130 results about "Predictive modelling" 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 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.

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 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 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 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 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.

A system monitors impression data from a plurality of media channels including attributes of content presented on a respective media channel. The system inputs the impressions data and conversion data into a machine learning predictive model. The machine learning predictive model is trained by determining an impact of historical impression data on historical conversion data for each media channel. A machine learning predictive model incorporates an impressions / conversions predictive model that generates coefficients to measure attribution of different channel / campaign combinations to predicted conversions. The machine learning predictive model also incorporates an optimization model to effectively allocate marketing budget among various channels and campaigns in order to realize incremental conversions. The optimization model utilizes a constrained optimization framework based upon user-inputted budgets and budget constraints. Disclosed mixed media marketing predictive modeling provides a better understanding of budget allocation strategy for mixed media marketing factors.

The invention discloses a multi-energy management control method for a hybrid power system, and the method comprises the steps: S1, multi-source information processing, S2, working condition prediction modeling, S3, energy distribution optimization, S4, control execution and state monitoring, and S5, model and parameter adaptive adjustment. Future working conditions and required power are accurately predicted through multi-source information fusion and an LSTM model, and a prospective basis is provided for energy distribution. A dynamic weight multi-objective optimization algorithm is adopted, fuel consumption, battery SOC and emission indexes are planned as a whole, an optimal power distribution scheme is solved, and the energy efficiency and the environmental protection property are remarkably improved. Meanwhile, the temperature change rate of the component is calculated in real time through a heat flow evolution algorithm, a cooling system is dynamically adjusted, and precise heat management is achieved. The system further introduces a multi-dimensional deviation closed-loop correction mechanism, key parameters are adaptively adjusted through feedback data, and efficient, stable and reliable operation under different working conditions and environments is ensured.

The invention provides an NFC information interaction and management method for an intelligent signboard of power equipment, and the method comprises the steps: extracting field problem feedback data from an obtained synchronous data flow, carrying out the mode recognition and priority sorting of the feedback data through a support vector machine algorithm, and determining a high-priority problem list; according to the determined high-priority problem list, a deep neural network algorithm is adopted to carry out prediction modeling on list data, the future equipment fault trend is judged, and a predictive maintenance suggestion data set is obtained; the obtained predictive maintenance suggestion data set is fused with the real-time interaction content, a closed-loop feedback instruction is generated in a background system, and guidance information update for field operation is obtained; and extracting verification parameters from the obtained guidance information update, performing integrity verification on the parameters through a hash function, judging the reliability of the updated data, and obtaining the finally confirmed closed-loop management information.

The present invention is directed to a software-based technology system for managing and executing high volumes of work on complex projects across multiple organizations involving multiple users that results in superior outcomes at significantly reduced time and cost. Litigation is the initial application of this invention; each case is handled as a project. As applied to litigation, the system of the invention empowers a novel method of end-to-end case assessment, planning, execution and management by integrating tools, processes and procedures allowing for more informed decision-making and deliberate execution of all case-related actions as well as enhanced coordination and communication among all constituents, resulting in efficient achievement of targeted outcomes. The system provides intuitive, menu-driven functionality for inputting, processing and utilizing vast amounts of information, including company and case-specific information, attorney discretionary evaluation and input, document templates and monitoring and reporting requirements. A threat algorithm cross-correlates via multivariate analysis critical data points with customized company-specific information and business logic to generate a numeric threat score as an output. The system also predicts the expected settlement range with a high degree of precision for a specific case as the output of one or a combination of multiple artificial intelligence (AI) / machine learning algorithms. The system then generates a unique case strategy (which includes an expected outcome, including projected outcome, Case Plan and estimated attorneys' fees and costs). System logic learns from thousands of examples of similar cases and directs highly effective and efficient execution of a specific Case Plan providing each attorney and staff member assigned a role in a case with a prioritized task list that is updated in real time as other users complete tasks and litigation priorities change. For each user, the system integrates assigned tasks across multiple cases and multiple client portfolios into a single, prioritized task list. The execution features become increasingly valuable given the volume and complexity of projects across multiple organizations and users, allowing users to focus on their area of expertise and avoid the wasted time and distraction of trying to determine what they should be working on or recording notes to communicate what they have been doing since the system automatically performs these functions and directs the various users' next steps while monitoring their progress against the plan. Integration of administrative functions, including automated time tracking, billing and reporting without the need to rely on users to update this information, further increases quality and efficiency of legal services. Key objectives of the system of the present invention include eliminating gaps between business objectives and litigation strategy and optimizing communication and coordination of case-related tasks and priorities in a fully automated fashion, which are all achieved by leveraging data and technology to enable a fundamentally different approach that becomes smarter and more precise via machine learning and predictive modeling.

