254 results about "Predictive modelling" patented technology

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.

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 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 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 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.

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.

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.

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.

The invention relates to the field of multi-machine operation control systems, and discloses a multi-machine operation energy-saving intelligent control system for an air blower, and the system comprises a data collection and fusion module which is used for collecting and fusing operation parameters and environment parameters of the air blower; the prediction modeling module constructs a system load prediction model based on historical data; the multi-agent decision module generates a control strategy according to the current state; the collaborative optimization module carries out non-cooperative strategy coordination among the multiple air blowers; and the parameter updating module updates the prediction model and the control strategy through the control error and training feedback. All the modules work cooperatively, and efficient energy-saving control and dynamic optimization of the system are achieved. The multi-agent collaborative optimization technology is adopted, the overall energy efficiency of the system is guaranteed, meanwhile, the load fluctuation of a single air blower is reduced to the maximum extent, the service life of equipment is prolonged, the problem of disordered scheduling when the load fluctuation is large in a traditional control method is solved, and therefore energy consumption is greatly reduced.

An automated adaptive radiotherapy system based on machine learning for designing and adapting personalized dosing plans and a corresponding system, the system comprising: • a data acquisition module to collect multimodal patient data such as anatomical images, genomics, physiological signals and electronic health records; • a preprocessing department that performs the normalization, alignment, segmentation and transformation of the acquired data into data structures suitable for predictive modeling; • a dose prediction engine containing at least one machine learning model trained to predict personalized three-dimensional radiotherapy dose distributions and dose-volume histograms from the preprocessed data; • an adaptation module to receive updated clinical data and automatically adapt the dosing schedule to intra-fraction and inter-fraction changes in patient anatomy and response to treatment; • a clinician dashboard that displays predicted dosing schedules, allows clinician interaction, and can make the model interpretable through explainable AI; • a data security and compliance layer that protects data through encryption, access control, and regulatory compliance.

The invention provides a cross-omics sparse feature selection system and method based on hierarchical causal modeling, and the system comprises a data input and preprocessing module which is used for receiving multi-omics original data of a multivariate sample; the hierarchical causal structure learning module is connected with the data input and adaptive preprocessing module and is used for constructing a cross-omics hierarchical causal topology; the causal-oriented sparse feature selection module is connected with the hierarchical causal structure learning module; and the model retraining and integration module is used for constructing a three-layer weighted integration discrimination model based on the screened markers, optimizing the fusion weight of each layer through a gradient descent algorithm, and outputting a final prediction result. According to the method, the protein-metabolism biological hierarchy relationship and serum-urine complementary information are fully utilized, and the method has good generalization ability and can be widely applied to marker mining and prediction modeling of cancers, metabolic diseases and the like, so that the accuracy and reliability of precise medical treatment are improved.

The invention discloses a multi-mode induction display screen response method fusing voice interaction, and relates to the technical field of man-machine interaction, and the method comprises the following steps: in a user interaction process, calling a built-in sensor through an interface component real-time state monitoring mechanism, continuously monitoring a rendering process of a target interface component at a millisecond-level time granularity, and displaying the rendering process of the target interface component; collecting multi-dimensional availability state parameter information of the interface component; the method comprises the following steps: preprocessing collected interface component multi-dimensional availability state parameter information, and extracting key indexes reflecting potential asynchronous triggering risks from preprocessed data through a feature engineering method; by sensing the state of the interface component in real time, extracting the asynchronous risk index and combining machine learning recognition and TCN prediction modeling, dynamic regulation and control of the induction triggering window and the animation rhythm are achieved, the problem of mispointing or dislocation of the induction component is avoided, the accuracy and safety of interaction response are improved, and the user experience is improved. The method is suitable for high-precision scenes such as vehicle-mounted, medical and industrial control.

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.

