1245 results about "Predictive analytics" patented technology

The invention discloses an electrical equipment fault diagnosis and prediction analysis system, which relates to the field of intelligent operation and maintenance of a power system and comprises an acquisition and preprocessing module, an extraction fusion module, a fault diagnosis modeling module, a prediction evaluation module and an update feedback module. According to the invention, through fusion of structured sensing data and unstructured image data, multi-modal depth feature joint representation is realized, and the accuracy and robustness of fault identification are significantly improved; a fusion time sequence prediction model is introduced, and a health degree scoring system is combined, so that accurate prediction of key parameter trends and quantitative estimation of the residual life of equipment are realized; a transfer learning and incremental learning mechanism is adopted, when a new fault or small sample data appears, model parameters can be quickly updated, and efficient adaptation to a new scene is achieved; a data alignment mechanism with time-space synchronization and an auto-encoder anomaly detection algorithm are constructed, and the multi-source heterogeneous data processing capacity and the real-time fault early warning capacity are remarkably improved.

The invention discloses a cloud-edge collaborative intelligent storage node dynamic deployment method and system. The method comprises the following steps: monitoring performance indexes such as edge node data traffic and storage resource state in real time; a deep learning algorithm combining time sequence analysis and an LSTM neural network is adopted to analyze traffic information, and a data access demand is predicted; edge nodes and cloud storage resource configuration are dynamically adjusted based on a prediction result, and an intelligent scheduling algorithm, a data cold and hot separation strategy and a self-adaptive fragmentation technology are introduced to allocate resources; the storage node layout is optimized in real time, and an optimal data distribution path is selected through a reinforcement learning strategy; data security and access control are realized by adopting end-to-end encryption, multi-level access control and block chain technologies. The system comprises a monitoring acquisition module, a prediction analysis module, a resource scheduling module, a path optimization module and a security control module. According to the method, the utilization efficiency of storage resources is improved, data delay is reduced, system stability and data security are enhanced, and the method is suitable for a cloud edge collaborative storage scene.

The invention relates to the technical field of greenhouse gas monitoring, in particular to a greenhouse gas collaborative monitoring and analysis platform. According to the technical scheme, the system comprises a cross-modal data fusion module, a causal reasoning analysis engine, a holographic dynamic visualization system, a distributed edge computing node and a self-adaptive decision optimization module, and the cross-modal data fusion module is used for integrating satellite remote sensing data, a ground sensor network, unmanned aerial vehicle mobile monitoring data and industrial Internet of Things emission source data in real time; a dynamic weight distribution algorithm is adopted, the multi-source data fusion weight is automatically adjusted according to environmental parameters, and a space-time continuous greenhouse gas concentration field is generated; a causal reasoning analysis engine is based on a hybrid architecture. According to the method, data privacy is guaranteed and delay is reduced through multi-source data fusion, causal reasoning analysis, holographic visualization and edge calculation, and comprehensive and accurate monitoring, scientific prediction analysis, efficient decision execution and risk prevention and control of greenhouse gas are realized based on decision optimization of dynamic games and block chain smart contracts.

A dynamic optimization system for energy consumption of a refrigeration house based on digital twinning is characterized by comprising a data acquisition module used for acquiring basic structure data of the refrigeration house, technical parameters of a refrigeration system, real-time operation data and historical operation data, preprocessing the data and then outputting a standardized multi-dimensional real-time data stream; the model construction module is used for constructing a 3D geometric model, a thermodynamic transfer model and a refrigeration system mathematical model according to the multi-dimensional real-time data flow, performing machine learning calibration on model parameters through historical operation data, and performing fusion to construct a refrigeration house digital twin model; the prediction analysis module is used for predicting future energy consumption demand and load change according to the refrigeration house digital twin model and the real-time operation data, and outputting an energy consumption prediction result and a load analysis report; a strategy generation module; an execution feedback module; and a learning optimization module. Overall energy consumption of the refrigeration house is reduced, energy utilization efficiency is remarkably improved, and goods storage safety is guaranteed.

