3248 results about "E infrastructure" patented technology

A portable agent package apparatus for coupling to one or more environment, energy or water infrastructure or water body sensors produce timestamped or temporal process data, includes a physics surrogate world model trained to predict at least one hydraulic, chemical, or biological state variable of the sensed water system, a connection memory that stores metadata describing data source identifiers, units, and sampling cadence, pointers to available analytical tools or peer agent packages, or streams of operational experience or a hierarchical options library, an emotion tensor continuously encodes normalized metrics comprising at least one of model accuracy, computational load, data quality, latency, and uncertainty, or further including an exploration bonus channel, a value estimate error, an anomaly score, or an alignment divergence flag, and a bidirectional, authenticated communication interface that receives the temporal or timestamped process data from the one or more sensors, transmits Memo updates, and accepts goal directives.

A system and method for adaptive edge-cloud data processing dynamically distributes computational tasks between edge devices and cloud infrastructure in response to changing conditions. The system continuously monitors resource availability, network parameters, and workload characteristics while predicting future conditions using hierarchical forecasting models. A multi-objective optimization approach determines optimal task distribution, balancing processing latency, energy consumption, bandwidth utilization, and result quality. The system implements a partitionable processing pipeline that enables seamless task migration through state synchronization protocols and checkpoint mechanisms. During migration, the system preserves processing continuity by establishing dependencies, creating execution checkpoints, and verifying successful state transfer. Performance metrics may be continuously collected and analyzed to improve future decision-making. The system maintains operational resilience during connectivity disruptions through local decision-making capabilities and eventual consistency protocols, making it suitable for diverse applications including industrial IoT, connected vehicles, healthcare wearables, and smart city infrastructure.

An artificial intelligence driven system of systems may include a layered architecture for providing transaction support to various types of enterprises. A governance layer implements automated governance and policy enforcement through specialized governance modules utilizing generative AI technology. An enterprise layer supports enterprise functions by integrating management and control platforms with digital infrastructure. An offering layer creates and manages system offerings via content generation, personalization, and smart product modules. A transactions layer enables automated transaction orchestration through API integration, execution, and fulfillment modules. An operations layer manages AI systems through generation, training, verification and orchestration modules. A network layer provides adaptive networking capabilities through routing, protocol selection and communication modules. A data layer processes fused data from multiple sources using machine learning and AI systems. A resource layer manages computing, storage, and other resources through specialized resource modules.

An Al -based energy edge platform is provided herein with a wide range of features, components and capabilities for management and improvement of legacy infrastructure, coordination, and orchestration with distributed systems to support important use cases for a range of enterprises. An Al -based energy edge platform may include a graph neural network including a set of nodes respectively representing at least one distributed energy resource (DER) and a set of edges respectively interconnecting the set of nodes, wherein each edge represents at least one energy - related feature among at least two nodes of the set of nodes. The platform may incorporate emerging technologies to enable ecosystem and individual energy edge node efficiencies, agility, engagement, and profitability. Embodiments may forecast, plan for, and manage the demand and utilization of energy in greater distributed environments. Embodiments may employ intelligent provisioning, data aggregation, and analytics to leverage energy market connection, communication, and transaction enablement platforms.

A portable agent package apparatus for coupling to one or more environment, energy or water infrastructure or water body sensors produce timestamped or temporal process data, includes a physics surrogate world model trained to predict at least one hydraulic, chemical, or biological state variable of the sensed water system, a connection memory that stores metadata describing data source identifiers, units, and sampling cadence, pointers to available analytical tools or peer agent packages, or streams of operational experience or a hierarchical options library, an emotion tensor continuously encodes normalized metrics comprising at least one of model accuracy, computational load, data quality, latency, and uncertainty, or further including an exploration bonus channel, a value estimate error, an anomaly score, or an alignment divergence flag, and a bidirectional, authenticated communication interface that receives the temporal or timestamped process data from the one or more sensors, transmits Memo updates, and accepts goal directives.

