313 results about "Traffic load" patented technology

This disclosure provides systems and methods for provisioning resources in a wireless network. In some implementations, an apparatus obtains, from a first station (STA) on a first communication link associated with an access point (AP) multi-link device (MLD), a request for association all communication links of the AP MLD, and provisions either a single communication link or multiple communication links of the AP MLD for communications with the first STA based on at least one of a latency, a level of interference, or a traffic load associated with each of the communication links of the AP MLD. The apparatus generates a frame indicating whether the single communication link or the multiple communication links of the AP MLD are provisioned to the first STA for communications with the AP MLD, and outputs the frame for transmission to the first STA.

The invention relates to the technical field of energy scheduling, and discloses a smart city energy dynamic scheduling system and method based on big data analysis, and the method comprises the following steps: the operation state of a transformer substation, the basic parameters of a charging pile and regional load prediction data are collected in real time, data cleaning and abnormal value filtering are carried out; constructing a complete power grid-charging facility dynamic information base; calculating the power supply margin of each region based on the capacity loss of the faulty transformer substation, establishing a weight scoring system of charging pile power adjustment in combination with the charging demand urgency, and determining the reduction or recovery priority of each charging load; and generating a charging pile power adjustment instruction through a multi-target optimization model, and iteratively correcting a power distribution scheme and generating a final scheduling instruction set by taking minimization of user satisfaction loss as a target while meeting the power grid capacity. According to the invention, by constructing a dynamic response mechanism and a multi-target collaborative optimization model, accurate and rapid regulation and control of the traffic load are realized in the scene of sudden power shortage of the power grid.

The invention provides a cluster network flow prediction method based on multi-scale time feature fusion, and belongs to the technical field of computer network flow prediction. The method comprises the following steps: determining a multi-index prediction sequence based on traffic load characteristics of cluster IP instances, and constructing a high-quality time sequence data set; fourier transform and discrete wavelet transform are used for time-frequency feature analysis, and noise filtering and data dimension reduction are completed; projecting sequences of different time granularities to a unified model dimension, performing one-dimensional channel convolution merging, inputting the merged sequences into a time encoder and a cross-channel encoder, and capturing cross-scale long-term time dependence and a coupling relationship between variables; in the loss function design, time domain and frequency domain loss are fused, double-domain error calculation is carried out on a prediction result and a label through Fourier transform, and the robustness of a model to non-stationary fluctuation is enhanced; and through linear layer decoding and reverse normalization processing, the abstract feature is converted into an actual flow prediction value. According to the invention, the precision and reliability of cluster network flow prediction are significantly improved.

The present disclosure provides connection management techniques based on graph neural networks (GNN) and deep reinforcement learning (DRL) to optimize user association and load balancing. A graph structure of a communication network is considered for the GNN architecture and DRL is used to learn parameters of the GNN algorithm / model. Connection management is defined as a combinatorial graph optimization problem, and the DRL mechanism uses the underlying graph to learn weights of the GNN for an optimal user connections or associations. The connection management techniques can consider local network features to make better decisions to balance network traffic load while network throughput is also maximized. Implementations are provided based on edge computing frameworks include the Open RAN (O-RAN) architecture. Other embodiments may be described and / or claimed.

The invention discloses a server component durability test system, method and device, a medium and a product, and relates to the technical field of computer hardware testing and reliability engineering.The server component durability test system comprises an intelligent control center used for sending an environment parameter control instruction to a test environment construction module, a load time sequence graph is sent to a load simulation module, so that the test environment construction module generates a composite test environment based on the environment parameter control instruction, and the load simulation module applies a network flow load and a calculation task load based on the load time sequence graph; and the data monitoring and analysis module generates a degradation evaluation result of the target server component through the collected data, so that the intelligent control center adjusts a test strategy based on the degradation evaluation result. Visibly, by adopting the technical scheme, the cooperative scheduling precision of the network flow load and the calculation task load can be improved, the test strategy can be dynamically adjusted, and the accuracy of the durability test of the server component is effectively improved.

