587 results about "Network topology" patented technology

Systems and methods are provided for analog hardware realization of neural networks. The method incudes obtaining a neural network topology and weights of a trained neural network. The method also includes transforming the neural network topology into an equivalent analog network of analog components. The method also includes computing a weight matrix for the equivalent analog network based on the weights of the trained neural network. Each element of the weight matrix represents a respective connection between analog components of the equivalent analog network. The method also includes generating a schematic model for implementing the equivalent analog network based on the weight matrix, including selecting component parameter values for the analog components.

This invention relates to the field of equipment safety diagnosis and data processing technology, specifically a method and system for safety diagnosis of transportation equipment in coal port areas. The method includes: acquiring port environment data and transportation equipment data to generate a disturbance behavior data sequence, and constructing an environmental directional entropy field; generating a disturbance phase vector set of transportation equipment in the environmental directional entropy field; using an information resonance identification model to perform combined calculations on disturbances in all directions of the disturbance phase vector set, performing back-folding inference on resonant disturbance nodes that satisfy the continuous offset resonance condition, and calculating the asymmetric stability index; constructing a disturbance network topology, determining the disturbance propagation path and information impedance, and diagnosing the safety of transportation equipment based on the information impedance of the disturbance propagation path. This invention achieves dynamic safety diagnosis of transportation equipment in coal port areas by performing information resonance identification between the coal port environment and transportation equipment operation data.

The application relates to the technical field of multi-spacecraft cooperative control, and discloses a multi-spacecraft grouping consistency restraint control method and device under optimized FLS, which comprises the following steps: acquiring the network topology of a heterogeneous multi-spacecraft system and constructing a multi-subgroup directed communication topology network, constructing a spacecraft attitude control system model based on modified Rodrigues parameters; approximating unknown nonlinear terms in the model through a fuzzy logic system, constructing a fuzzy logic system based on a gradient descent algorithm and Armijo line search criterion; constructing a restraint control term and using a backstepping method to construct a virtual control law, combining the output of the fuzzy logic system based on the gradient descent algorithm and the Armijo line search criterion to construct an actual control input law, so that grouping consistency restraint control of the heterogeneous multi-spacecraft system is realized. The application can improve real-time approximation precision and adaptive step length adjustment capability while guaranteeing grouping consistency, and can enhance topology applicability and task cooperation flexibility.

This invention belongs to the field of artificial intelligence hardware architecture design and discloses a method, system, device, and medium for cross-layer optimization of multi-core packaging and optical interconnect clusters for large language model training. The method includes: constructing a design space, including a core hardware architecture layer, an optical interconnect network layer, and a training parallel strategy layer; performing an outer-layer search to search for the architecture parameters of the multi-core modules within the core hardware architecture layer; for each outer-layer search sampling point, performing an inner-layer search to collaboratively optimize the optical interconnect network topology and training parallel strategy within the optical interconnect network layer and the training parallel strategy layer; and outputting the Pareto optimal design point on the training performance and cluster cost plane. The technical solution described in this invention can guide the design of related training clusters, thereby fully leveraging the advantages and potential of core technology and optical interconnect technology in large language model training.

The application discloses a kind of multi-node server network topology and energy consumption perception's task scheduling method, belong to data center and high performance computing technical field.It includes: constructing the weighted directed graph topology model including server node and switch node, the dynamic energy consumption weight function of link is defined;Establish the three-dimensional energy consumption and thermal interference evaluation model of node, introduce space thermal interference factor;Analysis the characteristics of task to be scheduled;Based on energy consumption potential field screening candidate node;Build the global energy consumption cost function including computing energy consumption, network transmission energy consumption and thermal coupling penalty energy consumption;According to the principle of minimum total energy consumption, select target node to execute scheduling and update topology state.The application solves the problem of splitting computing and network energy consumption, ignoring the space coupling effect of heat dissipation in the prior art by the fusion of network topology perception and multi-dimensional energy consumption model, and realizes the optimization of global energy consumption of data center.

This application relates to an IoT-based intelligent land remediation monitoring method and system. The monitoring method includes: acquiring satellite remote sensing elevation data and historical soil composition data of the land area to be monitored; dividing the land area to be monitored into multiple grid units and assigning grid identifiers; deploying fixed soil sensor nodes and mobile drone nodes to generate a sensor network topology; collecting real-time soil parameters of each grid unit and performing noise filtering and anomaly detection to generate a soil composition dataset; inputting the soil composition dataset and historical soil composition data into a cloud-based prediction model to output a land degradation risk score and remediation strategy; controlling the execution equipment to perform land remediation operations and monitoring changes in soil parameters after execution in real time; updating the parameters of the cloud-based prediction model and optimizing the sensor network topology based on the deviation between the changes in soil parameters after execution and the expected values. This application improves agricultural production efficiency and soil health.