The invention relates to a power grid construction management and control method based on three-dimensional digital twinning, and relates to the technical field of power grid construction management and control, and the construction management and control method comprises the following steps: S1, a three-dimensional digital twinning model construction stage: constructing a power grid project high-precision three-dimensional model based on BIM, and superposing GIS geographic data to generate a construction scene three-dimensional digital twinning model; s2, in a construction process dynamic mapping stage, processing data of the multi-source data acquisition module in real time through an edge computing node; s3, optimizing a construction management and control decision; s4, a dynamic risk management and control stage; and S5, an execution feedback stage: forming a closed loop of three-dimensional digital twinborn model construction-construction rehearsal analysis-decision-execution-model updating. According to the power grid construction management and control method based on the three-dimensional digital twinning, the construction management and control process can be transformed from extensive experience driving to data intelligent driving, and high-reliability and high-adaptability digital infrastructure support is provided for novel power system construction.

The invention relates to an infrastructure carbon emission dynamic monitoring and prediction system based on a digital twinborn technology, and aims to realize dynamic monitoring, trend prediction and optimal management of infrastructure full life cycle carbon emission through a virtual-real combined digital twinborn model. The system comprises a data acquisition module, a digital twinborn model module, a carbon emission evaluation module, a carbon emission prediction module, an optimization decision module and an interaction and visualization module. Compared with the prior art, the digital twinborn technology is applied to dynamic monitoring and prediction of infrastructure carbon emission, and the method has the characteristics of real-time monitoring, accurate prediction, scene simulation and dynamic optimization, is suitable for the infrastructure fields of roads, waterways, buildings and the like, and provides scientific support and an efficient solution for realizing a low-carbon target.

The invention provides a disaster chain prediction method and system based on infrastructure cascade failure, and the method comprises the steps: S1, carrying out the screening and concrete of extreme natural disasters, and constructing an extreme natural disaster scene which accords with the characteristics of a research region; s2, constructing a single-layer network topology structure of various infrastructures and a cascade failure mechanism based on coupling characteristics of various infrastructures, and constructing an infrastructure multi-layer coupling network conforming to spatial distribution characteristics and actual operation logic of a research area based on the single-layer network topology structure and the cascade failure mechanism; and S3, superposing the constructed extreme natural disaster scene on the infrastructure multi-layer coupling network, and predicting a disaster chain spatio-temporal evolution process under the impact of the extreme natural disaster by taking the infrastructure multi-layer coupling network as a disaster propagation carrier. According to the invention, the disaster chain can be predicted.

A distributed indexing and resolution architecture is disclosed for decentralized systems requiring modular, trust-scoped mutation control and dynamic alias governance. The system comprises a plurality of index entries arranged in a parent-child hierarchy, each associated with a structured alias and governed by one or more anchor nodes. Anchors perform localized resolution, mutation validation, and restructuring operations under deterministic policy constraints, enabling semantic scope enforcement and entropy-sensitive adaptation without requiring global consensus or centralized control. The architecture supports scoped alias traversal, asynchronous mutation proposals, and elastic anchor registration based on system state metrics. The indexing substrate may be integrated into heterogeneous infrastructures, including systems comprising distributed software agents, semantic execution platforms, or pseudonymous identity frameworks. Anchors coordinate within defined trust domains to ensure lineage continuity, dynamic rekeying, and semantic integrity across independently governed segments of a decentralized namespace.

The present invention relates to a system and computer implemented method for cost-aware autoscaling of artificial intelligence workloads using predictive queueing models, designed to achieve proactive and economically optimized scaling of computational resources across cloud and edge environments. The invention introduces a predictive queueing-based technique that anticipates future workload congestion by modeling dynamic task arrivals and service times using a stochastic queueing process. A cost estimation unit computes the total projected operational cost of potential scaling actions by integrating real-time infrastructure pricing data, predicted delay penalties derived from service-level objectives, and estimated energy consumption. A scaling decision unit applies reinforcement learning-based optimization to select the scaling action that minimizes total cost while ensuring compliance with latency and throughput constraints. The system includes a hardware-integrated autoscaling controller device comprising a predictive computation processor, cost-decision processor, and scaling actuation interface configured for real-time execution of predictive and scaling operations.

