911 results about "Data aggregator" patented technology

The invention discloses a railway station facility full life cycle information query method based on digital twinning. The method comprises the steps that a unified facility object with a unique logic identifier is generated based on multi-source heterogeneous data aggregation; constructing a facility tense version model containing time sequence nodes by using an event-driven rule; in combination with an engineering constraint knowledge base and statistical characteristics, evaluating physical feasibility and statistical association strength between events, introducing life cycle stage risk factors, and constructing a facility full life cycle causal chain model; and based on the model, responding to the explanatory query, executing a constrained path search in the facility full life cycle causal chain model, and generating a life cycle query result set containing a causal path. According to the method, the pseudo-correlation problem generated by pure data mining can be effectively solved, the cumulative influence of early-stage risks in the design and construction stage on later-stage operation is quantified, and accurate tracing and explanation of cross-stage fault causes are realized.

The invention discloses a distributed time sequence library data management method supporting cold and hot data level-to-level management, and relates to the technical field of computer databases, comprising: receiving a write-in request of time sequence data, and executing preliminary write-in data aggregation and sorting; dynamically identifying data cold and hot attributes based on a multi-dimensional data cold and hot degree calculation model in combination with a write-in behavior and an access behavior of time series data; according to the cold and hot attribute recognition result, in combination with the hierarchy boundary of self-adaptive division, automatic hierarchical storage of the time series data is executed; based on a cold and hot data dynamic migration and scheduling mechanism, according to the access frequency and time decay characteristics of the time series data, dynamically adjusting the storage hierarchy of the time series data, and executing a data migration task; and constructing a hierarchical index system adaptive to different cold and hot attribute time series data, and combining query frequency dynamic identification and index structure automatic upgrading. By adopting a cold and hot data automatic identification and hierarchical storage mechanism, the overall performance and the resource utilization rate of the system are improved.

Computerized systems and methods are disclosed for automating Configure to Order (CTO) and Quote to Order (QTO) processes. Methods include receiving user inputs for desired product configurations, retrieving corresponding data from a bill of materials database, and calculating optimized pricing through intelligent rules based on real-time market data. Automated quotes are generated and transferred to orders in a vendor system, selected based on pre-set criteria like vendor reputation and delivery time. Validation steps reduce errors, and real-time reports are generated. The system integrates a Real-Time Data Mesh for data aggregation, a Single Pane of Glass User Interface for user interactions, and Advanced Analytics and Machine Learning Modules for implementing rule-based and learning algorithms. The system is accessible across various devices and standardizes data for uniform consumption, while also employing machine learning models to continually optimize processes. Notifications are sent to users upon successful execution of orders or completion of quotes.

The invention relates to the technical field of data transmission of the Internet of Things, in particular to a self-adaptive data compression and transmission method of a low-power-consumption wide-area Internet of Things. According to the method, a three-layer collaborative architecture comprising an equipment layer, a fog computing layer and a cloud computing layer is constructed, data preprocessing and feature analysis are performed on the equipment layer, and data types, entropy values and repeatability information are extracted; the fog calculation layer selects an optimal compression algorithm based on a multi-dimensional decision engine, and reduces redundancy through spatial-temporal correlation analysis and data aggregation; the cloud computing layer collects compression performance data, adopts reinforcement learning and federal learning to train a global optimization model, and dynamically issues strategy parameters; and the fog node adaptively adjusts a compression strategy in combination with the system state to realize the optimal balance between the compression ratio and the reconstruction precision. The method is suitable for field deployment environments with limited electric quantity and network, has the advantages of low power consumption, high efficiency, strong adaptability and the like, and can be widely applied to Internet of Things scenes such as remote monitoring, smart energy and the like.

In some examples, there may be provided systems, devices, and methods for using data from disparate databases to determine characteristics of a set of users within an organizational unit and generate customized user interface elements for display within a user interface.