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 multi-modal data processing, and discloses a multi-modal data fusion processing method and system, and the method comprises the steps: collecting a multi-modal data set containing the activities of the old through an intelligent old-age care robot; constructing a timestamp calibration matrix based on the multi-modal data set, and carrying out dynamic weight correction on the timestamp calibration matrix by adopting a three-modal data envelope delay compensation algorithm to obtain a three-modal data stream with a synchronous time sequence; performing hierarchical feature extraction through a nested feature extractor to obtain a three-mode tumble feature vector group; performing vector fusion on the three-mode tumble feature vector group to obtain tumble fusion feature vectors; and inputting the tumble fusion feature vector into a multi-modal forward prediction model to perform tumble risk trajectory prediction, and outputting a tumble detection result, the method realizes prediction analysis of body posture changes in a key time window before the old man tumble, and improves the ability to distinguish a slow getting-up action and a real tumble behavior of the old man.

The invention relates to the technical field of digital information transmission, and provides a communication network security situation prediction method and system based on big data, which break through the limitation of single data in the aspect of data fusion, integrate four large classes and 12 subclasses of multi-source heterogeneous data, and combine a dynamic weighting mechanism to make feature extraction more comprehensive and accurate; on the aspect of model architecture, CNN-Bi-LSTM-Attention three-level fusion and PSO optimization are adopted, spatial and temporal features are effectively captured, the convergence speed is increased, the prediction accuracy is high, and the false alarm rate is low; in the risk assessment aspect, a layered assessment system is constructed, and early warning is realized in combination with a dynamic threshold value and Monte Carlo simulation; the model training module innovatively adopts a federated learning mode, and the model generalization ability is improved while data privacy is guaranteed; the prediction analysis module deploys an optimization hybrid model, supports high-concurrency prediction and is short in response time; the visual decision-making module provides three-dimensional visualization and geographical drilling functions, and output data can be seamlessly connected with a third-party platform.

The invention relates to the technical field of industrial safety, in particular to a factory safety intelligent management and control method based on a heterogeneous multi-system cross service fusion technology, and the method comprises the steps: collecting factory multi-source heterogeneous data, and carrying out the time-space alignment and fusion; constructing a risk assessment model based on a deep learning algorithm, and performing quantitative risk analysis on personnel behaviors, environmental parameters and equipment states; a dynamic grading early warning mechanism is established, and warning and automatic handling are achieved according to the risk grade; introducing risk trend prediction, and analyzing a risk evolution trend; and a safe closed-loop optimization mechanism is constructed, and self-adaptive adjustment of the management and control strategy is realized. According to the method, a full-process and dynamically-optimized intelligent safety management and control scheme is constructed, and comprehensive perception, quick response and continuous optimization of factory safety management are realized.

Predictive Maintenance Digital Twin with Carbon Footprint Optimization System, consisting of: a digital twin framework for real-time asset monitoring, performance simulation, and failure prediction; an IoT-enabled data acquisition layer with sensors to collect operational and environmental data; an edge computing module for low-latency anomaly detection and diagnostics; a cloud-based AI engine that uses deep learning for predictive maintenance and remaining useful life (RUL) estimation; a carbon footprint optimization engine for tracking energy consumption, emissions, and sustainability metrics; an LLM-supported NLP interface for analyzing maintenance data, generating prescriptive recommendations, and optimizing CO2 efficiency; a predictive analytics engine for early fault detection, energy optimization, and automatic alerts; an interactive dashboard for visualizing asset health, predictive insights, and tracking carbon impacts; a hybrid cloud-edge architecture for secure, scalable, and efficient real-time processing.

The invention provides a logistics distribution demand prediction analysis method and system based on big data, and relates to the technical field of logistics distribution, and the method comprises the steps: dividing a distribution region into hexagonal grid units, building an order density evaluation model based on historical order data, extracting order propagation features through combining with a graph attention network, and carrying out the prediction analysis of the logistics distribution demand. And an external factor encoder is introduced to obtain an influence factor, probability correction is carried out by adopting kernel density estimation, and finally order demand prediction distribution with a confidence interval is obtained. The logistics distribution demand can be accurately predicted, the distribution efficiency is improved, the operation cost is reduced, and intelligent scheduling is realized.

The invention relates to the field of image recognition, in particular to a wind power construction intelligent safety management method and system based on intelligent AI monitoring. The method comprises the following steps: obtaining an omnibearing real-time image flow of a wind power construction area, carrying out super-resolution deep convolution optimization and operator three-dimensional image segmentation, and extracting an operator three-dimensional image frame; three-dimensional point cloud modeling of the construction area is carried out based on the image flow, real-time image frame position positioning rendering is carried out according to a three-dimensional image frame, and a real-time twinborn model of the construction area is constructed; performing operation dynamic behavior analysis and behavior deviation degree quantitative analysis based on a twin model to obtain the behavior deviation degree of the operator; and according to the behavior deviation degree, carrying out early prediction analysis on illegal behaviors, making an adaptive risk early warning decision, and constructing an operation behavior risk early warning strategy. According to the invention, through real-time operation behavior identification and environmental risk analysis, the intelligence and safety level of wind power construction are improved.

