310 results about "Predictive modelling" patented technology

The invention presents a system for geospatial topographical modeling and real-time system control to be used with subterranean slurry injection technology. The system consists of a predictive modeling subsystem, which processes land characteristics to generate optimal site plans for drilling and injection. The real-time control subsystem implements these plans, dynamically adjusting slurry compositions and injection parameters based on sensor feedback. A feedback loop between the subsystems allows continuous refinement of both predictive models and operational controls, enhancing the precision with which the technology shapes the ground and the efficiency with which the technology sequesters carbon.

The invention discloses a pulmonary infectious disease prediction system based on multi-modal data fusion, and aims to improve the accuracy and reliability of disease prediction through comprehensive analysis of multi-dimensional data. The system comprises a data acquisition module, a feature extraction module, a dynamic fusion module and a prediction modeling module. The data acquisition module is used for acquiring clinical and medical images and environmental factor data of a patient in real time; the feature extraction module extracts feature vectors of all data sources; the dynamic fusion module performs adaptive weight distribution on the multi-modal features and fuses the multi-modal features into multi-modal feature representation; the prediction modeling module is used for carrying out dynamic change trend modeling on the multi-modal feature representation through time sequence analysis so as to generate a probability prediction value of suffering from pulmonary infectious diseases and a disease trend prediction result of the patient; the invention provides a comprehensive and efficient disease prediction method by utilizing the collaborative advantage of multi-modal data and combining an attention mechanism and time sequence analysis, and can be widely applied to the fields of medical diagnosis and public health management.

The invention discloses a distributed optical storage micro-grid control system based on a large model and an energy management method, and the system collects various data through a data collection module, captures a time sequence long-term dependence relation based on a self-attention mechanism through a large model prediction system, and predicts the photovoltaic power generation amount, the load demand and the energy storage charging and discharging demand. The network-forming inverter integration module dynamically adjusts the output power, the energy storage strategy and the interaction power of the power generation system according to a prediction result, the distributed control strategy module adopts a distributed consensus algorithm to realize information sharing and collaborative decision making, and the energy management module makes a multi-time scale plan and introduces an economic optimization model. The energy management method comprises the steps of data collection, real-time monitoring, prediction modeling, plan making, distributed control, economic optimization, system monitoring, fault processing and the like. The method can improve the new energy utilization rate, the electric energy quality and the system stability, adapts to the change of environmental factors, and maximizes the economic and environmental benefits of the micro-grid.

A system for the temporal ordering of multi-channel data streams, comprising the following: a multi-channel data reception module configured to receive real-time data streams from multiple sources, extract temporal metadata, and detect discrepancies in timestamps; a time synchronization module configured to align data packets using timestamp normalization, adaptive buffering, and AI-based predictive modeling; an AI-powered predictive modeling module trained to estimate missing or delayed timestamps and to reorder out-of-order data packets based on learned patterns; an adaptive buffer module configured to dynamically adjust buffer sizes based on real-time data arrival rates and network conditions; an ordering and output processing module configured to reconstruct the correct order of the data packets and transmit them to a processing or analysis system; and a module for error detection and correction that is configured to detect inconsistencies in timestamps, transmission delays and missing data points, thus ensuring increased reliability of the ordered data sequence.

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 invention relates to the technical field of energy scheduling, and discloses an intelligent optimization scheduling method of an informatization energy management system, which comprises the following steps of: data acquisition and fusion: acquiring real-time data of each node in an energy system through distributed Internet of Things equipment and a multi-mode sensor network, and fusing and processing the acquired data to obtain a data fusion result; a high-quality data set in a unified format is generated; and prediction modeling and demand response: based on user behavior analysis and a machine learning algorithm, establishing a demand response model, and constructing a multi-stage energy demand and supply prediction model including short-term, medium-term and long-term time dimensions. Through combination of a collaborative optimization algorithm and a distributed decision-making mechanism, a global optimal energy scheduling scheme is realized on the basis of local optimization of each distributed energy unit, high collaboration and optimal resource allocation among the energy units in a complex energy network are ensured, and the energy scheduling efficiency is improved. Therefore, the energy utilization efficiency and the operation stability of the whole system are improved.