The invention provides a mechanism model and data driven air compressor energy consumption hybrid estimation method and system, and relates to the technical field of industrial energy conservation and intelligent control. The method comprises the steps that firstly, a large number of obviously irrelevant characteristic quantities are rapidly removed through Pearson correlation analysis, and a set is obtained; capturing a nonlinear relationship by using a random forest feature analysis method, and comprehensively mining potential features to obtain a set; a correlation analysis result is taken, and an optimal feature subset is prepared for subsequent modeling; on the basis that a core mechanism model framework is reserved, a Lasso intelligent algorithm is used for learning and compensating for residual errors which cannot be covered by a mechanism model, and air compressor energy consumption prediction modeling is achieved. The hybrid model has high precision and strong generalization ability; meanwhile, the model is lightweight, calculation is efficient, and a real-time control system can be embedded conveniently.

The invention provides an asphalt material softening point prediction modeling method based on an infrared spectrum, and belongs to the technical field of road engineering material performance prediction. The method comprises the following steps: collecting infrared spectrum data of a multi-source asphalt sample and carrying out standardization pretreatment; fusing and extracting functional group quantitative features, wavelet multi-scale features and principal component dimension reduction features; constructing a high-stability feature subset by adopting a progressive feature optimization strategy guided by a physical mechanism; a self-adaptive Stacking fusion model is constructed and trained, the model integrates four base learners including PLSR, SVR, random forest and gradient boosting regression, and Bayesian ridge regression is used as a meta-learner to realize uncertainty quantification of a prediction result. The method can quickly output the predicted value of the softening point without a physical test, solves the technical problems of low efficiency and poor consistency of a traditional method, and is suitable for production quality control, on-site quick detection and formula optimization of asphalt materials.

This invention provides a method and system for intelligent mixing of metallurgical dust and sludge based on deep machine learning. By mining data from metallurgical dust and sludge strong mixers, preprocessing data acquisition, real-time modeling of strong mixers, multi-step predictive modeling of strong mixers, intelligent control of water addition, intelligent control of binder, and intelligent control of return material, this invention solves problems such as uneven mixing and unsatisfactory mixing effects in the metallurgical dust and sludge field. It improves the processing capacity of the mixing system, enhances production quality and capacity, and paves the way for the digital and intelligent transformation of metallurgical dust and sludge.

The invention discloses a digital solid mineral resource exploration and data acquisition internet-of-things transmission method and system, and the method comprises the steps: carrying out the denoising and normalization processing of multi-source sensing data through an edge calculation node, and guaranteeing the data quality; a low-power wide area network and 5G and LoRa converged communication are combined to construct an efficient transmission channel, and a standby link is switched by detecting radio interference to guarantee the transmission stability; cloud edge collaborative data encryption is utilized, and a block chain technology is combined to record and upload a log, so that data security and traceability are ensured; and finally, performing three-dimensional geologic body modeling and geographic information system analysis based on the traceable data, and generating accurate scheduling instruction data. According to the invention, through the whole process innovation from data processing, transmission optimization, safety guarantee to prediction modeling, the efficiency of geological data processing and the accuracy of metallogenic prediction are significantly improved, and reliable technical support is provided for geological exploration.

A digital documentation system for preparation of engineering documents utilizing one or more artificial intelligence (AI) algorithms is provided. The system includes a user interface for selecting and populating templates with data, and one or more AI algorithms for creating and recommending templates, and preparing documents based on the recommended templates. The system uses natural language processing and semantic analysis algorithms to understand the content of the templates, documents, and associated engineering data, and to generate and recommend relevant templates to the user based on user prompts. The system also uses machine learning and predictive modeling and decision-tree algorithms to assist with the preparation of documents, by generating suggestions for data fields and values based on the user's previous inputs and the overall context of the document and available engineering data, including model data and metadata from digital models accessed in a zero-trust framework.

A method and system for a generative machine learning (ML) framework generating predictive results regarding financial transactions. The method includes generating the generative ML framework by connecting: a base layer; a data processing layer; at least one large language model (LLM) layer; a ML processing layer; and an applications layer. The method further includes executing the generative ML framework by: storing and receiving a first data; performing data processing procedures on the first data resulting in a standardized data, wherein the standardized data includes at least one specific case involving the financial transactions. The operations further include parsing the standardized data to generate analytical results with natural language descriptions; inputting, into the ML processing layer, the analytical results; performing predictive modeling of the analytical results to generate the predictive results; transmitting the predictive results; and generating at least one application model based on the predictive results.

