1728 results about "Transmission network" patented technology

The invention discloses an optical transmission network hidden danger detection method and device and a computer program product, which can realize dynamic adjustment of differentiated bandwidth requirements by acquiring optical network state information in real time, evaluating service priority and establishing an optical channel resource allocation model. And when a topology anomaly is detected, constructing a service quality evaluation model by using a long-short-term memory neural network, and quantitatively analyzing the influence of the anomaly on the service. When a fault occurs, distributed link fault detection and a fault diagnosis engine based on a decision tree are adopted, and fault hidden dangers are quickly positioned and classified. And then, through an elastic recovery mechanism oriented to service sensitivity, the service quality level of a standby optical path is adjusted, and an optical layer topology connection relationship is optimized. According to the method, the resource utilization efficiency, the fault recovery capability and the service quality of the optical network are remarkably improved, and an innovative solution is provided for intelligent optical network management.

The invention discloses a transmission network fault positioning method, system and device and a storage medium, and relates to the field of communication, and the method comprises the steps: firstly, collecting network data to construct a multi-granularity network topology model, then obtaining original alarm data, analyzing the topology association relationship between alarms, and obtaining a multi-granularity network topology model; and mining horizontal peer association and vertical upstream and downstream association among the alarm objects, and identifying multi-alarm data of each alarm object. Thirdly, constructing an associated alarm directed graph based on the multi-alarm data, and performing associated alarm compression on the associated alarm directed graph to obtain a compressed alarm set; and finally, constructing an alarm propagation graph based on the compressed alarm set and the multi-granularity network topology model, querying the alarm propagation graph through a reverse hierarchical sorting algorithm, and determining a root alarm. According to the method, through innovative methods such as topological modeling, association mining, graph compression and reverse sequencing, alarm data and a topological structure are closely combined, an alarm propagation rule is analyzed, and fault root causes are diagnosed.

The invention relates to the technical field of atmospheric pollutant detection, and discloses a method and a system for detecting excessive emission of atmospheric pollutants. The method comprises the following steps: acquiring pollutant concentration data of multiple monitoring points in a target area to form an original data set; abnormal value detection and correction are carried out on the data set, sensor fault outliers are eliminated, and a preprocessed data set is obtained; extracting concentration change trend characteristics in a preset time window of each monitoring point, and constructing a spatial-temporal characteristic matrix; inputting the matrix into a pollutant diffusion model, calculating a transmission path and strength between monitoring points, and generating a regional transmission network; identifying a potential source region of abnormal fluctuation of pollutant concentration based on a network, and marking the potential source region as a candidate region to be checked; performing multi-scale concentration gradient analysis on the candidate area, and determining a key monitoring area; arranging mobile equipment in the key monitoring area, and collecting high-precision component data; and comparing the data with a standard emission source feature library, matching emission source types of which the similarity exceeds a threshold value, judging whether the emission exceeds the standard or not, and generating a detection report.

The present application discloses a calculation method for multi-scale electric-carbon energy efficiency optimization for a power system, which includes designing a carbon flow calculation improvement model of the power system that takes into account a photovoltaic cluster auxiliary service, converting a power lossy transmission network of the power system equivalent to a lossless power consumption network, and calculating a carbon flow of the power system that takes into account a photovoltaic reactive power cluster auxiliary service; designing a decision-making system for low-carbon operation of the power system based on a coupling of electric-carbon energy efficiency, and calculating a correlation between electric quantity and the carbon flow of the power system that takes into account the photovoltaic cluster auxiliary service; the output varies in the range of 0-1, larger values represent a stronger correlation between electric-carbon energy efficiency.

The invention belongs to the technical field of automatic industrial sorting, and discloses an automatic sorting digital twin system and a construction method. The construction method comprises the following steps: constructing physical entities for executing different operation functions in automatic sorting based on a physical entity layer, and sending the physical entities to a digital twin platform layer; the digital twin platform layer constructs a three-dimensional virtual model synchronous with different operation functions executed by the physical entity through three-dimensional design software; the three-dimensional virtual model is dynamically updated in real time; and loading the three-dimensional virtual model to a three-dimensional engine of a visual application layer through a communication protocol or a transmission network, monitoring the real-time states of different operation functions of the physical entity in an industrial automatic sorting simulation scene, and visually displaying the real-time states through a user interface. The digital twinning platform of the automatic sorting system is constructed, and visual interaction, real-time data monitoring and virtual and real synchronous operation of the automatic sorting digital twinning system are achieved.