The invention discloses a cable-stayed bridge tension and compression support service life evaluation method and system under random traffic flow load, and belongs to the field of bridge engineering and structure health monitoring, and the method comprises the steps: carrying out the statistical analysis of traffic load data, building a random traffic flow load model, and generating a load spectrum; correcting stay cable force and modal frequency based on bridge monitoring data, and constructing a refined multi-scale finite element model; a unit fatigue vehicle load is applied to the model, a stress influence line is obtained, a rain flow counting method is adopted to calculate stress amplitude, cycle index and average stress, the stress amplitude, the cycle index and the average stress are converted into daily average equivalent stress amplitude and equivalent daily cycle index through a fatigue damage equivalence principle, and the fatigue life of the tension-compression support is evaluated by utilizing a Miner criterion. According to the method, the problem that the influence of three-way alternating stress on bolt fatigue damage cannot be accurately reflected in the prior art is solved, the service life prediction precision of the cable-stayed bridge tension and compression support is improved, the damage of the tension and compression support can be early warned in advance, and data support is provided for safe operation and maintenance of the cable-stayed bridge.

The invention discloses an asphalt pavement maintenance strategy intelligent generation method and system, and relates to the technical field of road engineering.The maintenance strategy generation method comprises the steps that traffic analysis data information and traffic load data information are extracted from a multi-source traffic monitoring system, and the traffic analysis data information and the traffic load data information are obtained through a laser scanning and image intelligent recognition algorithm; analyzing the pavement structural disease level in each grid unit; according to the road surface structural disease level, the road condition risk level of each grid unit under the traffic load and damage superimposed effect is analyzed, a risk response area is divided, and a service life loss analysis instruction is generated; analyzing an accelerated decline trend of pavement structure deterioration of each grid unit in the risk response area to determine a life reduction value of each grid unit in the risk response area; after comparison, generating a pavement maintenance strategy of a corresponding grade; the maintenance strategy generation method has the advantages of being high in real-time performance, fine in area recognition and dynamically adjustable in strategy response.

The invention relates to the technical field of communication networks, in particular to a dynamic resource allocation method and system for an industrial 5G network. The method comprises the following steps: acquiring industrial terminal data; identifying network congestion nodes based on industrial terminal data; detecting a link congestion fault according to the network congestion node, and determining a bandwidth utilization rate; allocating spectrum resources based on the bandwidth utilization rate; performing frequency spectrum slicing on the frequency spectrum resources, and determining a flow load amount; adjusting load balance according to the traffic load, and generating a load resource report; dynamically adjusting time slot allocation by using a load resource report, and determining a task priority; task scheduling simulation is executed according to the task priority, and if congestion occurs, a congestion snapshot is stored; reducing scheduling conflicts according to the congestion snapshots, releasing bandwidth resources, and determining available network slice capacity; power resources are dynamically adjusted based on available network slice capacity. The bandwidth utilization rate and the task scheduling efficiency of the industrial 5G network are improved based on the communication network technology.

The invention provides an intelligent prediction method and system for differential settlement of an expressway broadening roadbed, and relates to the technical field of expressway broadening. The method comprises the following steps: acquiring data at a joint of new and old pavements in real time, constructing a three-dimensional stress field model, identifying a weak area and generating a topological structure, mapping coordinates to filler particle size parameters by combining a pore water pressure gradient, dynamically distributing creep parameter weights, and generating a creep cloud picture. Extracting a strain rate characteristic value, establishing a traffic load-shear strength degradation incidence matrix, correcting a shear strength factor, constructing a settlement differential model, and predicting settlement distribution. And comparing the optimized parameters by using triple residual errors, when the similarity coefficient reaches the standard, outputting a settlement early-warning map, and dividing early-warning grades. According to the method, multi-source data are fused, the early warning coincidence degree is improved, and the settlement prediction precision and the prevention and control timeliness are improved.

The invention provides an edge server dynamic activation method and system based on deep reinforcement learning, and the method comprises the following steps: S1, building system models, including building a network model, an energy consumption model, a communication and service delay model and a state switching cost model, the network model including RES and MES; s2, optimizing a network model by calculating the sum of the cost of the energy consumption model, the communication and service delay model and the state switching cost model; s3, constructing a Markov decision process model, and improving RES stability; s4, predicting a traffic load; s5, calculating a baseline value; s6, training the network model through a centralized intelligent scheduling algorithm CDDS to obtain a trained strategy network model; s7, dividing the trained strategy network model through a federated distributed intelligent scheduling algorithm FDDS, and training the model based on federated learning to obtain a DDPG model; and S8, the DDPG model is deployed to each RES.

