66 results about "Congestion management" patented technology

The invention discloses an urban tunnel intelligent safety operation and maintenance method and system combined with multiple data, particularly relates to the field of intelligent traffic operation and maintenance, and is used for solving the problems of prediction deviation and congestion management caused by tunnel flow fluctuation. In combination with traffic parameters of a multi-point detector, a wave similarity analysis model is utilized to accurately evaluate a propagation path and a target section of fluctuation; meanwhile, by means of a time sequence superposition correction mechanism, a historical fluctuation rule and real-time data are fused, a traffic flow transmission prediction curve after dynamic correction is generated, and balance between physical constraint and prediction precision is achieved; finally, key indexes are output through dynamic updating of the flow prediction model, efficient linkage of signal coordination and induction management and control modules is guided, congestion accumulation and traffic risks caused by fluctuation propagation can be reduced to the maximum extent, the tunnel operation and maintenance efficiency is optimized, and the traffic safety guarantee capacity is enhanced.

Multipathing for session-based remote direct memory access (SRDMA) may be used for congestion management. A given SRDMA session group may be associated with multiple SRDMA sessions, each having its own unique 5-tuple. A queue pair (QP) associated with the SRDMA session group may provide a packet for transmission using the SRDMA session group. The SRDMA session group may enable the packet to be transmitted using any of the associated SRDMA sessions. Congestion levels for each of the SRDMA sessions may be monitored and weighted. Therefore, when a packet is received, an SRDMA session may be selected based, at least, on the weight to enable routing of packets to reduce latency and improve overall system efficiency.

The invention discloses a congestion management system and method based on vehicle-road cooperation and dynamic group path optimization, and relates to the technical field of intelligent traffic systems. The method comprises the following steps: S1, collecting original traffic data through a multi-modal sensor array to carry out space-time alignment, and constructing a microscopic traffic flow data set; s2, bidirectional information interaction is carried out through a vehicle-road cooperative communication network; s3, calculating a multi-modal fusion confidence coefficient parameter based on the data quality index, and generating traffic state information and road section real-time saturation; s4, fusing historical and static road network data, constructing a dynamic digital twinborn model and a road network state feature map, and generating a road network load balancing index; and S5, making a decision by adopting a hierarchical-distributed architecture, and generating a group path induction strategy through multi-objective optimization. The efficient collaborative decision is realized through the graph convolutional network and the attention mechanism, and the road network traffic efficiency is comprehensively improved through stable and reliable multi-objective optimization on the premise of guaranteeing the user fairness.

Techniques for adaptively adjusting retry limits and modulation and coding scheme (MCS) thresholds are provided. A first network device receives a traffic flow from a second network device via a wireless network. The first network devices analyzes the traffic flow to identify a network service associated with the traffic flow. The first network device examines one or more predefined network service policies to determine one or more characteristics of the network service. The first network device detects a network congestion within the wireless network. Responsive to detecting the network congestion, the first network device switches to a congestion management mode, which further comprises that the first network device determines one or more network transmission settings for the traffic flow based on the one or more characteristics, and communicates the one or more network transmission settings to the second network device.

Examples described herein relate to a network element comprising an ingress pipeline and at least one queue from which to egress packets. The network element can receive a packet and generate a congestion notification packet at the ingress pipeline to a sender of the packet based on detection of congestion in a target queue that is to store the packet and before the packet is stored in a congested target queue. The network element can generate a congestion notification packet based on a queue depth of the target queue and likelihood the target queue is congested. The likelihood the queue is congested can be based on a probabilistic function including one or more of Proportional-Integral (PI) or Random Early Detection (RED). The network element can determine a pause time for the sender to pause sending particular packets based at least on a time for the target queue to drain to a target level.

The invention discloses an expressway congestion prediction method and system based on fusion of a cellular transmission model and a space-time convolutional network. According to the method, an expressway is divided into continuous cellular units, speed and density data of each cellular in multiple periods are collected, and a space-time input sequence is constructed. Local state correlation between cells is extracted through spatial convolution, and then a causal convolution network is used for modeling a time evolution trend of a traffic state, so that joint prediction of speed and density in a plurality of time steps in the future is realized. According to the invention, the spatial discretization idea of the cellular transmission model and the feature extraction capability of the convolutional neural network are combined, so that the provided method not only retains the interpretability of traffic flow physical evolution, but also has the capability of learning a nonlinear complex mode; the method is suitable for application requirements of traffic situation monitoring, intelligent scheduling, congestion management and the like in various expressway operation scenes.