The present disclosure relates to a vehicle electrical architecture processing method and device, electronic equipment, storage medium and program product, and relates to the technical field of intelligent networked vehicles; in response to a vehicle electrical architecture change request, automatic identification processing of network topology configuration data is performed, corresponding network topology data is determined, and based on the corresponding network topology data, an architecture change network topology is automatically generated. Through the network topology data, the architecture change network topology is automatically generated, improving the efficiency of vehicle electrical architecture processing; and the processing process can be applied to various processing scenarios such as intelligent troubleshooting and manual repair of signal conflicts, and has high flexibility; and based on the automatic data identification and processing capability, it can be applied to different vehicle models and diverse vehicle electrical architectures, and has high applicability. The technical scheme can improve the efficiency, flexibility and applicability of vehicle electrical architecture processing, thereby effectively ensuring the intelligentization, networking and efficient collaboration of vehicle functions.

This invention discloses a method for compressing and reconstructing distribution network data based on graph transform compressed sensing, belonging to the field of distribution network measurement data technology. The method includes the following steps: inputting the network topology and historical operating data of the distribution network; constructing a power-voltage relationship parameter matrix; determining the number of principal components and obtaining the projected principal component coefficient matrix; calculating the graph Fourier transform basis; using the graph Fourier transform basis as the sparse basis after projecting the principal component coefficient matrix, calculating the sparsity and initial compression of each principal component under the sparse basis, and obtaining spatially compressed observations; reconstructing the voltage amplitude measurement data of distribution network nodes, and further reconstructing the active power measurement data and reactive power measurement data of branches. This invention employs the above-mentioned method for compressing and reconstructing distribution network data based on graph transform compressed sensing, achieving high data reconstruction accuracy while maintaining a high compression ratio, reducing computational load, and making it more suitable for applications on edge-side devices.

Certain aspects of the present disclosure provide techniques for determining a topology of an electric vehicle supply equipment (EVSE) network. An example method includes obtaining, for a first time period, for each of a plurality of pieces of equipment of the EVSE network, a respective magnitude of a respective current phasor, wherein for each of one or more pieces of equipment of the plurality of pieces of equipment, a respective phase angle of the respective current phasor is unknown; and determining, based on at least the respective magnitude of the respective current phasor for each of at least one piece of equipment of the one or more pieces of equipment, a respective subset of lines of the EVSE network to which the respective piece of equipment is coupled.

This invention relates to the field of electronic digital data processing and digital information transmission technology, specifically to a cloud-based intelligent data processing system for multiple nodes. The system includes a data preprocessing unit, a topology-aware scheduling engine, a graph neural network device, and a task execution unit. The data preprocessing unit extracts the temporal dependencies and logical associations of the input data stream to generate a directed acyclic graph (DAG). The topology-aware scheduling engine maps the graph to the physical network topology and calculates link bandwidth margin and congestion status. The graph neural network device jointly encodes node computational load and edge link status to output a task allocation strategy. The task execution unit schedules strongly dependent tasks to adjacent nodes with sufficient link margin and idle computing power according to the strategy, and dynamically migrates tasks to alternative path nodes based on link packet loss rate during execution. This invention reduces data transmission latency across switches in multi-node cascading and avoids node computing power idleness caused by local link congestion.

The application discloses a road centerline determination method and system based on a deep neural network satellite map, and relates to the technical field of satellite image processing; the method comprises the following steps: preprocessing time-phase satellite images of a collected to-be-processed region, vehicle GPS track data and street view image sequences to obtain a dynamic region in the to-be-processed region, a track density map and a street view feature mapping table; inputting the time-phase satellite images into a pre-trained road segmentation model to obtain multiple road probability maps; based on the multiple road probability maps, performing dynamic region weighted fusion, non-dynamic region logical or operation and threshold determination to obtain an initial road mask; vectorizing the initial road mask into a centerline network and extracting cross nodes to obtain an initial road network topology; and the application has the advantages of improving the accuracy of the road network topology and guaranteeing the rationality of path planning.

