2163 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 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.

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 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.

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 data center infrastructure personnel intelligent configuration system, and belongs to the technical field of data center operation and maintenance management. Comprising an acquisition module, a man-hour prediction module, a configuration module and a feedback module, the man-hour prediction module is used for calculating the health coefficient of the equipment according to the equipment list and performing man-hour prediction; the configuration module is used for configuring the number of personnel according to the prediction result; the feedback module is used for outputting an executable personnel configuration scheme through a preset scheduling system; according to the method, a multi-dimensional configuration scheme meeting the SLA requirement is output through a real-time optimization algorithm of precise man-hour modeling and fusion of dynamic factors, real-time generation of the scheme is achieved, and traditional manual measurement and calculation which consumes several days are replaced.

The invention discloses an electric power detection method and system for multi-modal enhanced fine tuning, and relates to the technical field of vision-driven intelligent electric power inspection, and the method comprises the steps: 1, obtaining an electric power inspection image, and marking the image; 2, inputting the marked image into the initial vision-language model, and carrying out cold start supervision fine tuning to train the initial vision-language model to obtain a model subjected to supervision fine tuning; 3, performing reinforced fine tuning on the model subjected to supervised fine tuning, and updating model parameters to obtain a PowerGPT-R1 model; and 4, evaluating the performance of the PowerGPT-R1 model based on the test set, and verifying the reliability of power detection. According to the method, the detection precision is remarkably improved under the condition of few samples, meanwhile, the multi-modal reasoning capability which a visual perception model does not have is shown, and the method has important application value in autonomous inspection of the infrastructure of the smart power grid.

Systems and methods are disclosed for real-time anomaly prediction using near real-time data. The invention addresses delays in telemetry data collection from infrastructure components, by collecting metrics and logging this data in real-time. Extracted logged data undergoes initial analysis to identify patterns and anomalies, followed by cleaning to remove noise and errors. Feature engineering enhances the data, creating or modifying features to improve machine learning model performance. The system calculates weighted means of previous data values and computes first and second-order differences to capture immediate changes and trends. These calculations adjust the extrapolated value to accurately reflect current conditions. The adjusted data is integrated into the dataset and validated. The validated data trains and tests a machine learning model, which is then finalized and deployed for real-time anomaly detection. This system ensures accurate and timely anomaly prediction, enabling automated incident response to maintain the reliability and performance of infrastructure components.

The invention provides a system and method for managing the lifecycle of machine learning models, from development to deployment and ongoing operation, across various environments including on-premises, cloud, and hybrid infrastructures. The system features a model build platform for data processing, feature generation, model development, training, and hyperparameter tuning. A model analytics engine extracts metadata, performs complexity analysis, and generates configuration files specifying environment settings and resource needs. A secure model repository enables version-controlled storage, while a deployment platform retrieves, validates, and deploys models in containerized environments like OpenShift or Kubernetes. The platform dynamically allocates resources, supports real-time and batch scoring, and monitors model performance with guardrails. Customizable agents provide real-time feedback and automated optimization, and the system can securely decommission models while maintaining detailed lifecycle records. The invention enhances the efficiency, security, and scalability of machine learning operations with continuous performance improvement and compliance automation.

A system can receive user input data that comprises an identification of a computer infrastructure component of a group of computer infrastructure components that are associated with a user account. The system can identify information about the group of computer infrastructure components based on the identification of the computer infrastructure component. The system can convert text of the user input data into a first numerical vector. The system can create a context of the user input data based on identifying a match between the first numerical vector and a second numerical vector of a group of numerical vectors, wherein the context corresponds to a natural-language version of the second numerical vector. The system can combine the context and the information about the group of computer infrastructure components into a combined context, and input the combined context and the user input data to a large language model to produce a result.

