79 results about "Space Network" patented technology

The invention relates to the technical field of satellite-borne edge computing and simulation, and provides a satellite-borne edge computing task unloading semi-physical simulation system. Comprising a physical satellite-borne edge computing device, a software virtual satellite-borne edge computing device, a ground user device, a space environment simulator, a container and cloud management base, a resource data distribution module and a task unloading scheduling module. The physical satellite-borne edge computing device and the software virtual satellite-borne edge computing device jointly construct a cloud side end cooperative computing cluster, the ground user device is used for initiating a computing task request, the space environment simulator is used for simulating spatial network characteristics, and the container and the cloud management base are used for achieving all-node unified management and control. The resource data distribution module is used for issuing and subscribing node resources and environment information, and the task unloading scheduling module is used for executing a task unloading decision. The problems that in the prior art, on-orbit verification cost is high, a pure software simulation environment is distorted, algorithm credibility is insufficient, and a satellite-borne data set is scarce are solved.

A method for measuring disaster resilience of urban public services includes the following steps: constructing a residence-service-transportation space network under normal conditions; removing, through disaster simulation, failed road segments and function nodes to construct a damaged residence-service-transportation space network; calculating a per capita accessible public service of each residential node to represent network performance; calculating a change rate of the per capita accessible public service level before and after the disaster; and drawing a relation curve between the change rate and the disaster intensity to measure the disaster resilience of the urban public services.

The invention relates to a deep learning-based structure anti-seismic performance evaluation system, which comprises a data acquisition module, a data preprocessing module, a feature extraction module, a numerical simulation module, a deep learning model training module, an evaluation and verification module, a visualization module and a model updating and maintenance module, the data preprocessing module is used for data preprocessing, the feature extraction module is used for data feature extraction, the numerical simulation module is used for simulating seismic response of a structure, the deep learning model training module is used for constructing a deep learning model, the evaluation and verification module is used for model evaluation and verification, and the visualization module is used for evaluation result display. And the model updating and maintaining module is used for updating and maintaining the model. According to the invention, an improved seismic data feature fusion extraction algorithm is proposed for feature extraction, an improved seismic perception depth state space network is proposed for model construction, and a better scheme is provided for a deep learning-based structure seismic performance evaluation system.

The invention discloses a multi-connection management method suitable for a high-orbit and low-orbit fusion satellite communication terminal, and the method comprises the steps: building a communication connection with a high-orbit satellite, and obtaining the ephemeris information of a low-orbit satellite; a visible time window of the target low earth orbit satellite is predicted, and the composite link quality at the future moment is dynamically evaluated; performing look-ahead multi-connection state management based on the visible time window and the composite link quality; wherein the multi-connection state management comprises a preparation state of triggering antenna pre-pointing before a target low-orbit satellite is visible, and a transition state of triggering high-orbit and low-orbit double-link simultaneous transmission before low-orbit satellite coverage is finished. According to the method, the defect that a communication blind area exists in the link handover period of traditional passive response type switching is overcome, the cross-track link is firstly established and then broken, and the continuity and high reliability of service data transmission in a complex space network are effectively guaranteed.

The present application relates to a kind of based on heat-power collaborative perception's crystallizer sticking breakout intelligent prediction method, belong to continuous casting safety monitoring and intelligent manufacturing technical field.It includes: in the matrix arrangement MEMS thermal sensor array on crystallizer copper plate, copper plate local temperature and heat flux density are synchronously detected online;Online detection crystallizer friction force;Single time network model and group space network model are constructed, and thermal quantity dynamic characteristic and space propagation characteristic are extracted;Thermal quantity neural network model is constructed to the mode recognition of characteristic waveform;Friction force signal is decomposed by empirical mode, and trend quantity change characteristic is extracted;Friction force neural network prediction model is constructed, and abnormal state is identified by forecast deviation;Steel grade composition crack sensitive index and other auxiliary judgment index are calculated;Finally, through the weighted score and the multi-source information fusion decision maker of dynamic threshold correction output sticking breakout prediction grade.