The application provides a multi-terminal interconnection method, system, electronic device and storage medium, priority of each wireless communication device is preset, and a role state containing independent, central and peripheral three states is initialized, a dynamic self-organizing mechanism taking priority as the only decision basis is constructed. When a first device in the independent state discovers a second device through a device discovery process, priority comparison is triggered: if the first device has higher priority, the first device is switched from the independent state to the central device state after actively establishing a connection, and the second device is assigned to enter the peripheral device state and stop the active device discovery process. The first device in the central device state continuously performs device discovery, actively discovers and connects all other devices with lower priority, and automatically constructs a star-shaped topology network with itself as the center. By completely giving the connection decision-making right to the high-priority device, the network topology from disorder to order is determined to converge.

This application provides a wireless ad hoc network transmission method and system for VOCs monitoring data from oil storage tanks, relating to the field of wireless transmission technology. The method includes: deploying monitoring nodes in the tank area to collect VOCs monitoring data and meteorological data, and deploying relay nodes to collect wind speed and direction data; correcting the VOCs monitoring data based on the meteorological data to generate a data packet to be transmitted, and simultaneously generating link state parameters based on the wind speed and direction data; then determining the stability parameters of the wireless link based on the link state parameters and constructing a dynamic network topology; selecting a first transmission path for the data packet to be transmitted; and finally updating the stability parameters when the number of consecutive wind direction data changes exceeds a preset threshold, selecting a second transmission path, and switching the data packet to be transmitted to the second transmission path, thus completing the wireless ad hoc network transmission of VOCs monitoring data. This application improves the continuity and reliability of data transmission under complex meteorological conditions.

The application discloses a kind of based on pipe network topological structure's medium pressure pipe network breakpoint analysis method, the method comprises: simplifying first pipe network topological graph, in the first pipe network topological graph after simplification, obtain the object to be analyzed, according to the object to be analyzed, second pipe network topological graph is constructed;Obtain the ID information and position information of the object to be analyzed, according to the ID information and position information, the comprehensive ranking of several occlusions is calculated;According to the comprehensive ranking, the breakpoint risk degree of medium pressure pipe network is determined.The application is by simplifying the object to be analyzed in original pipe network topological graph, to construct a new pipe network topological graph, while each object to be analyzed in new pipe network topological graph is assigned a position information, to calculate the comprehensive ranking of each occlusion, and then determine the breakpoint risk degree of size of the position of the occlusion, realizes the quick analysis and positioning of medium pressure pipe network breakpoint.

This invention discloses a voice line fault analysis method based on big data, belonging to the field of line fault analysis technology. The invention includes the following steps: synchronously acquiring raw voice signals and network jitter data from the voice line; preprocessing the voice and network data; extracting amplitude fluctuation features from the processed voice signal; converting voice fluctuations and network jitter into frequency domain signals and calculating the correlation strength between the two within a key frequency range; comparing the calculated fault index with an adaptive dynamic threshold to determine whether a fault has occurred; analyzing the key frequency components leading to the fault and, combined with network topology information, determining the specific physical location of the fault; and generating a structured report containing fault details and location information. This invention, by analyzing the coherence of the voice signal modulation spectrum and network jitter sequence within a key frequency range and constructing a dynamic fault index, can achieve the identification and location of voice line faults.

The application discloses a fault distance measurement method and system based on synchronous sampling of power distribution terminals, relates to the related technical field of fault distance measurement, and comprises the following steps: acquiring transient electrical quantity data synchronously sampled by multiple power distribution terminals after a fault of a power system; constructing a dynamic graph structure with the power distribution terminals as nodes and electrical connection relationships as edges according to real-time network topology information, inputting the transient electrical quantity data into a phase sequence disorder correction model to correct phase sequence disorder and extract spatial fault features; fusing zero-mode components of the transient electrical quantity data and node enhanced features; globally aggregating the fusion features of the nodes to obtain graph-level fusion features, extracting timing features, and regressing to obtain a fault distance. The application solves the technical problem that, in the prior art, it is difficult to fully utilize multi-source transient information to perform high-precision fault distance measurement in a complex power distribution network environment with dynamic topology and phase sequence disorder, and achieves the technical effects of improving fault distance measurement precision and adaptability to complex power distribution network environments.

The application discloses a kind of based on whitelist occupation monero network topology structure mapping method, the method includes: for each reachable node in monero network, the address in address set A is used to the white list of the reachable node is occupied, and after waiting for set time period, the white list of the reachable node is obtained;The address in the white list obtained is compared with address set A, and the first difference address set is obtained;According to the first difference address set, the outgoing connection node of the reachable node is determined;According to the outgoing connection node of each reachable node determined, the topology structure of the monero network mapped out is obtained.The application can not be necessary to send a large number of transactions to the network, avoid the invasiveness to network, and monero network topology structure can be accurately obtained at high speed.