The invention discloses a smart city and intelligent traffic collaborative management method based on artificial intelligence, and relates to the technical field of traffic collaborative management, and the method comprises the following steps: S1, data processing; s2, performing congestion assessment and road section division; s3, all-weather congestion management is carried out; step S4, peak period management and information optimization; and S5, carrying out comprehensive evaluation and optimization. The invention also discloses a smart city and intelligent traffic collaborative management system based on artificial intelligence. The system comprises a data acquisition unit, a historical database, a big data processing unit, a traffic light control module, an information display module, an early warning information sending module and a communication unit. According to the invention, abundant and accurate traffic data are acquired through multiple channels, and a wCGwNN improved algorithm is introduced into all-weather congestion road section management, so that traffic signal lamp intelligent agents cooperate with each other, and an optimal traffic management decision which comprehensively considers multiple factors is obtained.

Devices, systems, methods, and processes for fabric congestion management are described herein. At each ingress switch, virtual output (“VO”) queues are created for egress ports based on identifiers, state indicators, and encapsulation values of the egress ports received via an Ethernet Virtual Private Network (“EVPN”) control plane. When a data packet is received at the ingress switch, an egress port for the data packet is determined, an identifier and an encapsulation value of the egress port are added to the data packet, and the data packet is stored in a corresponding VO queue. The data packet remains at the ingress switch until an egress switch is available. At the egress switch, one or more tags are added in the data packet based on the encapsulation value, whereas the destination egress port is identified based on the identifier. Thus, a quick egress through the egress switch is achieved.

Devices, systems, methods, and processes for incast congestion management are described herein. Typically, in a Packet Sequence Number (PSN) based Remote Direct Memory Access (RDMA) network, Priority Flow Control (PFC) is asserted upstream when an incast congestion event occurs, which can victimize unrelated flows. Thus, instead of asserting PFC, a switch in the PSN based RDMA network detects an incast congestion event and directly notifies one or more Reliable Connection (RC) Queue Pairs (QPs) of various sending devices, associated with the incast congestion event using Receiver Not Ready (RNR) negative acknowledgements (NACKs). These RNR NACKs are associated with unique pause time-periods. The associated RC QPs receive the RNR NACKs and pause packet transmission. The associated RC QPs resume packet transmission upon expiration of corresponding pause time-periods. Thus, the packet transmission from the contributing RC QPs is spaced out, avoiding all packets reaching a switch output port at the same time.

The invention discloses an urban traffic flow prediction method fusing a graph attention network and a Transform, and belongs to the field of traffic flow prediction. The method comprises the following steps: (1) constructing an urban refined characteristic traffic flow high-quality data set based on actual license plate recognition (LPR) probe data; (2) building an efficient space-time traffic flow prediction model of a fusion graph attention network GAT and a Transform model; and (3) traffic flow prediction model training based on time-space fusion. According to the method, the traffic flow prediction model fusing the graph attention mechanism and the Transform is established, the space-time dependency relationship of the traffic flow in the urban complex road network is accurately described, the traffic flow prediction level under the urban complex road network is improved, and efficient data support is provided for intelligent traffic management, signal control optimization and urban congestion management.

According to one aspect of the present application, in connection of a mobile terminal device to a non-public network (NPN) of a 5GS, the mobile terminal device and the device within the core network do not make congestion management effective. Alternatively, in connection of a mobile terminal device to a non-public network (NPN) of a 5GS, the mobile terminal device and the device within the core network provide a NAS-level congestion management method to the mobile terminal device and the device within the core network in connection to the NPN (Non-public Network) of the 5GS by making congestion management effective only in a specific PLMN (Public land mobile network) of the congestion management.

The application relates to the technical field of data transmission, and discloses a congestion management method based on a novel network congestion announcement and rate adjustment strategy, which comprises the following steps: S1: constructing a flow statistical system for counting receiving rates and generating accurate congestion announcement data packets at a receiving end; S2: judging whether accurate congestion announcement data packets are generated for a specified queue pair according to statistical information of the system, and sending the accurate congestion announcement data packets to a specified sending end; and S3: constructing a rate adjustment system at the sending end, wherein the flow statistical system comprises a queue pair rate statistical module and an RDMA rate statistical module, and each RDMA network card port needs to contain an RDMA rate statistical module. In the application, the calculated statistical rate is carried in a 16-byte reserved field of a standard congestion announcement data packet, which is compatible with an existing RoCEv2 congestion management architecture, does not need an additional detection marking function provided by a data center switch, and is easy to deploy and expand.