The invention discloses a mechanical arm digital twinning system for deep space exploration, and relates to the technical field of digital twinning simulation. The mechanical arm comprises a physical layer, a data acquisition and transmission layer, a digital twin model layer and an application layer, the physical layer comprises a mechanical arm body and a sensor network, and the sensor network is used for acquiring operation state information of the mechanical arm body; the data acquisition and transmission layer comprises data acquisition equipment and a data transmission network, wherein the data acquisition equipment is respectively connected with the sensor network and the data transmission network. According to the invention, the physical layer, the data acquisition and transmission layer, the digital twinborn model layer and the application layer of the mechanical arm are constructed, so that the actual mechanical arm body can be guided to perform related actions through virtual simulation data, and virtual and real body configuration comparison can be completed through a visual interface; and the digital twinning requirements of various spacecraft operations in the future deep space exploration field are met.

The invention provides an edge fusion diagnosis system and method for multi-source heterogeneous power transmission monitoring data, and belongs to the technical field of power transmission line monitoring. The multi-source heterogeneous power transmission monitoring data edge fusion diagnosis system comprises a multi-source heterogeneous data access module, a data preprocessing and standardization module, an edge computing node, an edge node collaborative diagnosis module, a data compression and feature extraction module and a cloud fusion analysis platform. The method comprises the following steps: accessing multi-source heterogeneous data, preprocessing and standardizing, locally diagnosing by an edge computing node, triggering multi-node collaborative diagnosis when the credibility is insufficient, and uploading key feature data to a cloud for global analysis and model optimization. According to the method, comprehensive integration and efficient processing of multi-source data are realized, the real-time performance, accuracy and comprehensiveness of power transmission monitoring diagnosis are improved, and the efficient monitoring requirement of a complex power transmission network can be met.

The invention discloses a power backbone transmission network fault diagnosis method and system based on a knowledge graph, and the diagnosis method comprises the steps: building a fault and alarm knowledge graph, collecting the historical fault data, alarm information and network topology structure data of a power backbone transmission network, and carrying out the entity recognition, relation extraction and attribute filling technologies, thereby achieving the fault diagnosis of the power backbone transmission network. Constructing a fault and alarm knowledge graph; building a fault diagnosis algorithm model based on the knowledge graph through alarm vectorization in combination with model building of the knowledge graph and a graph neural network and building and optimization of a fault reasoning model, and performing network fault diagnosis; a simulation environment is built and experimented, the simulation environment is built according to the actual operation condition of the power backbone transmission network, and fault reasoning is conducted through a built model; and verifying the effectiveness and accuracy of the model in fault diagnosis. The relevance between alarms and a fault propagation path can be more accurately captured, so that a fault source is quickly positioned, and the accuracy and efficiency of fault diagnosis are remarkably improved.

The invention belongs to the technical field of power systems, and particularly relates to a transmission and distribution cooperative scheduling optimization method based on multi-agent deep reinforcement learning. The method comprises the following steps: firstly, establishing a transmission and distribution cooperative scheduling distributed framework, and respectively establishing distributed optimization models taking the minimization of the operation cost of a power transmission network and the minimization of the operation cost of each active power distribution network as targets; secondly, converting a transmission and distribution collaborative scheduling problem into a Markov game model with a multi-agent benefit balance characteristic, and designing a collaborative multi-agent architecture; then interacting with a transmission and distribution interconnection environment through each agent to obtain historical interaction information, storing the historical interaction information into an experience pool, and training by using an MADDPG algorithm to realize iterative updating and optimization of an Actor network and a Critic network of each agent; and finally, realizing local optimization control of the power transmission network and each active distribution network by using an Actor network.

