186 results about "Predictive systems" patented technology

The invention discloses an operation collaborative optimization method for an optical storage direct current flexible interaction system. Comprising the steps of collecting operation data such as photovoltaic output, an energy storage state, household load power and direct current bus transmission power, fusing power market price information, and constructing a multi-dimensional time series data set; then, predicting an adjustable load capacity interval of the system based on a coupled physical constraint neural network model embedded with DC bus power balance, voltage constraint and equipment operation limitation; further constructing a state-action space, solving a Pareto frontier by adopting a multi-objective optimization algorithm, and generating a light storage and home load collaborative scheduling strategy set; then combining the real-time operation state and the prediction deviation information, applying a voltage-power droop control mechanism to carry out strategy decoupling, and generating an energy storage power correction amount and a flexible load priority control instruction; and finally, a control instruction is issued to the optical storage direct flexible system, so that collaborative optimization operation with consideration of economical efficiency, safety and comfort of the system is realized.

The invention discloses a boiler state monitoring and predicting system based on artificial intelligence, and relates to the technical field of state monitoring. The system comprises a data acquisition part, a combustion stability analysis part, a hydrodynamic instability risk assessment part, a steam drum water level fluctuation prediction part and a fault probability and service life assessment part. The data acquisition part is used for acquiring boiler operation data and water circulation data; the combustion stability evaluation part is used for calculating a thermodynamic efficiency index; the combustion stability analysis part is used for calculating a combustion stability coefficient; the hydrodynamic instability risk assessment part is used for calculating a hydrodynamic instability risk index; the steam drum water level fluctuation prediction part is used for calculating and predicting steam drum water level fluctuation; and the fault probability and service life evaluation part is used for evaluating the fault probability and the residual service life of the boiler. According to the invention, the comprehensiveness, accuracy and predictability of boiler operation state monitoring are obviously improved.

The invention provides a virtual power plant energy storage system co-scheduling and control method and system, and relates to the technical field of power management, and the method comprises the steps: obtaining power grid scheduling data, calculating the charge and discharge income, collecting the real-time parameters of a battery, analyzing the performance attenuation law through deep reinforcement learning, determining the initial working parameters, predicting the system state based on a rolling time domain, and obtaining the real-time parameters of the battery. An optimal scheduling scheme is calculated through mixed integer linear programming and model prediction control in combination with a power distribution strategy and is corrected in real time, operation is executed according to a time-phased scheduling instruction and is monitored in real time, and an emergency strategy is started when necessary, so that the economic benefit and the safety of an energy storage system are improved, and the service life of a battery is prolonged.

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

A method for process control in association with a production system. The method leverages a large language model (LLM) that has been trained or fine-tuned on production data in a manner that avoids or minimizes use of numerical sensor data. In particular, during training, historical sensor data is received. In lieu of using the historical sensor data to train the model directly, the data is first encoded into a grammar-based sequence of characters before it is applied to train or fine-tune the model.

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

A system for AI-supported cash flow management and forecasting, consisting of: a data acquisition unit configured to retrieve transaction data from multiple financial data sources, the data acquisition unit incorporating encrypted API connections and secure data transfer protocols to ensure data security; a data preprocessing unit that is operationally linked to the data acquisition unit and includes a data cleansing module for removing anomalies, duplicates and missing values, as well as a normalization engine for standardizing data formats, thereby ensuring a uniform data structure for processing; a predictive analytics unit configured to receive processed data from the data preprocessing unit, wherein the predictive analytics unit comprises an ensemble of machine learning models, including recurrent neural networks and long short-term memory networks, each model being optimized for different prediction horizons; an adaptive learning component embedded in the predictive analytics unit and configured to regularly retrain the machine learning models based on updated historical data and feedback on financial performance to ensure the accuracy of predictions through iterative improvement; a decision support unit that is operationally coupled with the predictive analytics unit and configured to provide real-time recommendations for cash allocation and liquidity adjustments based on forecasted cash flow patterns; a liquidity warning module configured to continuously monitor cash balances, compare them with pre-configured liquidity thresholds, and generate alerts when balances approach critical limits, thus enabling proactive management of cash flow risks; a visualization module configured to present insights into cash flow, trend forecasts, and liquidity recommendations in an interactive graphical format to facilitate user evaluation and strategic decision-making; and a network interface integrated into the system, configured to connect to external data sources for dynamic updates.