The application discloses a congestion-aware routing method for a deep space network. In order to solve the problem of redundant inter-satellite forwarding and transmission efficiency reduction caused by the lack of remote link queue state awareness in a distributed long-delay deep space network, an exogenous mapping load element is maintained for each contact at each node. When the node perceives the loop risk caused by inconsistent queue states through the quasi-loop data packet, the delay increment interval of unknown remote traffic is solved by using the locally known routing information without explicit information exchange, and the exogenous mapping load is updated accordingly. Then, the load is mapped to the subsequent route estimation time cost calculation as an available capacity penalty term, so as to actively suppress the selection of contacts with potential congestion risk in the forward routing stage. The application can improve the remote traffic awareness capability of the long-delay distributed network, reduce the redundant inter-satellite relay, and improve the end-to-end transmission efficiency and reliability of the deep space network without increasing the signaling overhead.

This application discloses a training method, apparatus, device, and storage medium for a joint angle prediction model, relating to the field of computer technology. The method includes: acquiring training sample data, which includes skeletal parameters, sample wrist joint pose sequences, and arm angle labels for the sample wrist joint pose sequences; obtaining arm angle feature information based on the sample wrist joint pose sequences using a selective state-space network of the joint angle prediction model to be trained; obtaining elbow position prediction information based on the arm angle feature information and skeletal parameters using a differentiable kinematic layer of the joint angle prediction model to be trained; and iteratively updating and training the joint angle prediction model to be trained based on a preset objective function, the training sample data, the arm angle feature information, and the elbow position prediction information to obtain a trained joint angle prediction model.

The application discloses a space network networking method and device, a network resource arrangement platform and a storage medium. The method comprises the following steps: in the case that a UAV service accesses a network, the position of the UAV performing the UAV service is determined; the service score corresponding to the cell within the preset distance range from the position of the UAV is determined; the preset number of cells with the highest service score are determined as the space candidate working cells, and the number of adjustable beams of the space candidate working cells is determined; and the space-to-air beam and the ground-to-air beam of the space candidate working cells are determined and set according to the number of adjustable beams. The application solves the technical problems that the UAV space coverage scheme in the related art mostly needs to add equipment for transmission transformation, the cost is too high, the scheme of covering the space by directly using the existing site resources has too great influence on the ground coverage, and the beam resources for covering the space are wasted.

The application discloses a self-iteration-based deep subspace training method and device, which comprises the following steps: a pre-training step, in which a first optimization function is minimized to train a deep subspace network, so as to initialize a similarity matrix; a training step, in which when the iteration number t is less than a first iteration number, a self-expression coefficient matrix is fixed, the second iteration number is iterated, the deep subspace network parameters are updated by minimizing a second optimization function; the deep subspace network parameters are fixed, the third iteration number is iterated, the self-expression coefficient matrix is updated by minimizing a third optimization function, the similarity matrix generated in the tth iteration is updated according to the updated self-expression coefficient matrix, and finally the update of the similarity matrix in the first t iterations is realized; and the iteration number t is increased by 1, and the training step is repeated. The application introduces the idea of self-iteration, supervises the training of the model through the results of the previous training, and can discard the previous training results, so that the memory and the calculation resources are saved.

The embodiment of the invention provides a hypoxia risk spatio-temporal distribution prediction method and device, equipment and a medium, and belongs to the technical field of safety monitoring. The method comprises the following steps: collecting geographic coordinates, low-oxygen threshold values and real-time oxygen contents of all monitoring nodes in a region; calculating an edge generation index based on the node coordinates and a threshold value, and if the index is lower than a preset value, connecting the two nodes (regarded as similar oxygen change trends) to form a node network; the connected nodes are clustered into monitoring sets, and the number of low-oxygen nodes (the nodes with the oxygen content lower than the threshold mean value of all the nodes) in each set is counted; the oxygen growth rate of each node is calculated, and a hypoxia condition prediction index of the set is generated by combining the number of hypoxia nodes in the set, the total number of nodes, the average growth rate and the average oxygen content; and finally, plotting a set area of which the prediction index exceeds a threshold value on the map as a hypoxia risk area. According to the method, the low-oxygen high-risk area is identified and drawn based on the spatial network and set division and comprehensive indexes, and accurate prediction of the low-oxygen area is realized.