The invention, which relates to the technical field of traffic safety dispersion, discloses an anti-vehicle-blocking traffic safety dispersion method for reconstruction and expansion of an expressway, and the method comprises the following components: S1, constructing a multi-dimensional information collection system, S2, establishing an information intelligent transmission network, S3, carrying out a traffic jam prediction algorithm, and S4, carrying out precise early warning and intelligent guidance. According to the invention, rapid and efficient transmission of information is realized through the converged communication technology, the communication technology not only improves the speed and stability of data transmission, but also can realize intelligent switching in different signal environments, thereby providing timely and reliable traffic information for a traffic management center, enabling the traffic management center to make a response rapidly, and improving the traffic management efficiency. Accurate early warning and intelligent guidance are implemented, and traffic jam and safety accidents are effectively avoided.

The invention discloses a high-frequency data remote transmission method and system, and the method comprises the steps: dynamically adjusting the collection frequency and mode according to environment conditions, and obtaining high-frequency data in real time; based on the data type and the transmission network state, a compression algorithm, a filtering mode and a coding strategy are automatically selected; the congestion condition of the transmission network is predicted through a built-in machine learning model, and a packaging strategy and transmission parameters are dynamically adjusted according to network conditions; in the data receiving process, the bit error rate in the transmission process is monitored in real time, abnormity in transmission is automatically detected, repair is carried out through an error correction algorithm, and if repair fails, the system automatically requests the data packet again; and the recovered high-frequency data is classified and stored according to the importance, real-time performance and storage cost of the data, and automatic storage strategy adjustment is realized in a remote database. According to the technical scheme, the high efficiency, reliability and safety of remote transmission of high-frequency data are improved.

The invention discloses a transmission path planning method and system for a box-type logistics system, and the method comprises the steps: constructing a four-dimensional space-time transmission network model which comprises the three-dimensional space coordinates of the box-type logistics system and department operation time window constraints, and quantifying the node traffic capacity; a hierarchical planning architecture is adopted, and a dynamic Bayesian network is introduced to predict the node congestion probability; a multi-agent cooperation mechanism is established, an online layer adjusts a path weight in real time by improving an ant colony optimization algorithm, and an offline layer optimizes global strategy parameters by using deep reinforcement learning and constructs a knowledge distillation channel to realize cross-period experience migration; and a multi-objective evaluation function is applied, transmission time cost, system energy consumption indexes and path load balance degree are synchronously optimized, a conflict resolution mechanism based on the game theory is designed, and transmission path planning of each box body is realized. According to the invention, through dynamic path planning and real-time flow monitoring, materials can be rapidly and accurately delivered to a destination, and the overall efficiency of a logistics system is improved.

In some implementations, a network device may receive an indication of a next-next-hop of the network device and link quality information of the next-next-hop. The network device may transmit network traffic based on the indication of the next-next-hop and the link quality information of the next-next-hop.

The invention relates to the technical field of network communication security, and discloses a real-time encrypted transmission network communication security guarantee system, which comprises a dynamic fragmentation quantum resistance encryption module, a flow perception-based adaptive encryption engine, a zero-handshake continuous identity authentication module and a hardware acceleration fragmentation processing architecture, and the dynamic fragmentation quantum resistance encryption module is configured to segment a data stream into a plurality of fragments according to a time window, and independently generate an unassociated key for each fragment based on a quantum security NTRU algorithm. According to the network communication security guarantee system for real-time encryption transmission, the traditional network communication security guarantee technology is comprehensively innovated, and the problems of static key management, encryption algorithm adaptation, data fragmentation encryption, identity authentication and encryption cooperation, quantum computing threat response and the like are successfully solved; and the performance of the system in key performance dimensions such as security, real-time performance, adaptability, reliability and anti-attack capability is obviously improved.

The invention provides an ecological environment dynamic monitoring method based on big data, and relates to the technical field of environmental pollution detection, and the method comprises the steps: collecting multi-source sensor data of a fixed monitoring station in an urban region, and carrying out the time alignment and space registration of the multi-source sensor data, thereby generating a standardized environmental monitoring data set. And on the basis of the data set, constructing a pollutant transmission network between the monitoring points to obtain a regional pollutant distribution characteristic graph, calculating a diffusion trend coefficient of pollutants in combination with regional wind direction data, constructing a pollutant migration path network, and generating a key supervision region map. According to the method, the pollutant concentration distribution can be dynamically partitioned, and the mobile monitoring equipment is scheduled to carry out gradient type point distribution monitoring, so that the accurate positioning of the pollution source is realized; the accuracy of pollutant diffusion trend analysis and the precision of pollution source positioning are improved, dynamic supervision of key areas is facilitated, the efficiency of environment monitoring and the fine management level are improved, and therefore the risk of environmental pollution is effectively reduced.