The invention relates to a self-adaptive pressure regulation vacuum pump closed-loop control system and method. The system comprises a model building module which is used for acquiring real-time data flow of a vacuum pump and building a nonlinear dynamic model to obtain a pressure-flow-power feature mapping relation; the predictive analysis module calculates a pressure-flow predictive value based on the relationship and compares the pressure-flow predictive value with the real-time power data for analysis. And generating a parameter adjustment instruction once the deviation value exceeds a preset threshold value. And the control scheme generation module corrects the control parameters according to the parameter adjustment instruction and generates an optimized power adjustment scheme. And if the data is abnormal, generating a state update value. And the control scheme updating module fuses the state updating value and the pressure-flow prediction value to obtain an optimized model parameter so as to adjust a weight coefficient of an adaptive control strategy and generate a control scheme. The system effectively improves the accuracy of vacuum pump pressure control, greatly enhances the ability of the system to deal with abnormal conditions and equipment performance changes, and ensures long-term stable operation of the vacuum pump.

Computer-implemented methods and systems are disclosed for Information Technology Service Management (ITSM). The pioneering AI-driven system revolutionizes IT service management by uniquely validating, suggesting, and inferencing incident and problem data. At its core are cutting-edge generative AI techniques like GANs and LLMs, requiring intricate training and iterative refinement on varied data sets, showcasing a depth of expertise in database structures and AI concepts. It bridges critical gaps in incident resolution and classification through cognitive computing, AI, NLP, and deep learning, applied to both historical and current data. The system comprises modules for Incident Validation & Classification, Resolution Validation, Generative Intelligence, Problem Probability Calculation, and Prevention Recommendation, each employing AI to enhance standard compliance, predictive analysis, and proactive management, thereby setting new standards for IT service management efficiency and effectiveness.

The invention provides an unmanned ship charging cable control method based on visual identification, and relates to the technical field of data processing, and the method comprises the steps: collecting continuous image frames of an unmanned ship charging area, identifying the spatial displacement and inclination angle change of an unmanned ship, analyzing the attitude offset of the unmanned ship caused by sea waves, calculating a water surface disturbance factor, and calculating the water surface disturbance factor. The method comprises the following steps: identifying the position of a charging interface, obtaining an initial positioning coordinate, carrying out prediction analysis on a disturbance trend in a preset time window, predicting a position change range of the charging interface, obtaining prediction coordinate data, planning a butt joint path of a cable and the charging interface, obtaining a compensation path sequence, and generating a cable propulsion instruction. And collecting an area image of the charging interface to obtain path tracking image data, judging whether the butt joint of the charging interface is successful or not, and if not, dynamically correcting the compensation path sequence. According to the invention, the cable is controlled through visual identification to charge the unmanned ship.

A predictive incident management system, consisting of: a sensor input module configured to receive heterogeneous telemetry data streams from mechanical, thermal, electrical and cyber sources; a temporal correlation control unit operationally coupled to the sensor input module, wherein the temporal correlation control unit is configured to normalize received data into a uniform time series envelope that includes identifiers, microsecond-precision timestamps, metric names, values, and context markers, and is further configured to compute sliding window-cross-sensor correlation matrices, event motifs, and lead-lag dependencies across multiple time granularities; a scalable rule processor that is communicatively linked to the control unit for temporal correlation, wherein the rule engine includes an in-memory runtime environment for processing complex events and a domain-specific declarative language, and is configured to apply rules that reference primitive sensor metrics, derived correlation features, and motive-based early warning vectors to classify, escalate, or resolve predicted incidents; A historical repository that is communicatively connected to both the temporal correlation control unit and the rule engine. The repository is configured to store tagged event histories, correlation motif dictionaries, rule versions, and rule origin metadata to ensure the verifiability and explainability of predictions; and An incident response interface is operationally connected to the rule engine. The incident response interface is configured to trigger automated workflows, including the generation of tickets for IT service management, chat ops notifications, the execution of orchestration playbooks, and direct machine control via industrial protocols. the system is configured to perform predictive analyses based on temporal correlations between sensors and to execute context-aware, rule-based incident management in real time.