The invention discloses an anti-open circuit optimization method and device for an AI computing architecture and a medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: monitoring operation state parameters of each link in a current communication path in real time, and dynamically updating the operation state parameters to a congestion lookup table; based on the congestion lookup table, calculating a comprehensive abnormal index and a stability index of the current communication path to predict a link disconnection probability of the current communication path in a future time period; when the disconnection probability exceeds a preset threshold value, screening a minimum risk path in the network topology through a Hamiltonian path analysis method and replacing the minimum risk path; according to the task type of the AI calculation task, calculating a dynamic waterline threshold value of a plurality of links corresponding to the minimum risk path, and dynamically adjusting a flow load waterline threshold value of the plurality of links based on the dynamic waterline threshold value; and when a certain link is congested or disconnected, calculating an optimal flow recovery scheme by using a convex optimization algorithm, and executing recovery.

The invention provides a network-on-chip transmission information arbitration method and related equipment, and belongs to the technical field of computers, and the method comprises the following steps: determining a request to be sent by a target node in each input direction in a current round of arbitration period, and obtaining a target set; determining a weight value corresponding to each to-be-sent request in a target set according to a fault state of a node closest to a target node in a message transmission path of the target request, a space idle number of a flit size, a historical request number in the same transmission direction and a historical weight value; the historical weight value is a corresponding weight value when the target node sends each to-be-sent request in the last round of arbitration period; and sorting the to-be-sent requests in the target set according to a sequence of the weight values from high to low so as to respond to the sorted to-be-sent requests by using a crossbar switch on the target node. According to the method, the technical problem that the flow load of the network-on-chip is unbalanced in the related technology can be solved.

A server may obtain teacher artificial intelligence (AI) models from source base stations; obtain target traffic data from a target base station; obtain an integrated teacher prediction based on the target traffic data by integrating teacher prediction results of the teacher AI models based on teacher importance weights; obtain a student AI model that is trained to converge a student loss on the target traffic data; update the teacher importance weights to converge a teacher loss between a student prediction of the student AI model on the target traffic data, and the integrated teacher prediction of the teacher AI models on the target traffic data; update the student AI model based on the updated teacher importance weights being applied to the teacher prediction results of the teacher AI models; and predict a communication traffic load of the target base station using the updated student AI model.

The invention discloses a dynamic base station dormancy / wake-up method based on a flow load and a channel state, which improves the accuracy and reliability of dormancy and wake-up judgment by comprehensively considering multi-dimensional factors such as a service load, a wireless channel state, user distribution, adjacent region bearing capacity and the like. By designing a multi-level dormancy and wake-up mechanism and combining flexible management of different functional modules, reasonable balance between energy consumption reduction and service continuity is realized; by introducing a neighbor cell cooperation mechanism and a pre-wake-up strategy, the wake-up time delay is shortened while the coverage and switching stability are maintained. Meanwhile, key indexes such as energy consumption, access time delay, offline rate and switching success rate are continuously monitored in the operation process, and the feedback information is used for dynamically optimizing parameters such as criterion weight, threshold value and residence time. Through a closed-loop adjustment mechanism, the system can keep stable and effective operation in different scenes, and long-term balance between energy-saving control and service performance is realized.

The invention discloses a bridge structure monitoring method based on multi-source data and digital twinning and a related device, and relates to the technical field of bridge engineering.The method comprises the steps that an initial finite element model of a target bridge is constructed based on a design drawing and structural parameters of the target bridge, and the initial finite element model of the target bridge is constructed based on the initial finite element model and preset load conditions; calculating to obtain simulation mechanical response data of each grid node of the target bridge; based on the simulation mechanical response data and the obtained actually measured mechanical response data of each grid node of the target bridge, obtaining a corrected finite element model; inputting the obtained traffic load data passing through the target bridge into a digital twinborn calculation model constructed based on the corrected influence line data for numerical calculation to obtain an overall structure response cloud picture and a structure response-time curve of the key monitoring point, and fusing the overall structure response cloud picture and the structure response-time curve with the obtained three-dimensional visualization model to obtain a three-dimensional visualization model; and obtaining the structural state of the target bridge. According to the invention, accurate monitoring and real-time state evaluation of the bridge structure are realized.