The invention belongs to the technical field of intelligent traffic and deep learning modeling, and particularly relates to a cross-layer low-rank fusion space-time traffic flow prediction modeling method. The method comprises the following steps: firstly, carrying out standardization, complementation and coding processing on historical traffic data, time labels and road topology, and constructing a space-time embedding representation; then, through node aggregation and agent number adaptive estimation, a double-stage attention modeling structure is established so as to reduce the calculation complexity; efficient fusion of cross-layer information is realized through output pairing and feature splicing of adjacent layers in combination with low-rank bottleneck compression and gating residual fusion; and a robust prediction result is generated by adopting a hybrid expert decoding structure and combining time dimension aggregation, routing weight initialization and multi-target constraint. According to the method, the problems of high-dimensional redundancy, calculation bottleneck and distribution drift in large-scale traffic flow prediction are effectively solved, and the prediction precision and the model efficiency are improved.

The invention relates to the technical field of wind speed prediction, in particular to a space wind speed modeling prediction method and system for a multi-fan matrix, and the modeling prediction method comprises the steps: building a three-row and three-column initial measurement fan matrix, respectively setting a plurality of measurement points in front of a plurality of set mapping positions on a central fan at equal intervals, the wind speed mean value and the turbulence degree corresponding to the measuring points are obtained under different fan rotating speed combinations of the initial measuring fan matrix, a measuring point-wind speed-turbulence degree sample library is constructed and used for training a multi-fan matrix prediction model, and the multi-fan matrix prediction model can predict the wind speed and the turbulence degree of any position in the k-row k-column multi-fan matrix; according to the modeling prediction system, the prediction modeling method is applied, enough fan rotating speed-wind speed distribution sample data are obtained within finite time, a high-precision multi-fan matrix prediction model is trained and obtained, the multi-fan matrix prediction model is applied to larger-scale multi-fan matrix configuration, and the applicability is high.

The invention relates to the technical field of photovoltaic power generation system monitoring, in particular to a photovoltaic power generation power medium and short term prediction method and system. Preprocessing the photovoltaic power generation power data, and performing feature selection; carrying out similarity evaluation by adopting a DTW algorithm, and carrying out modeling on a relationship between weather conditions and generated power; similar day clustering is carried out through K-Medoids in combination with a DTW algorithm; and training the prediction model and evaluating the model effect. According to the method, firstly, the correlation degree of meteorological factors and photovoltaic power generation power is analyzed through K-Medoids-DTW, and similar day clustering analysis is carried out according to weather conditions; a TimeXer model is used for performing eco-eco variable and eco-eco variable joint time sequence prediction modeling, and accurate prediction of photovoltaic power generation power under three weather conditions of sunny days, cloudy days and cloudy and rainy days is realized. Medium-short term photovoltaic power generation power prediction can be carried out under three common weather conditions, and the accuracy and efficiency of online monitoring and energy management of a photovoltaic system are improved.

The invention discloses a liquid cooling heat dissipation control system of a charging pile, relates to the technical field of energy storage charging piles, and solves the problems of low-frequency oscillation and energy consumption fluctuation caused by independent closed-loop adjustment of a pump and a fan and fault false alarm and failure alarm caused by fixed threshold diagnosis in the prior art. The collection module is used for synchronously collecting and filtering the cooling liquid inlet and outlet temperature, the power module temperature, the pump current and the fan rotating speed; a first prediction modeling module is adopted to generate thermal load prediction and time delay compensation parameters; constructing a self-adaptive baseline relationship between the pump flow and the heat exchange efficiency of the fan through a second baseline modeling module; performing dynamic anomaly judgment based on the residual sequence through a residual detection module; the cooperative scheduling module is used for solving the control track of the pump and the fan by combining the prediction result, the baseline parameter and the abnormal label on the basis of a model prediction control framework; according to the invention, the coordination of liquid cooling heat dissipation control and the adaptability of fault diagnosis are greatly improved.

The invention discloses an AI large model and competitiveness analysis-based address pre-warning and intelligent matching method, which comprises the following steps of: (a) dynamic portrait construction: constructing five-dimensional indexes including enterprise vitality, market expansion capability, resource adsorption capability, innovation growth potential and risk resistance capability, based on historical enterprise data quantitative indexes and in combination with industry characteristic dynamic distribution weights, an enterprise dynamic competitiveness portrait is formed; (b) migrated address prediction modeling: taking the five-dimensional index in the step (a) as a core, fusing a regional land cost fluctuation rate and a policy matching degree, inputting an industry special prediction model optimized by transfer learning, and outputting an enterprise migrated address dynamic risk value; and (c) industrial chain collaborative early warning: when the risk value in the step (b) exceeds a preset threshold value, starting an industrial chain collaborative early warning mechanism. According to the method, the enterprise competitiveness portrait is dynamically constructed, the address migration risk is accurately predicted, the industrial chain influence is cooperatively pre-warned, and a dynamic optimization closed loop is formed.

