33317results about "Particular environment based services" patented technology

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

A decentralized event detection and geolocation system is disclosed, utilizing a dynamically adaptable Spontaneously Emergent Geolocation Network (SEGNet) comprising mobile, stationary, and other devices equipped with multi-sensor capabilities. The system operates by obtaining sensor data from distributed devices, applying trained models to detect potential events of interest (EOIs), and dynamically forming device networks for collaborative geolocation and event validation. The system employs synchronization mechanisms and geolocation techniques, including Time Difference of Arrival (TDOA) and / or Angle of Arrival (AoA), to determine event locations through multimodal data fusion and iterative validation processes. The architecture supports multiple operational modes, including decentralized, centralized, and hybrid configurations, enabling operation in connectivity-limited environments and enhanced processing when server access is available. The system is designed to accommodate dynamic task delegation and real-time threshold adjustments based on environmental conditions, providing reliable event detection and geolocation across diverse scenarios.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Aspects presented herein relate to perception data collection and curation. In one aspect, a network entity receives, from a first set of user equipments (UEs), a first set of perception data collected by the first set of UEs. The network entity configures, for a second set of UEs based on the first set of perception data, at least one of: a set of planned routes and time plans for collecting a second set of perception data or a set of embeddings centroids. The network entity receives, from the second set of UEs, the second set of perception data based on at least one of the configured set of planned routes and time plans or the configured set of embeddings centroids.

An improved system and method of defining speeding severity levels, receiving over a wireless network from a vehicle device speed data and metadata, determining associated speeding severity levels, based at least in part on the determined associated speeding severity levels, determining whether an alert communication is to be transmitted the vehicle device, and in response to determining that an alert communication is to be transmitted the vehicle device, transmitting the alert communication via a wireless interface to the vehicle device, the alert communication configured to cause the vehicle device to provide a corresponding in-vehicle alert.

The invention provides a gas leakage time-space correlation early warning method and system based on multi-modal data fusion, and relates to the technical field of data processing, and the method comprises the steps: collecting real-time concentration data and a historical leakage accident database through an NB-IoT combustible gas sensor network disposed at a key node of a gas pipe network; according to the real-time concentration data and a historical leakage accident database, a UTM projection coordinate system is adopted to carry out registration on geographic space data, a three-dimensional space mapping model of a pipe network topological structure is established, timestamp alignment is carried out on all dynamic monitoring data streams through an NTP clock synchronization protocol, and a time-space correlation standardized data set is formed. According to the invention, full-chain intelligent management of the gas leakage risk from sensing, prediction to disposal is realized.

The invention discloses a traffic transportation operation monitoring early warning and decision analysis method based on vehicle infrastructure cooperation, and relates to the technical field of intelligent traffic. According to the method, real-time collection of dynamic traffic elements is realized through a multi-dimensional sensing network of a vehicle end, a road side and an environment and V2X communication, time-space reference unification of multi-source data is ensured, a road-vehicle-environment-event semantic network is constructed, multi-dimensional recognition of abnormal events such as accidents, congestion and severe weather is realized in combination with hierarchical feature extraction, and the method has the advantages of being high in practicability and high in practicability. The method is advantaged in that identification accuracy is improved, abnormal event propagation paths can be predicted, global road network situation prediction capability is realized, differential early warning is generated based on a comprehensive risk index, multi-level responses such as traffic signal adjustment and path planning are triggered, response time is greatly shortened, emergency response efficiency is optimized, a decision execution effect real-time feedback mechanism is established, and the method is suitable for popularization and application. The system performance is continuously optimized along with data accumulation, and the defect that a big data platform lacks an intelligent decision closed loop is avoided.

The invention provides a data transmission method and transmission device applied to a vehicle-road cloud collaborative road network system, and the method comprises the steps: collecting vehicle operation state data through a vehicle-mounted terminal, collecting real-time road network topology data through road side equipment, and transmitting the data to a cloud server for data preprocessing; performing feature extraction on the preprocessed data based on a preset time-space correlation model, generating dynamic road network feature data, calling a cooperative transmission model to perform transmission path optimization on the dynamic road network feature data, generating a path optimization parameter set, and performing dynamic adjustment on a target transmission path according to the path optimization parameter set. Generating a self-adaptive transmission path adaptive to the current road network state; and distributing the target vehicle data packet to the roadside node and the cloud node through the self-adaptive transmission path, and carrying out retransmission scheduling on the data packet which is not successfully transmitted based on a feedback confirmation mechanism. According to the invention, the communication efficiency and the system robustness in a vehicle-road cloud collaborative scene can be improved.

