one patient-entity node based on the nutrients-medications correlation parameters.

The invention provides a transient stability control method for a power distribution network containing high-proportion distributed energy, and belongs to the technical field of electrical variable adjustment and control, and the method comprises the steps: obtaining real-time monitoring data, distributed energy output information and external environment data of the power distribution network, carrying out the fusion processing of the obtained data, and generating comprehensive monitoring data; performing transient stability prediction analysis on the comprehensive monitoring data, and outputting a prediction result; performing collaborative decision on the prediction result and the comprehensive monitoring data, and generating a dynamic adaptive adjustment strategy for cooperatively controlling the distributed energy equipment, the energy storage equipment and the controllable load; and executing the dynamic self-adaptive adjustment strategy, collecting feedback data after execution, and performing secondary adjustment by using the feedback data, so that the voltage parameter or the frequency parameter approaches a preset reference value. According to the invention, the transient stability of the voltage or frequency of a distributed energy grid-connected point and a sensitive load node can be realized by adopting a real-time monitoring and predictive analysis technology and combining a dynamic adaptive adjustment strategy.

The invention discloses a dynamic pricing optimization method and system for a coal industry chain and a program product, and the method comprises the steps: automatically obtaining multi-source data of the coal industry chain through a data collection and fusion module, and carrying out the fusion processing of the obtained spatio-temporal data; a pricing prediction module predicts the market price trend based on the fused data, and quantifies the response relationship of the price change to the supply and demand quantity; performing prediction analysis on a supply end, a demand end and an inventory end of the industry chain based on the fused data through a supply and demand analysis module; an evaluation strategy is dynamically selected through a quantitative evaluation module according to the market environment state, and quantitative scoring is carried out on the pricing scheme based on the pricing prediction and supply and demand analysis results; and solving by adopting a multi-objective optimization algorithm based on the quantitative scoring result through an optimization solving module, and outputting an optimal pricing scheme. According to the method, collaborative optimal pricing under the condition of balancing multiple targets is realized, and systematicness and overall benefits of decision making are improved.

The invention discloses a GIS partial discharge fault diagnosis method based on a double neural network model, and the method comprises the steps: obtaining a partial discharge signal ultrahigh frequency signal feature vector and a gas decomposition product concentration change feature vector through analyzing a GIS partial discharge signal and a gas decomposition product absorption spectrogram, respectively inputting the feature vectors into an LSTM network and a Transformer model, and carrying out the fault diagnosis of the GIS partial discharge fault. And outputting respective diagnosis results, building a prediction fusion model, carrying out prediction analysis by using the model, and finally outputting a fault diagnosis result. According to the GIS partial discharge fault diagnosis method based on the double neural network model disclosed by the invention, the problem of inaccurate partial discharge type identification caused by single detection method and incapability of avoiding external interference in the prior art is solved.

The invention discloses a building light storage energy economic scheduling and power compensation management system and method, and relates to the technical field of building energy management, and the method comprises the following steps: obtaining scheduling data in a future preset scheduling period, carrying out the prediction analysis based on the scheduling data, and generating a macroscopic charging and discharging scheduling strategy; the method comprises the following steps: acquiring a sky image sequence by using a visual sensor, analyzing the movement of a cloud cluster based on the sky image sequence to predict the descending trend of photovoltaic power, and sending a pre-compensation instruction to an energy storage converter and controlling the energy storage converter to charge and discharge before the actual descending of the photovoltaic power. According to the method, the descending trend of the photovoltaic power can be accurately predicted in advance, the pre-compensation instruction is sent to the energy storage converter before the actual falling of the power, the peak-valley electricity price arbitrage income is maximized through the combination of high-precision prediction and accurate pre-compensation, and the optimization and improvement of the overall performance of the building optical storage system are further realized.

The present disclosure provides systems and methods for predictive analytics. For example, a system provided herein includes a data retriever implemented on a first unit configured to communicate over a data network with a second unit, a database coupled to the first unit and configured to store deal data, and a forecast toolkit implemented on a second unit configured to communicate over the data network with a remote computing device. The forecast toolkit may include a future deal manager configured to identify, within enhanced deal information, future open deals; a machine learning engine; a forecast manager configured to union the future open deals with an initial forecast to produce a second initial forecast, the initial forecast constructed by applying the machine learning engine to historical deal information; and a table builder configured to construct a predicted revenue output based on the second initial forecast.

