37 results about "Distributed intelligence" patented technology

The application discloses a kind of real-time detection method and system of food pollutant based on intelligent sensor, comprising: through the multimodal sensor array of deployment in food processing production line key station, sensor response signal is collected;Baseline response under the condition of no pollution is predicted by establishing dynamic baseline prediction model;Signal separation network based on variational decoupling autoencoder is constructed, residual signal is mapped to low-dimensional latent space by sharing encoder and is divided into baseline error subspace and pollutant signal subspace, respectively by two independent decoders reconstructing baseline prediction error signal and pollutant candidate signal;Multi-scale time-frequency transform is carried out to pollutant candidate signal to identify pollutant type and estimate concentration;Through distributed intelligent agent cooperation, pollution traceability and hierarchical early warning are carried out;Cross-scene adaptive calibration is realized using meta-learning framework.The application realizes the real-time decoupling of pollutant signal and matrix change signal in food processing process.

This invention relates to the field of artificial intelligence technology and discloses a heterogeneous terminal interaction method and system based on distributed intelligent agent collaboration. The method includes: a first terminal sensing at least one second terminal within its communication range in real time and exchanging intelligent agent configuration files with the at least one second terminal to determine the state information of the at least one second terminal; wherein the state information includes capability description information, a previous state, and a resource state list; the first terminal determining a collaboration mode between itself and the at least one second terminal based on the state information and the current task to be processed; the first terminal generating task instructions corresponding to the at least one second terminal by calling a lightweight large language model based on the collaboration mode, and issuing them to the at least one second terminal respectively; the at least one second terminal executing the task according to the task instructions. This invention improves the effects of intelligent agent collaboration and dynamic adaptation.

The application discloses a kind of wind power variable pitch system control parameter self-adapting setting method and system, it is constructed as dynamic graph to wind farm, and the wake influence between units is quantified by dynamic adjacency matrix.Meanwhile, macroscopic dispatching instruction of power grid is encoded as context vector.For any wind turbine in the field, first observe the machine data, and quantify its topological role based on dynamic adjacency matrix.The topological role is used to generate context modulation gate to differentiate adjustment global context vector, so that it adapts to the current wind turbine role.Subsequently, graph neural network fuses the local state of the wind turbine, dynamic adjacency matrix and personalized context modulated by role, to generate enhanced state vector.Finally, the enhanced state vector is input into the cooperative decision network, and is parsed into optimal PID parameter increment to update local PID parameter.The scheme makes each wind turbine execute control strategy matched with its topological role and global target through this distributed intelligent decision, to optimize the performance of the whole field.

This invention relates to the field of converter protection technology. It discloses a method, system, equipment, and medium for calculating protection principle settings for multiple types of converters. The method includes: acquiring multi-dimensional electrical signals from multiple types of converters; identifying fault characteristics and classifying levels using a multi-level converter fault hierarchy model; determining the boundary conditions of a valve group short-circuit current attenuation prediction model; obtaining dynamic characteristic parameters of short-circuit current attenuation through the prediction model; performing distributed collaborative optimization of protection parameters using a distributed intelligent collaborative protection algorithm; conducting multi-scenario simulation tests using a converter fault simulation calculation platform; and correcting setting parameters based on test data to form a protection setting value system adapted to multiple types of converters. This invention integrates hierarchical modeling, dynamic prediction, collaborative optimization, and simulation verification technologies, adapting to the topological differences and operating conditions of multiple types of converters, and improving the universality, accuracy, and reliability of protection setting calculations.

This invention discloses a distributed intelligent fire location system based on the Internet of Things (IoT), relating to the field of fire location technology. It includes: sensing devices comprising multiple environmental information acquisition units installed on each floor of a building to collect environmental data, including smoke concentration and ambient temperature; a cloud platform connected to the sensing devices; the cloud platform is configured to: acquire environmental data fed back by the sensing devices; execute preset processes for estimating the fire source location and predicting the fire spread direction based on the environmental data, obtaining the estimated fire source location and the estimated fire spread direction respectively; and send the estimated fire source location and the estimated fire spread direction to pre-connected management personnel and designated third parties. This application has the effect of intelligently determining the core location and spread direction of a fire by analyzing data from multiple sensing devices, providing crucial information for rescue efforts.