According to one aspect of the present invention, a communication control method is provided for determining whether to continue congestion management in a destination PLMN when a PLMN change is performed during congestion management application, and the PLMN before the change is the home PLMN. Furthermore, if a PLMN change occurs while a backoff timer received along with a congestion management reason value is active, and the PLMN before the change is the home PLMN, whether to permit transmission of a UE's SM request message in the destination is determined based on the presence or absence of an information element notified by the network.

This invention discloses a concurrency control and traffic scheduling system for card data interaction, relating to the fields of Internet of Things communications and distributed data processing technology. By adding a semantic addressing intent field (PIF) to the IPv6 extension header, the system enables switches to complete parsing and direct traffic to SmartNICs within 16ns. The SmartNIC generates a time-slot micro-ledger with a 500µs cycle and pre-allocates transaction locks using the Edge-Lock Weaving algorithm. Backend nodes complete strongly consistent transaction submissions using a single round of RDMA using a fast two-phase commit protocol. Dynamic congestion management is achieved through a dual-layer token bucket combined with flight windows. This solution achieves nanosecond read and write speeds, sub-millisecond submissions, and highly available decentralized scalability, significantly improving latency, throughput, and fault tolerance.

A network interface controller (NIC) facilitating incast management at a computing system is provided. During operation, the NIC can receive, via a network, a request to send data from a remote computing system. The NIC can determine that the request is among a plurality of requests from a plurality of remote computing systems accessible via the network. Based on a descriptor in the request, the NIC can determine a storage location of the data at the remote computing system. The NIC can then determine a level of congestion associated with the plurality of requests at the computing system. The NIC can schedule a data retrieval in response to the request based on the level of congestion and with respect to the plurality of requests. The NIC can then retrieve the data from the storage location based on remote access.

The invention relates to the technical field of virtual power plants, and provides a virtual power plant-based congestion management and operation control method and system, and the method comprises the steps: obtaining the electric energy data of nodes and market participants in a power distribution network; determining a blocking management strategy when the blocking management cost is minimized according to the electric energy data through a blocking management optimization model; wherein the congestion management cost is defined as the sum of the total adjustment compensation cost for adjusting the available capacity of each market participant participating in the congestion management, the total output cost for enabling each reactive power compensation device participating in the congestion management of the power distribution network to output power for the congestion management, and the network loss cost of the power distribution network related to the congestion management; the congestion management strategy comprises a target regulation capacity of a market participant set and a target output of the reactive power compensation device; and executing a blocking management action according to the blocking management strategy. Therefore, congestion caused by the fact that the virtual power plant participates in energy transaction in the market is eliminated, and safety is guaranteed.

The described technology is generally directed towards a short message service (SMS) congestion manager that can evaluate, predict, and mitigate SMS congestion. The SMS congestion manager can be implemented within a short message services function (SMSF) of a fifth generation (5G) or subsequent generation cellular network. The SMS congestion manager can monitor a volume of non-access stratum (NAS) SMS messages in order to detect potential overload conditions wherein the volume of messages exceeds a capability of a network function. In response to detecting potential overload conditions, the SMS congestion manager can inhibit messages directed to the network function in order to prevent overloads from developing. The SMS congestion manager can use machine learning to learn to detect the potential overload conditions as well as to learn actions to take to address the potential overload conditions.

A substrate processing congestion management apparatus includes at least one processor configured to divide a process sequence of a substrate processing apparatus into specific sections and determine whether congestion occurs in the specific sections, to set an initial suspected region corresponding to a section in which the congestion occurs, analyze outliers in events occurring in the initial suspected region, deduce a final suspected region while updating the initial suspected region based on an event in which an outlier is identified, and determine, among the events, an event with a greatest contribution to the final suspected region as a congestion cause, and to notify the congestion cause and automatically control the substrate processing apparatus to reduce process congestion and improve substrate processing throughput, based on the determined congestion cause.