The invention discloses an autonomous positioning method and terminal equipment, and the method comprises the steps: obtaining a first observation position of a mobile body in a world coordinate system at a current moment, the first observation position being determined according to a positioning signal of a positioning sensor of the mobile body at the current moment; matching the first observation position with local road information in a road network map to obtain a target road point where the mobile subject is currently located, the target road point being a projection point of the first observation position in the road network map; calculating road network observation noise at the current moment by taking the position of the target road point as road network observation quantity according to the spatial network attribute information of the target road point; and according to the road network observation noise, determining an estimated position of the mobile body in the world coordinate system at the current moment so as to update the state of the mobile body according to the estimated position.

The invention discloses an unsupervised anomaly detection method for an aviation composite material hole wall, and relates to the technical field of aviation composite material processing detection, and the method comprises the steps: carrying out the fusion of public texture data and a gray-scale map of a hole wall through an anomaly mask to construct an anomaly training sample, and carrying out the manual annotation, in addition, the Mama state space model is introduced into the anomaly detection field to construct a visual state space network model of a Unet structure, and the difference between a prediction mask generated by the visual state space network model and an anomaly mask is evaluated to obtain a loss function which can be used for training the model; by means of the method, under the condition that defect data does not need to be marked, CFRP complex fiber textures and unknown defect types can be effectively handled, and the accuracy of detection is improved. And an efficient and accurate solution with a wide application prospect is provided for automatic detection of the inner wall defects of the aviation composite hole.

In one embodiment, a method is disclosed, comprising: receiving a packet; processing the packet in a Control Unit–User Plane (CU-UP) as a user-space application with kernel-bypass networking; performing packet Input / Output (I / O) by the CU-UP user-space application, wherein the CU-UP performs Internet Protocol (IP) validation on received user-plane packets before processing Packet Data Convergence Protocol (PDCP), Service Data Adaptation Protocol (SDAP) and General Packet Radio Service Tunnelling Protocol (GTPU) protocol stack and performing direct I / O to a Network Interface Controller (NIC) for sending the packet over a network to a Distributed Unit (DU). The method may further comprise using a containerized CU-UP. The method may further comprise using a plurality of worker threads. The method may further comprise using a polling user space networking accelerator framework.

The present application relates to the technical field of data transmission management, and more particularly to a data flow circulation method across data spaces, which determines a plurality of data flow circulation spaces by acquiring node parameters of adjacent device nodes on a data transmission path, determines data flow circulation characteristic time periods by determining a plurality of circulation state explicit nodes on traffic variation curves of different data flow circulation spaces, determines whether there is an invalid circulation phenomenon by performing continuity analysis on the data flow circulation characteristic time periods determined according to a first constraint condition, and determines whether to transmit data of the space network cache node to a target device node having an interface mapping relationship according to a second constraint condition by setting a space network cache node between adjacent device nodes. The present application performs associated recognition analysis on the data flow circulation states of adjacent nodes across data spaces, thereby avoiding waste of data flow circulation resources caused by redundant transmission, response delay and other problems.

The application discloses a UE dedicated dynamic resource pool communication method and system based on a dynamic security root. The method establishes a dedicated dynamic resource pool for each user equipment, and provides a logically independent resource index space. The network side and the user equipment calculate the same pool index at a logical time based on a sharing rule, and are mapped to physical resources and time through a predetermined method. When the user moves, the network side dynamically updates the mapping relationship, adds new coverage resources to the pool, and removes resources away from the pool without switching signaling. The dedicated pools of multiple UEs are allowed to overlap in physical resources, and conflicts are solved by two-step deterministic arbitration. The application realizes complete decoupling of the UE and the physical network, eliminates switching, requires zero signaling, and allows the UE to move without feeling, and can be widely applied to the fields of 6G, satellite internet, industrial internet and the like.