This invention provides an on-chip integrated intelligent actuation control device for aircraft based on a software-defined and hardware-accelerated collaborative paradigm using silicon photonic interconnects, belonging to the field of avionics and flight control technology. The device uses software-defined methods for functional configuration and reconfiguration, while hardware acceleration ensures link determinism and performance. Its core features include: achieving highly interference-resistant signal sensing and isolated transmission through a software-configurable silicon photonic interconnect intelligent interface; integrating a reconfigurable sensor computation and a hard real-time control law engine through a heterogeneous computing acceleration center, providing deterministic and high-performance sensor computation and control law execution; achieving efficient and safe energy conversion through a software-defined intelligent power drive module; and performing global collaborative configuration and online optimization of the above modules through an edge intelligent management unit. This invention resolves the contradiction between task adaptability, extreme real-time performance, and operational reliability in actuation control, and is applicable to distributed intelligent actuation systems for multi-electric / all-electric and hypersonic aircraft.

The application provides a multi-intelligent cooperative method and device for distributed terminal equipment. The system comprises: a distributed global network perception module, which is used for collecting and synchronizing global network states and intelligent agent capability information in a distributed autonomous network, and outputting standardized information to a distributed intelligent decision module; the distributed intelligent decision module is used for generating cluster configuration and task execution strategy based on global information and task targets provided by the perception module, and issuing the cluster configuration and the task execution strategy to a multi-intelligent task execution module; the multi-intelligent task execution module is used for feeding back intelligent agent states and task progress to the perception module and the decision module in real time according to the cluster configuration and the strategy issued by the decision module, so as to form a closed-loop cooperative link of "perception-decision-execution-feedback". Cross-layer data interaction and standardized information transmission are realized through a Hongmeng distributed soft bus and Google A2A protocol adaptation and extension between the modules.

This invention relates to the field of network communication technology, specifically to a flow control optimization algorithm for an FPGA-based intelligent conferencing system. It constructs a central control unit (CCU) with the FPGA as its core, and uses a cascaded CAM to segment the 48-bit MAC address into six 8-bit sub-keys for parallel searching, outputting the matching result in one clock cycle. A system cycle of 1-5ms is set, and data is transmitted in time slots according to the order of SOC frames, real-time data frames, SOA frames, non-real-time data frames, and TAIL frames, isolating real-time and non-real-time data. A dynamically maintained MAC address table based on an aging mechanism is established to achieve adaptive topology changes for slave devices. Dynamic priority polling arbitration is used to fairly allocate shared storage resources across multiple PHY interfaces. This invention achieves zero-conflict, high-bandwidth utilization network data transmission at a rate of 1000Mbit / s, significantly reducing FPGA resource consumption, improving the real-time performance and reliability of the conferencing system, and is suitable for multi-slave distributed intelligent conferencing scenarios.

The utility model relates to a bus duct based on a tunnel distributed intelligent power distribution and utilization technology, which comprises a bottom shell, ear plates arranged at the tops of the outer walls of the two sides of the bottom shell, a top cover covering the top of the bottom shell, and an assembling assembly arranged at the top of the top cover and comprising an assembling box, an assembling block and a moving block, the assembling box is fixedly arranged on the outer wall of the top of the top cover, the multiple assembling blocks are fixed to the outer wall of the lug plate and the outer wall of the bottom shell correspondingly, the moving block is slidably connected into the assembling box, an inserting block is arranged on the outer wall of the moving block, the inserting block is inserted into the assembling block, and the interior of the assembling box is rotationally connected with a two-way lead screw through a bearing. According to the assembling assembly, when the bottom shell and the top cover are assembled, a plurality of screws do not need to be used for connecting and fixing the bottom shell and the top cover, so that when parts in the bus duct need to be overhauled, a disassembling tool does not need to be used for disassembling the plurality of screws one by one, the disassembling operation steps are relatively convenient, and the overhaul efficiency of the bus duct can be improved.