The application discloses a network fault root cause positioning method and operation and maintenance equipment, relates to the technical field of network fault positioning, and is used for improving the accuracy of network fault root cause positioning.In the method, the operation and maintenance equipment constructs an elastic backtracking window associated with a network topology delay, combines a time-space alignment drift degree to quantify a cause-effect relationship, establishes a more reasonable log searching interval for each candidate root cause node, reduces time axis out-of-range time sequence misplacement of a preset window range, avoids a situation that related technology is difficult to capture the correlation between the two, and improves the accuracy of network fault root cause positioning.

The present application relates to epidemic prevention communication technology field, especially to a duplex cluster communication interaction method and system and epidemic prevention helmet, which comprises: using the air guide pickup component arranged in the helmet body to obtain the voice signal of the wearer, and outputting the audio signal through the air guide sound production component arranged in the helmet body; performing echo cancellation and noise reduction processing on the obtained audio signal; using an adaptive filtering algorithm to suppress the acoustic feedback between the air guide pickup component and the air guide sound production component; based on a distributed channel access mechanism, allocating communication resources for multiple communication nodes to access in duplex; relying on a mesh self-organizing network topology without a central node to perform multi-hop forwarding and route maintenance of data packets, solving the problems of unclear communication, howling and signal attenuation in the strong noise, wind noise and shielding scene in the epidemic prevention helmet, effectively guaranteeing the clarity, stability and safety of cluster communication in harsh working environment.

The application provides a PCIe extension card-based hardware monitoring device and method, and relates to the technical field of hardware monitoring.The device comprises a PCIe communication interface for performing PCIe protocol communication with a host;an independent power supply unit for taking power through an auxiliary standby power supply interface and being isolated from the host side through a digital isolator;an isomorphic processing unit;multi-stage storage units for storing data in stages;a switchable network topology unit having a network physical layer interface and a topology switching circuit;the isomorphic processing unit receives host instructions, controls the working mode of the switchable network topology unit, and schedules the multi-stage storage units to perform testing and store the test results in stages.The application can solve the problem of monitoring failure and data loss caused by operating system crash or power failure in the prior art, continuously monitors and ensures data safety in abnormal system conditions, reduces testing cost, and improves the comprehensiveness of system stability verification.

The application relates to the technical field of algorithm and network cooperative scheduling, and discloses an algorithm and power cooperative scheduling method and system based on multi-dimensional classification of task value and time delay sensitivity, which comprises the following steps: acquiring multi-source data streams of computing power tasks, power markets and network topologies; performing multi-dimensional classification processing based on a time delay sensitivity index and a comprehensive value score to generate a task type code; extracting power market environment features to dynamically generate a multi-target optimization weight vector, and screening network topology nodes to generate a candidate data center set; combining the code and the weight vector to calculate the comprehensive cooperative utility of each node, generate a to-be-verified scheduling decision, input the decision into a consortium chain network to perform a quoted price tolerance check and consensus determination, and generate a scheduling instruction after the check and determination; collecting actual operation parameters according to the instruction to construct an experience four-tuple, and inputting the experience four-tuple into a meta-learning network for parameter fine-tuning. Through multi-dimensional feature mapping and distributed checking, the application realizes closed-loop cooperative scheduling of heterogeneous computing power and dynamic power.

Disclosed are a network traffic load balancing method and apparatus for a data center, which are applied to the data center including a switch, a host, and a server. The method includes the following steps: collecting connection information between the switch, the host, and the server, and constructing a network topology structure of the data center according to the connection information; monitoring working information about each network link under the network topology structure, and correspondingly determining a congestion condition of each network link according to the working information about each network link; and allocating pre-transmitted data traffic packets to each network link according to the congestion condition to balance data traffic on each network link.

A system is provided for detecting and remediating computing system breaches using computing network traffic monitoring. In particular, the system may identify one or more technology elements within a network as well as relationships between computing systems associated with said elements to determine a network topology. Based on the network topology, the system may use historical network traffic data associated with the technology elements in the network to generate predicted entry points and lateral pathways of a security breach that may take place within particular computing systems. Then, based on the technology elements affected as well as entry points and path traversals of the breach, the system may generate and / or implement one or more remediation steps to address existing and / or future breaches. In this way, the system may provide an intelligent method of augmenting the security of a computing network.