Methods and systems for managing data processing systems are disclosed. A data processing system may include and depend on the operation of hardware and / or software components. Inference models may be implemented to predict future system infrastructure outcomes (e.g., component failures) using information recorded in logs that reflect the operation of the components. However, the models may be complex “black boxes” and may generate critical outcome predictions for downstream consumers without explanations of how the predictions are determined, resulting in downstream consumers having low confidence in the predictions. Therefore, hidden knowledge (e.g., structured knowledge attributes) of the models may be extracted and / or used to understand the underlying processes that the models use to predict the system infrastructure outcomes. The hidden knowledge may be stored in a repository and may be provided for downstream use in order to increase the likelihood of preventing and / or mitigating future data processing system failures.

The invention discloses a modularized intelligent tubular column conveying system based on digital twinning and self-adaptive control, and belongs to the technical field of digital twinning. The modularized intelligent tubular column conveying system is characterized in that a digital twinning model integrating a mechanical structure, electrical control and hydraulic power attributes is constructed; acquiring multi-source sensing data including an image sequence, a three-dimensional point cloud and vibration acceleration in real time, driving the digital twin model to perform synchronous simulation, and calculating a deviation value between the digital twin model and internal simulation data; on the basis of the deviation value, a control logic parameter adjusting instruction is generated through a predefined mapping rule, then updated control logic parameters are executed, and a complete pipe column conveying process is simulated to generate a predictive system state sequence; and finally, the safety-related state subsets are issued to a field controller to guide and execute preventive actions. According to the invention, deep fusion and closed-loop control of digital twinning and physical entities are realized, and the debugging efficiency, the environmental adaptability and the operation safety of a tubular column conveying system are effectively improved.

The invention provides a deep reservoir physical property prediction and oil and gas resource evaluation system based on artificial intelligence. And the problems of physical property prediction and resource evaluation of low-porosity and low-permeability reservoirs in oil-gas exploration are solved. The system fuses earthquake, well drilling and well logging data, and generates a high-quality reservoir database through data cleaning, normalization and interpolation processing. A deep learning model is constructed by adopting a convolutional neural network and a long-short-term memory network, a nonlinear relationship between reservoir physical properties and multi-source data is automatically captured, and high-precision prediction is realized. The system is integrated with a real-time economic evaluation module, and an economic evaluation result is dynamically adjusted through a gradient enhanced tree model in combination with real-time market data. In addition, an automatic feedback mechanism is introduced into the system, and a region with a large error is re-sampled and the model is updated. According to the scheme, the reservoir prediction precision and exploration efficiency are remarkably improved, dynamic economic analysis is supported, and reliable technical guarantee is provided for exploration and development decisions of deep oil and gas resources.

The invention discloses an artificial intelligence-driven industrial Internet of Things equipment fault prediction system, and relates to the technical field of industrial Internet of Things and predictive maintenance, an edge-cloud collaborative architecture is adopted, real-time acquisition, preprocessing and online fault prediction of industrial equipment sensing data are realized, the system uses a Transform neural network to construct a hierarchical spatio-temporal model, and the fault prediction of the industrial equipment sensing data is realized. Time sequence data are processed in a segmented mode through a sliding window method, a causal reasoning enhancement mechanism is integrated, an industrial equipment causal atlas is constructed, attention masks are generated, a model is focused on key features, and therefore prediction accuracy and interpretability are improved, meanwhile, a closed-loop continuous optimization mechanism is established by the system, and prediction efficiency is improved. And an edge fault prediction result and actual operation feedback are uploaded to a cloud, a causal atlas and model parameters are updated, adaptive optimization of the model is realized, and the system provides efficient, accurate and explainable decision support for preventive maintenance of industrial equipment.