Predictive modeling is performed to predict the optimal input parameters for the manufacturing process. [Solution] The method includes receiving expected output data for a manufacturing process that defines the attributes of the output of the manufacturing process; accessing multiple machine learning models that model the manufacturing process; using a first machine learning model to determine input data for the manufacturing process, including values ​​for a first input and values ​​for a second input, based on the expected output data for the manufacturing process; and combining the input data determined using the first machine learning model and the input data determined using the second machine learning model to create a set of inputs for the manufacturing process, including candidate values ​​for a first input and candidate values ​​for a second input.

The application discloses a material formula optimization method and system based on rheological data and machine learning. The application takes rheological characteristics as the input of a machine learning model to optimize material parameters, formula variables and process parameters of a polymer. The method of the application does not directly statistically correlate a physical response with material variables, but embeds an intermediate layer based on basic polymer physics principles and rheological theories for connecting the bottom layer and the top layer of structure variables and responses, and realizes integration and prediction modeling of the three layers based on a rheological data set.

The invention discloses a silence cabin intelligent control method and system based on an AI digital matrix, relates to the field of silence cabin intelligent control, and constructs a high-dimensional silence cabin multi-modal feature tensor, namely the AI digital matrix, by obtaining multi-source heterogeneous data such as environment, biological features and equipment states and using time synchronization, matrix construction and normalization cleaning technologies. On the basis, performing user activity scene semantic analysis on the feature tensor to generate an activity weight vector reflecting the current demand of the user; and then prediction modeling of the acoustic-thermal coupling effect is carried out based on the weight vector, optimization is carried out in a prediction result set by using a multi-target Pareto optimal algorithm, and optimal control parameters giving consideration to noise sensitivity, thermal comfort and energy consumption are calculated. And finally, a PWM signal is generated through closed-loop feedback to drive a fan and light, so that the breathing effect of the fan is eliminated on the physical level, and active precise regulation and control of the environment of the mute cabin are realized.

The invention discloses a microwave penetration type online monitoring and feeding compensation method and system for the water content of gravel in a concrete mixing plant, and relates to the technical field of concrete production process control. According to the method, real-time monitoring of the water content of the gravel is achieved, the problems of hysteresis and insufficient precision of traditional off-line detection are solved, data processing and prediction modeling are carried out in combination with the sliding window algorithm and the Kalman filtering algorithm, the timeliness and accuracy of water content monitoring are remarkably improved, and real-time monitoring of the water content is achieved through a time synchronization mechanism and accurate time compensation parameters. By combining feedforward control and a PID control algorithm, accurate feeding of gravel and water is realized, the problem of proportion deviation caused by moisture content fluctuation in a traditional feeding mode is solved, the refinement level of production is improved, abnormity can be quickly identified and processed, the control performance can be continuously optimized, stable operation of the system and production continuity are ensured, and the production efficiency is improved. And the intelligent level of concrete production and the product quality consistency are obviously improved.

The invention provides an artificial intelligence-based industrial boiler coking prediction method and system. The method comprises the steps of infrared thermal image time sequence acquisition, multi-spectral dynamic threshold segmentation, acoustic impedance feature extraction, multi-modal working condition fusion, space-time attention prediction modeling and adaptive threshold decision triggering. According to the method, the local threshold values are adaptively calculated on different spectral bands such as short waves, medium waves and long waves, response characteristics of all temperature zones can be considered, the false detection and missing detection risks caused by the fixed threshold values are remarkably reduced, and high-temperature hot spots are extracted more accurately and reliably; acoustic impedance characteristics obtained through calculation quantitatively reflect the structure and viscosity change of a coking layer by using attenuation and time delay information of sound waves in an ash layer, a direct and fine physical basis is provided for subsequent rheological property inference, and the defect that mechanical changes of materials are difficult to capture through pure temperature monitoring is effectively overcome.

The invention relates to the technical field of tobacco industry, in particular to a tobacco multi-mode coding rate reduction and information theory evaluation method and system and a storage medium, and the method comprises the steps: obtaining tobacco sample data, and carrying out the preprocessing; constructing a multi-modal self-consistent coding rate reduction model, and training by adopting the preprocessed sample data; and adopting the trained multi-modal self-consistent coding rate reduction model to obtain fusion features, and carrying out modal redundancy and complementarity analysis. According to the embodiment of the invention, through the multi-modal fusion encoder, high-dimensional NIR and TGA data are mapped to a unified low-dimensional potential representation space, prediction modeling, coding rate estimation and mutual information analysis are carried out in the space, the problems of curse of dimensionality and noise interference are significantly relieved, and the stability and interpretation of information theoretical quantity estimation are improved.