The invention discloses a method for detecting the content of salidroside in a rhodiola rosea extracting solution based on artificial intelligence. The method comprises the following steps: S1, performing spectral scanning of different wavebands on an extracting solution sample in a plurality of near-infrared wavelength ranges; s2, performing multi-dimensional interference suppression processing and signal purification; s3, carrying out spectral feature compression calculation to obtain a salidroside feature intensity factor; s4, carrying out salidroside characteristic signal-to-noise ratio enhancement and purity index calculation; s5, predicting the content of salidroside and outputting a content prediction score; and S6, mapping the content prediction score into the final salidroside content. According to the method, spectral signal purification, nonlinear feature extraction, dynamic noise enhancement, artificial intelligence prediction modeling and a secondary recheck mechanism are utilized to realize adaptive analysis of extracting solutions from different sources, so that the detection accuracy and stability are remarkably improved, and rapid and traceable salidroside content detection can be realized under the condition that a large precise instrument is not needed.

Systems and methods are disclosed for identifying relationships between complex datasets and / or high-dimensional datasets for predictive modeling. The method includes clustering data associated with one or more entities in a first dataset based on distance data; clustering the data associated with the one or more entities in a second dataset based on longitudinal data; consolidating the first dataset and the second dataset into a third dataset based on weights assigned to one or more edges between one or more nodes in the first dataset and the second dataset; generating a diagnosis space indicating a condition of the one or more entities based on the third dataset; and determining, via input of the diagnosis space into a machine learning model, an optimization of a weighting scheme for assigning weights to one or more features within the longitudinal data.

A predictive modeling method may include determining a time interval of time-series data; identifying one or more variables of the data as targets; determining a forecast range and a skip range associated with a prediction problem represented by the data; generating training data and testing data from the time-series data; fitting a predictive model to the training data; and testing the fitted model on the testing data. The forecast range may indicate a duration of a period for which values of the targets are to be predicted. The skip range may indicate a temporal lag between the time period corresponding to the data used to make predictions and the time period corresponding to the predictions. The skip range may separate input data subsets representing model inputs from subsets representing model outputs, and separate test data subsets representing model inputs from subsets representing validation data.

The invention provides a disease population middle-aged and elderly category structure prediction method based on gray component prediction modeling, which comprises the following steps: collecting multi-component elderly population structure data influenced by a specific disease, and analyzing component data features; building a modeling framework integrating a time elastic mechanism and component structure analysis according to the data features; establishing a novel dynamic nonlinear gray component prediction model of multi-parameter combination optimization adaptive to the irregular time sequence component data based on the framework; and gray component prediction model parameters are solved, and the structural evolution of the old-age disease crowd with each component is estimated, so that the problems of old-age category structure prediction and health management of the disease crowd based on small sample sequence modeling are solved. According to the method provided by the invention, the proportion change rule of the old people in different health states of the disease population can be accurately described, so that the health management personnel can more accurately grasp the change trend of the old people in the disease burden, and thus a more reasonable resource allocation and intervention strategy is formulated.

The invention discloses a rolling time domain reactive power optimization method and system containing a distributed photovoltaic power distribution network, and the method comprises the steps: firstly, employing a probabilistic prediction model to carry out the prediction of photovoltaic active power, and obtaining a future output expected value and an uncertainty interval; secondly, the prediction result is substituted into a multi-objective optimization problem with line loss, voltage deviation and reactive compensation as optimization objectives, and opportunity constraints are established by using the uncertainty interval so as to ensure the safety margin of the power grid voltage; and finally, performing rolling solution on the problem by adopting a multi-objective evolutionary algorithm guided by a power grid sensitivity index to obtain an optimal reactive compensation scheme. According to the method, through a closed-loop rolling mechanism of prediction, modeling and solving, prospective, collaborative and robust control of reactive power resources is realized, and the stability and economical efficiency of operation of the power distribution network under high-proportion photovoltaic access are improved.