A system for predicting the fundamental period for reinforced concrete buildings on slopes, consisting of: a data input module configured to retrieve building and soil parameters from datasets, including building height, field width, number of fields, slope angle, and soil conditions; a modal analysis module configured to perform a modal analysis of the input parameters using finite element analysis software and to generate results for different slope angles, soil conditions, and building configurations; a soil-structure interaction (SSI) module that includes a foundation modeling component that applies spring stiffness to column foundations based on Gazetas' formulas and simulates soil conditions categorized as stiff, medium or soft, thus incorporating soil-structure interaction effects; a regression and machine learning prediction module configured for developing predictive models using multiple linear regression, random forest, and XGBoost algorithms; and an output module configured to output the predicted basic period of the building based on the trained prediction models.

The invention relates to the technical field of inventory optimization, in particular to an intelligent supply chain demand prediction and inventory optimization system, which comprises a supply chain dynamic demand prediction module for intelligently predicting the demand quantity of a supply chain, and an inventory intelligent optimization module for intelligently optimizing the inventory according to the demand prediction result of the supply chain. The prediction correction and optimization adjustment module is used for dynamically correcting demand prediction and intelligently optimizing and adjusting inventory; according to the invention, through establishment of a three-channel cross validation prediction and anomaly elimination mechanism, single-channel prediction anomaly can be resisted, the stability and accuracy of supply chain demand prediction are improved, and inventory risks caused by prediction errors are reduced; the safe inventory level is dynamically set according to different stages, so that the inventory strategy better conforms to the commodity market change, and the shortage rate and the unsalable inventory are reduced; the correction amplitude is dynamically adjusted through error fluctuation, excessive correction is avoided, and the self-learning and self-evolution ability of a long-term supply chain prediction system is improved.

The present disclosure relates to the field of Controlled Environment Agriculture (CEA). It details a predictive method and system for optimizing the use of resources and crop productivity in indoor farming across an entire crop cycle. The present disclosure provides for a predictive method and system for optimizing resource use and crop productivity in indoor farming. According to one aspect of the present disclosure, a predictor for optimizing resource use and crop productivity in indoor farming. According to a second aspect of the present disclosure, a predictive system for optimizing resource use and crop productivity in indoor farming. According to a third aspect of the present disclosure, a method of using a predictive system for optimizing resource use and crop productivity in indoor farming.

The present invention discloses an energy-saving optimization control method for an air-conditioning rotary wheel dehumidification system based on a mechanism model, belonging to the technical field of air-conditioning energy-saving control. Specifically, it includes: establishing and validating a mechanism model by collecting historical operation data; based on the validated model, using a model predictive control method to predict the system state, and applying an adaptive learning algorithm and a deep reinforcement learning algorithm to adaptively adjust the model and the predictive control method; using the adjusted model and predictive control method, combining the current state and load prediction, and calculating the optimal control input through an optimization algorithm; real-time monitoring the system operation data, and using an intelligent decision-making algorithm and a deep reinforcement learning algorithm to correct and optimize the model; applying the optimal control input to the system, implementing the control strategy, evaluating the effect and optimizing the control strategy to achieve efficient and intelligent dehumidification control.

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

A method for process control in association with a production system. The method leverages a large language model (LLM) that has been trained or fine-tuned on production data in a manner that avoids or minimizes use of numerical sensor data. In particular, during training, historical sensor data is received. In lieu of using the historical sensor data to train the model directly, the data is first encoded into a grammar-based sequence of characters before it is applied to train or fine-tune the model.