The invention relates to the technical field of communication, in particular to an underground power distribution room communication system and method based on heterogeneous network and multi-modal fusion, and the system comprises a three-dimensional heterogeneous network cooperation module, a cross-modal feature fusion module, a dynamic routing decision engine module, a self-adaptive interference suppression system and a cross-layer cooperation optimization module. The three-dimensional heterogeneous network cooperation module comprises a 4G / 5G wireless communication sub-layer, a power line carrier communication sub-layer and an edge computing management sub-layer; the cross-modal feature fusion module comprises a multi-modal encoder group, a shared feature projection layer, a twin network structure and a cross-modal attention fusion module; and the dynamic routing decision engine module comprises a state space monitoring unit, a routing optimization unit and a millisecond-level path switching strategy. Through the arrangement, the reliability, the real-time performance and the energy efficiency of communication of the underground power distribution room are systematically improved, and a high-robustness communication infrastructure is provided for an intelligent power grid.

The invention discloses a data processing method based on computer software development, which belongs to the technical field of data processing, and comprises the following steps: S1, constructing an adaptive analysis engine driven by a knowledge graph, and carrying out multi-modal data semantic modeling and context labeling by adopting a multi-modal data feature fusion technology; and S2, designing a cross-node distributed data cleaning and dynamic fragmentation optimization strategy based on a differential privacy and feature space alignment technology, and through hierarchical deployment of a national cryptographic algorithm and homomorphic encryption, realizing data full life cycle security protection, ensuring data asset security by static storage encryption, and supporting security calculation requirements by ciphertext operation; attribute-based encryption fine-grained access control accurately matches a data use permission, and a block chain technology ensures traceability and tamper-proofing of data operation; the problem that a traditional encryption scheme is insufficient in flexibility is solved while the compliance requirement is met, and a trusted infrastructure is established for cross-domain data sharing.

Systems and methods for decentralized orchestration of event records within decentralized computing environments. A processing system including decentralized execution nodes dynamically allocates microservice instances based upon real-time node performance metrics. Transaction requests including transaction data digitally associated with decentralized identifiers (DIDs) are processed by allocated microservice instances. The processing includes verifying transactions, generating cryptographically-linked immutable memorialization records associated with respective DIDs, and distributing these records to a tamper-evident distributed ledger configured to enforce immutability through consensus nodes. The processing system dynamically reallocates microservice instances among decentralized execution nodes based upon monitored utilization metrics and predefined scaling thresholds. Each memorialization record is cryptographically secured and linked permanently to the DID for data immutability, enhanced security, and dynamic scalability for event data processing in distributed infrastructures.

Systems, methods, and computer-readable storage media for providing a composable cyber resilience object. A method can include identifying, by one or more processing circuits, a posture state of at least one entity, generating or selecting a first plurality of code blocks corresponding with a parameters of at least one third-party, and determining the entity qualifies for protection based on the posture state and the rules or conditions. The method can include generating or selecting a second plurality of code blocks including functions to provide the protection based on the rules or conditions and generating the composable cyber resilience object by integrating a portion of the first plurality of code blocks and the second plurality of code blocks into at least one data structure. The method can include linking the composable cyber resilience object and a computing or networking infrastructure of the entity using a communication interface or structure.

A system (100) for securing cloud-based large language models through cyber threat defense and compliance monitoring, comprising: (a) an access control and authentication module configured to authenticate users and applications using role-based access control (RBAC) and multi-factor authentication (MFA); (b) a data protection and encryption module configured to encrypt data in transit and at rest and to anonymize sensitive input / output data using cryptographic techniques; (c) a threat detection and behavior monitoring module employing machine learning algorithms to identify anomalies and detect potential cyber threats in real time;(d) a regulatory compliance monitoring module configured to continuously assess system operations against applicable regulatory frameworks and generate alerts in the event of deviations from the regulations; (e) an incident response and mitigation module configured to automatically execute predefined response actions upon detection of threats or regulatory violations; (f) a logging, auditing, and forensic analysis module configured to securely log system activities, interactions, and threat events with cryptographic integrity protection; and (g) a governance and policy management module configured to define, update, and enforce organizational AI usage and compliance policies via the cloud-based LLM infrastructure.

The invention provides an infrastructure security analysis method and system based on a large model, and the method comprises the following steps: collecting multi-source heterogeneous data of an infrastructure, including physical equipment sensor data, network flow data, operating system logs and external threat intelligence; converting the multi-source data into standardized feature vectors of a unified time axis through a space-time alignment module; and inputting the standardized feature vector into the fine-tuned large language model for semantic understanding, and generating an equipment behavior semantic description and threat intention analysis result. According to the method provided by the invention, a large language model and reinforcement learning collaborative architecture is provided, and the large language model is improved, so that end-to-end mapping from multi-source data to threat semantics is realized, and the attack detection rate is improved.