The application discloses a kind of automated self-healing UI test method and system based on multi-agent AI, belong to automated UI test technical field, including: through test inference interpretation class, original test script is semantized, and output expected intent;When test fails, multiple-source data is synchronously collected and multi-modal feature is extracted, fault propagation graph is constructed according to expected intent and multi-modal feature, fault propagation analysis is carried out using graph attention network, and fault diagnosis result is obtained;According to fault diagnosis result, repair strategy is negotiated and decided using deep Q network, greedy strategy and byzantine fault tolerance algorithm by distributed agent collaborative mechanism, and feedback is obtained;Based on feedback, repair strategy and test inference interpretation class are optimized by meta-learning algorithm, and dynamically adjusted;Through the construction test inference interpretation class, test intent is deeply understood, and distributed agent collaboration is used to complete fault diagnosis and automatic repair, improve semantic understanding ability, diagnostic accuracy and repair success rate.

The present application belongs to the technical field of distributed power grid, and particularly relates to a grid-connection control method and system of an active distributed intelligent microgrid. The method comprises: when the active distributed intelligent microgrid meets the starting grid-connection condition, the microgrid is controlled to grid-connection as follows: 1) adjusting the voltage amplitude and frequency of the microgrid to the range allowed for grid-connection, and adjusting the phase difference between the microgrid waveform and the power grid waveform to a decreasing trend, and then performing step 2); 2) calculating the time difference between the current time and the time of the next synchronous forward zero-crossing point, the synchronous forward zero-crossing point being the synchronous forward zero-crossing point of the microgrid waveform and the power grid waveform; and judging whether the time difference is within the time range allowed for grid-connection, if yes, then grid-connection is performed; otherwise, when the time difference is greater than the upper limit of the time range allowed for grid-connection, waiting until the time difference is within the time range allowed for grid-connection, and when the time difference is less than the lower limit of the time range allowed for grid-connection, performing step 1).

This invention relates to the field of data management technology, specifically to an intelligent flow scheduling method for high-performance oil circuits in sheet metal pipeline networks. The specific implementation process includes: establishing a distributed intelligent execution entity and a resource optimization model based on a sheet metal pipeline network containing schedulable units and production management information including historical performance baselines and production plans; the distributed intelligent execution entity acquires and evaluates the operating data of the schedulable units to generate real-time status data; combining the real-time status data and historical performance baselines to analyze drift trends and generate performance degradation data; based on the production plan and performance degradation data, quantifying the comprehensive operating cost as the objective function to calculate and generate resource allocation strategies and work instructions; and the distributed intelligent execution entity combining work instructions, real-time status data, and performance degradation data to execute dynamic compensation management and generate scheduling instructions. This invention optimizes resource allocation by evaluating the real-time and long-term status of equipment, achieving risk avoidance and dynamic cost optimization.

The application discloses a retail equipment distributed intelligent decision system and method based on cloud and edge node cooperation, relates to the technical field of Internet of Things intelligent retail equipment, and comprises a cloud intelligent layer, an equipment end intelligent layer and a physical hardware layer, wherein the cloud intelligent layer, the equipment end intelligent layer and the physical hardware layer are sequentially connected; the cloud intelligent layer comprises one orchestration agent and multiple professional sub-agents and is used for performing strategy analysis, decision optimization and on-site operation management on all-network retail equipment; the equipment end intelligent layer deploys one node agent for each retail equipment and directly controls the physical hardware layer through a hardware abstraction layer. The application realizes unified intelligent operation management of self-service retail equipment distributed in multiple geographical regions. Through comprehensive use of artificial intelligence multi-agent system, edge computing, an MQTT Internet of Things communication protocol, a hardware abstraction layer, a model context protocol and other technologies, efficient cooperation between cloud strategy decision and equipment end autonomous execution is realized.