The invention provides a computing power metering method and device of an intelligent computing center cloud platform based on a multi-dimensional performance index, and relates to the technical field of intelligent computing centers, intelligent computing centers, computing power infrastructures and intelligent computing clouds, and the method comprises the steps: S1, obtaining a first task type of a first computing power operation task; s2, determining a plurality of resource indexes corresponding to the first task type and a weight coefficient corresponding to each resource index; s3, collecting a parameter value corresponding to each resource index in each of the N periods; s4, calculating a computing power value corresponding to the first computing power operation task based on the weight coefficient corresponding to each resource index and the parameter value corresponding to each resource index in N periods; and S5, generating a first verification data packet based on the first task identifier of the first computing power operation task, the weight coefficient corresponding to each resource index and the parameter value corresponding to each resource index in N periods. The computing power credibility of the intelligent computing center cloud platform can be greatly improved.

An AI-based energy edge platform is provided herein with a wide range of features, components and capabilities for management and improvement of legacy infrastructure and coordination with distributed systems to support important use cases for a range of enterprises. 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 use AI, IoT, and technologies that filter, process, and move data more effectively across communication networks. Embodiments of the platform may leverage energy market connection, communication, and transaction enablement platforms. Embodiments may employ intelligent provisioning, data aggregation, and analytics.

The invention discloses a heterogeneous resource energy-saving optimization scheduling method and system based on computing power perception, and relates to the technical field of modern computing infrastructure, the system comprises a computing power-energy consumption evaluation module, the computing power-energy consumption evaluation module is used for carrying out computing power and energy consumption evaluation on heterogeneous resources, generating and dynamically updating a computing power-energy consumption mapping table, the heterogeneous resources comprise ARM architecture resources, X86 architecture resources and GPU resources. According to the method, the computing power-energy consumption mapping table is generated and updated in real time, so that the scheduling decision is always based on the latest energy efficiency state of the resources, the accuracy of the basic data is ensured, intelligent task classification and dynamic constraint construction are combined, the system can flexibly balance the task performance and the energy consumption target, and particularly, the strategy can be adaptively adjusted for a high-emergency task; the timeliness of key services is guaranteed, the differentiated scheduling strategy serves as an efficient pre-screening mechanism, task requirements and resource architecture characteristics are accurately matched, and the pertinence and efficiency of resource allocation are remarkably improved.

The invention provides a distributed quantum security encryption method, system and device. The method comprises the following steps: monitoring the quality of a quantum key distribution link in real time, and dynamically selecting a quantum key, a post-quantum session key or a fusion key of the quantum key and the post-quantum session key as a working key; and when the node cannot be directly reached, the trusted relay node generates an end-to-end key seed by using a quantum key and a post-quantum cryptography shared key which are respectively established with the two ends, the end-to-end key seed is encrypted and distributed by a public key and then a session key is independently derived by the two communication parties, and the relay node cannot decrypt. The system adopts a distributed Mesh network architecture supporting a terminal / relay dual mode. The device integrates a quantum random number generator, a post quantum cryptography coprocessor and a mixed key engine. The method integrates the advantages of quantum physical security and post quantum cryptography, has high security and availability, and is suitable for constructing a key infrastructure security communication network resisting quantum computing attack.

The invention relates to a data center equipment type selection method based on electrical and heating ventilation load linkage, and belongs to the technical field of data center infrastructure design. The model selection method comprises the steps of setting basic data, obtaining the number of machine rooms and the number of IT devices in a single machine room, obtaining the total power of the IT devices, setting an energy efficiency index set value, obtaining the project power consumption scale, obtaining the total cooling load, selecting models of air conditioners at the tail ends of the machine rooms and substations, selecting models of water chilling units, selecting models of heat exchangers, selecting models of cooling water pumps and chilled water pumps and selecting models of cooling towers. And obtaining the total power of the air conditioner, obtaining an energy efficiency index accounting value, and carrying out PUE verification. According to the method, through closed-loop iteration of electrical load-heating and ventilation load-PUE, model selection and quantity selection of all key equipment are completed at a time; professional splitting, repeated adjustment and modification and capacity redundancy caused by traditional serial design are avoided, the design period is remarkably shortened, and initial investment is reduced.