The invention relates to the technical field of intelligent retail and big data analysis, and discloses a multi-retail store sales volume prediction method and system based on an improved Transform architecture, and the method comprises the steps: collecting historical retail store sales data, historical weather characteristics and holiday and festival data, carrying out the correlation, and carrying out the preprocessing of the collected data; dynamically constructing and training a store sales volume prediction model based on a deep learning neural network, and obtaining gated store-level features and product cluster features which have important influence on sales volume prediction through a multi-level attention mechanism; and fusing external covariables with the gated store-level features and the product cluster features to enhance the prediction capability of the model, and predicting the sales volume of the multiple retail stores based on the trained store sales volume prediction model. According to the multi-retail store sales volume prediction system realized through the method, more comprehensive and more accurate sales volume prediction is realized through interactive analysis of stores and products and comprehensive modeling of external environment variables.

The invention discloses an efficient circulating water heating system based on a heat pump and a control method thereof.The system comprises a multi-stage heat pump system, a circulating water heating loop and an intelligent control unit, and the multi-stage heat pump system is used for increasing heat energy of a low-temperature heat source stage by stage to heat circulating water; the intelligent control unit is integrated with a data acquisition module, a load prediction module, a system modeling module, a parameter optimization module, an instruction issuing module and a performance evaluation module, and can realize thermal load dynamic prediction and thermal performance modeling and optimization control of the heat pump system. The control method is based on deep learning and model prediction control, combines real-time data and an external scheduling instruction, calculates an optimal operation parameter to improve the system energy efficiency ratio or reduce the operation cost, and continuously optimizes prediction and modeling precision through an adaptive learning mechanism. The system has the capacity of intelligent prediction, accurate control, efficient heat transfer and self-optimization, is suitable for occasions such as industries and buildings with high requirements for hot water supply energy efficiency, and remarkably improves the operation efficiency and stability of the system.

The invention relates to the technical field of geothermal energy utilization and underground heat exchange, in particular to a medium-deep layer buried pipe heat exchanger seepage thermal response rapid prediction method based on artificial intelligence, and the method comprises the steps: obtaining temperature field snapshot data of a medium-deep layer buried pipe heat exchanger system under different operation conditions; performing orthogonal decomposition on the temperature field snapshot data to obtain main modal characteristics and corresponding amplitudes; the parameters of all the operation conditions serve as input variables, all the main modal features and the corresponding amplitudes serve as output variables, a BP neural network is trained, and an artificial intelligence agent model is constructed; and obtaining parameters of a target operation condition, inputting the parameters into the artificial intelligence agent model, outputting the amplitude of each main mode under the target operation condition, reconstructing a temperature field, and completing rapid prediction of the seepage thermal response of the medium-deep layer buried pipe heat exchanger under the target operation condition. According to the method, the temperature field distribution of the DBHE system under the action of underground water seepage can be quickly predicted.

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

The present invention embodiments provide a forecasting system that derives carbon intensity, emissions, and environmental impact forecasts from a life cycle assessment (LCA). The forecasting system offers an intuitive, user-friendly interface that simplifies the complexity of forecasting methodologies, making generating forecasts of carbon intensity, emissions, and environmental impacts accessible to a broader audience. It incorporates advanced algorithms and real-time data integration, ensuring accurate and up-to-date forecasts. Additionally, the forecasting system is designed with a modular architecture and is highly configurable, allowing seamless integration with various industries and accommodating diverse products and processes.

The invention relates to a data-driven criminal period prediction system, belongs to the technical field of artificial intelligence, solves the problems of insufficient accuracy, poor regional adaptability and the like of prisoner classification and criminal period prediction in the prior art, and provides a multi-model fusion provincial prisoner classification and criminal period prediction system based on real judicial sentencing logic. The system comprises an input display module, a legal judgment element extraction module, a sentency and insignment prediction module, a sentency judgment prediction module and a judgment output display module, and can effectively improve the prediction precision and optimize judicial decision support by fusing multiple advanced algorithms and combining judicial practices of different provinces. The method has a wide application prospect and a strong regional adaptation capability.