The invention discloses an asynchronous motor energy-saving monitoring and dynamic early warning method based on situation awareness, and relates to the technical field of asynchronous motor energy-saving monitoring. The method comprises the steps that S1, multi-source heterogeneous data are synchronously collected, power supply parameters and operation parameters are obtained in real time, and a frequency conversion working condition self-adaptive deviation value and a current stability coefficient are obtained; s2, generating four-dimensional situation factors including an overtemperature danger value, a current stability coefficient, a voltage stability coefficient and a frequency deviation value; s3, constructing a fourth-order transmission network, and generating a primary early warning signal based on the path mark combination; s4, calculating the motor efficiency in real time, triggering an efficiency abnormity mark, fusing the efficiency abnormity mark with the primary early warning signal, and reconstructing a final early warning instruction; and S5, executing hierarchical control according to the final early warning instruction. Through multi-parameter coupling and dynamic early warning transmission, the early-stage capture capability of the composite fault is improved, the early warning response delay is shortened, energy-saving optimization and fault protection of the motor are realized, and the method has a remarkable practical value.

The invention relates to the technical field of computing power distribution, in particular to a computing network energy integrated collaborative task scheduling platform and scheduling method. The scheduling platform comprises an infrastructure layer, a management and control layer and an application service layer. The infrastructure layer is composed of a computing power supplier, a network supplier and an energy supplier; the application service layer at least comprises a user platform, and the user platform is at least used for receiving and analyzing task requests initiated by all users and reporting the task requests to the management and control layer; the management and control layer at least comprises a sensing module, a measurement module and a computing network energy task scheduling module, the sensing module is used for sensing computing power resources, transmitting network resources and energy resources, and the measurement module is used for measuring the computing power resources, the network resources and the energy resources required for processing each task; and the calculation network energy task scheduling module is used for selecting an optimal calculation power supplier and an optimal network supplier of each task based on the total time delay, the total energy consumption and the total carbon emission. According to the platform, the computing resource utilization rate and the task processing speed can be improved, and carbon emission can be effectively reduced.

The present disclosure relates to data transmission methods. In one example method, a first gateway device obtains a RoCEv2 packet to be sent to a second gateway device, generates a target request, and sends the target request to a network controller. The two gateway devices perform data transmission based on an SRv6 network. The network controller obtains a path computation result based on the target request, and returns the path computation result to the first gateway device. The path computation result includes a network path between the two gateway devices and a reserved network resource. The first gateway device extends the RoCEv2 packet based on the path computation result. An extended RoCEv2 packet carries a first identifier and a second identifier representing RDMA data. The network controller performs bandwidth assurance based on the second identifier and a QoS policy. An extended RoCEv2 packet is forwarded on the network path.

Provided in the present application are a power load regulation and control system and method based on a high-speed wireless transmission network. The power load regulation and control system comprises a regulation and control resource layer, an aggregation transmission layer and a load terminal layer, wherein the load terminal layer is connected to each terminal device, and is used for collecting information of each terminal device and fusing the information of the terminal devices, and also issuing a control instruction to each terminal device or transmitting fused information to the aggregation transmission layer; the aggregation transmission layer is used for transmitting the fused information to the regulation and control resource layer by using multi-service multi-hop access technology and secure transmission technology, and transmitting to the load terminal layer the control instruction issued by the regulation and control resource layer; and the regulation and control resource layer is used for generating the control instruction for each terminal device on the basis of the fused information, and transmitting the control instruction to the aggregation transmission layer. The present application can realize the unification of a stable connection and a high transmission rate, can also meet the high requirements for data collection and the transmission rate, and can also cope with complex and variable channel transmission scenarios.

