3888 results about "E infrastructure" patented technology

An emergency response and crisis management system comprises a computing system which performs steps comprising: aggregating real-time and historical data from multiple data sources; processing the data using artificial intelligence techniques; analyzing the processed data to detect and classify active crises to predict the likely development and trajectory of the ongoing crises, and assess potential short-term and long-term impacts across one or more sectors comprising one or more of public health, economy, infrastructure, environment, and social stability; generating multi-dimensional, interactive visualizations of the analysis results comprising one or more of geospatial maps, time series charts, and network diagrams; providing recommendations for crisis management and response tailored to different stakeholder roles; facilitating integration with external systems and applications through a comprehensive application programming interface (API) layer; and continuously adapting the system's models and algorithms based on one or more of real-world outcomes, expert input, and user feedback.

The present disclosure relates to a blockchain-based authentication system for electric vehicles and drones, and a method for authenticating electric vehicles or drones in a smart city. The proposed blockchain-based authentication system enhances security, privacy, and scalability for electric vehicles (EVs) and drones in smart city environments. It employs a consortium blockchain managed by city authorities and stakeholders, utilizing smart contracts for identity registration, credential issuance, and access control. Device nodes store cryptographic keys securely, while an optional authentication server facilitates off-chain integration. The authentication operation of the system includes, ensuring tamper-proof identity verification through on-chain validation. Authentication attempts are immutably recorded for auditing and anomaly detection. By eliminating single points of failure and strengthening data privacy, this decentralized authentication framework offers a scalable and secure solution for integrating EVs and drones into smart city infrastructures.

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.

Various automated techniques are disclosed herein for monitoring and assessing real-time and future operational and health statuses of real-world assets (e.g., physical devices, components, equipment, structures, buildings, machines, infrastructure, piping systems, etc.). A monitoring system incorporates the use of intelligent Monitoring Devices for monitoring pipe systems and / or other infrastructure for leaks / issues using IoT devices and machine learning. One or more Monitoring Device(s) are attached to specific physical equipment / structure(s) to be monitored. A Monitoring Device performs comprehensive field data gathering. The collected field data is used to train a customized ML model, which is stored locally in that Monitoring Device. The Monitoring Device monitors current conditions of the pipe using various sensors, and uses its locally stored customized, trained model to perform real-time, edge-based analysis of the monitored data to identify possible issues in real-time and / or to predict future maintenance / service needs without relying on continuous cloud connectivity.

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.

Techniques are described for a computing system configured to obtain a plurality of candidate logs for a plurality of layers of a computing infrastructure. The computing system may, for each candidate log of the plurality of candidate logs, map the candidate log to a log template of a plurality of log templates, wherein each log template to which a candidate log is mapped is a mapped log template. The computing system may rank mapped log templates based on properties of the mapped log templates. The computing system may select, based on the ranking, one or more candidate logs as critical logs. The computing system may output at least one of (1) an indication of the critical logs to determine a potential root cause associated with a performance issue of a network application or (2) an indication of the potential root cause associated with the performance issue of the network application.

The invention discloses an intelligent digital operation and maintenance management system and method for a charging pile, and relates to the technical field of new energy infrastructure operation and maintenance, and the system comprises an information obtaining module which determines a delay information circulation channel, obtains real-time discussion content, processes a discussion text through employing a word segmentation technology, and carries out the operation and maintenance of the charging pile. If the confidence coefficient of the semantic feature vector is higher than a preset threshold value, the semantic feature vector is judged to be a specific question type through state space definition, classified question labels are obtained, the historical matching module obtains features associated with a historical question library, historical question records are matched, and the matching precision is evaluated through reward function design. Determining a historical solution most similar to the current problem; according to the intelligent digital operation and maintenance management system and method for the charging pile, the accuracy of problem classification and solution matching is continuously improved, and intelligentization and high efficiency of operation and maintenance management of the charging pile are achieved.

The invention relates to an MCP-based urban rail transit intelligent operation and maintenance service construction method. The method comprises the steps of obtaining an input question of a user; inputting the input question into the large language model for processing and extracting key information; the big language model judges data required for solving the problem according to the key information; acquiring data required for solving the problem from different MCP Clients through an MCP protocol; and outputting a final result by combining the big language model with data required for solving the problem. According to the method, the MCP protocol is introduced into the field of urban rail transit intelligent operation and maintenance for the first time, and a unified model context communication interface is constructed to realize structured interaction between a large model and multi-source operation and maintenance data, so that connection between various operation and maintenance services and intelligent agents is more efficient and standardized, and the operation and maintenance efficiency is improved. The technical problems that in a traditional system, interfaces are heterogeneous, data are closed, and a model is difficult to call external services are solved, and infrastructure support is provided for modular deployment and cross-system integration of an intelligent operation and maintenance platform.