The application provides a distributed intelligent tire lateral force estimation method and system based on Kolmogorov-Arnold, step S1, a distributed intelligent tire system is established, sensitive positions are determined through finite element simulation and Spearman correlation coefficient analysis, a plurality of three-axis acceleration sensor nodes are embedded in the tire structure, multi-point signals are captured, and spatial resolution measurement of tire-pavement interaction is realized. Step S2, entropy-guided feature extraction is performed on the collected nonlinear non-stationary acceleration signals, and experience mode decomposition and permutation entropy calculation are combined to adaptively filter high-entropy noise components and retain time spectrum information related to the lateral force. Step S3, a Kolmogorov-Arnold enhanced gated skip connection network is designed, Kolmogorov-Arnold nonlinear extension is used to realize smooth and efficient approximation, multi-head self-attention mechanism and gated skip connection are combined to dynamically adjust information flow, and local and global time sequence dependence is fused, and only 12,428 parameters are required. Step S4, model parameters are optimized through MTS Flat-Trac laboratory testing and road verification, and tire lateral force is predicted.

This invention proposes a method for constructing and training a neural network based on heterogeneous quantum computing resources, relating to the fields of quantum machine learning and distributed intelligent computing. The method includes: constructing identical classical feature extraction networks on quantum computing nodes; masking the output feature vectors of the classical feature extraction networks to obtain local input features based on the number of available qubits on each quantum computing node; constructing a quantum neural network corresponding to each quantum computing node based on the number of available qubits on each quantum computing node; sharing the network parameters of the classical feature extraction networks equally across the quantum computing nodes, and collaboratively updating the parameter prefixes of the quantum neural networks across the quantum computing nodes; alternately training the feature extraction networks and quantum neural networks corresponding to each quantum computing node and performing global parameter aggregation and updates until the quantum neural network converges. This invention can improve the feasibility of quantum neural network models.

This invention discloses an intelligent diagnostic system for collecting abnormal electricity consumption information, specifically relating to the field of power system monitoring technology. It addresses the waveform distortion and broadening problems caused by transmission line distribution parameters during long-distance wired transmission in existing systems. The system collects abnormal characteristic waveform data through distributed intelligent terminals, selects characteristic harmonic components from the power line frequency voltage to construct an inherent test signal, and extracts the envelope shape similarity between the response signal and the test signal as a first distortion evaluation index through envelope analysis. After statistical significance judgment, it performs instantaneous frequency stability analysis on signals with significant distortion to obtain a second distortion evaluation index. Finally, based on the dual indices, it generates corresponding waveform compensation parameters to achieve real-time compensation and correction. This effectively maintains the integrity of the abnormal characteristic waveform during transmission, significantly improving the accuracy and reliability of abnormal electricity consumption diagnosis.

This invention discloses a microservice-based cloud-network-edge intelligent collaboration and management orchestration method, applied to a cloud-network-edge intelligent collaboration framework system. This system comprises three domains: cloud, network, and edge. Each domain has a four-layer, three-plane architecture. The four layers consist of an infrastructure layer, a virtualization layer, a functional layer, and an application layer, designed from bottom to top. The three planes include a control plane, an intelligence plane, and a MANO plane. The infrastructure, virtualization, functional, and application layers interact with the control plane, intelligence plane, and MANO plane, respectively. The control plane, intelligence plane, and MANO plane also interact with each other. The corresponding methods include: implementing the distributed intelligent algorithm training and inference process based on microservices; and implementing the network function template instantiation process based on Kubernetes. This invention achieves efficient deployment.

The application belongs to the technical field of block chain, and particularly relates to a lightweight consensus method for large-scale distributed intelligent computing scenarios. The method is as follows: firstly, a threshold key is initialized among replica nodes and a signature share is generated. In the chain consensus stage, the leader node broadcasts a proposal, and the replica verifies and generates a threshold signature share and returns after verification; the leader node aggregates enough shares to form a legal certificate to promote the pipeline. If a certificate is not formed within a timeout, the view synchronization and recovery stage is entered. When adjacent view chain certificates are reached, the final submission can be accelerated; otherwise, the block needs to be submitted after meeting the continuous three-view condition. In abnormal conditions, the view synchronization mechanism is enhanced to aggregate the view switching threshold share, so that the whole network quickly converges to the new view and recovers the consensus. The technical scheme of the application can be widely applied to large-scale distributed sharded block chain networks through the collaborative optimization of the cryptographic primitives and the sharding structure level.