The invention relates to an intelligent power grid distribution line fault prediction analysis method, and the method comprises the steps: carrying out the synchronous collection and standardization processing of the real-time load, electrical parameters, environment information and component aging states of each sampling point of a distribution line through distributed sampling and precise space positioning; and based on multi-time scale dynamic feature and aging feature extraction, realizing automatic generation of a structured and layered feature library and scene labels. A label-driven historical model parameter migration and meta-learning mechanism is utilized to quickly adapt to new working conditions and finely adjust the weight of the model, multi-time scale features are fused to carry out contribution degree weighting, and finally the accuracy and robustness of fault prediction are improved. The system also continuously optimizes the model performance through real-time A / B comparison and automatic parameter switching, has the capabilities of data tracing and result interpretation, and significantly enhances the timeliness, reliability and intelligent level of power distribution network fault diagnosis.

The invention discloses an enterprise digital management method based on data mining. The method comprises the following steps: acquiring information flow data and resource allocation data; analyzing according to the information flow data and the resource allocation data to obtain an initial network diagram; performing clustering analysis according to the initial network diagram, and performing path optimization to obtain a dynamic network diagram; performing feature extraction on the dynamic network diagram to obtain prediction analysis data; according to the dynamic network diagram and the prediction analysis data, network nodes are identified, and key nodes and high-risk nodes are obtained; in combination with the key nodes and the high-risk nodes, identifying a key propagation path to obtain the key propagation path; and based on the key nodes, the high-risk nodes and the key propagation paths, predicting key positions where resource bottlenecks and conflicts occur, and generating resource optimization suggestions. According to the method, the dynamic change of the implicit dependency relationship of the enterprise can be identified, high-risk nodes and key propagation paths are positioned, and resource configuration is optimized.

The invention relates to the technical field of digital engineering, and particularly provides a BIM-based engineering construction dynamic three-dimensional visual management method and system, and the method comprises the steps: collecting construction multi-dimensional data, carrying out the correlation mapping with an initial BIM model component after preprocessing, and forming a structured construction element data set; dynamically reconstructing an initial BIM model based on the data set, generating a three-dimensional dynamic twinborn body, and dynamically displaying information such as superposition progress, quality, resources and environment; a prediction analysis model is called to generate progress prediction, quality early warning and resource optimization suggestions, and a final scheme is determined after visual simulation verification; and updating the data set and the twin according to an execution result, and iteratively optimizing the prediction model. According to the scheme, dynamic integration and visual management of construction total elements are achieved, the timeliness, accuracy and intelligent level of construction management are effectively improved through intelligent prediction and closed-loop optimization, the construction efficiency is improved, and the management cost is reduced.

The invention discloses an intelligent control system and method based on an AI intelligent home model, and the system comprises a data acquisition module which is used for collecting home related data in real time based on a multi-dimensional sensing system, and the data comprises indoor and outdoor environment parameters and habitant physiological feature data; the data analysis module is used for performing resident comfort demand prediction analysis on the home related data based on the AI smart home model to obtain the comfort demand of the resident; and the environment regulation and control module is used for carrying out working parameter setting and working mode adjustment on the household electrical equipment based on the comfort degree requirement, so as to realize personalized environment adjustment for different residents and scenes. The AI smart home model accurately predicts the comfort demands of the residents, and the system can provide customized environment adjustment schemes for individual differences of different residents, meets the differentiated demands of special groups such as old people, children, pregnant women and the like, and improves the living experience.

The invention relates to a brain inspired data service system and a brain inspired data service method, which are characterized in that a layered architecture is constructed, the layered architecture comprises a data node cluster deployed in a mixed manner, the data node cluster specifically comprises a core node, an acquisition node and a gateway node, and the core node, the acquisition node and the gateway node are respectively used for processing core service data, real-time edge data and cross-domain connection; the small-world connection layer realizes same-domain efficient communication through high-clustering local connection, and breaks a data island through dynamic long-range cross-domain connection; the intelligent control layer integrates a DMN control center, a collaborative state monitoring engine, a prediction analysis module and a reinforcement learning driven resource scheduler. The system initializes a topological structure through a small-world network manager, and when the communication frequency of a cross-domain node exceeds a threshold value, long-range connection is dynamically inserted to ensure that the hop count of a cross-domain path is stably not higher than a preset value. The DMN control center realizes dual-mode switching based on a system load threshold; in an idle period, data pre-cleaning, knowledge graph construction, predictive pre-fetching and resource pre-allocation are executed; and optimizing routing in combination with a graph traversal algorithm in a task period.