The invention discloses an energy-based economic key element linkage prediction method and system, and relates to the technical field of economics and prediction modeling, and the method comprises the steps: determining and collecting a data source, and carrying out the preprocessing of the obtained data; exporting key business data based on the multi-dimensional data model, and constructing an energy consumption data and economic growth prediction model; training an energy consumption data and economic growth prediction model, and performing result evaluation and model optimization; and economic growth and energy prediction are carried out by using the optimized model, and analysis is carried out according to a prediction result and a corresponding decision is provided. By constructing the data cube model, data from different fields can be efficiently integrated, multi-dimensional comprehensive analysis is realized, and the comprehensiveness of the prediction model is improved; according to the method, the optimized model is utilized, a more flexible nonlinear modeling method is adopted, the complex relation between economic growth and energy consumption is better captured, and the prediction accuracy is improved.

A system and method are disclosed for measuring, analyzing, and optimizing organizational interaction dynamics as a proxy for inclusion and collaboration. Interactions between individuals are recorded and tagged with demographic attributes (e.g., gender, age, ethnicity, role, location, department) and categories of interest (e.g., innovation, sustainability, safety, production, wellness, mentoring, training, career development). The tagged data are stored in a structured repository and processed using analytics, artificial intelligence, and machine learning to generate inclusion scores, collaboration scores, and other indicators. Outputs include charts, heat maps, dashboards, and compliance reports aligned with standards such as ISO 30415. Embodiments provide real-time monitoring, trend analysis, team formation recommendations, predictive modeling, and automated feedback loops. The disclosed approach enables organizations to identify strengths and gaps, implement corrective actions, and track progress toward more inclusive and collaborative environments.

The application discloses a coagulant dosing prediction method combining sparse coding and graph space-time attention, and relates to the technical field of time series data modeling and prediction.The application combines the Transformer coding-decoding architecture with sparse self-attention and a top-k sparsification strategy, effectively improves the missing data completion accuracy, and provides high-quality and non-missing input data for subsequent prediction modeling; in the aspect of spatial feature expression, the graph attention network is used to capture the node correlation of the water production system multi-subsystem, the correlation weight between nodes is dynamically calculated, the neighbor features are aggregated, the information entropy of the spatial features is improved, and the expression capability of the spatial features to the multi-node coupling relationship is greatly enhanced; in the aspect of time series prediction performance, the time attention mechanism can dynamically focus on key time series nodes, further improves the utilization rate of key time series features, and improves the accuracy of coagulant dosing prediction.

This invention provides a modeling method for predicting the construction period of high-altitude ballastless track based on hybrid simulation, belonging to the field of computer simulation technology. The method includes: S1: defining agent attributes and interaction rules; S2: constructing an adaptive mesh simulation environment; S3: constructing an efficiency dynamic evolution model based on SD; S4: constructing a Bayesian prior probability network based on historical data to introduce perturbations; S5: introducing PERT construction speed distribution to obtain the probability distribution for construction period prediction; S6: building a coupled simulation model and solving for the construction period distribution. This invention achieves unified modeling of construction efficiency evolution, the impact of random perturbations, and spatial interaction constraints through multi-model coupling, dynamic optimization, and probabilistic analysis, improving the accuracy and engineering applicability of construction period prediction, and providing technical support for the planning and risk management of high-altitude railway tunnel construction.

A modelling platform including at least one processor in communication with a memory device and a payment processor is provided. The at least one processor is configured to retrieve subsets of data from a transaction history database, derive training data sets from the subsets, apply model input data fields of each training data set as inputs to one or more machine learning models, and apply a machine learning algorithm to adjust parameters of the one or more machine learning models. The at least one processor is also configured to upload at least one trained machine learning model to an operational predictive model module, apply a stream of real-time authorization request messages as inputs to the at least one trained machine learning model, and transmit, to the payment processor in real-time or near real-time, values of at least one output obtained by applying the stream.

The application discloses a kind of mechanism and data collaborative driving-based copper electrolysis effluent concentration accurate prediction modeling method, specifically related to artificial intelligence empowerment nonferrous metallurgy clean production field, including: the creation, cleaning and screening of input data set, output data set;Carry out data conversion processing;Build theoretical model, use theoretical formula to build Me concentration prediction model;Optimize model parameters by least square method;The residual error of theoretical model is learned using machine learning algorithm, and the prediction result of theoretical model is corrected;Save the model as training model, change the hyperparameter of initial prediction model, train multiple times, select the training model with the smallest average absolute percentage error and the largest determination coefficient as the final prediction model;Evaluate the prediction effect of training set model applied to test set.The present application can accurately predict the effluent concentration of copper electrolysis refining Me ion, and lay a model foundation for the quality control of electrolyte and the optimization of electrolysis process.