The invention discloses a vehicle-mounted electrical control unit (ECU) debugging simulation system and method based on digital twinning. The system is composed of a data aggregation and time reference module, a consistency evaluation module, a synchronization and correction module, a semantic communication and scheduling module, an arbitration and security debugging module, and a cross-layer adaptive degradation and execution and iteration module. Channel-level time delay is estimated through a unified time reference, a consistency difference spectrum containing amplitude, time sequence, event and derivative components is constructed, confidence is given, time delay compensation and event-level alignment are calculated, and deterministic and priority scheduling is implemented according to semantic benefits; actions such as parameter recharge, message injection, task rhythm perturbation and environmental stimulation are generated and limited under the constraint of a security invariant set, control right soft handover and gating are realized based on confidence, and grading and degrading are performed when the confidence is low or the constraint is triggered; millisecond-level bidirectional collaborative debugging of twin and physical ECUs is realized, and consistency convergence, safety and bandwidth time arrival are improved.

The invention relates to a water-light storage area power grid optimization method based on federated and layered multi-agent reinforcement learning. The method comprises the steps of constructing a federated and layered multi-agent model, defining states, actions and reward spaces of layered agents, implementing federated reinforcement learning offline training with privacy enhancement, deploying the model and executing online distributed optimization. Through the federal learning framework, it is ensured that private data of all the energy main bodies are locally calculated, the privacy leakage risk caused by data collection in a traditional centralized method is avoided, and a technical basis is provided for building collaborative trust among multiple main bodies. Meanwhile, the designed hierarchical intelligent agent architecture decouples a complex power grid system into subproblems with clear hierarchies, and when the scale of the system is expanded, new energy units can be accessed in a modular mode, so that the overall maintenance and upgrading difficulty of the system is reduced, and the expandability of the system is improved.

The invention discloses a method for aggregating ocean buoy data, which comprises the following steps of: establishing a space-time correlation model according to the spatial distribution density of buoy clusters and the dynamic characteristics of an ocean environment, and adaptively dividing buoy groups with a data compression cooperative relationship; performing a data-related compression operation within the buoy group; constructing a transmission priority dynamic sorting engine, and calculating a dynamic weight coefficient; performing hierarchical packaging processing on the differential coding data, distributing enhanced forward error correction coding resources for high-priority data, and reserving redundant transmission time slots; the method comprises the following steps: receiving a hierarchical data packet through a low-orbit satellite and executing on-satellite preprocessing, reconstructing an original data stream by using a distributed decoder, and meanwhile, fusing multi-dimensional space-time related data to generate an aggregated data product; and finally, the aggregated data product is transmitted to a ground data center through a satellite downlink. According to the invention, uplink data volume is reduced, disaster monitoring timeliness is improved, ground station processing load is reduced, and system environment adaptability is enhanced.

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.

It is provided an apparatus comprising interface circuitry, machine-readable instructions, and processing circuitry to execute the machine-readable instructions. The machine-readable instructions include instructions to receive data from a remote entity for handling by a computing system. The machine-readable instructions further include instructions to instantiate a first TEE and a second TEE. The machine-readable instructions further include instructions to generate log data corresponding to predefined activities of the computing system and to generate system record data of a system log of the computing system at predetermined times. The machine-readable instructions further include instructions to generate first audit data by the first TEE and second audit data by the second TEE. The machine-readable instructions further include instructions to transmit the first and the second audit data to a detection system for data aggregation and anomaly detection.

The invention belongs to the technical field of unmanned aerial vehicles, and particularly relates to an unmanned aerial vehicle low-altitude collaborative inspection method and device based on data acquisition, electronic equipment and a storage medium, and multi-aerial vehicle collaborative flight and data aggregation are realized by calling a plurality of unmanned aerial vehicles to form a logic formation set and constructing a communication topological structure by using relay nodes. According to the method, the formation is quickly established after task area analysis, parallel path coverage is realized through unified scheduling, and the inspection area in unit time is remarkably increased. In a multi-unmanned-aerial-vehicle cooperative mode, the unmanned aerial vehicles share environment sensing information, the flight path is dynamically adjusted, repeated flight and path conflict are avoided, and resource utilization is further optimized. Therefore, the method can effectively solve the problem of low inspection efficiency in the prior art, and realizes efficient, rapid and complete coverage of a large area.