A system for an automated real-time digital identity verification based on predictive analytics of user identity data, including a processor of a digital identity verification (DIV) node configured to host a machine learning (ML) module and connected to at least one verifier entity node over a network and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: acquire an identity verification request associated with a user from the at least one verifier entity node, wherein the identity verification request comprising user biographic data and a truncated facial biometric hash extracted from a digital identity feature embedded into an identification document of the user and user biographic data acquired from the identification document and a facial biometric hash generated from the user identification document; parse the identity verification request to derive a plurality of key features; generate at least one feature vector based on the plurality of the key features; and provide the at least one feature vector to the ML module configured to execute a predictive model based on underlying neural network configured to produce at least one user identity verification parameter for generation of a user identity verification verdict.

The invention discloses a tumor chemoradiotherapy reaction automatic prediction system, and particularly relates to the technical field of medical automation, the tumor chemoradiotherapy reaction automatic prediction system comprises a data acquisition module, a data preprocessing module, a feature extraction and selection module, a model training module, a prediction analysis module, an early warning module and a feedback module; the multi-source data acquisition module comprehensively gathers patient information, the quality is improved through the preprocessing module, a foundation is built for the feature extraction and selection module to accurately screen key features, the data dimension is greatly reduced, and the model training efficiency is remarkably improved; the model training module applies a machine learning algorithm to deeply mine a data complex relationship, so that the prediction is more accurate and reliable; on the basis, the prediction analysis module outputs detailed chemoradiotherapy reaction prediction and provides confidence evaluation to help doctors to make decisions and select; and the early warning module is responsible for predicting, warning abnormity in time and helping doctors to plan intervention in advance.

Systems and methods are configured to generate a set of potential responses to a prompt using one or more data models with data from at least a plurality of data domains of an enterprise information environment that includes access controls. A deterministic response is selected from the set of potential responses based on scoring of the validation data and restricting based on access controls in view profile information associated with the prompt. These enterprise generative AI systems and methods support granular enterprise access controls, privacy, and security requirements. enterprise generative AI providing traceable references and links to source information underlying the generative AI insights. These systems and methods enable dramatically increased utility for enterprise users to information, analyses, and predictive analytics associated with and derived from a combination of enterprise and external information systems.

The invention belongs to the technical field of unmanned aerial vehicle technology and intelligent operation and maintenance management, and discloses an unmanned aerial vehicle maintenance task scheduling and resource management platform and method, and the method comprises the steps that a data collection and fusion module obtains and fuses multi-source data; the fault diagnosis and prediction analysis module uses a deep learning model and a CNN-LSTM hybrid model to diagnose faults, predict life and generate a maintenance task list; an optional task dynamic priority evaluation module performs priority ranking on the maintenance task list; the intelligent scheduling decision module uses an improved NSGA-II and VNS mixed algorithm to generate an optimal allocation scheme according to priority and resource constraints; the resource dynamic management and cooperation module realizes dynamic allocation and cooperation of personnel, spare parts, tools and sites, and can integrate AR guidance; and the optional visual monitoring and feedback optimization module displays the state, feeds back data and realizes closed-loop optimization. The maintenance efficiency of the unmanned aerial vehicle can be remarkably improved, the operation cost is reduced, and the flight safety is guaranteed.

The invention discloses a dynamic water quality monitoring system based on an unmanned ship and a data acquisition and distribution algorithm. Comprising an unmanned ship platform module, an initial navigation path setting module, a water quality data acquisition module, a dynamic adjustment and optimization module, a control interaction module and a dynamic adjustment and optimization module, wherein the dynamic adjustment and optimization module dynamically adjusts the navigation strategy and sampling frequency of the unmanned ship in a target to-be-detected water area through a second branch of a first neural network model; the unmanned ship can sail autonomously, continuous and global water quality monitoring is achieved, through an efficient data processing algorithm, the accuracy and reliability of data are improved, a scientific basis is provided for water quality management, a distributed algorithm model is achieved to conduct predictive analysis on each specific target sampling point, the accurate predictability of unmanned ship monitoring is improved, and the water quality monitoring efficiency is improved. The monitoring strategy is dynamically adjusted according to actual requirements, optimal configuration of monitoring resources is achieved, the monitoring cost is reduced, the water quality change trend is found in advance through prediction analysis of the neural network model, and powerful support is provided for early warning and emergency response.