The invention relates to the technical field of power systems and energy management, in particular to a source network load storage collaborative interaction optimization system for high-proportion new energy. Comprising a multi-source information sensing data acquisition module used for monitoring various objects and an external environment in real time; the data modeling prediction analysis module is used for constructing a system parameter model and carrying out prediction analysis; the source network load storage collaborative optimization decision-making module is used for realizing multi-time-scale and multi-main-body optimization scheduling; according to the invention, the multi-source information sensing data acquisition module, the data modeling prediction analysis module, the source network load storage collaborative optimization decision module, the execution control instruction issuing module and the like are organically combined, so that the multi-source information sensing data acquisition module, the data modeling prediction analysis module, the source network load storage collaborative optimization decision module and the execution control instruction issuing module are integrated; omnibearing perception and prediction of new energy output, load demand, energy storage operation and power grid state are realized.

The invention relates to the technical field of power cable intelligent monitoring, and aims to solve the technical problems that in an existing cable state monitoring system, multi-source heterogeneous data fusion is insufficient, the coupling relation between physical quantities is difficult to reveal, and insulation aging, current-carrying capacity and fault probability cannot be quantitatively predicted according to evaluation results. The invention discloses a cable state multi-source data fusion and prediction system. The system comprises a data acquisition module, a feature processing module, a prediction analysis module and a cloud management platform. The system collects partial discharge, surface temperature and vibration data of the cable through the distributed terminal; performing spectrogram decomposition and fusion of the multi-source data by adopting a non-subsampled contourlet transform algorithm to generate multi-scale fusion features; building a prediction model based on a particle swarm optimization least square support vector machine, and outputting an insulation aging state prediction value, a current-carrying capability evaluation value and a fault probability prediction value of the cable; and data management and full-life-cycle service are realized through the cloud platform.

The application discloses a differential pressure sticking probability prediction method and system based on a Bayesian belief network, and comprises the following steps: collecting historical drilling data of a target oil reservoir block, preprocessing sample data, and creating a sample training set and a test set; determining characteristic input variables and characteristic output variables from the sample data; using the sample training set to establish a differential pressure sticking training model based on the Bayesian belief network; and verifying the differential pressure sticking probability result after calculation by using the verification set. The differential pressure sticking probability prediction model based on the Bayesian belief network is used to input the fact drilling characteristic input parameter value of the target oil reservoir development area into the model for prediction analysis, so that the differential pressure sticking probability of instant drilling is obtained, drilling guidance is provided for field engineering and technical personnel, a decision scheme is timely adjusted, the differential pressure sticking probability is reduced, and unnecessary operation time and economic loss are reduced.

A system and method for determining optimal sampling parameters is described. The method gathers soil data from a soil data source, which is associated with a soil organic carbon (SOC) project area. The crop prediction engine then determines that the soil data is less than optimal, but that the soil data is sufficient to generate an optimal sampling plan. The method completes a Monte Carlo simulation, which generates an empirical sampling distribution. The optimal sampling plan is determined by defining a margin of error, which provides a deviation from a predictive analysis of measured soil chemistry for a plurality of collected soil samples, and performing Monte Carlo simulations that include one Monte Carlo simulation having a lowest sampling density that satisfies the margin of error. The optimal sampling plan has the lowest sampling density and includes one or more sampling locations.

A predictive analytics apparatus, engine, system and method capable of providing real time analytics in a manufacturing system that may include a data input capable of receiving raw data output from at least one machine operable to effect the manufacturing system embodiments, and a processor to execute code from a computing memory. The code may comprise an adaptor to push the received raw data to a database to processed data; an extractor to extract the processed data from the database; predictive analytics to receive the extracted processed data and apply thereto a predictive model comprised of target data for the at least one machine, and to provide feedback to the at least one machine to modify performance of the at least one machine based on the application of the predictive model; and a visualizer capable to provide at least a visualization of the feedback and the performance.

A system is provided for artificial intelligence based predictive analytics of electronic user data. In particular, the system may comprise an artificial intelligence (“AI”) engine that continuously aggregates user data associated with a user and / or a group of users based on internal and external data sources across various different communication channels. The AI engine may then parse and tokenize the user data to generate a complete user snapshot associated with the user and / or the group of users. Based on analyzing the user data, the AI engine may generate a probability score associated with a predicted user action within the network environment. Based on the predicted user action and the probability score associated with the predicted user action, the system may drive decisioning processes within the network environment and / or generate one or more outputs to be presented on one or more user computing devices in and out of the network environment.