The invention discloses a low-altitude integrated management system based on a grid digital twin model and an intelligent algorithm, and relates to the technical field of low-altitude aircrafts. The method comprises the following steps: multi-source sensing data fusion and grid coding are carried out, and an airspace environment space-time database is constructed; constructing an airspace environment space-time database to carry out three-dimensional subdivision on the airspace; generating a multi-scale grid twinborn body, and associating a corresponding attribute for each level of grid according to a management requirement; generating an airspace risk thermodynamic diagram in the future 5-30 minutes; establishing a grid state change triggering rule; and generating a multi-target optimal path set by taking the grid navigation cost as a weight. According to the invention, by combining the real-time operation data of the low-altitude aircraft, the meteorological environment and other data with the artificial intelligence algorithm and the navigation rule base, the airspace traffic flow is analyzed, the flight plan is optimized, the flight route is intelligently distributed, the automatic route setting of any two points is realized, and the response speed of the low-altitude flight service system and the processing capability of various flight data are improved.

The invention relates to the technical field of signal devices, and discloses a traffic signal control method and system based on vehicle-road cloud multi-modal data fusion, and the method comprises the steps: collecting multi-modal traffic data synchronously in real time through a vehicle-end sensor, road-side sensing equipment and a cloud Internet platform; fusing the heterogeneous data by adopting a space-time alignment algorithm, and constructing a standardized space-time feature matrix; traffic flow prediction is carried out based on a multi-layer space-time diagram neural network trained by a federated learning mechanism, and a signal control instruction is generated through reinforcement learning and a multi-objective optimization model; and issuing the green wave parameter, the dynamic timing scheme and the cross-domain coordination strategy to a roadside signal machine through the cloud edge coordination architecture to execute control. The problems that in the prior art, low-delay private network communication cannot be achieved, the data fusion efficiency is low, unmanned driving is not supported, and the deployment cost is high are solved, and the purposes of low-delay communication, high reliability and low risk are achieved.

The invention discloses a slope geological disaster multi-mode early warning method and system, and relates to the technical field of slope monitoring, and the method comprises the steps: laying a distributed sensor network, and collecting slope multi-source monitoring data; the multi-source monitoring data comprises displacement data, stress data and vibration frequency data; feature extraction is performed on the multi-source monitoring data by using a graph neural network, and the feature extraction comprises capturing spatial correlation among sensor nodes and identifying an abnormal mode, identifying a potential instability area of the slope according to the abnormal mode, and outputting features of the potential instability area; and generating a slope stability risk grade assessment report based on the characteristics of the potential instability region and a disaster evolution graph constructed by combining historical disaster data. According to the invention, the comprehensive monitoring of the slope from the outside to the inside and from the static state to the dynamic state can be realized, the abnormal mode and the potential instability area can be accurately identified, and the accurate assessment and timely early warning of the slope risk can be realized based on the historical data.

The invention relates to the technical field of traffic management, in particular to a smart city intelligent traffic management system based on big data, which comprises a multi-source data acquisition module, a data fusion module, a dynamic feature generation module, a situation prediction module, a multi-target optimization module and an instruction execution module, the multi-source data acquisition module acquires multi-source data in real time; the data fusion module generates a standardized traffic data flow and outputs a three-dimensional data cube; the dynamic feature generation module outputs a traffic feature matrix; the situation prediction module generates a dynamic situation map; the multi-objective optimization module outputs an optimization instruction set; and the instruction execution module issues a control instruction to the traffic signal controller, the variable information board and the vehicle-mounted terminal. According to the invention, traffic jam can be relieved, road traffic efficiency can be improved, bus priority scheduling can be optimized, vehicle delay can be reduced, and emergency response speed can be improved.

A method of optimizing dependent task offloading in an Internet of Vehicles using deep reinforcement learning is provided, including the following steps: S1: constructing a vehicle system network; S2: constructing a task model for an application program; S3: constructing a task load model: calculating delay, energy consumption and incentive compensation for local computing, offloading to nearby vehicles and offloading to nearby RSUs three offloading methods based on the vehicle network system and the task model, respectively; S4: determining task priorities: first determining the priorities of each of subtasks according to allocation of predecessor nodes of the subtasks combining with dynamic network environment, and then scheduling according to a multi-queue algorithm; and S5: finding an optimal offloading strategy by using the deep reinforcement learning.