The invention discloses an AI infrastructure performance detection method and device and a storage medium, and belongs to the technical field of data processing. The method comprises the steps that according to chip information of container nodes, dynamic instrumentation is conducted on a kernel layer and a calculation layer by expanding a Burkley packet filter, first performance indexes of the kernel layer and the calculation layer are collected, a hardware management library function is called, second performance indexes of a chip hardware layer are collected, and on the basis of a control group mechanism, the kernel layer and the calculation layer are subjected to dynamic instrumentation; and obtaining meta-information of the business process group in the container node, constructing an association relationship between the first performance index and the business process group and between the second performance index and the business process group through association marking according to the meta-information, and performing association analysis on the first performance index and the second performance index based on the association relationship to obtain a performance detection result of the business process group. According to the method, the performance parameters are collected through a non-intrusive scheme, the performance indexes of different service processes are determined through a service association mode, and the accuracy of AI performance problem positioning is improved.

The invention discloses an intelligent RAG enhanced large model auxiliary writing system, and relates to the technical field of artificial intelligence auxiliary writing. In order to overcome the defects of an existing writing auxiliary tool, the adopted scheme comprises an interface and user layer for receiving a writing demand and redisplaying a generation result; the service layer is used for deploying three types of intelligent agents including writing, interactive writing enhancement and checking; the platform layer is used for carrying out industry adaptation analysis, mixed retrieval and RAG enhancement on writing demands and then sending the writing demands to the large model layer; three intelligent agents of writing, interactive writing enhancement and checking are called in sequence based on the output of the large model layer, and then a text is redisplayed to the interface and user layer after fact accuracy verification is completed through a tense knowledge graph; the data layer is used for providing a multi-type data storage system; the large model layer outputs a paragraph-level text and a reference index based on a writing large model, the paragraph-level text is returned to the platform layer, and reference index information is fed back to the interpretability and security layer for tracing; and the infrastructure layer is used for providing bottom layer resource support for each layer.

The invention relates to the field of intelligent transportation and urban landscaping management, and discloses an LIM transportation junction landscaping landscape intelligent operation and maintenance method and system and electronic equipment, and the method comprises the following steps: constructing a landscaping landscape information model integrating a three-dimensional space model, physiological attributes and infrastructure topology; deploying a multi-source sensing network based on the model, and collecting environment, vegetation and operation and maintenance data; performing time-space association on the data and the model entity, and establishing a dynamic data management platform; constructing a vegetation digital twinborn prediction engine in combination with the physical information neural network and outputting a result; based on the prediction result, adopting multi-target reinforcement learning to generate a maintenance strategy; and executing an intelligent maintenance task bound with the model space coordinates according to a strategy. According to the method, unified integration and dynamic management of greening landscape data are realized, vegetation state sensing precision and prediction capability are improved, intelligent decision-making of multi-objective optimization is supported, and scientificity, efficiency and resource utilization rate of greening maintenance are effectively improved.

The invention belongs to the technical field of data security and block chains, and discloses a physical infrastructure node continuous data chaining and traceability method and system, and the method comprises the steps: generating a unique asymmetric key pair for each node; continuously collecting physical sensing data and generating a unique data identifier; performing digital signature on the current physical sensing data to form a continuous signature chain; storing the original physical perception data, the corresponding digital signature and the continuous signature chain in an off-chain service platform database; uploading the data identifier, the digital signature and the hash value of the original physical sensing data to a block chain network based on a dynamic anchoring strategy; and comparing the calculated hash value with the hash value of the corresponding original physical sensing data in the block chain network, and backtracking the continuous signature chain. According to the invention, a safe, efficient and traceable complete data guarantee scheme can be provided for a decentralized physical infrastructure network.