The invention discloses a river ecology small sample enhancement method based on a generative diffusion model, and the method comprises the steps: obtaining observation data in an ecological environment field, and carrying out the data preprocessing; performing time variation diffusion modeling by adopting a generative diffusion model architecture; constructing an ecological constraint function, and solving an optimal solution curve; carrying out data sampling, and outputting ecological time sequence data; and performing data simulation verification and prediction analysis. By introducing a time variation diffusion process and an ecological process modeling constraint mechanism, high-precision interpolation and long-period trend deduction of ecological variables are realized while the model generation capability is maintained.

Systems and methods for callback queue management are provided. An example system is configured to determine a maximum size of a callback queue based on a formula. The system determines eligibility of an inbound query for the callback queue based on a current number of calls in the callback queue being less than the maximum size. The system causes a callback call associated with the inbound query to be performed based on the inbound query being eligible for the callback queue.

A method of predictive analysis of one or more characteristics associated with multiple neurostimulators used by respective multiple users, the method comprising: processing at a first site first input data collected from a first plurality of neurostimulation devices and / or neurostimulation users, and generating first machine learning model data from the first input data, the first machine learning model data comprising first model parameters; processing at a second site second input data collected from a second plurality of neurostimulation devices and / or neurostimulation users, and generating second machine learning model data from the second input data, the second machine learning model data comprising second model parameters; processing the first model parameters and the second model parameters to generate third machine learning model data representative of at least one neurostimulation characteristic; and using the third machine learning model data to predict evolution of at least one neurostimulation characteristic.

Systems, methods, and apparatuses for industrial equipment diagnostics are disclosed. Specifically, a bus controller operatively connected to a processor is physically connected to two or more controller area network (CAN) buses, wherein each CAN bus operates according to a different protocol. The bus controller is configured to control the rate at which messages are received on certain buses by adjusting parameters such as a baud rate and sample rate. The bus controller is further configured to filter or mask certain CAN bus messages based on identifiers within the messages. The bus controller is operatively connected to a remote processing system for processing the CAN bus messages from their raw data format into a useable format. The remote processing system performs diagnostic analysis on the processed CAN bus messages, and furthermore performs predictive analytics on the processed CAN bus messages to identify patterns or discrete events indicative of future system failures.

This invention relates to the field of network detection and optimization technology, and discloses a method, system, storage medium, and device for network anomaly detection and automatic processing. The method includes: collecting multi-dimensional data related to network performance; preprocessing the collected data and constructing feature engineering on the preprocessed data; constructing a multi-dimensional anomaly detection model; performing time-series anomaly detection, multi-dimensional feature clustering analysis, and rule engine detection through the multi-dimensional anomaly detection model; classifying and automatically processing the anomalies detected by the multi-dimensional anomaly detection model; embedding data points on network performance indicators and anomaly events; monitoring the status of each layer of the network in real time and tracking and recording anomaly events; simultaneously performing predictive analysis on network performance data; mining data value through trend and correlation analysis; and generating optimization suggestions based on the predictive analysis results. Through the above method, a network anomaly detection and automatic processing system is realized, improving network quality and user experience.

The invention discloses a construction economic index data analysis method based on deep learning, relates to the technical field of data analysis, and carries out paver health index analysis based on paver operation condition data during road construction. Based on the paver health index analysis result and the paver screed plate historical wear condition data, paver screed plate wear rate prediction analysis is carried out; inputting the current wear thickness data and the wear rate prediction analysis result of the paver screed into the paver screed damage condition prediction analysis deep learning model, and outputting a paver screed damage grade probability prediction result; outputting a damage grade probability prediction result of the paver in a future work period according to the paver screed damage grade probability prediction result, and outputting related maintenance cost; according to the invention, a clear action basis and cost expectation are provided for a user, predictive maintenance of the paver is realized, sudden faults are effectively avoided, and continuity and stability of construction are guaranteed.

The invention provides an illness state tracking and early warning system based on a knowledge database, and the system comprises a data module which is responsible for collecting and integrating clinical knowledge and patient data information in the cardiovascular field; the survival rate prediction analysis module is used for extracting pathological features from the patient model, performing survival rate prediction analysis by using a deep learning algorithm and a neural network model, and displaying a prediction result to a physician in a visual report form; and the illness state auxiliary tracking, studying and judging module monitors the illness state change of the patient in real time, analyzes the illness state development trend and risk factors in combination with a knowledge graph and a patient model, and provides treatment scheme recommendation and early warning notification functions. Through the beneficial effects of integrating medical resources, improving data security, accurately predicting the survival rate of a patient, monitoring and early warning in real time, widely applying a knowledge graph, being friendly to user interaction, feedback circulation and continuous optimization and the like, powerful support is provided for prevention, diagnosis and treatment of cardiovascular diseases.