The invention discloses a CIM-based pipeline state intelligent monitoring method, system and equipment and a medium, belongs to the technical field of urban infrastructure management, and aims to solve the technical problem of how to realize comprehensive, real-time and intelligent monitoring and management of the pipeline state of an underground pipe network, automatically analyze potential risks and abnormities and improve the management efficiency of the underground pipe network. In order to improve the management efficiency and level of the pipeline state of the underground pipe network, the adopted technical scheme is as follows: constructing a data acquisition and transmission network: constructing the data acquisition and transmission network by deploying multiple types of sensors along the pipeline, and obtaining and transmitting the real-time operation data of the pipeline; preprocessing data and constructing a model: preprocessing the pipeline operation data, and constructing a precise three-dimensional model of the pipeline by using a CIM (Common Information Model) technology; and intelligent analysis and prediction: in combination with big data analysis and data mining technologies, deep analysis and prediction of pipeline operation data are realized, and potential risks are identified and early warned.

The invention relates to the technical field of cable fault diagnosis and positioning, in particular to a cable fault positioning method and system based on multi-source data space-time correlation, and the method comprises the steps: obtaining partial discharge, traveling wave and temperature multi-source monitoring data from a high-voltage cable-overhead line hybrid power transmission network; on the basis of space-time correlation analysis of partial discharge and temperature data, heat-electricity associated defect points are calibrated; comparing traveling waves of the cable and the overhead line, and identifying and inhibiting cross-domain propagation interference; positioning the traveling wave data which are not identified to obtain temporary coordinates, judging fault characteristics by fusing information such as sheath ring current and the like, and comparing the temporary coordinates with defect points to generate matching identification; and inputting multi-dimensional information such as fault features, defect matching identification and temperature into the fusion model, calculating the confidence coefficient of a fault mode, and outputting a cross validation positioning result. According to the invention, high-reliability positioning of mixed line faults and active early warning of insulation defects are realized by carrying out space-time correlation and fusion decision on multi-source monitoring quantities such as traveling waves, partial discharge, circulation, temperature and the like.

The invention relates to the technical field of networks, in particular to a traffic transmission method and device, computer equipment and a readable storage medium. The method comprises the following steps: acquiring transmission time period information and transmission network information corresponding to flow to be transmitted in a communication network; wherein the communication network comprises a plurality of communication nodes, and according to transmission time period information, determining that each transmission sub-time period takes transmission network information as a constraint condition, and according to node information of each communication node when the communication network transmits the to-be-transmitted flow in each transmission sub-time period, determining a target transmission path corresponding to the to-be-transmitted flow; and transmitting the to-be-transmitted traffic based on the target transmission path.

The invention discloses a production enterprise-oriented pollutant degradation scheme recommendation method and system and a medium, and relates to the technical field of environmental protection, and the method comprises the steps: collecting multi-source pollution related data in real time in an enterprise production area, and uploading the data to a data processing center through an Internet of Things transmission network; a pollutant prediction model is constructed, pollutant generation prediction is carried out according to the preprocessed multi-source pollution related data, and the migration and diffusion trend of generated pollutants is analyzed; a pollutant generation prediction result and a pollutant migration and diffusion trend are used to construct a pollution portrait, a degradation technology is selected, and a degradation scheme is generated; and executing the degradation scheme to degrade pollutants, and acquiring operation parameters and degradation effect data of degradation equipment to perform equipment fault monitoring and degradation scheme optimization. According to the invention, through real-time monitoring, accurate data analysis and dynamic adjustment of a degradation scheme, and in combination with a novel composite degradation technology, continuous, efficient and stable treatment of pollutants is realized, and the influence of the pollutants on the environment is effectively reduced.

The invention discloses a backfill soil foundation compactness nondestructive testing method based on an intelligent sensor. The backfill soil foundation compactness nondestructive testing method comprises the following steps of S1, the intelligent sensor, S2, a GPS positioning system, S3, a wireless transmission network and S4, an information monitoring and collecting system. An on-site road roller is modified, an RTK-GPS receiver, a wireless communication antenna and an acceleration sensor are installed, accurate measurement of the position and the running speed of the rolling machine can be achieved through an automatic rolling system based on RTK-GPS and closed-loop feedback control, the acceleration sensor is fixed to a vibrating wheel, vertical acceleration signals of the vibrating wheel are collected in real time, and the vibration speed of the vibrating wheel is measured. A CMV value and a field compaction degree evaluation result are analyzed in real time through a background development system, then a correlation relation is established between a compaction degree value and the CMV value which are measured by using a standard compaction degree inspection method in a test section, and real-time compaction degree monitoring control can be carried out.