The invention relates to the technical field of unmanned aerial vehicles, in particular to a meshing method and system based on flight trajectory data of an unmanned aerial vehicle, and the method comprises the following steps: constructing a spatial network subdivision and coding system based on the operation characteristics of the unmanned aerial vehicle, and obtaining a target geographic region corresponding to a target airspace; dividing the target geographic area according to a target space grid level, and generating an initial space grid list corresponding to the target geographic area; generating a target space grid list corresponding to a target airspace according to the initial space grid list and historical flight path data of a target unmanned aerial vehicle; therefore, differentiated rasterization processing is carried out on the airspace according to the flight activity density of the unmanned aerial vehicle, and the problem that the unmanned aerial vehicle flight activity dense area and the unmanned aerial vehicle flight activity sparse area both adopt airspace grids of the same size, and great inconvenience is brought to unmanned aerial vehicle flight activity management under dense flight activities is solved.

Systems and techniques for optimizing utilization of hostlers of a hub based on a dual-stream resource optimization (DSRO). In embodiments, a dual-stream optimization model is utilized, incorporating a consolidated time-space network and a deconsolidated time-space network spanning a planning horizon. The model identifies hostler operations and pairs them based on proximity criteria, forming operation pairs that lead to higher unit throughput within the hub. An optimized hostler operation schedule is generated for implementation during the planning horizon. The system also includes a feature for automatically transmitting a control signal to a hostler control system to direct a hostler to execute the paired operations in accordance with the pairing.

The invention relates to the technical field of space network communication and satellite intelligent management, in particular to an LEO satellite network intelligent routing system and method based on intention recognition. Comprising a natural language instruction input module, a text preprocessing module, a BERT semantic modeling module for RoPE position coding enhancement, a bidirectional LSTM context enhancement module, an intention recognition module, an entity recognition module, a structured result generation module and an interface interaction module. According to the method, a path request instruction in a natural language form is mapped into a structured scheduling parameter, the problems that a parameter configuration process in a traditional satellite scheduling system depends on manpower, the response timeliness is poor and the analysis capability is weak are solved, meanwhile, rotation position coding is introduced to improve the sequence structure sensing capability, and local context features are captured in combination with BiLSTM, so that the real-time performance of the satellite scheduling system is improved. Through intention recognition and entity recognition dual-task joint modeling, high-precision output of a structured request analysis result is realized, and key support is provided for satellite network intelligent scheduling.

The invention discloses a cloud platform system based on edge computing and an edge computing implementation method. The system comprises an edge computing node, a multi-source heterogeneous fusion gateway connected with the edge computing node, an intelligent scheduling module connected with the multi-source heterogeneous fusion gateway, and a central cloud server. The edge computing node unifies the file format and the file coding mode of the computing data of the terminal equipment, so that the forwarding efficiency is improved; the multi-source heterogeneous fusion gateway pre-processes the preliminarily pre-processed data again, so that the storage space and the network bandwidth occupied by the preliminarily pre-processed data are reduced; and the intelligent scheduling module determines execution equipment of the calculation task according to the standard data type and the processing requirement related to the calculation task included in the scheduling instruction, the resource occupation condition of the edge calculation node and the central cloud server, and the network condition of the edge calculation node and the central cloud server. And the occupation condition of the storage space, the complex condition of the scheduling task and the occupation condition of the network bandwidth are reduced.