This application provides a preventative dispatch method, terminal, and storage medium for an integrated electric and thermal energy system, belonging to the field of power system dispatching technology. It constructs an uncertainty set based on the potential failure of transmission lines under extreme weather conditions and employs a two-stage robust optimization framework. First, a first-stage constraint set is established based on known extreme weather forecasts. Then, a second-stage constraint set is established based on the actual occurrence of extreme weather. The objective function is to minimize the sum of the electrical load gap and the thermal load gap under extreme weather conditions. By establishing the uncertainty set and introducing extreme events into the constraints, a complete two-stage robust preventative dispatch model is formed, which is then solved to obtain the preventative dispatch results. Therefore, backup and energy storage are arranged in advance before extreme weather, and network topology is adjusted to reduce the load gap caused by sudden outages. Robust optimization ensures that the system meets both heating and power supply needs under all possible outage scenarios, improving the overall reliability and resilience of the system.

The application relates to the technical field of data processing, and discloses a multi-source pipeline data registration and fusion labeling method for heterogeneous coordinate systems, which comprises the following steps: obtaining multi-source pipeline data and source coordinate system description information to establish a coordinate transformation model. Pipeline nodes, pipeline sections, connectivity relationships and semantic attributes are extracted, source and target pipeline network topology semantic constraint graphs are constructed, a registration error model is established, parameters are jointly solved through iterative optimization, and a globally consistent registration result is obtained. Pipeline nodes and pipeline sections from different pipeline data sources and corresponding in spatial position, topology structure and semantic attributes are clustered into unified pipeline objects and assigned with unified identification. A quality-driven attribute fusion model is constructed, geometric attributes are corrected within engineering constraints, classified attributes are weighted, and finally, a fusion labeling result is obtained. The application obtains unified pipeline objects which are globally consistent, topologically correct, semantically unified and provided with quality labeling.

The application provides a line loss analysis method and system based on carbon flow tracking, and belongs to the technical field of power data anomaly analysis. CE = CE in ‑ CE o , CE The total loss carbon amount of the node i ; CE in The total input carbon amount of the node i ; CE o The total output carbon amount of the node i ; an XGBoost model for carbon amount anomaly screening is constructed based on a gradient decision tree XGBoost, and a target function of the model; each node in a network topology is analyzed based on the XGBoost model, and a carbon amount anomaly node is predicted based on the power and carbon emission density of each node; each user corresponding to the carbon amount anomaly node is subjected to power analysis, the power analysis includes high-voltage user abnormal power analysis and low-voltage user abnormal power analysis, and the type of abnormal situation existing in the user is obtained.

This invention discloses a network security situation awareness method and system based on deep learning, relating to the field of deep learning technology. The method includes deploying a network security data acquisition component within a regional network to collect and parse network data packets, generating security event data; constructing a communication graph of network communication relationships based on the security event data, and embedding and mapping the communication edge features in the communication graph to generate communication edge embedding vectors as a vectorized representation of communication relationships; performing topology encoding based on the communication graph to generate position codes, and fusing the communication edge embedding vectors with the position codes to form fused edge vectors containing network topology information; introducing a multi-head attention mechanism to update the fused edge vectors to obtain the final edge vectors. This invention can not only more accurately identify and distinguish different types of attack behaviors, but also significantly improve the accuracy of network security status perception.

The application discloses a critical patient sedation state evaluation method based on a multi-modal network model, relates to the technical field of medical monitoring and signal processing, and comprises the following steps: analyzing a plurality of channel physiological signals of a patient, constructing a dynamic time-varying function network, and performing multi-scale community detection to identify stable function modules in the network and dynamic connection modes thereof. The network topology mode is fused with instantaneous phase information of the signals to form a fusion feature space-time atlas. After the atlas is subjected to deep feature extraction and structured coding, a sequence modeling network is used to capture time sequence dependence, and finally a quantitative sedation evaluation signal is output by a decoder. Through dynamic modular structure analysis of the brain function network and cross-level feature fusion, the method realizes more continuous and accurate objective evaluation of the sedation state.

This application proposes a cloud-based collaborative method for remote operation and maintenance and fault prediction of CPE devices, belonging to the field of fault operation and maintenance prediction technology. The method includes: collecting operational indicator data from multiple target devices to obtain network topology data and constructing a heterogeneous correlation graph; constructing multiple degradation indicators based on the operational indicator data within a continuous time window; identifying target base station nodes as predicted fault points based on the degradation indicators in the heterogeneous correlation graph; and treating the target device nodes directly connected to the predicted fault points as a device cluster; determining the fault type based on composite judgment logic executed by the target devices; generating a pre-diagnostic instruction set; and executing automated recovery operations. By deeply fusing topology location, root cause score distribution, and real-time link status information, fine-grained differentiation of fault types is achieved, significantly reducing the misjudgment rate.