The invention discloses a quantum key dynamic management method and system of a power wireless communication network. The method comprises the following steps: constructing a three-level service security model: dividing power services into high, middle and low three-level security levels, and differentially configuring key basic parameters; secondly, proposing a dynamic key updating algorithm, adjusting a key updating period and a quantum key window in real time based on a communication state and a flow load, and dynamically expanding the updating period and the size of the key window according to the change of throughput; and finally, setting an attack response strategy, automatically shortening an update period and enhancing key strength when an anomaly is detected, and improving the security of a power wireless communication scene. According to the quantum key dynamic management method disclosed by the invention, the problem of security risk caused by the fact that a traditional fixed quantum key update period cannot meet the communication state of a power wireless communication network and the real-time change of traffic load is solved.

A method for controlling congestion in intermittently-connected and lossy computer networks comprising: determining, at a local network node, a payoff score for each of a plurality of active flows of network traffic, wherein each active flow consists of a stream of in-transit packets at the local network node that come from a common source and share a common destination, wherein each active flow's payoff score is based on a pricing model that considers both a sojourn time and a position in a queue of each of an active flow's constituent packets; allocating unused buffer space across all active flows in the local network node based on relative traffic loads with a buffer-space allocation (BSA) agent; and controlling a rate at which packets from all active flows are received at the local network node with a hop-by-hop local-flow-control (LFC) agent according to each flow's payoff score.

The invention relates to the technical field of network communication, and discloses an internet protocol television (IPTV) server management and control method and system driven by an internet multicast protocol, which comprises the following steps of: firstly obtaining multicast data flow and analyzing protocol characteristics of the multicast data flow, identifying multicast nodes and then fragmenting the flow, constructing a flow on-demand framework, extracting flow load data in the framework and grading and marking; calculating a bandwidth allocation value to detect a traffic abnormal point, determining a congestion area corresponding to the abnormal point and marking a speed limit, calculating a conflict threshold after data is obtained through protocol analysis, identifying a redundancy parameter based on the threshold, optimizing the parameter in combination with a preset QoS standard, and finally making a multicast control strategy according to the protocol analysis data. According to the invention, the resource scheduling efficiency of the IPTV server can be improved.

The invention provides a bridge health monitoring and intelligent control method and system based on machine learning, and the method comprises the steps: carrying out the distributed sensing data collection of a bridge structure, generating a structure monitoring data set, carrying out the spatial-temporal feature extraction, and obtaining a vibration response feature sequence and a traffic load feature sequence of the bridge structure; performing correlation analysis on the two sequences, generating a structural damage sensitive feature matrix, performing damage evolution trend prediction based on the structural damage sensitive feature matrix, generating damage development probability distribution of the bridge key component, and generating a dynamic traffic control instruction according to the damage development probability distribution. The dynamic traffic control instruction comprises a vehicle passing parameter dynamically adjusted based on a damage degree quantitative index, and sending the dynamic traffic control instruction to a traffic management system. According to the invention, the accuracy of bridge health monitoring and the dynamic adaptability of control measures can be improved.

The invention provides a model training method and a steel bridge temperature field prediction method and device. The steel bridge temperature field prediction method comprises the steps of obtaining temperature, stress, environmental parameters and traffic load data of each target monitoring point of a to-be-predicted steel bridge; inputting the temperature, the stress, the environmental parameters and the traffic load parameters into a temperature field prediction model of the target service steel bridge structure; and outputting the temperature field prediction values of the to-be-predicted steel bridge at different monitoring points and different time. The service steel bridge structure temperature field prediction model is constructed based on a dynamic graph convolution and time convolution network, and comprises a dynamic graph convolution module, a feature fusion module LSTM and a time sequence feature extraction module. Through the dual-drive dynamic graph, mechanism constraint and bidirectional verification, the adaptability of the complex working condition is enhanced, the prediction precision of the steel bridge temperature field under the complex working condition can be improved, a reliable basis is provided for safety evaluation, and safe operation of the steel bridge is guaranteed.