The embodiment of the invention discloses a COE interconnection simulation method and system based on a DPDK and a full user mode protocol stack and a medium. Constructing a user state simulation environment comprising a DPDK driver and a full-user state network protocol stack on at least two network interconnected nodes; encapsulating a memory access request of the CXL protocol into frame data conforming to a predetermined format, and adding network layer and transport layer protocol headers through a full-user-state network protocol stack to form an Ethernet data frame; the Ethernet data frame is submitted to a physical network card for sending through a DPDK interface; receiving an Ethernet data frame from a physical network card through a DPDK interface, stripping protocol heads of a network layer and a transport layer by a full-user-state network protocol stack, and de-encapsulating to obtain CXL protocol data; and performing response processing according to the decapsulated CXL protocol data, generating confirmation information when needed, and sending the confirmation information to an opposite-end node through a full-user-state network protocol stack and a DPDK interface.

The invention relates to the technical field of building ventilation and environment control, and provides an indoor environment quality optimization method and system based on a space network pressure gradient. The internal space of a house is divided into a clean space, a pollution source space and a transition space, a ventilation network is constructed, and a mechanical exhaust device is arranged in the pollution source space; outdoor fresh air is guided to sequentially flow through the cleaning space, the transition space and the pollution source space, and after indoor pollutants are collected, the outdoor fresh air is finally discharged out of the pollution source space; carbon dioxide concentration data are collected through a monitoring unit arranged on an airflow path key interface, the stability of the pressure gradient is judged according to the concentration gradient among the cleaning space, the transition space and the pollution source space, and the exhaust air rate is automatically adjusted to maintain directional airflow. The fluid network theory is introduced for quantitative analysis and design, so that the system performance can be predicted and optimized, and blindness and uncertainty of a traditional ventilation mode are eliminated.

The invention discloses a high-speed processing and forwarding simulation method for a spatial network, and belongs to the technical field of network simulation. The implementation scheme comprises the following steps: constructing spatial network simulation nodes for a to-be-simulated spatial network scene, configuring and initializing a vector data packet processing component for each simulation node, and realizing scale deployment of the simulation nodes; according to link communication requirements and scale deployment requirements, vector data packet processing components are configured in simulation nodes at the two ends of a simulation link to construct the simulation link; constructing a simulation link attribute control mechanism, and performing dynamic configuration of simulation link attributes in each time slot; simulation link switching is carried out based on a smooth switching mechanism in cooperation with a vector data packet processing component; capturing and analyzing target data in a simulation process by adopting a simulation data automatic acquisition method; according to the invention, while a space network simulation function is realized, the requirements of high-speed processing and forwarding of a communication link in a high throughput scene are met, and a more accurate network simulation effect is realized.

The application discloses a method for enhancing position monitoring of a closed space PNT network and a high-precision map, and relates to the field of wireless positioning. The method comprises the following steps: establishing a semantic unit grid of a target closed space; establishing a mapping relationship database of semantic information of any one entity object element and position information of the semantic unit where the entity object element is located; acquiring a grid code of a semantic unit where a target entity object is located; and obtaining position semantic information of the target entity object based on the grid code of the semantic unit and the mapping relationship database. According to the technical scheme, the grid code of the semantic unit where the user is located is calculated only once, the semantic information is obtained by reading the mapping relationship database index based on the grid code of the semantic unit, and the calculation amount and response waiting time of the user position monitoring information are greatly reduced. Furthermore, the rationality of the user position monitoring information is improved, and the calculation amount of the generated user position density monitoring information is reduced.

Embodiments of the present application provide a real-time population distribution-based emergency shelter availability assessment method and system. The method comprises: obtaining real-time population spatial distribution data of a city; establishing an emergency shelter simulation space network based on city roads; calculating the real-time per capita emergency shelter area of each regular grid at a specific time without considering the disaster scenario; establishing a city emergency shelter simulation space network under different intensity disaster scenarios; recalculating the real-time per capita emergency shelter area of each regular grid under different intensity disaster scenarios; determining whether the real-time per capita emergency shelter area of each regular grid at a specific time under a specific disaster intensity reaches the planning set target, and performing spatial visualization expression. In this way, the real-time availability level of the emergency shelter can be accurately determined based on the population spatio-temporal distribution characteristics, the weak links and vulnerable areas can be quickly identified, and technical support can be provided for planning compilation and monitoring and early warning.