The invention provides a communication and interaction method of a digital twinning system in a compressor blisk machining process, and relates to the technical field of digital twinning and aeronautical manufacturing, and the method comprises the steps: collecting vibration, temperature and acoustic emission data in the compressor blisk machining process in real time, analyzing machine tool machining data, carrying out the alignment and noise reduction of the collected data, and obtaining a digital twinning result; then time-frequency domain mixed feature extraction is carried out, and time-frequency domain mixed feature data are optimized and uploaded in a graded mode; a deterministic transmission network is constructed, data transmission priorities of 5G communication and LoRa dual channels are dynamically adjusted according to a network load, and intelligent switching between 5G communication and a LoRa protocol is realized based on a switching decision model; analyzing normal processing data of the whole stage of the processing process, and establishing a processing data thread; and predicting the abnormal processing risk in the processing process, and updating the model parameters. According to the method, the communication and interaction efficiency of the digital twin system in the machining process of the compressor blisk in the aero-engine can be improved.

A device assembly having a syndicated data service interface that is communicatively coupled to a vendor-specific syndicated data application platform and having an installed vendor specific syndicated data application, with the interface receiving point-to-multipoint non-addressed transport bearer channels of different syndicated data channel transport networks initially only including the monitoring of the syndicated data channels and not decoding the syndicated data messages or syndicated data informational data contained therein, identifying from the transport bearer channels a received syndicated data channel having an syndicated data channel identifier of the specific syndicated data device, and reading the syndicated data messages within the received syndicated data channel and extracting the syndicated data supplier-independent informational data from the syndicated data message and providing the extracted syndicated data informational data to at least one of the installed vendor specific syndicated data applications of the syndicated data device.

The invention discloses a static IR Drop intelligent prediction system based on heterogeneous feature fusion, and aims to solve the problems that a traditional IR Drop analysis method is high in calculation complexity and an existing deep learning method cannot effectively capture global spatial features and unit instance topological information of a power transmission network (PDN). According to the framework, double branches of a convolutional neural network (CNN) and a graph neural network (GNN) are combined, and effective extraction and feature fusion compensation of PDN multi-scale global to local space power features are realized through a long-distance and local detail encoder (LLE) and a hierarchical and adjacent compensation group (HACG) module of the CNN branches; pDN topological features of heterogeneous neighbors of different orders are adaptively aggregated through a unit instance voltage aware (CVA) module of a GNN branch. Experimental results show that compared with an existing advanced method, the framework has remarkable advantages in prediction precision, and prediction errors are greatly reduced.

Methods, systems, and devices for fast beam selection via user equipment (UE) receive (Rx) beam selection reporting are described. A UE may transmit a beam selection report conveying a selected Rx beam, position, and orientation to a network entity. The network entity may build a database with the conveyed information. The network entity may “tag” each position for which a UE has transmitted a beam selection report and, if another UE gets close to a “tagged” position, the network entity may indicate the associated position, orientation, selected Rx beam, and a corresponding transmit (Tx) beam to that UE. The UE may use the information to select Tx and Rx beam search windows and window centers. Compared to other methods, the process described herein may result in a smaller search window, decreased complexity of the beam selection process, decreased power consumption, decreased latency, and more accurate tracking of UE dynamics.

The invention belongs to the technical field of power supply transmission, and relates to a POE-POF power supply transmission method and system, and a storage medium. The method comprises the following steps: collecting electric quantity data of POE-POF power supply transmission network nodes in real time, and extracting a dynamic power feature set; identifying an abnormal power loss event based on the feature set and extracting an abnormal mode feature; abnormal path nodes are positioned by combining a POE-POF power supply transmission network topological structure, voltage and current distribution parameters are optimized, and an optimized power distribution scheme is generated; dynamically adjusting voltage and current values of a power supply path, evaluating an energy consumption trend and generating a trend data set; and adaptively adjusting the electric quantity acquisition parameters according to the energy consumption trend, updating the acquisition configuration and circularly executing the steps. According to the invention, intelligent detection, accurate positioning and dynamic optimization of abnormal loss of the power grid are realized, the stability and energy efficiency of the power supply network are improved, and the method is suitable for fine management and energy-saving optimization of the intelligent power grid.