The invention discloses a bridge health monitoring system which comprises a sensing layer, a transmission layer, a processing layer and an application layer which are in communication connection in sequence. The sensing layer synchronously collects structural mechanics, environmental influence and traffic load data through multi-source heterogeneous sensing nodes, and the sensing nodes adopt a solar energy and vibration energy double-source energy supply and low-power-consumption mechanism; the transmission layer adopts a'wireless private network + edge gateway 'mixed mode, and has data caching and breakpoint resuming functions; the processing layer analyzes data through a deep learning fusion algorithm based on an attention mechanism, and realizes dynamic early warning threshold adaptive adjustment in combination with a Bayesian reasoning model; and the application layer provides a visual interface, graded early warning and targeted maintenance decision suggestions. The system achieves the precise monitoring of the full life cycle of the bridge, improves the evaluation accuracy and system stability, reduces the operation and maintenance cost, provides scientific support for the safe operation and maintenance of the bridge, is suitable for the health monitoring scenes of various bridge types, and greatly improves the accuracy of the evaluation of the health state of the bridge.

The invention discloses a powder spraying pile composite roadbed deformation prediction method, and particularly relates to the field of roadbed settlement prediction, comprising the following steps: in an edge calculation node, respectively constructing an underground water level change event, a construction stage event, a soil consolidation stage event and a traffic load cycle event into independent event time chains; the event time chain is recorded by taking the occurrence moment of the event, the duration of the event and the interval duration between the events as a time step unit; and performing splitting operation on the time step units of each event time chain, and forming a time granularity matrix according to the duration difference between the time step units. A plurality of types of discrete events such as water level, construction, consolidation, traffic load and the like are uniformly disassembled into time step length units, and a time granularity matrix and a time dislocation vector are constructed, so that the rhythm difference and dislocation relationship of the events on different time scales can be quantitatively expressed; and solving the transition of the settlement stage by taking the result as the observation input of the variational Markov model.

The invention relates to a self-adaptive optimization decision-making method for preventive maintenance opportunity of a road surface, which comprises the following steps of: establishing a road surface performance prediction model based on multi-source data fusion based on historical road surface performance data, traffic load, climate environment and material structure characteristics; based on the performance data of the pavement before and after maintenance construction, extracting the instantaneous performance resilience value after maintenance completion and the performance attenuation rate after maintenance, and establishing a maintenance effect prediction model; constructing a double-layer optimization decision model taking the total cost minimization of the whole life cycle as an optimization target; the upper layer takes the maintenance opportunity threshold value as a decision variable to generate a corresponding maintenance demand; the lower layer solves the optimal maintenance schedule under the corresponding threshold value; in the lower-layer solving process, a maintenance effect prediction model is called to predict the maintenance effect; and an optimal maintenance opportunity threshold value is obtained through simulation optimization search. The method aims to realize closed-loop coupling of performance prediction, maintenance effect quantification and maintenance decision, and minimize the total cost of the whole life cycle on the premise of ensuring the pavement service level.

The invention provides a bridge deformation detection method based on three-dimensional laser scanning and a multi-scale algorithm, and the method specifically comprises the steps: carrying out the detection of the overall and local deformation of a bridge through employing the fusion of a three-dimensional laser scanning technology and the multi-scale algorithm, the method comprises the following steps: firstly, by introducing a geometric constraint registration algorithm, a cascade noise reduction model and a multi-stage slice center point offset analysis technology, realizing synchronous accurate quantification of local settlement of a bridge floor and overall inclination of a bridge pier; and in combination with load distribution verification, the spatial correlation between the deformation rule and the traffic load is revealed, and a scientific basis is provided for bridge safety assessment and reinforcement decision making. The bridge safety monitoring precision is remarkably improved, intelligent system development is supported, and guidance is provided for bridge maintenance.

Existing roaming protocols overlook certain factors such as direction and speed of mobile devices, client mix, and profiles during assisted roaming, leading to less relevant access point (AP) suggestions. Therefore, devices, systems, methods, and processes for motion-aware and load-balanced assisted roaming are described herein. Multiple mobile devices roaming in an area and having similar motion attributes are grouped. A roaming candidate list including potential APs is identified for the group. This list is further segmented into unique sub-lists, which are then transmitted to the mobile devices based on the motion attributes and connectivity needs. Furthermore, traffic load for each potential AP is predicted and traffic alert notifications are transmitted to the potential APs. The traffic alert notification indicates the predicted traffic load or mobile device types expected to connect to the potential AP. Thus, ensuring optimal network performance, efficient resource management, and enhanced roaming experience in dynamic environments.