The embodiment of the invention provides a quantum key distribution method based on a cryptographic infrastructure system, relates to the technical field of quantum secret communication, and provides a quantum key distribution method based on fusion of quantum key distribution and post quantum cryptography, which integrates a classical commercial cryptographic algorithm, a PQC post quantum cryptographic algorithm and quantum key distribution. A QKD-based quantum key secure distribution process combining SM2 and PQC mixed signature, SM2 and PQC mixed key packaging, PQC collaborative signature, PQC collaborative decryption, token access and other cryptographic technologies is designed, and anti-quantum cryptographic capability support is provided for voice applications through collaborative signature and collaborative decryption, so that the security of software for realizing a PQC cryptographic algorithm is improved, and the security of the PQC cryptographic algorithm is improved. Moreover, the software implementation mode does not have the inherent security problem of the IP, the dependency on the server is low, and the standardization program is high.

A system and method for enabling real-time dispatch of flexibility resources to unlock grid capacity through AI-based orchestration. The invention addresses the challenge of connecting high energy demand users, such as data centers, to constrained electricity grids without requiring infrastructure upgrades. The system establishes a marketplace where flexible asset holders set temporal compensation prices and boundary conditions, enabling true market-based participation. An AI orchestration engine analyzes real-time grid conditions and modifies flexible asset behavior to create inverse consumption profiles that counterbalance new demand loads. The platform integrates hardware and software solutions for remote control and APIs for autonomous systems like electric vehicles. Aggregators and off-takers can establish long-term contracts for flexible capacity at agreed prices. The AI system ensures flexible assets meet user-defined boundary conditions while simultaneously masking high energy demand, making new loads invisible to the grid and enabling immediate connection of data centers essential for industrial deployment.

The invention discloses a power monitoring system anomaly detection method based on a multi-model voting mechanism, and the method mainly comprises the following steps: firstly, collecting network flow data and equipment behavior data in a power monitoring system, and converting the network flow data and the equipment behavior data into multi-dimensional feature vectors; secondly, multiple types of anomaly detection models are constructed, and each model independently judges the anomaly of the input data; performing fusion processing on judgment results of the models through a majority voting or weighted voting mechanism, and outputting a comprehensive anomaly judgment result; and finally, dynamically adjusting the weight and detection parameters of each model based on a feedback mechanism, outputting alarm information when a fusion result exceeds a set threshold value, and linking a system response module. The power monitoring system anomaly detection method provided by the invention for the first time has the advantages of high detection accuracy, low false alarm and missing report rate, dynamic adjustability, high adaptability, flexible deployment and the like, and is particularly suitable for industrial control network security protection scenes in key infrastructures.

The invention discloses an industrial Internet of Things time sequence self-supervision anomaly detection method and a monitoring and early warning system, and relates to the field of industrial Internet of Things, and the method comprises the steps: S1, constructing an anomaly detection model, and S2, obtaining a training data set; s3, training and optimizing an anomaly detection model; s4, acquiring to-be-detected data in real time; s5, performing anomaly detection analysis on the to-be-detected data, and outputting an anomaly detection result; through a time sequence and relation learning module, a dynamic graph topological structure learning module and an enhancement module, internal characteristics of a time sequence in a time domain and a space domain are deeply mined. The time sequence and relation learning module comprehensively captures a multi-scale time pattern, and the dynamic graph topological structure learning module eliminates dependence on a predefined graph structure; the enhancement module enhances the invariant representation under noise, and improves the recognition capability of the model to a normal mode; through wide experiments, the advancement of the method in detection performance is verified, and reliable support is provided for intelligent manufacturing and infrastructure diagnosis.