The invention relates to the technical field of image segmentation, in particular to a plant salt tolerance response modeling prediction method and system based on a time sequence image. The method comprises the following steps: preprocessing acquired plant sample image data; performing image segmentation on the preprocessed image data by using a plant semantic segmentation model based on U-Net; a plant salt tolerance response prediction model based on TimeSform is constructed; and predicting the plant salt tolerance response grade by using the plant salt tolerance response prediction model. According to the time sequence image-based plant salt tolerance response prediction modeling method provided by the invention, a prediction process integrating image acquisition, preprocessing, dynamic feature extraction and depth time sequence modeling is constructed, so that the efficiency, precision and automation level of plant salt tolerance phenotype recognition are remarkably improved.

The invention provides a line loss optimization system and method based on distributed photovoltaic topology analysis, and relates to the technical field of distributed photovoltaic, and the system comprises a topology connection module which is used for carrying out the topology connection, and building a distributed photovoltaic power distribution network topology model; the prediction modeling module is used for performing prediction modeling to obtain a photovoltaic output-power grid load space-time prediction model; the access position optimization module is used for carrying out photovoltaic access position optimization and obtaining a distributed photovoltaic optimization power distribution network topology model through iteration; the strategy analysis module is used for carrying out strategy analysis and determining power distribution network line loss control strategy parameters; and the feedback optimization module is used for carrying out simulation evaluation and carrying out feedback optimization on the power distribution network line loss control strategy parameters. The technical problem that the line loss control effect is poor because the line loss control strategy of the power distribution network in the prior art is often set based on experience and is not fully combined with the dynamic characteristics of photovoltaic power generation and load for real-time adjustment is solved.

Discussed herein are methods and systems for an interdependent series / suite of AI models. In one embodiment, a processor receives projection assumption inputs from a user device and executes a population builder machine learning model to predict a dynamic adjustment table. It applies the model to population data to generate a value population file, which simulates a subset of the population based on the predicted table. The file contains value cells representing instances of a product. The device then runs a mortality machine learning model to determine mortality data for the product using the simulated population. Finally, it executes a flow projection model to generate a projection report for the product, incorporating mortality data and projection assumptions.

The invention discloses a medical health cost prediction system and method based on multi-source data fusion, and relates to the technical field of medical data analysis, the system firstly fuses a plurality of heterogeneous medical information sources to generate structured data; then, multidimensional features related to the cost are extracted to construct a feature representation vector; and the cost prediction modeling module constructs a cost prediction model through graph structure modeling and semantic embedding, carries out joint optimization by combining a graph attention mechanism and semantic similarity, and outputs a prediction result by fusing a time sequence and static characteristics. A joint optimization algorithm of graph semantic comparison loss and prediction deviation loss is introduced into model training, and positive and negative sample pairs are constructed through a cost label distance. And the feedback optimization module triggers model updating when the prediction deviation exceeds a threshold value, and dynamically adjusts a model structure and parameters through a deviation index and a sample confidence factor. And the prediction interpretation module analyzes influence factors based on intermediate layer features or gradient propagation, outputs an interpretation report, and improves the transparency and credibility of the model.

The invention provides a food freshness evaluation method and system based on image processing. The method comprises the following steps: acquiring a multi-view image of food, transmitting the image to a central processing center, setting personalized image acquisition parameters, and forming a food image database; based on the food image database, performing high-precision dynamic monitoring on color and texture changes of the food in different time periods, and generating a preliminary evaluation result; according to the preliminary evaluation result, combining food types, storage conditions and historical sales data, performing prediction modeling on the freshness of the food to obtain a final freshness evaluation report; and utilizing the freshness evaluation report to adjust an inventory strategy in real time, optimizing a food distribution plan, and generating an optimized management process. According to the technical scheme provided by the invention, the accuracy of food freshness evaluation is improved.

The invention discloses a behavior-driven twinborn prediction method, and relates to the technical field of intelligent information processing and prediction modeling, and the method comprises the following steps: building a unified event time baseline, collecting nanosecond clock offset information of each data source, building a cross time sequence suspicion chart, recognizing time synchronization abnormal nodes, and forming a credible time anchor point set; and based on the trusted time anchor point set, executing anti-fact playback, reconstructing a historical evolution process of behavior data, generating a time offset vector set, and constructing a corresponding time sequence offset spectrum. According to the method, through construction of a unified time baseline, trusted anchor points, anti-fact replay, causal topology and time reversal control, time sequence dislocation identification, calibration and false trajectory elimination of multi-source behavior data are realized, a dynamic self-healing closed-loop prediction mechanism is established, and the twin system prediction accuracy and stability are improved.