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 communication infrastructure intelligent operation and maintenance management system based on cloud computing. The system collects multi-source operation and maintenance data through the data acquisition module, and the multi-source operation and maintenance data is transmitted to the cloud computing center module after being processed by the data transmission and preprocessing module. The cloud computing center module performs deep analysis on data by using a plurality of intelligent algorithm-based sub-modules such as equipment state evaluation, fault prediction, resource optimization and allocation, operation and maintenance decision suggestion and the like to generate an operation and maintenance strategy, the operation and maintenance strategy is implemented by the operation and maintenance operation execution module, and the operation and maintenance condition is visually presented by means of the visual display and monitoring module. According to the method, the powerful computing power and storage capacity of cloud computing are utilized, and multiple algorithm advantages such as machine learning and time sequence analysis are combined, so that intelligence and automation of operation and maintenance management of the communication infrastructure are realized, the operation and maintenance efficiency and quality are effectively improved, and stable and reliable operation of communication service is guaranteed.

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.

The invention provides a method for realizing agent development MCP service through computing power of an intelligent computing center, and relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructure, the method comprises the following steps: S1, determining interface information of a target application programming interface API, the target API being an interface of a target platform; s2, generating a first coding task based on the interface information through an intelligent agent of the intelligent computing center; s3, generating a first MCP service component corresponding to the first coding task through the intelligent agent; and S4, registering the first MCP service component to the intelligent computing center. According to the invention, the development efficiency of the MCP service of the intelligent computing center can be greatly improved.

The invention relates to an infrastructure carbon emission evaluation method and system based on dynamic and static fusion prediction. The method comprises the following steps: calculating carbon emission in a design stage and a construction stage through life cycle evaluation; obtaining carbon emission related data in the whole life cycle of the infrastructure, wherein the carbon emission related data comprises static data and dynamic data collected by a sensor; aiming at heterogeneous data of different data sources in carbon emission related data, screening data playing a key role through an attention mechanism, calculating a fusion weight of a sensor level based on reliability, information entropy and historical accuracy of a sensor, and realizing fusion of multi-source heterogeneous data through adaptive weight distribution; extracting features of the time sequence type data by utilizing Bi-GRU, and extracting features of the space correlation type data by utilizing GCN to obtain fused features; on the basis of the fused features, carbon emission in the operation and maintenance stage is predicted through a deep learning algorithm; and realizing infrastructure carbon emission evaluation by combining the carbon emission of each stage.

The invention provides a method and device for an intelligent computing center to provide computing power resources through computing power packages, and relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructure, and the method comprises the steps: S1, determining a computing power package corresponding to a to-be-processed computing power task under the condition that the computing power resources need to be called, the computing power task is a task needing to call computing power resource processing, and the computing power package is used for metering the computing power of the intelligent computing center according to the calculated amount; s2, computing power resources are called based on an application program interface corresponding to the computing power package, the application program interface comprises a code program corresponding to operation for executing the computing power task, and the computing power resources called by the application program interface are matched with the operation environment of the computing power task. According to the method, the scheduling efficiency of the computing power resources can be improved, the computing power is metered according to the calculated amount through the computing power package, and wide application of the general computing power is achieved.

Systems and methods for mathematical modeling of the hardware and software stack of commodity computer platforms are provided, enabling provable guarantees on memory, device, and program execution. This approach addresses the technical problem of reliance on system agents that rely on implicit trust in the operating environment, which can be exploited by sophisticated attackers using complex threats such as memory access exploits and code / data integrity exploits. The solution provides a proactive, mathematically-backed security solution that eliminates entire classes of cyberattacks by design, ensuring realizable guarantees on commodity computer platforms running hardware and software stack elements at the lowest operating level. This approach has significant advantages over current reactive cybersecurity methods, including reduced complexity and overhead, and increased confidence in the integrity of the system. The solution's main uses include providing mathematically-backed security and availability guarantees for critical infrastructure, financial institutions, and other organizations vulnerable to cyberattacks.

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.

In an embodiment, a method may be implemented in a computer system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, the computer system interconnected with a telecommunications system, the method comprising: receiving, at the computer system, data relating to operation of the telecommunication system, obtaining, at the computer system, at least one machine learning model trained to detect and predict faults in the operation of the telecommunication system, selecting, at the computer system, computing infrastructure upon which to execute the at least one machine learning model, wherein the selected computing infrastructure comprises a mesh of interconnected micro-applications;, executing, at the computer system, the at least one machine learning model using the selected computing infrastructure to detect and predict faults in the operation of the telecommunication system, and automatically correcting at least some of the detected faults.

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.