The invention provides a 5G technology-based fleet local area network and satellite communication cooperation system, and relates to the technical field of data processing, and the system comprises a ship terminal which is used for obtaining ship communication data, including environment perception data, ship state data and instruction interaction data, and carrying out the classification processing of the data; the fleet local area network base station is used for carrying out priority division according to data categories and transmission requirements; the satellite communication module is used for dynamically adjusting a data transmission mode according to the transmission state to obtain complete satellite transmission data; the shore-based control center is used for receiving the complete satellite transmission data and performing fusion processing on the complete satellite transmission data to obtain global fleet situation information; the shore-based control center generates a navigation adjustment instruction according to the global fleet situation information, and issues the navigation adjustment instruction to the ship terminal through the satellite communication module to obtain synchronous adjustment data; the ship terminal receives the synchronous adjustment data and adjusts a navigation strategy according to the synchronous adjustment data; according to the invention, the autonomy and accuracy of the fleet cooperation system are improved.

The invention relates to the technical field of power equipment state monitoring and the Internet of Things, in particular to an SF6 gas state intelligent diagnosis and early warning system based on the Internet of Things, which comprises a sensor array module, an edge computing module, an anti-interference communication module, a cloud platform intelligent analysis module, an early warning and linkage control module, an energy management module and a self-checking and fault-tolerant module. The sensor array collects gas state and equipment environment data in real time, and the data are stably transmitted to the cloud platform through the anti-interference communication module after being preprocessed through edge computing. The cloud platform uses a multi-modal data fusion and deep learning algorithm to realize gas leakage trend analysis, leakage source positioning and risk level evaluation; and the early warning and linkage control module triggers graded early warning according to the diagnosis result and is linked with related equipment for emergency response. According to the invention, omnibearing intelligent monitoring and management of the SF6 gas equipment are realized, the monitoring accuracy and the system reliability can be effectively improved, and the equipment fault risk is reduced.

The invention relates to the technical field of ship communication, in particular to a multi-link converged communication method and system for a ship. The method comprises the following steps: receiving navigation information broadcasted by other ships in a preset range, evaluating the availability of a communication frequency band, and generating sea area communication channel state data; acquiring geographic position data of each ship; space communication link potential analysis is carried out according to the geographic position data of each ship and the sea area communication channel state data, and cross-chain space communication potential data is generated; constructing a multi-link ad hoc network topological structure according to the cross-chain space communication potential data; and task adaptation communication link allocation is carried out based on a multi-link ad hoc network topological structure, and a multi-link fusion transmission strategy is constructed, so that real-time communication link safe switching management is realized. According to the invention, on the basis of communication service adaptation and a multi-link fusion transmission strategy, real-time communication safe switching and high-reliability transmission of the marine formation are realized.

The invention relates to the technical field of traffic flow regulation and control, and discloses an electronic information traffic flow automatic regulation and control system based on a wireless sensor network. The multi-source sensing module collects road network multi-dimensional traffic data through a wireless sensing network, and traffic situation characteristics are generated through a specific data fusion method. The spatio-temporal feature fusion module utilizes a spatio-temporal attention mechanism to associate cross-modal features, and the collaborative decision-making module inputs fusion features into a pre-trained distributed decision-making model to generate a regulation and control instruction set. The dynamic game optimization module constructs a multi-target game optimization model, and traffic signal parameters are optimized by adopting a dynamic game strategy decomposition algorithm. And the hierarchical execution module executes regulation and control instructions in a distributed manner through a three-level control architecture of a central decision-making layer, a regional coordination layer and an intersection execution layer. The system can comprehensively collect and fuse traffic data, realizes scientific decision making and accurate regulation and control, effectively improves the road passing efficiency, balances the road network load, and relieves traffic jam.