The invention relates to the technical field of knowledge graph construction, and discloses a knowledge graph-based agent behavior description method, which comprises the following steps of: firstly, analyzing an unstructured agent operation log data stream through a processor, and mapping the unstructured agent operation log data stream into a discretized module access sequence by utilizing a topological structure of a code knowledge graph so as to remove text redundancy; performing multi-dimensional time sequence analysis on the sequence, and calculating resident distribution data representing operation continuity, frequency domain fluctuation data representing switching rhythm and multi-scale coverage extension data representing a traversal range; and then aggregating the data into a fixed-length multi-dimensional index vector, constructing an index node associated with a session entity in a graph database, and writing the vector as a binary structured attribute into a storage field. According to the method, massive unstructured logs are converted into compact graph structured indexes, and quick positioning and direct retrieval of a complex agent operation mode are supported while the storage space is remarkably saved.

The invention relates to a wind power transmission fault early warning method and device based on single machine-cluster model interaction. The method comprises the steps that historical operation data of wind turbine generators of the same type in the same wind power plant are collected; aggregating to obtain a first data set corresponding to a single wind turbine generator set and aggregating to obtain a second data set of the same type of wind turbine generator sets; performing model training according to the first data set to obtain a single-machine early warning model, and performing model training according to the second data set to obtain a cluster early warning model; interactively correcting an output result by using the single-machine early warning model and the cluster early warning model; and adopting a voting fusion strategy to obtain a final prediction result. Through interaction and fusion of the single machine-cluster model, personalized features of the single machine model and a common rule of the cluster model are fully utilized, result misjudgment and missed judgment are avoided, and the accuracy of fault prediction of the wind turbine generator transmission system is remarkably improved. The method can better adapt to complex and changeable operation conditions of the wind turbine generator, and the reliability of fault early warning is enhanced.

The invention provides a wearable exercise and rehabilitation monitoring system based on multi-modal AI, which relates to the field of electrical digital data processing and comprises a distributed multi-modal sensing acquisition module, a hierarchical wireless data aggregation and transmission module, a 3D human body dynamics reconstruction and modeling module and an intelligent action evaluation and feedback module. The distributed multi-modal sensing acquisition module is responsible for realizing multi-dimensional cooperative sensing of human body motion through heterogeneous sensors, and the hierarchical wireless data aggregation and transmission module adopts a master-slave base station architecture to realize efficient aggregation and low-delay transmission of heterogeneous sensor data. The 3D human body dynamics reconstruction and modeling module is used for realizing high-fidelity 3D skeleton model reconstruction based on inverse kinematics solution of multi-sensor fusion; the intelligent action evaluation and feedback module is used for providing personalized training guidance by constructing a standard action library and realizing multi-dimensional similarity quantitative scoring; according to the system, deep fusion of the multi-modal sensor can be realized, and accurate motion monitoring is provided.

The invention discloses a power demand response optimization method based on time sequence big data aggregation analysis. The method comprises the following steps: S1, collecting time sequence big data of a user side, an equipment side and an environment side, and preprocessing the time sequence big data; s2, constructing a heterogeneous graph structure comprising users, equipment and time nodes, embedding time sequence features into node attributes, and fusing geographic information and behavior information between the nodes; s3, dynamically capturing a space-time dependency relationship by using a gating unit, and generating a preliminary gating chart neural network parameter; s4, performing joint optimization on the key hyper-parameters of the gating chart neural network by adopting an improved grey wolf optimization algorithm to obtain optimal gating chart neural network parameters; s5, configuring a collaborative decision-making unit for each grey wolf individual, and optimizing the power demand response scheduling strategy network through a centralized training and distributed execution mechanism; and S6, based on the optimal demand response scheduling instruction, dynamically updating the heterogeneous graph structure and gated graph neural network parameters. According to the method, the improved grey wolf optimization algorithm, the gated graph neural network and the time sequence big data aggregation technology are combined, and power demand response optimization based on time sequence big data aggregation analysis is realized.