A method and system for AI based insight extraction from format-bound financial transaction data includes transforming a structured dataset in ISO format into a transformed dataset having metadata interpretable by a LLM Metadata includes descriptions of field names, expected values, entity relationships, and business rules. The transformed dataset is analyzed using machine learning models such as regression analysis, principal component analysis, predictive modelling, or anomaly detection. An intent and content of a natural language query are determined using an NLP model. Based on the intent, context, and metadata, the LLM generates a database query, which is executed on the structured dataset to retrieve relevant data. Insights are generated by combining the retrieved data with machine learning results.

The invention relates to the technical field of prediction modeling, in particular to an ultra-short-term prediction method and system based on a satellite inversion technology, and the method comprises the following steps: obtaining satellite inversion earth surface solar incident radiation data and ground observation data, calculating the matching degree, classifying and extracting stability indexes according to meteorological conditions, and screening short-term prediction applicable data. An applicable radiation data set is obtained. According to the method, a stability index is calculated by using satellite inversion and ground observation matching degree, an error mapping relation is extracted by combining an error trend and probability density, data stability is enhanced, noise is reduced by numerical adjustment and smoothing processing, prediction continuity is improved, multi-mode meteorological data is fused, dynamic weight optimization is performed, and precision is improved. Photovoltaic prediction is combined with radiation characteristics, error adjustment and abnormity elimination are carried out to reduce extreme fluctuation, regional fragmentation is combined with error feedback to dynamically optimize prediction parameters, adaptability is enhanced, and timeliness and accuracy of short-term prediction are improved.

An inverse design system combines a large language model (LLM) with a task-specific optimizer, which includes a search function, a forward model, and a comparator. The LLM adjusts parameters of the optimizer's components in response to a design scenario. Then the optimizer processes the design scenario to produce design candidates. Optionally, the LLM learns from the design candidates in an iterative process. A stochastic predictive modeling system combines an LLM with input distributions and a forward model. The LLM adjusts one or more of the input distributions and / or the forward model in response to a forecast scenario. Then the forward model processes a sampling of the input distributions to produce a forward distribution. Optionally, the LLM informs the sampling process. Optionally, the LLM learns from the forward distribution.

A navigation system and method are disclosed for operation in GPS-denied environments using quantum-inspired sensor fusion, dynamic virtual anchor points (VAPs), and predictive environmental modeling. The system represents multiple position hypothesis using wavefunction-like expansions and integrates VAP-based triangulation for drift correction. A predictive modeling module ingests solar, geomagnetic, and environmental data to proactively adjust sensor weighting. A cybersecurity module employs quantum-algebraic key generation and location-derived ephemeral keys to secure inter-device communication. The system includes an augmented reality (AR) interface to visualize and edit anchor references, and a neurofeedback module that adapts the AR interface based on real-time physiological signals from the user. The method further enables anchor optimization via AI-driven repositioning and supports low-power edge execution using approximate amplitude filtering. Additional modules may include fractal antennas, neuromorphic processors, and adaptive forecasting layers to maintain positional accuracy and user experience in subterranean, multi-floor, or magnetically complex environments.

Analytics dashboards for critical event management systems that include artificial-intelligence (AI) functionalities, and related software. AI functionalities disclosed include pattern recognition and predictive modelling. One or more pattern-recognition algorithms can be used, for example, to identified patterns or other groupings within stored critical events, which can then be used to improve response performance and / or to inform the generation of predictive models. One or more predictive-modeling algorithms can be used to generate one or more predictive models that can then be used, for example, to make predictions about newly arriving critical events that can then be used, among other things, to provide optimal response performance and allow users to efficiently and effectively manage responses critical events. These and other features are described in detail.

In general, various aspects of the techniques enable predictive modeling for forged components. A computing device comprising a memory and a processor may be configured to perform the techniques. The memory may store a trained machine learning model that associates training features extracted from data representative of a plurality of training forged components to a plurality of training model results. The memory may also store data representative of a target forged component. The processor may perform a geometrical analysis with respect to the data representative of the target forged component to extract target features, and apply the trained machine learning model to the target features to obtain predicted model results for the target forged component. The processor may also output the predicted model results.

The invention provides a charging pile power distribution method and system based on dynamic load balancing, and the method comprises the following steps: obtaining the local state information of each charging pile, the state information at least comprises the charging queuing length, the current vehicle charge state, the current load power, the battery health state, the predicted residence time, the electricity price information and the power grid load state; based on the historical charging behavior, the electricity price fluctuation and the traffic flow data, the vehicle access frequency, the electricity price trend and the power demand of each charging pile in the future time period are predicted through a time sequence neural network; a prediction model is constructed; according to the method, forward-looking power planning is realized by entering a multi-dimensional prediction mechanism; the power distribution efficiency is improved by adopting a local collaborative game; the self-adaption and generalization ability is realized through reinforcement learning, and the robustness of a scheduling strategy in multiple scenes is enhanced; through a multi-target scheduling index, the electricity price, the battery life, the waiting time and the power grid load are optimized at the same time.