The invention provides a heterogeneous computing power normalization metering method and device for an intelligent computing center cloud platform, and relates to the technical field of intelligent computing centers, intelligent computing centers, computing power infrastructures and intelligent computing clouds, and the method comprises the steps: S1, obtaining a parameter value of a first related parameter of a first GPU and a parameter value of a second related parameter of a first TPU, the first GPU and the first TPU are used for executing a first computing power operation task; s2, determining a first weighting coefficient of the first GPU, and determining a second weighting coefficient of the first TPU; s3, based on the first weighting coefficient, the parameter value of the first related parameter and a preset reference value, calculating to obtain a first computing power value of the first GPU; s4, based on the second weighting coefficient, the parameter value of the second related parameter and a preset reference value, calculating to obtain a second calculation force value of the first TPU; and S5, based on the first computing power value and the second computing power value, calculating to obtain a third computing power value of the first computing power operation task. According to the method, the computing power metering accuracy of the computing power operation task is greatly improved.

The invention relates to a high-precision crack segmentation method based on double-flow visual basic model collaboration, belongs to the technical field of computer vision and deep learning, and aims to solve the problems that an existing crack segmentation model is poor in generalization under a complex background, fine cracks are missed and disconnected under high resolution, and the fine tuning cost of a large model is high. The method comprises the following steps: firstly, preprocessing an acquired infrastructure surface image into standard input of 1024 * 1024 pixels, and extracting features by adopting a double-flow encoder containing parallel SAM3 and DINOv3 double branches; freezing double-bone intervention training parameters, and embedding a lightweight adapter to complete efficient fine tuning of the parameters; double-branch multi-scale features are extracted, and multi-scale splicing and dimension reduction fusion are completed after channel alignment; and outputting a pixel-level crack binary segmentation result through a lightweight decoder based on depth separable convolution. According to the method, segmentation precision, cross-domain generalization ability and training deployment cost are considered, and the engineering landing requirement of infrastructure health monitoring is met.

The invention provides a BIM-fused port infrastructure digital twinborn operation and maintenance management system and method. The method comprises the steps of collecting a port infrastructure BIM full-life-cycle multi-source heterogeneous data set, constructing a port infrastructure real-time three-dimensional digital twinborn body, constructing an equipment health prediction model and generating a fault diagnosis result; simulating tide influence to form a stress distribution prediction result; a three-level linkage optimization model is constructed, a scheduling scheme is generated, a multi-energy collaborative optimization engine optimization scheme is used, a three-dimensional safety matrix is constructed through unmanned aerial vehicle inspection and visual recognition, risks are monitored in real time, and safety early warning is generated. The prediction model is constructed, faults are accurately diagnosed, the tide influence is quantified, and the prediction precision is improved; multi-target collaborative optimization is realized through a linkage optimization model and an engine, and the problem of single-target optimization is solved; and on the basis of a monitoring and early warning system, rapid identification and hierarchical response are realized, a closed-loop link is formed, and the port operation and maintenance adaptability is improved.

The present application discloses a method, system, and computer system for proactively discovering malicious domains through a guided crawling of attack infrastructure. The method includes (i) determining a set of toxic network neighborhoods on the internet, (ii) expanding one or more network graphs for the set of toxic network neighborhoods; (iii) determining a set of domains expected to be malicious from the set of toxic network neighborhoods, and (iv) performing an action based at least in part on the set of domains expected to be malicious. A particular toxic network neighborhood shares a plurality of hosting environments.

Techniques are described for maintaining data consistency when failure events occur during file storage replications between file systems in different cloud infrastructure regions. In certain embodiments, two generation numbers (or different identifications) are assigned to two groups of processed B-tree key-value pairs, one before and one after a failure event, within a key range. In some embodiments, the two generation numbers are assigned to a group of B-tree key-value pairs processed by a failed thread and another group of B-tree key-value pairs processed by a substitute thread taking over the failed thread to avoid potential data corruption.