A train operation intelligent dispatching system and method are provided, where the system includes: a data acquisition and control module, configured to collect multi-source data affecting train operation; a data processing module, configured to perform data aggregation on the multi-source data, and process the multi-source data to form multi-source data in a unified data format; a multi-disciplinary data resource pool, configured to perform real-time storage and updating of the multi-source data in the unified data format, and store historical multi-source data; an intelligent fault analysis module, having intelligent fault analysis models therein, configured to perform fault analysis based on the multi-source data to obtain fault analysis results of corresponding disciplinary systems, and output fault advance warnings of corresponding disciplinary systems according to the fault analysis results; and a process preplan module, configured to provide preplan handling processes corresponding one-to-one with external multi-disciplinary systems.

The invention discloses an insulin pump safety reminding system based on remote communication, and the system comprises an insulin pump terminal which is used for collecting equipment operation parameters, and receiving and executing a verified operation instruction; the patient monitoring terminal is bridged with a data transfer interface, a local data encryption cache interface, a graded early warning display interface and a remote man-machine interaction interface of the insulin pump terminal and the hospital comprehensive management platform; the graded authorization user terminal receives early warning information related to the insulin pump terminal, checks the state of a patient and initiates a remote operation request; the hospital comprehensive management platform is in communication connection with each terminal, manages each terminal, performs data aggregation and intelligent analysis, generates early warning information based on a multi-dimensional rule, and performs authority management and auditing and multi-source data fusion application; and the safety communication and control module is used for guaranteeing data transmission safety, operation instruction compliance and system emergency fault-tolerant operation. According to the invention, cross-time-space safety protection and active early warning can be realized, the treatment safety and management efficiency are improved, and the method is suitable for medical and pension institutions at all levels.

The invention relates to the technical field of medicine management, and discloses a medicine informatization whole-process management system based on big data analysis. According to the system, a drug full-life-cycle multi-dimensional data stream is captured through a distributed sensing node network, and a drug state tensor is generated after noise filtering and missing value compensation are carried out through a real-time stream processing engine. The risk identification module scans and locates an abnormal data cluster through a multi-scale mode and constructs a drug quality hypersurface model; and the data aggregation module establishes a mapping relation between a state tensor and a quality model by using a space-time diagram attention network and calculates a global consistency metric value. The intelligent decision-making module generates an optimal drug management action sequence based on the measurement value under multiple constraint conditions, the closed-loop control module executes the action sequence and monitors system state deviation in real time, data acquisition parameters are dynamically calibrated, and autonomous optimization and closed-loop management of the whole process of drugs are achieved.

The invention relates to the technical field of storage system IO performance optimization, and provides a redirection data aggregation method and equipment, and the method comprises the steps: recognizing random small IO data from a written IO request, aggregating and writing back-end storage objects, and constructing a storage mapping relation in an index table; traversing the rear-end storage object in which the random small IO data is stored, and distributing data from the same logic address space to point to the same target rear-end storage object through a hash algorithm; writing the data distributed to point to the same target back-end storage object into the continuous physical space of the target back-end storage object in sequence, and updating the mapping relation in the index table; the step S2 and the step S3 are repeated before a preset termination triggering condition is met, and a hash algorithm different from all previous times is adopted when the step S2 is repeatedly executed each time; when the data are read, the storage mapping relation is obtained from the index table, and the read requests corresponding to the data located in the same back-end storage object are aggregated and then read at a time.

The invention discloses a Web-based visual editing method, belongs to the technical field of industrial configuration, and aims to solve the problems of complex deployment of traditional configuration software and insufficient Web scheme performance. The method comprises the following steps: constructing a front-end architecture (Vue. Js + Konva.js) containing a graphic element module, a canvas management module and the like; the primitive adopts JavaScript prototype chain inheritance + SVG / Canvas double-layer rendering, and a canvas is divided into four layers and is provided with a quadtree index; an editor and an expression engine are generated by using a factory pattern for attribute configuration; page management supports state capture recovery; real-time communication is based on WebSocket + PubSub, and data aggregation and incremental updating optimization are combined. Cross-platform zero deployment and Web environment near-desktop performance are achieved, editing efficiency is improved, industrial protocols such as OPC UA are compatible, and the method is suitable for the field of energy and intelligent manufacturing.