A computer-implemented method for optimizing a distributed database includes receiving, at an aggregator, one or more query logs comprising past queries received by the distributed database. The method includes generating, by the aggregator, a query prediction model based on the one or more query logs. The method includes predicting, by the aggregator, a future query using the query prediction model. The predicted future query is predicted to be received by an edge device. The method includes causing, by the aggregator, data for responding to the predicted future query to be transmitted to the edge device.

The invention discloses an asynchronous time series data processing and predictive modeling method, system, device and medium, which are mutually corresponding schemes, and in the scheme, by combining the powerful spatial modeling capability of a graph attention network (GAT) and the advantages of a gating cycle unit (GRU) processing time series data, the time series data processing capability of the GAT is improved, and the time series data processing efficiency is improved. According to the method, the space and time relevance between complex and changeable asynchronous time sequence data in the multi-stage continuous production process can be comprehensively captured, so that the prediction precision of the production data is remarkably improved, an enterprise can more accurately predict key parameters and state changes in the production process, and powerful support is provided for optimizing production control. In addition, the interpretability of the hybrid model also provides an important basis for transparent and intelligent transformation of the production process, and is helpful for an enterprise to construct a more efficient, green and sustainable production system.

The invention discloses a heat supply regulation and control method based on side cloud cooperative computing and a related device, and relates to the technical field of comprehensive energy heat supply, real-time data is obtained and then input into a current heat supply control model, and after control parameters are obtained and sent to a local side to be executed, the indoor temperature reaches a target value of the indoor temperature. The current heat supply control model is obtained by selecting the preset modeling algorithm for training according to the characteristics of the current historical meteorological data and the current historical room temperature data stored in the cloud platform and the current historical local operation data of the local side, and the training data of the model is comprehensive compared with the training data in the prior art; the method is a prediction modeling algorithm selected based on characteristics of historical data. Therefore, the method can be used for training different heat supply systems and establishing adaptive current heat supply control models with relatively high control precision.

An inverse design system combines a large language model (LLM) with a task-specific optimizer, which includes a search function, a forward model, and a comparator. The LLM adjusts parameters of the optimizer's components in response to a design scenario. Then the optimizer processes the design scenario to produce design candidates. Optionally, the LLM learns from the design candidates in an iterative process. A stochastic predictive modeling system combines an LLM with input distributions and a forward model. The LLM adjusts one or more of the input distributions and / or the forward model in response to a forecast scenario. Then the forward model processes a sampling of the input distributions to produce a forward distribution. Optionally, the LLM informs the sampling process. Optionally, the LLM learns from the forward distribution.

The invention provides an interior wall tile glaze mill control method and system based on intelligent energy consumption analysis, and belongs to the technical field of ceramic tile process control. Aiming at the problems of high energy consumption and difficulty in quality control in a ceramic tile glazing process, an energy optimization and product quality dual-channel control framework is designed. According to each channel, a # imgabs0 # controller is adopted to provide basic control, a # imgabs1 # network is adopted to carry out prediction modeling, and a # imgabs2 # controller is adopted to realize a hybrid scheme of optimal control. And by designing a special channel coordination mechanism, the coordination of energy consumption optimization and quality control is realized. Simulation results show that compared with traditional # imgabs3 # control, the method has the advantages that the thickness consistency of the glaze layer is guaranteed, meanwhile, the mean square error is reduced by 62%, the energy consumption is reduced by 21.6%, and the effectiveness of the method is proved.

The invention discloses a tensor Tucker decomposition-based soft package lithium ion battery temperature field prediction method and system, and the method comprises the steps: carrying out the time-space separation of the two-dimensional temperature field data of the surface of a battery through employing a tensor Tucker decomposition technology, and then building a neural network prediction model for estimating the future low-order time characteristics of a nominal system; and the prediction information of the future temperature field of the battery is reconstructed through Tucker decomposition inverse transformation. According to the method, a model simplification method based on tensor Tucker decomposition is adopted, the complex calculation problem that high-order data are directly modeled one by one is avoided, meanwhile, the two-dimensional space-time characteristics of the battery surface temperature field are considered, and the prediction modeling difficulty is greatly reduced while the model precision is guaranteed. The method disclosed by the invention is simple and easy to implement, is particularly suitable for the temperature field prediction problem of the large-scale soft package lithium ion battery, can better adapt to the modeling requirement of a computing resource limited system, and has a wide application prospect.