The invention provides a dynamic load network attack detection method based on adaptive Kalman filtering. The method comprises the following steps: step 1, establishing a power network space network attack detection model; 2, designing an adaptive Kalman filter; and step 3, carrying out attack detection. According to the technical scheme, a forgetting factor is added on the basis of traditional Kalman filtering, and an efficient detection algorithm is designed for the concealment and complexity of D-LAA attacks.

According to the sealing component with the three-dimensional network structure surface coating, the surface coating comprises the base body and the surface coating, the surface coating is of the three-dimensional network structure, the three-dimensional space network structure of the coating is independent of the base body, the coating is combined with the base body through the thermal spraying and surface coating technology, and therefore the surface coating is formed. The coating is prepared by a method of alternating additive processing and patterning processing. The sealing component has the advantages that the coating can obtain a three-dimensional network structure at the magnitude of several millimeters, so that the performance of a friction pair formed by the sealing component is greatly improved, and the surface of the sealing component has more beneficial tribological performance.

Embodiments of the present application disclose a kind of power transmission line three-dimensional deformation monitoring method and system, equipment and storage medium, method includes: using the preliminary elevation estimate of permanent scatterer point on power transmission line and in the plane coordinate of multi-temporal synthetic aperture radar image, construct the three-dimensional space network of permanent scatterer point;For each arc segment of three-dimensional space network, establish fine phase model, and calculate the coherence coefficient of the observation value of each arc segment differential interferometric phase and the fitting value of differential interferometric phase under different model parameter combinations, to generate the coherence coefficient solution space of each arc segment;Using the preset adaptive threshold detection method, according to the local background statistical characteristics of coherence coefficient in coherence coefficient solution space, dynamic decision threshold is determined to identify high-quality deformation arc segment;According to the model parameters corresponding to high-quality deformation arc segment, the three-dimensional deformation of power transmission line is determined.A kind of high-precision, strong robustness improved time series interferometric synthetic aperture radar deformation monitoring method is realized.

The invention relates to the field of wireless communication networks, and provides a self-adaptive sum-difference channel phase compensation method based on double-flow feature fusion, which comprises the following steps: receiving a non-cooperative target satellite signal, constructing a spatial network pre-training data set in combination with antenna beam pointing, and pre-training a spatial network model to output a static phase difference compensation predicted value; constructing a time network pre-training data set based on the historical phase deviation cached by the register and the temperature difference value of the environment sensor, and pre-training a time network model to output a phase difference change trend predicted value; and carrying out feature fusion on the static phase difference compensation predicted value and the phase difference change trend predicted value, carrying out training through a fusion network model, outputting a final phase compensation value of a sum-difference channel, and driving a digital phase shifter to carry out real-time phase correction. Through heterogeneous feature separation of a spatial stream and a time stream, the phase compensation precision and the environmental robustness are greatly improved, and particularly, accurate output of sum-difference channel phase compensation is ensured in a complex dynamic scene.

This application relates to a high-resolution reconstruction method for photovoltaic power generation data. The method includes: collecting photovoltaic data at a resolution lower than a first preset level and time-aligned weather data; performing frequency and time domain modeling on the photovoltaic data and weather data using Fourier transform to obtain input frequency domain features; selectively enhancing the input frequency domain features using learnable frequency domain channel attention to obtain a frequency-modulated sequence; and modeling the frequency-modulated sequence using a Mamba state-space network to obtain reconstructed frequency domain features; restoring the reconstructed frequency domain features to a time domain output through inverse frequency domain transform; and fusing the time domain output with the original time domain features to generate photovoltaic data and weather features at a resolution higher than a second preset level based on the fused features. This solves the problems in related technologies, such as the lack of an effective multimodal feature fusion mechanism, which makes it difficult to deeply integrate key external information such as meteorological data with the power sequence, and the bias in frequency domain modeling, tending to learn low-frequency trends while ignoring high-frequency details, leading to rapid fluctuation distortion in the reconstructed signal.