A cloud deployment automation system consisting of: a deployment automation device housed in a rack-mountable enclosure, the device comprising: a multi-core orchestration processor configured to execute deployment logic as compiled execution graphs; a storage module operatively coupled to the orchestration processor, the storage storing a set of deployment templates, real-time execution states, telemetry logs, and policy configurations; a secure credential management processing unit embedded in the device, configured to generate, store, and rotate cloud access tokens, API keys, and user-specific credentials, and to provide encrypted access to those credentials during deployment execution; an in-memory workflow execution engine executed by the orchestration processor, configured to analyze a user-defined deployment configuration that includes a declarative specification of infrastructure resources and compile that configuration into a directed acyclic graph (DAG) that represents the resource deployment order, dependency mapping, and rollback relationships, a cloud provider interface subsystem communicatively connected to multiple heterogeneous cloud platforms via appropriate API adapters, the subsystem enabling the orchestration processor to send provisioning requests and receive status events from the platforms; a customizable workflow compiler unit configured to convert graphical workflow definitions or domain-specific language (DSL) scripts into execution sequences that can be used by the workflow execution engine, where the workflow compiler unit supports conditional branching, asynchronous operations, and runtime variable resolution; and A policy enforcement control unit integrated into the deployment automation device, with the policy engine configured to apply organization-specific compliance rules, tagging conventions, security group configurations, and runtime resource limits to all deployment actions in a context-aware manner prior to execution.

The invention provides an electric power infrastructure construction progress self-adaptive scheduling method based on digital twinning. The method comprises the steps that a construction progress digital twinning model is constructed according to preset historical data and construction data fed back in real time on a construction site; predicting a construction progress trend based on the construction progress digital twinborn model to obtain construction progress prediction data; performing construction safety assessment according to the construction progress prediction data in combination with uncertainty of human factors, a construction process and environmental conditions to obtain a construction safety assessment result; designing an adaptive scheduling method according to the construction safety evaluation result and the construction progress prediction data to optimize a construction progress scheduling scheme; performing iterative optimization on the optimized construction progress scheduling scheme by using an optimization algorithm to obtain an optimal scheduling scheme; and applying the optimal scheduling scheme to actual construction management. According to the method, the adaptive scheduling algorithm is combined with the digital twinborn model, so that intelligent progress management in power infrastructure construction can be realized.

Techniques for facilitating connectivity to vPLCs created in a CSP-provided infrastructure in a region. Within the CSP-provided infrastructure in a region, when the destination of a packet is determined to be an endpoint associated with a particular vPLC, the packet is tagged with information related to the particular vPLC. The vPLC-related information for the particular vPLC can include, for example, a vPLC identifier identifying the particular vPLC, an identifier identifying a customer associated with the endpoint, a virtual cloud network identifier identifying a virtual cloud network (VCN) belonging to the particular vPLC and where the endpoint is part of the VCN, and other vPLC-related information. The packet is then routed or communicated within the CSP-provided infrastructure in a region along with the tagged vPLC-related information. The vPLC-related information is used as part of the connectivity and for routing of packets within the CSP-provided infrastructure in a region.

Disclosed are various embodiments for automated deployment and management of distributed units (DUs) in a radio-based network. In one embodiment, it is determined to deploy a radio-based network at least partially using infrastructure of a cloud provider network. A radio access network (RAN)-enabled edge server of the cloud provider network is deployed to a physical location of the radio-based network. The RAN-enabled edge server is configured to perform a set of distributed unit (DU) functions for the radio-based network.

The invention discloses a charging pile load prediction method based on deep learning. The method comprises the following steps: S1, constructing a regional grid and initializing a cellular basic attribute vector; s2, collecting historical load data and external influence factors, and unifying the historical load data and the external influence factors into an input tensor; s3, generating a time continuous evolution state by using an ordinary differential equation modeling module; s4, constructing a state vector at the current moment; s5, the differentiable cellular automaton module executes primary spatial state propagation; s6, performing multiple rounds of spatial propagation iteration, and outputting enhanced state representation; s7, the load prediction module outputs a future multi-moment prediction value; s8, errors are calculated, and end-to-end training optimization model parameters are executed; and S9, outputting a final optimization model as a load prediction method. According to the method, the response capability of the model to sudden disturbance and the stability of multi-region collaborative prediction are remarkably improved, and the method can be widely applied to multiple application scenes such as smart energy management, electric traffic scheduling and urban public infrastructure intelligent optimization.