This invention relates to an optimal scheduling method and system for an electro-hydrogen coupling system based on truck flexibility and relaxation-repair warm-start technology. The method includes: acquiring basic data of the electro-hydrogen coupling system; constructing a power system model, a hydrogen energy system model, a pipeline congestion management model, and a hydrogen truck transportation model, wherein the pipeline congestion management model is a truck-based pipeline congestion management model that treats hydrogen trucks as flexible resources; establishing constraints and constructing an optimal scheduling model with the goal of minimizing the total system operating cost over the entire scheduling cycle; transforming the optimal scheduling model into a convex programming model using a mixed-integer second-order cone programming reconstruction technique; and solving the convex programming model using a relaxation-repair warm-start algorithm, considering the physical state information of the system, to obtain the optimal scheduling result. Compared with existing technologies, this invention has the advantages of actively alleviating pipeline physical congestion and high computational efficiency.

Multipathing for session-based remote direct memory access (SRDMA) may be used for congestion management. A given SRDMA session group may be associated with multiple SRDMA sessions, each having its own unique 5-tuple. A queue pair (QP) associated with the SRDMA session group may provide a packet for transmission using the SRDMA session group. The SRDMA session group may enable the packet to be transmitted using any of the associated SRDMA sessions. Congestion levels for each of the SRDMA sessions may be monitored and weighted. Therefore, when a packet is received, an SRDMA session may be selected based, at least, on the weight to enable routing of packets to reduce latency and improve overall system efficiency.

The invention relates to the technical field of intelligent path planning, in particular to an automatic guided vehicle path planning method based on swarm intelligence. The method comprises the following steps: acquiring position information of multiple carriers based on swarm intelligence, identifying candidate paths and drivable space channels, judging a low-speed stable state in combination with a driving speed and a space crowding degree, screening stable channels, distributing residence time, and determining a stable state; and integrating the channel superior degree and the residence time to realize dynamic path conversion and optimal scheduling. According to the invention, through introducing swarm intelligence, multi-dimensional dynamic perception, low-speed stable state detection, stable space channel screening and a path conversion and short-time obstacle avoidance mechanism of smooth constraint, collaborative optimization of multi-carrier path planning is realized; the overall transportation efficiency and the operation reliability of the system are remarkably improved in the aspects of congestion management, channel safety, path switching smoothness and multi-vehicle ordered passing.

The present disclosure relates to converging to enable congestion management. A network interface controller (NIC) that facilitates incast management at a computing system is provided. During operation, the NIC can receive a request to send data from a remote computing system via a network. The NIC can determine that the request is one of a plurality of requests from a plurality of remote computing systems that are accessible via the network. Based on a descriptor in the request, the NIC can determine a storage location of the data at the remote computing system. Then, the NIC can determine a degree of congestion at the computing system associated with the plurality of requests. The NIC can schedule data retrieval in response to the request based on the degree of congestion and with respect to the plurality of requests. Then, the NIC can retrieve the data from the storage location based on remote access.

This invention belongs to the field of airport congestion management and time slot resource allocation technology, specifically involving a method and system for assessing airport flight capacity based on congestion cost. This invention constructs an airport flight capacity assessment method based on congestion cost to achieve closed-loop linkage optimization of airport time slot capacity calculation and multi-airline demand response. Compared with existing technologies, this invention achieves rolling online updates of time slot cost signals and demand adjustments through interactive modeling of upper-layer time slot management agents and lower-layer airline response agents; it improves solution efficiency and scalability by learning multi-agent strategies in parallel under a centralized training and distributed execution framework; and it enhances the executability and stability of congestion management strategies through state interaction embedding, thereby improving airport congestion mitigation and time slot resource utilization efficiency. This provides feasible algorithmic support and application paths for real-time demand control during airport tactical operations, filling a technological gap in related fields.

Embodiments of the present application provide systems, apparatus and methods for predictive congestion management using signals from packet sources. According to a method, a network element predicts future traffic loads by receiving signals from multiple packet sources that indicate the size and timing of incoming data flows. By analysing these signals, the network element forecasts potential traffic surges. Before the predicted traffic arrives, the network element takes preventive actions to manage the data load. By addressing potential overloads in advance, the method may allow for smooth and efficient network operation, maintaining stability and preventing congestion.