The invention relates to a photovoltaic support adaptive regulation and control system and method based on artificial intelligence and a readable storage medium, and relates to the field of photovoltaic panel regulation and control. Comprising a control unit, an analysis unit, an environment detection unit, an evaluation unit and an execution unit. The environment detection unit is used for detecting surrounding environment data and feeding back the data to the analysis unit, the evaluation unit and the control unit; the analysis unit is used for receiving data of the environment detection unit and generating decision suggestions; and the control unit is used for receiving data of the environment detection unit, extracting environment characteristics and obtaining adjustment data according to decision suggestions of the analysis unit. According to the invention, predictive feedback is realized, the analysis and prediction of environmental data are carried out through the analysis unit and the control unit, and the system can make an accurate decision, thereby realizing predictive feedback, adjusting the angle of the photovoltaic panel in advance, adapting to future weather changes, and improving the stability and power generation efficiency of the system.

Methods and systems for managing data processing systems are disclosed. A data processing system may include and depend on the operation of hardware and / or software components. Inference models may be implemented to predict future system infrastructure outcomes (e.g., component failures) using information recorded in logs that reflect the operation of the components. However, the models may be complex “black boxes” and may generate critical outcome predictions for downstream consumers without explanations of how the predictions are determined, resulting in downstream consumers having low confidence in the predictions. Therefore, hidden knowledge (e.g., structured knowledge attributes) of the models may be extracted and / or used to understand the underlying processes that the models use to predict the system infrastructure outcomes. The hidden knowledge may be stored in a repository and may be provided for downstream use in order to increase the likelihood of preventing and / or mitigating future data processing system failures.

The invention provides a prediction system and method for gestational hypertension risk, and belongs to the technical field of gynaecology and obstetrics. According to the system, a data list generation module is used for generating a pregnant woman personal data list; the scoring module is configured to be used for processing and analyzing a pregnant woman personal data list, matching and contrasting each item of data in the pregnant woman personal data list with items in a preset judgment list, identifying matched data, scoring according to a corresponding preset rating standard to obtain main element scores, and sending the main element scores to the pregnant woman personal data list; summarizing the main element scores corresponding to the data to obtain a total score result; and the judgment module is configured to compare the total scoring result with a preset threshold value and judge the risk level of the hypertension risk in the gestation period of the pregnant woman. According to the method, personalized comprehensive risk assessment can be provided, the limitation of a traditional method is overcome, a doctor can formulate corresponding prevention and intervention measures according to specific conditions, and therefore the intervention effectiveness is improved.

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

A hydrogen-powered refrigeration system 900 and related intelligent and predictive control methods, comprises various subsystems integrated to create synergies and interdependencies providing high efficiency, mission anticipation and recommendations to minimize fuel consumption and increase the durability of the system while refrigerating a cargo volume during transportation at low or no carbon emissions; wherein the intelligent and predictive controls are achieved by the use of a control system based on deep neural networks that optimizes operation by anticipating the future state of the system and adjusting its configuration based on models to operate under an optimal control policy that considers external information obtained from calls to APIs linked to external information providers while ensuring cargo is maintained under conditions required or regulated as a part of the cold chain. The invention has particular application to the vehicular and multi-modal transportation of items requiring refrigeration.

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

A computer-implemented system for short-term stock price prediction, consisting of: • a data acquisition module (101) configured to retrieve historical intraday stock data including closing prices, trading volumes and technical indicators; • a data preprocessing module (102) configured to normalize the data and generate temporally consecutive samples using a sliding window approach; • a neural network module (103) comprising a Gated Recurrent Unit (GRU) architecture configured to receive the temporally consecutive samples and output intermediate stock price predictions; • a gradient boosting module (104) comprising an Extreme Gradient Boosting (XGBoost) regressor configured to receive the output of the GRU module along with associated residual features and to refine the predictions by modeling nonlinear patterns and residual errors; • and an output module (105) configured to present the final refined stock price prediction, where the GRU module is trained to minimize a loss function for the mean squared error, and the XGBoost module is trained to minimize an objective function for the squared error, thereby providing improved predictive accuracy for financial time series forecasts.