The invention discloses a wireless network data reliability transmission system, which is used in the technical field of data transmission and comprises a slave gateway deployment and data aggregation module. A scheduling protocol and priority management module; a hierarchical caching and breakpoint resuming module; and a trend prediction optimization decision module. According to the invention, in combination with priority-based scheduling, link quality evaluation, data caching and retransmission mechanisms, reliable transmission of data with different priorities in a wireless network can be ensured, and normal transmission can be recovered through an efficient data recovery mechanism when the network is unstable; through real-time environment state estimation and prediction, communication parameters and resource scheduling can be dynamically optimized, network performance is effectively improved, and communication delay is reduced. The hidden state of a communication environment is estimated in real time through comprehensive analysis of link quality, event records and cache state data, future hidden state changes are predicted through a time sequence convolutional network, and potential performance degradation risks are recognized in advance.

The invention discloses an AI visual identification-based landscaping pest and disease damage intelligent monitoring and early warning system, and particularly relates to the technical field of AI visual identification pest and disease damage, and the system comprises the steps: obtaining time sequence visual image data, space-time labels and environment sensing data of a target garden area; inputting the time sequence visual image data into a pre-trained disease and insect pest AI identification model, and outputting an identification result; calculating initial occurrence density and local diffusion trend parameters of disease and pest species, and generating a spatial distribution thermodynamic diagram; constructing a multi-factor risk assessment model; dynamically generating a graded early warning signal; according to the invention, the landscaping pest and disease damage intelligent monitoring and early warning system is constructed through the multi-source heterogeneous data acquisition module, the feature extraction and identification module, the data aggregation and space association module, the multi-factor risk assessment and time sequence prediction module and the dynamic early warning and decision support module; the problems of single monitoring dimension, weak data fusion and space association capability, fragmentation of decision support information and the like are solved.

The invention relates to the technical field of data processing, in particular to a real-time data aggregation optimization method and system based on incremental calculation. The invention discloses a real-time data aggregation optimization method and system based on incremental calculation. The method comprises the following steps of data receiving and preprocessing, incremental calculation model construction, data aggregation and storage, data updating and maintenance, and query and analysis. Data receiving and preprocessing: receiving data streams from a plurality of data sources in real time, performing cleaning operation on the data streams to remove invalid data, performing format conversion to unify a data format, and allocating a unique identifier and a record timestamp to each processed data record; according to the method, only the change part of the data needs to be calculated through the incremental calculation model, so that the calculation amount is greatly reduced, and the efficiency of real-time data aggregation is improved; compared with a traditional method and system, the calculation time can be shortened by more than 50% when large-scale real-time data are processed.

The invention discloses a mine multi-source data fusion transmission method and device based on distributed edge agent collaboration, and relates to the field of data transmission. The method comprises the following steps: collecting heterogeneous data through a plurality of data collection terminals deployed in a mine production environment, and generating a data voucher based on the heterogeneous data; analyzing a value mark through a master agent to split a data processing task into task sub-pieces of different processing types, selecting a slave agent from bidding results of other edge computing nodes for bid invitation information, and constructing the master agent and the slave agent into an edge agent alliance for processing heterogeneous data; performing real-time interaction of event features in the edge agent alliance, and executing cross-modal joint coding on the associated heterogeneous data streams based on the event features; and aggregating and uploading the data subjected to joint coding to the cloud in a transmission window returned by the 5G base station. By implementing the technical scheme, mine multi-source heterogeneous data can be effectively processed, and on-demand distribution and dynamic scheduling of network resources can be realized.

The invention discloses a vehicle-mounted distributed data storage and management method for an intelligent vehicle, which comprises the following steps: in a dynamic election mode, acquiring performance parameters of each node by a default main storage node, calculating a comprehensive score, electing a main storage node and an alternative storage node according to the comprehensive score, and taking the rest as slave storage nodes; the main storage node formulates a differential fragmentation storage strategy and a network QoS strategy according to the data multi-dimensional label and distributes the strategies; each node stores collected data in a local designated area according to a strategy, and data aggregation and preprocessing in the nodes are completed; the alternative storage node and the slave storage node converge the preprocessed data to the main storage node at different priorities according to a preset rule; and the main and alternative storage nodes periodically synchronize data. According to the method, the distributed architecture is combined with the intelligent fragmentation, the single-point bottleneck is avoided, the data is stored and processed nearby, the network delay is effectively reduced, and the storage and network resource utilization efficiency is improved.