The concrete-filled steel tube arch bridge detection system and method based on multi-modal fusion and quantum optimization are used for bridge structure health monitoring. The system comprises a multi-modal data acquisition module, a quantum optimization data fusion module, a defect self-growth simulation damage prediction module, a low-power-consumption intelligent sensor network and a quantum safety remote monitoring module. The multi-modal data acquisition module is fused with an ultrasonic radar, a millimeter wave radar, a hyperspectral image, a LiDAR and a wireless sensor, and bridge surface and internal damage detection is realized; the data weight is dynamically adjusted by adopting a quantum optimization algorithm, and the fusion precision is improved; in combination with a graph neural network and deformable grid calculation, crack propagation and fatigue damage development are predicted, the power consumption of the sensor is reduced through an energy collection technology, and the safety of remote monitoring data is ensured by adopting a quantum safety remote monitoring module. Compared with the prior art, the method improves the detection precision, prediction capability and data safety, and is suitable for health monitoring of various bridges and infrastructures.

The invention discloses an electric power spot market electric charge settlement system and method fused with a block chain technology, and the system comprises a core settlement layer which is used for storing the electricity price and user identity data, and executing an electric charge settlement rule through a built-in intelligent contract module; the high-frequency transaction processing layer is used for processing a transaction process through an under-chain caching technology and uploading data in the transaction process to the core settlement module; an external data interface is integrated on one side of the high-frequency transaction processing module and used for acquiring electric quantity and load data in real time, and a cross-chain gateway is arranged on the other side of the high-frequency transaction processing module and used for achieving synchronous settlement of electric charge and capital flow. According to the method, the efficiency and transparency defects of a traditional mode are overcome, the performance and energy consumption bottleneck of the block chain technology in an electric power scene is broken through, high efficiency, safety, expandability and environmental protection benefits are achieved, and a credible, real-time and compliant digital settlement infrastructure is provided for an electric power spot market.

The present invention provides a system and method for generating proposals for infrastructure modalities, such as electrical substations, using advanced artificial intelligence. It includes an input interface for data collection, a lightweight generative or rendering pipeline for creating preliminary 2D designs, and a generative model selected from diffusion, transformer-based, GAN or other architectures for refining these into detailed 3D models and generating preliminary designs. The system evaluates designs against predefined criteria to ensure compliance and feasibility. Supported by a cloud-based infrastructure for robust data processing and integration with third-party services, this system enhances the efficiency, accuracy, and compliance of modality planning and proposal generation.

The invention discloses a three-dimensional noise mapping method and system based on point cloud, and belongs to the technical field of urban environmental noise prediction.The method comprises the steps that point cloud data driven three-dimensional modeling and parameter extraction are carried out, an urban three-dimensional model with a geometric topological structure is constructed through laser scanning point cloud data, and related parameters are extracted; based on sound field calculation of ISO 9613 standards, a multi-path propagation model including an atmospheric effect, a ground effect and obstacle diffraction is established, a green infrastructure noise reduction effect is especially considered, and propagation attenuation of noise in a three-dimensional space is simulated; and performing visual expression of three-dimensional noise, and constructing fusion expression of a sound pressure level contour surface and a three-dimensional city model. In view of difficulty in simulation of three-dimensional reconstruction and sound waves in a spatial propagation process in the prior art, the method improves the precision of quantitative prediction, and fuses a green infrastructure noise reduction effect to realize rapid and efficient regional three-dimensional reconstruction and noise mapping.

The invention discloses a nature-based recreation infrastructure network construction method and device and electronic equipment, and relates to the technical field of geographic planning, and the method comprises the steps: recognizing a recreation resource center of a research area according to natural reserve data, park green land data, scenic area data, characteristic town data and river system data; constructing a comprehensive resistance surface of the research area according to the resistance factors; identifying a target corridor of the research area and a network strategy point of the target corridor according to the recreation resource center and the comprehensive resistance surface; and correcting and adjusting the network structure of the research area according to the recreation resource center, the target corridor and the network strategy point. According to the method, the recreation resource center meeting ecological functions and human recreation requirements is screened out according to the recreation resources, and the target corridor and the network strategy point are identified, so that the network structure of the research area is corrected and adjusted, and a recreation infrastructure network based on nature under recreation guidance is formed; based on the network, the experience of people in recreation can be improved.