This invention discloses a rule-based intelligent accounting strategy configuration method, relating to the field of distributed intelligent accounting technology. The method includes: collecting rule metadata and business characteristics, and constructing a full rule set based on the rule metadata; edge nodes retrieving business transaction logs and performing simulated accounting on the business transaction logs in a simulated accounting environment based on a rule logic tree, outputting simulated accounting data; performing benchmark accounting on the business transaction logs in a production accounting environment using the full rule set, outputting benchmark accounting data; comparing the simulated accounting data with the benchmark accounting data, and when the comparison results are consistent, replacing the full rule set with a lightweight rule set in the production accounting environment to generate a lightweight intelligent accounting strategy. This invention improves the accounting efficiency of edge nodes and the maintainability of rules.

An active noise reduction system for the passenger compartment (4) of a vehicle (1) includes: a reference sensor (16) designed to detect a noise reference signal indicating ambient noise to be eliminated; an error sensor (17) disposed within the passenger compartment (4) for detecting an error signal in a noise reduction area to achieve noise reduction feedback control; a loudspeaker (12) designed to reproduce sound within the passenger compartment (4); and a digital processing unit (14) designed to control the loudspeaker (12) to generate an anti-noise signal to be reproduced within the passenger compartment (4) through a noise reduction algorithm based on the reference signal, the error signal, and the determination of acoustic paths within the passenger compartment (4) to achieve noise reduction. In particular, the system has distributed intelligence, wherein the reference sensor (16) and / or the error sensor (17) have embedded digital processing capabilities and are configured to cooperate with the digital processing unit (14).

The application relates to the technical field of industrial automation system monitoring, and discloses a primary and backup dual-center high-availability health monitoring system and method based on a distributed intelligent probe, which comprises a probe collection layer, intelligent probes deployed on monitoring nodes, collection plug-ins for collecting the monitoring nodes according to preset collection strategies, collection data obtained, pretreatment of the collection data to obtain to-be-transmitted data, and reporting and / or forwarding of the to-be-transmitted data through primary and backup transmission channels; a data analysis layer, a primary and backup dual-center structure, real-time reception of the to-be-transmitted data by the primary center through the primary and backup transmission channels to generate analysis result data, keeping consistent with the primary center by the backup center through a state synchronization mechanism, and arbitration of the backup center to take over the primary center based on a multi-source evidence arbitration method when the primary center fails. The application realizes seamless takeover and state continuation in seconds when the primary center fails, and significantly improves the service continuity and availability of the monitoring system itself.

The present application relates to the field of power system automation and control technology, disclose a kind of virtual power plant collaborative scheduling system based on distributed resource aggregation, comprising: cloud collaborative scheduling platform and the distributed intelligent gateway connected via communication network, gateway collects resource equipment physical data, using nonlinear observation function mapping is promoted state vector, execute global linear evolution logic, and based on spectral feature drift determination result sends model parameter or state vector to cloud end;Cloud end constructs aggregated linear model, solves convex quadratic programming problem to generate control sequence;Gateway converts sequence into physical instruction to drive equipment by inverse mapping.The present application converts nonlinear control into convex optimization problem by Koopman operator, greatly improves the calculation efficiency, effectively reduces the cloud edge communication load using spectral drift trigger mechanism, and realizes the online model identification of grid-connected power without fluctuation by combining null space excitation injection.

The application provides a kind of intelligent ship broadcast and alarm system and method, system includes: distributed intelligent acoustic terminal network, fusion perception internet of things, central intelligent processing platform and immersive command console.Central intelligent processing platform contains digital twin ship model engine, intelligent alarm and broadcast strategy engine, adaptive broadcast optimization module and two-way communication and feedback analysis module.Through real-time acquisition of shipboard environmental noise, sensor alarm and personnel distribution data, fusion and visualization are carried out in the digital twin model;When receiving sensor alarm, intelligent strategy engine automatically generates broadcast strategy containing target area list and customized voice text;Adaptive broadcast optimization module calculates optimized audio parameters according to real-time environmental noise;Instructions are issued to intelligent terminal for accurate broadcasting;The system receives on-site personnel state feedback and updates the digital twin model in real time, significantly improving the accuracy, efficiency and intelligence level of ship emergency command.