The invention relates to the technical field of network slicing, and discloses a wireless network multi-link communication method based on dynamic link configuration. The method comprises the following steps: collecting state data of heterogeneous links such as cellular links, Wi-Fi links and satellites in real time, and constructing a global network state view; analyzing the network slice SLA and converting the network slice SLA into a quantitative performance constraint; predicting the future performance of the link by using a gating circulation unit neural network; generating a global optimal data flow routing and distribution strategy meeting the QoS (Quality of Service) requirements of the slices in the central controller based on reinforcement learning; and compiling the strategy into a configuration instruction and issuing the configuration instruction to the terminal and the access point for execution. The system comprises a state sensing module, a strategy analysis module, a prediction modeling module, a strategy generation module and a configuration execution module. By means of predictive planning and intelligent decision making, dynamic, fine and on-demand scheduling of heterogeneous resources is achieved, and the network resource utilization rate and the multi-service service quality guarantee capacity are remarkably improved.

The invention provides a hematologic tumor cord blood transplantation treatment prognosis index evaluation decision generation method and system based on prediction modeling, a medium and electronic equipment, and relates to the technical field of medical artificial intelligence. The method comprises the following steps: acquiring multi-source data of a key time point in a whole process of blood tumor umbilical cord blood transplantation treatment of a patient; integrating expert knowledge to form an expert knowledge network, taking the expert knowledge network as priori knowledge of multi-target prediction, and performing multi-dimensional prognosis prediction by using multi-source data; periodically acquiring time sequence multi-source data, capturing time evolution characteristics of patient states, and automatically triggering an updating mechanism at a key clinical time point to dynamically adjust a prediction result; generating a risk assessment grade, a feature importance analysis result and an individualized interpretation report based on the prediction result, and performing visualization; and generating a clinical decision using the large language model. According to the technology, accurate evaluation and individualized clinical decision support for hematologic tumor cord blood transplantation prognosis are achieved.

Analytics dashboards for critical event management systems that include artificial-intelligence (AI) functionalities, and related software. AI functionalities disclosed include pattern recognition and predictive modelling. One or more pattern-recognition algorithms can be used, for example, to identified patterns or other groupings within stored critical events, which can then be used to improve response performance and / or to inform the generation of predictive models. One or more predictive-modeling algorithms can be used to generate one or more predictive models that can then be used, for example, to make predictions about newly arriving critical events that can then be used, among other things, to provide optimal response performance and allow users to efficiently and effectively manage responses critical events. These and other features are described in detail.

A system for AI-driven predictive healthcare for pandemic prevention, which includes the following: a data collection module configured to capture health-related data from IoT-based wearable devices, electronic health records (EHRs), environmental sensors, and social media in real time; an AI-driven analysis module that is operationally linked to the data collection module and configured to use deep learning and natural language processing (NLP) techniques to process and analyze collected data in order to detect potential disease outbreaks; a predictive modeling module configured to use hybrid epidemiological and AI models to predict disease transmission patterns based on mobility data, population density, and historical infection trends; a warning and response module that automatically generates and disseminates notifications to health authorities, medical professionals, and vulnerable individuals based on predicted outbreak risks; a blockchain-based data security module configured to ensure the secure storage, access control, and verification of health-related data through decentralized ledger technology and smart contracts; and a decision support module that is operationally linked to the predictive modeling module and provides AI-driven recommendations for healthcare resource allocation, vaccination strategies, quarantine measures and public health measures.

The invention discloses a sparse coding and graph space-time attention fused coagulant addition prediction method, and relates to the technical field of time sequence data modeling prediction. According to the method, through the combination of a Transform encoding-decoding architecture and a sparse self-attention and top-k sparse strategy, the missing data complementation precision is effectively improved, and high-quality and missing-free input data is provided for subsequent prediction modeling; in the aspect of spatial feature expression, node association of multiple subsystems of a water production system is captured by means of a graph attention network, and by dynamically calculating association weights among nodes and aggregating neighbor features, the information entropy of spatial features is improved, and the expression ability of the spatial features to a multi-node coupling relationship is greatly enhanced; in the aspect of time sequence prediction performance, a time attention mechanism can dynamically focus on key time sequence nodes, the utilization rate of key time sequence features is further improved, and the accuracy of coagulant adding prediction is improved.