A system and method are disclosed for generating a privacy-preserving data corpus for Artificial Intelligence (AI) training. The system comprises a relying partner (RP) computing environment and a trusted, independent identity provider (IdP) computing system. Upon a user authentication request, the IdP provides the RP with only a PII-free, persistent pseudonymous identifier (gUserID) for the user. Any authentication artifacts containing Personally Identifiable Information (PII), such as an OAuth token, are programmatically neutralized by the IdP. This is achieved by generating a transient public-private encryption key pair, immediately destroying the private key, and encrypting the PII-laden artifact with the remaining public key, rendering the PII therein permanently irrecoverable. This enforcement of “PII unknowability” at the RP enables the aggregation of pseudonymous user data, linked by the persistent gUserID, from multiple independent RPs into a rich, cross-organizational corpus for AI training, without ever exposing user PII to the RP.

Methods, apparatuses, system, devices, and computer program products for AI-driven data aggregation and analysis for entity scoring and assessment are disclosed. In a particular embodiment, a controller generates an entity profile for an entity by iteratively retrieving data related to an entity, the data including structured data and unstructured data, determining a structure for the unstructured data by applying the retrieved data to the AI model, the structure including a plurality of fields, and populating one or more fields of the entity profile with a respective value that is extracted from the retrieved data using the AI model. The controller stores the entity profile in a database comprising a plurality of entity profiles corresponding to different entities. The controller generates, using the AI model, a score for the entity in relation to an attribute based on an AI-driven analysis of the plurality of entity profiles.

The invention relates to the technical field of big data processing bridge health monitoring, in particular to a distributed bridge disease data summarization bridge health monitoring method and system, and the method comprises the steps: obtaining vibration data and steel beam stress data of a bridge at a plurality of positions, and wind speed data near the bridge; obtaining the validity coefficient of each modal component of the vibration data to extract the de-noised vibration data in each time period of the current position, calculating the significant value of the dominant frequency down-shift characteristic of the de-noised vibration data in each time period of the current position, and further obtaining the abnormal coefficient of the bridge floor vibration in each time period of the current position; and obtaining the abnormal coefficient of the bridge deck steel beam stress at the current position in each time period by combining the random fluctuation degree of the de-noised steel beam stress data in each time period, and obtaining the significant value of the bridge load influence by combining the average distribution level of the wind speed data for monitoring the health state of the bridge. The bridge health monitoring precision can be improved.

The utility model discloses a tower crane radio environment monitoring device which comprises a shell installed at the position consistent with the height of a tower crane communication antenna, and a multi-frequency-band signal receiving module, an active frequency spectrum scanning module, a signal filtering processing module and a data aggregation and output module which are arranged in the shell. The multi-frequency-band signal receiving module is used for receiving radio signals of a plurality of preset frequency bands used for normal communication of the tower crane; the active frequency spectrum scanning module is isolated from the multi-band signal receiving module in a physical structure and is used for actively scanning a radio frequency spectrum in a tower crane operation area; the signal filtering processing module is in signal connection with the multi-band signal receiving module and the active spectrum scanning module; the data aggregation and output module is in signal connection with the signal filtering processing module, and the communication interface is in signal connection with the data aggregation and output module. The radio environment monitoring device can sense the radio environment experienced by the tower crane communication antenna in the actual operation process in real time.

Aspects relate to multiple access networks with non-integrated aggregation. In one aspect, a UE may communicate with a core network over a 3rd Generation Partnership Project (3GPP) network, transmit a request to the core network for a protocol data unit (PDU) session comprising a request for data aggregation across the 3GPP network and a non-integrated internet protocol (IP) network, and receive a response from the core network granting the requested PDU session, the response including identification of a core network aggregation entity and an offload percentage. The UE may also transmit data to the core network aggregation entity via the non-integrated IP network based on the offload percentage, and transmit data to the core network aggregation entity via the 3GPP network based on the offload percentage.