A computer-implemented method of updating a set of clusters representative of a classification of a text-based dataset into a plurality of different text types for use in a cyber security system is described as part of a classification pipeline. The method comprises receiving text data associated with an entity. The method further comprises generating one or more vector embeddings representative of the text data. The method further comprises using incremental learning to update the set of clusters based on the one or more vector embeddings.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

The invention relates to the technical field of wireless communication networks, in particular to a vehicle ad hoc network based on intelligent networked vehicle WiFi and a V2V interaction method, a vehicle motion state and network load data are acquired through a vehicle-mounted sensor, AP / STA modes are dynamically switched by combining a deep Q network (DQN) model and an epsilon-greedy strategy, and an adaptive topological structure is constructed. And expanding OLSR protocol message embedding path delay fields, calculating and optimizing multi-hop routing selection in combination with pheromone gradient, and realizing message priority classification and differential privacy protection through a federated learning model. And dynamically adjusting an AP / STA proportion threshold based on a particle swarm algorithm, and triggering node role reconfiguration to balance network load. According to the method, the existing WiFi hardware is reused, the deployment cost is reduced, cross-layer routing optimization and a distributed decision mechanism are combined, and the network real-time performance, the expansibility and the group cooperation capability in a dynamic scene are improved.

An enhanced multi-compartment connected logistics receptacle system includes a storage receptacle, a bridge node, and wireless accessory sensor node with a plurality of sensors. A first subset of the sensors is disposed relative to a first monitored receptacle compartment and a second subset of the sensors is disposed relative to a second monitored receptacle compartment. The plurality of sensors monitor for a change in state of the storage receptacle. The wireless accessory sensor node detects the storage receptacle's state change based upon sensor data and broadcasts a signal with a flag set indicating there is event information available for upload. The bridge node detects the flag set, retrieves the event information available for upload from the wireless accessory sensor node, and transmits the retrieved event information to a backend server to cause the backend server to initiate a dispatched logistics operation related to the storage receptacle.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

The invention provides a Beidou electric power data intelligent acquisition and dynamic communication scheduling method and system. The method comprises the steps that a master station constructs a database and trains a data priority classification model, a channel quality prediction model and an intelligent compression model; a substation collects power data through an edge calculation unit, constructs a skyline candidate set to perform data screening, and performs intelligent classification marking by using a data priority classification model; performing reliability evaluation on the data stream by using a Gaussian mixture model, selecting a compression strategy according to a reliability score, a data type and a priority, and packaging into a data frame; determining a transmission strategy in combination with a channel quality prediction result and a context-aware intelligent switching protocol, and sending a data frame; the master station receives the data frame, performs integrity verification, decompresses and reconstructs the data frame, and feeds back a communication state for model updating; and monitoring the operation state, performing early warning based on the anomaly detection model, and triggering a self-healing strategy. According to the invention, the Beidou communication resource utilization rate and the system reliability are improved.

The invention provides an agricultural environment monitoring method and system based on the Internet of Things. According to the method, multi-source data weights are dynamically distributed through a soil sensor array connected with Internet of Things nodes, and a global soil parameter set is generated; deploying a micro-electro-mechanical micro-fluidic chip in the coverage area to carry out soil solution selective permeation, converting the target ion concentration into an electric signal, and synchronously transmitting the electric signal and soil parameters; historical time series data are extracted, coherence is established through time correlation analysis, and undeployed area data are filled in combination with a spatial interpolation algorithm to construct a dynamic prediction model; according to the nutrient space change trend output by the model, crop growth requirements are matched to generate a fertilization amount adjustment instruction, and the fertilization amount adjustment instruction is issued to field fertilization equipment through the Internet of Things to execute dynamic regulation. Space-time precise sensing of soil nutrients and self-adaptive fertilization regulation and control are realized, and the utilization efficiency of agricultural resources and crop growth sustainability are improved.

The invention discloses a vehicle positioning method and system based on UWB and vehicle constraint, and relates to the field of vehicle positioning, and the method comprises the steps: deploying a UWB reference station in a well mining environment, and enabling a vehicle to be provided with a UWB tag; obtaining distance measurement data of at least two UWB reference stations, and detecting and compensating the distance measurement data to obtain a vehicle UWB position; collecting vehicle-mounted IMU (Inertial Measurement Unit) data, and obtaining a vehicle attitude quaternion through attitude calculation; collecting vehicle wheel speed meter data, and correcting the vehicle wheel speed meter data according to vehicle motion constraint conditions; according to the vehicle UWB position, the vehicle attitude quaternion and the corrected vehicle wheel speed meter data, the vehicle position is obtained through an adaptive Kalman filtering fusion algorithm. Aiming at low positioning precision caused by easy accumulation of errors and drifting of a traditional inertial navigation system in a well mine closed space in the prior art, the invention reduces the influence of a multipath effect in the traditional inertial navigation system on the positioning precision.