A system transmits, by a network access cluster, data to an upstream network device via a plurality of uplinks. The network access cluster comprises a first and a second network device, which each communicate with the upstream network device via a first and second group of uplinks. The first and second network devices communicate with each other via a link. The system updates a first bandwidth associated with the first network device in response to detecting a failure in the first group of uplinks. Responsive to the first bandwidth being less than a second bandwidth associated with the second network device, the system sets a forwarding cost (e.g., to zero) of the link. Based on the zero-cost link, the system allows an additional path via the second network device for transmitting data received by the first network device to the upstream network device.

Existing roaming protocols overlook certain factors such as direction and speed of mobile devices, client mix, and profiles during assisted roaming, leading to less relevant access point (AP) suggestions. Therefore, devices, systems, methods, and processes for motion-aware and load-balanced assisted roaming are described herein. Multiple mobile devices roaming in an area and having similar motion attributes are grouped. A roaming candidate list including potential APs is identified for the group. This list is further segmented into unique sub-lists, which are then transmitted to the mobile devices based on the motion attributes and connectivity needs. Furthermore, traffic load for each potential AP is predicted and traffic alert notifications are transmitted to the potential APs. The traffic alert notification indicates the predicted traffic load or mobile device types expected to connect to the potential AP. Thus, ensuring optimal network performance, efficient resource management, and enhanced roaming experience in dynamic environments.

The invention discloses an intelligent bridge monitoring method and system based on Multi-Agent architecture, a medium and a processor, and relates to the technical field of intelligent bridge monitoring. According to the method, a bridge state and environment monitoring Agent collects multi-dimensional environment data (such as water flow velocity, temperature, air temperature and humidity) in real time, a traffic flow Agent counts traffic load data by using an image processing technology, a diagnosis interaction Agent carries out fusion analysis on the data, an environment influence coefficient gamma is calculated, and a real-time monitoring result is obtained. And predicting the residual life of the key component in combination with material fatigue life data. The system adopts a distributed task scheduling architecture, realizes multi-Agent efficient cooperation, and avoids decision conflicts. Through data fusion and an intelligent early warning mechanism, the problem of data islanding of a traditional monitoring system is solved, the real-time performance, the accuracy and the intelligent level of bridge monitoring are remarkably improved, and reliable support is provided for bridge safety management.

The invention discloses a data transmission method and apparatus, a computer device, a medium and a product. The method comprises the steps of obtaining a data processing task carrying service representation data; the service characterization data is sent to an expert model in the second graphic processing unit; based on N first queue pairs associated with the expert model, performing block division processing on the service representation data to obtain data division blocks of the service representation data; if a target first queue pair meeting the data sending condition exists in the N first queue pairs, sending the target first queue pair to a first network transmission unit, sending a target division block matched with the target first queue pair to a second graphic processing unit through a target first network port in the N first network ports by the first network transmission unit; the target first network port is determined by the first network transmission unit based on the communication link parameter of the target first queue pair. Through the method, flow load balancing of multiple network ports can be realized.

The invention provides a traffic load monitoring management method and system for an electric power communication network, and the method comprises the steps: carrying out the initial multilayer analysis of a collected traffic data package based on a DPI probe, and obtaining the initial analysis data of the traffic; performing windowing processing on the flow initial analysis data based on the time sliding windows to obtain a flow load index and a service load index corresponding to each time sliding window, and associating the flow load indexes with the service load indexes to form structured flow feature data; inputting the structured traffic feature data into an anomaly detection model to obtain an anomaly recognition result output by the anomaly detection model; carrying out secondary analysis on an original abnormal data packet extracted from the traffic data packet based on an abnormal identification result, associating with the traffic primary analysis data, and constructing an abnormal traffic traceability chain; and determining a load regulation and control strategy based on the abnormal traffic traceability chain in combination with a pre-constructed traffic load regulation and control decision library. The fault recovery efficiency of the power communication network is improved.