The invention discloses a concurrency control and flow scheduling system for card data interaction, and relates to the technical field of Internet of Things communication and distributed data processing. According to the method, a semantic-addressing intention field (PIF) is added to an IPv6 extension header, and a switch can complete analysis within 16 ns and guide traffic to a SmartNIC; the method comprises the following steps of: generating a time slot micro account book by the SmartNIC in a period of 500 seconds, and pre-allocating a transaction lock by adopting an Edge-Lock Weving algorithm; the back-end node completes strong consistent transaction submission by means of one round of RDMA of a fast biphase submission protocol; and the double-layer token bucket is combined with a flight window to realize dynamic congestion management. According to the scheme, millisecond-level read-write, sub-millisecond-level submission and high-availability decentration extension are realized, and the delay, throughput and fault-tolerant performance is remarkably improved.

The invention discloses a power transmission congestion management partition pricing method and system considering dynamic topology recognition, and belongs to the field of power market operation and intelligent power grid control, and the method comprises the steps: obtaining real-time operation data and high-frequency synchronous measurement data, and recognizing a current operation mode fingerprint of a power grid through an unsupervised machine learning model; according to the method, the blocking section is analyzed based on the mode fingerprint, the evaluation result is generated in combination with the physical elastic margin, then the multi-target optimization algorithm with optimization of partition price distortion and partition internal physical elastic margin as targets is constructed, the dynamic partition set is solved and generated, and the optimal power flow is operated on the basis to calculate and generate the partition marginal electricity price. According to the method, a data-driven operation mode recognition and elastic safety coupling optimization mechanism is adopted, the market partition can be adaptively adjusted in real time, the economic value of physical limitation is accurately reflected, and the efficiency of power transmission congestion management and the robustness of power grid operation are improved.

The invention relates to the technical field of building digital design and construction management, and discloses a BIM-driven steel bar parametric modeling and blanking management and control system. The system comprises a data analysis module, a clustering fission module, a voxel construction module, a constraint solving module and an entity generation module, wherein the data analysis module is used for extracting BIM information to generate a steel bar path and establishing an inventory index; the clustering fission module generates a machining cluster based on homologous logic and fissions the end ultralimit steel bars; the voxel construction module establishes a three-dimensional voxel grid for maintaining a congestion state; the constraint solving module executes multi-dimensional constraint solving in a legal tolerance domain, and determines an optimal connection point meeting inventory matching and a space congestion target; and the entity generation module generates a three-dimensional entity, exports a processing instruction, updates a voxel grid state in real time, and reinjects excess material data into the inventory. According to the method, the voxel potential energy field and inventory coupling solution is adopted, and closed-loop management and control considering space congestion prevention and real-time excess material recycling are achieved.

The present invention discloses a port dispatching and anti-congestion management method based on an AI algorithm, which relates to the technical field of intelligent dispatching of logistics vehicles, and comprises the following steps: counting vehicles to be inspected at a port through intelligent sensors to obtain different vehicle types; analyzing the estimated inspection time of different vehicle types based on historical inspection data; predicting congestion events based on the estimated time and the number of vehicles to be inspected in the current port; and dispatching vehicles to be inspected at the port when a congestion event is predicted. The present invention is used to solve the problem that the existing port dispatching technology still has the problem that the dispatching method is not intelligent enough and the allocation of lanes is not intelligent enough, resulting in insufficient traffic efficiency at the port.

The invention relates to decoupling a congestion management state from a connection management state in high performance computing. A first network endpoint establishes a connection with a second network endpoint by sending a control packet including a first identifier associated with the connection and the first network endpoint. The first network endpoint stores a first connection state associated with the connection in a first data structure based on the first identifier, and stores a first congestion state associated with the connection in a second data structure based on the first connection state. The first network endpoint identifies, for a data stream associated with a first identifier, a congestion state corresponding to the data stream by obtaining a first connection state by searching a first data structure based on the first identifier; and identifying a first congestion state by searching a second data structure based on the obtained first connection state.

The invention discloses a reactance optimization credible capacity blocking relieving method, system and equipment and a medium, and the method comprises the steps: building a power grid impedance matrix and a power transfer distribution factor matrix, and defining a line fault distribution factor index to quantify a power flow transfer risk after a fault; a key line with unbalanced reactance and capacity distribution is accurately identified by using classification thresholds of credible capacity and line thermal capacity; based on a mixed integer linear programming model, line impedance distribution is optimized by dynamically adjusting fixed reactance, and the credible capacity of the line is improved; according to the method, through structural analysis and parameter collaborative optimization, in Monte Carlo simulation, feasible power generation plans are remarkably increased, the power generation disturbance variable is greatly reduced, the safety margin and economy of the power grid are remarkably improved, and the method is suitable for a modern power grid blocking management scene with high-proportion renewable energy access.