A system and method for providing reliable, legally compliant access to geographically restricted ATSC television signals via user-owned devices hosted in a datacenter. The invention verifies the physical address of each applicant to ensure eligibility within the designated market area before installation. It eliminates the need for rooftop or indoor antennas, resolving common ATSC reception challenges, including interference, terrain obstruction, and lightning risks. Designed for ease of use by non-technical users, the system provides consistent high-quality reception through a managed datacenter infrastructure. The invention complies with legal boundaries set by the U.S. Supreme Court's ruling in ABC v. Aereo by ensuring individualized device ownership and avoiding centralized retransmission. An optional encrypted group addressing mode further reduces energy and hardware redundancy while enforcing regional access through a conditional access system with access limited to persons with validated addresses and members of the datacenter.

The invention discloses a nuclear accident emergency three-layer coupling network model and a construction method, and relates to the crossing field of nuclear accident emergency management and complex system risk simulation. The nuclear accident emergency three-layer coupling network model comprises an RMN layer which is used for simulating the dynamic diffusion process of radionuclides leaked by a nuclear accident in the atmosphere; the IN layer is used for constructing a spatial distribution and dependency relationship network of infrastructures; the EN layer is used for topologically defining a command or cooperation relationship of the emergency organization; according to the three-layer network coupling mechanism, when RDR of a middle grid is larger than or equal to RDRmax-1, IN-layer nodes in the middle grid at the same position are marked to be in a damaged state; and quantifying the bidirectional dependence density of the IN layer and the EN layer through the coupling strength epsilon. The method is used for predicting the direct risk of radioactive contamination in a nuclear accident and the secondary risk caused by emergency evacuation, revealing the risk evolution law, quantifying the risk diffusion scale, determining key influence factors, and providing a theoretical basis for formulating an emergency strategy.

The invention provides a method and a device for dynamically metering computing power based on computing power operation tasks of an intelligent computing center cloud platform, and relates to the technical field of intelligent computing centers, intelligent computing centers, computing power infrastructures and intelligent computing cloud. The method for dynamically metering the computing power of the intelligent computing center cloud platform based on the computing power operation task comprises the following steps: S1, acquiring a parameter value of first operation data of a first GPU in a first time period, wherein the first GPU is a GPU for operating a first computing power operation task; s2, determining a first task type of the first computing power operation task based on a parameter value of the first operation data; s3, determining a weighting coefficient corresponding to the first task type; and S4, based on the weighting coefficient and the parameter value of the first operation data, calculating to obtain the calculation power value of the first calculation power operation task in the first time period. According to the method, the computing power metering accuracy of the computing power operation task in the intelligent computing center cloud platform can be greatly improved.

A system for recommendation and optimization of information technology (IT) server resources is disclosed. The system includes a server comprising at least one processor configured to access input datasets associated with an IT workload, determine types and counts of server resources based on predefined criteria and a server consolidation configuration, and access a multi-server performance database. The processor utilizes a trained deep learning model to predict infrastructure requirements and generate recommendations for an optimal server configuration by balancing performance, power consumption, and resource utilization. The processor automatically allocates server resources from multiple manufacturers based on the recommendations and generates data for display on a user interface dashboard. The dashboard presents server utilization patterns, recommended configurations, and real-time performance metrics of allocated resources. The system enables intelligent consolidation and efficient server management within a datacenter environment.

The present invention provides a multilingual, AI-powered healthcare system integrating a personalized medical assistant, real-time clinical decision support, and closed-loop device control. The system ingests multimodal patient-generated and institutional data, applies advanced language-model-driven reasoning for clinical insights and triage, and supports regulated, auditable actuation of medical devices. Key features include multilingual overlays, role- and jurisdiction-specific visualization, co-signature enforcement, audit traceability, and seamless integration with healthcare infrastructure. A “Multilingual Overlay” is a dynamically generated display layer that the system composites in real-time from clinical text, icons, and color-coded indicators, automatically adapting its language, reading direction, terminology, visual density, and role-based data visibility to the preferences, locale, and device form-factor of each authenticated viewer. The architecture supports provider- and patient-facing use cases, agentic AI for autonomous yet regulated decision-making, and robust safety and compliance features.