The invention discloses a knowledge model data management system based on artificial intelligence, and relates to the field of data management, and the system comprises the following components: a data collection and storage module, an intelligent prediction and analysis module, an abnormal comprehensive processing module, a knowledge graph and traceability module and a man-machine interaction management module. According to the invention, the intelligent prediction analysis module is combined with a time sequence prediction model and a causal reasoning algorithm, abnormal fluctuation and causal association thereof in data can be accurately judged, the accuracy of anomaly detection is effectively improved, and meanwhile, the knowledge graph and traceability module uses the constructed knowledge graph and reinforcement learning algorithm to improve the accuracy of anomaly detection. According to the method, the relation chain can be quickly traced from the abnormal data, the problem source can be positioned, the abnormal traceability efficiency is remarkably improved, and the functions act together, so that the system can more quickly and accurately discover and process problems when facing a complex data environment, and the stability and reliability of data management are guaranteed.

The invention discloses a crop growth real-time monitoring system and method, and relates to the technical field of crop monitoring. According to the crop growth real-time monitoring method, crop image data time sequence and soil state time sequence data are continuously acquired in real time, data analysis is performed respectively to obtain an initial agricultural healthy flow index and a soil conditioning index of each monitoring time period, comprehensive analysis is performed to obtain a comprehensive agricultural healthy flow index of each monitoring time period, and prediction analysis is performed to obtain a real-time crop growth monitoring result. According to the method, the comprehensive agricultural healthy flow index of the next monitoring time period is compared and analyzed with the preset comprehensive agricultural healthy flow index interval, and corresponding early warning measures are taken based on the comparison and analysis result, so that the growth state of crops and the change of the soil environment are tracked in real time; therefore, the health state of the crops and the influence of the soil on the growth of the crops are obtained, and the monitoring accuracy and predictability are improved.

The invention provides a wind power plant bird trajectory prediction and fan linkage control method based on a neural network, and relates to the technical field of intelligent power grids, and the method comprises the steps: recognizing a bird target in real time, generating trajectory data, processing the trajectory data through three-dimensional Hilbert-Huang transform and an adaptive decomposition algorithm, extracting feature parameters for prediction analysis, and obtaining a bird trajectory prediction result. A collision risk is predicted and evaluated based on a trajectory, a cooperative avoidance control algorithm is constructed by adopting an artificial potential field method, a control strategy is optimized by combining an adaptive fuzzy neural network and a sliding mode controller, the operation state of a fan is monitored in real time, normal operation of the fan is recovered after birds fly away safely, and intelligent bird protection of a wind power plant is realized.

The invention discloses a resource scheduling optimization method and system based on deep learning, and particularly relates to the technical field related to resource scheduling, real-time indexes such as CPU utilization rate, memory occupancy rate and network bandwidth are acquired through a lightweight monitoring agent, and resource demands in the future 3-10 minutes are predicted by using an improved LSTM (including a cross-cycle attention mechanism), so that resource scheduling optimization is realized. The heterogeneous resource matching degree is calculated in combination with a graph attention network, a hierarchical scheduling strategy and a dynamic fault-tolerant mechanism are implemented, and the scheduling effect is evaluated through a multi-objective optimization function. The system comprises a distributed sensing terminal, a predictive analysis engine, a decision center and other modules, and supports federated learning, elastic capacity expansion and contraction and visual evaluation. The resource utilization rate can be improved, delay and energy consumption are reduced, and the method is suitable for heterogeneous resource scheduling scenes such as cloud computing and edge computing.

The invention relates to the technical field of virtual power plants, and discloses a virtual power plant regulation and control method and system for realizing new energy consumption, and the system comprises a multi-source heterogeneous data collection module, an intelligent prediction analysis module, a resource aggregation modeling module, an optimization decision module, a block chain cooperation module, and a digital twinborn evaluation module. Multi-time-scale coupling prediction is carried out on new energy output and load demand through the deep space-time convolutional neural network, fluctuation and intermittency characteristics of new energy can be described, short-term and ultra-short-term prediction precision is improved, wind curtailment and light curtailment rate and load reduction risk are reduced, and the prediction efficiency is improved. According to the method, high matching between a virtual power plant scheduling plan and an actual operation condition is guaranteed, a flexible resource feature matrix is constructed, and distributed energy storage, interruptible load and electric vehicle multi-element resources are subjected to refined modeling and aggregation, so that a virtual unit capable of being efficiently scheduled can be formed, the resource utilization efficiency is improved, and the overall scheduling cost is reduced.