The present disclosure relates to a beamforming and user selection method based on a MIMO communication system assisted by distributed IRSs. The method comprises: constructing a MIMO communication system model assisted by distributed IRSs; constructing a joint optimization problem P of beamforming at a base station, phase shifts of IRSs, and a user selection strategy; decoupling the joint optimization problem P into a joint optimization subproblem P1 of the user selection strategy and the beamforming at the base station and an optimization subproblem P2 of the phase shifts of the IRSs; solving the joint optimization subproblem P1 and the optimization subproblem P2; obtaining an optimal user selection strategy, an optimal beamforming vector at the base station, and optimal phase shifts of the IRSs by alternately executing solving operations until the problem P converges, and implementing the optimal user selection strategy, the optimal beamforming vector, and the optimal phase shifts.

This application discloses an intelligent monitoring system and method for a high-voltage ice-melting cabinet, belonging to the field of power equipment condition monitoring and intelligent sensor technology. The system includes the following steps: deploying a distributed intelligent sensor network with self-diagnostic functions inside the high-voltage ice-melting cabinet; the distributed intelligent sensor network includes temperature sensing nodes, current / voltage sensing nodes, and environmental sensing nodes; acquiring multi-source data within the cabinet based on a unified clock reference, and filtering out abnormal data according to the self-diagnostic results; during the ice-melting start-up phase, determining whether there is a concurrent risk of near-condensation and local overheating based on the filtered data; if a concurrent risk exists, locking the ice-melting start-up; otherwise, predicting hotspot trends based on the cabinet's thermal inertia model and dynamically adjusting the ramp rate constraint value of the ice-melting current. Through concurrent risk determination and dynamic ramp rate adjustment, both ice-melting safety and operational efficiency are ensured.

The present application belongs to the technical field of energy planning, and discloses a coal mine area green electricity direct connection planning method and system. The present application comprises: integrating geographic spatial information to obtain a three-dimensional geographic spatial information database, processing in combination with a land reuse evaluation model to obtain a heat map, quantitatively analyzing the heat map to obtain power generation; processing fusion by using a space-time convolutional neural network and a distributed intelligent sensing model to generate a meteorological database; analyzing by using a wind power and photovoltaic space-time output model to obtain a photovoltaic joint sample; processing by using a load prediction model to obtain a load prediction curve set; inputting a source load time sequence mismatch quantization model for analysis to obtain an energy storage collaborative configuration scheme; inputting a multi-objective line optimization model for analysis to obtain a direct connection line topology structure diagram; data-driven optimization decision optimization to obtain a source storage load collaborative optimization model, and coal mine area green electricity direct connection planning is carried out through the source storage load collaborative optimization model. The present application directly obtains green electricity and reduces cost.

The application provides a power distribution automation device and a power distribution implementation method, and relates to the technical field of power distribution automation.The device comprises a circuit board, which is divided into a core board, a main board, an analog signal measurement board, a liquid crystal screen adapter board, a key control display board, a power module, a residual voltage board, a power voltage measurement switching board, an indicator light board and a PMU board.The application also comprises a power distribution implementation method, which realizes wide-area synchronization, high frequency and high-precision sampling functions;the FXU ground detection performance is significantly improved, the neighborhood protection is realized without relying on the main station, the precise positioning, isolation and second-level self-healing of faults are automatically completed;the distributed intelligent FA function realizes the absolute selectivity of protection without relying on the main station and the level difference cooperation of the main line, accurately completes the minimum isolation of the fault section and the second-level recovery of power supply of the non-fault section;the neighborhood protection makes up for the selectivity defects of the overcurrent protection and can trip the upstream and downstream switches of the fault point.