67 results about "Local congestion" patented technology

The invention discloses a self-adaptive dynamic routing method and system for an NoC (Network on Chip), and relates to the technical field of routing management. Comprising the steps that the local congestion degree of each routing node is collected through a communication link, the global congestion degree is generated through a weighted average mechanism, each routing node integrates delay counters in the east direction, the west direction, the south direction and the north direction according to a local router, the delay counters are used for monitoring the time interval when adjacent data packets leave a buffer area in real time, and the global congestion degree is generated; taking a time interval of leaving a buffer area of adjacent data packets as output delay, spreading the output delay to neighbor routing nodes, calculating local congestion degree and global congestion degree of the routing nodes, generating all possible shortest paths and a candidate direction set corresponding to output directions according to addresses of a source routing node and a destination routing node, and sending the candidate direction set to the destination routing node; screening an optimal path through the local congestion degree and the global congestion degree in sequence; and when a plurality of equivalent paths exist, the routing direction is randomly selected to balance the network load.

The invention provides a building interior design layout optimization method, and relates to the technical field of indoor modeling and layout optimization, and the method comprises the steps: obtaining building structure boundary data and current situation layout information, constructing an indoor three-dimensional space model, and completing the marking of node passing attributes and a spatial topological structure. On the basis, crowd behavior data of a daily scene and an emergency scene are collected, behavior characteristics such as walking speed, obstacle avoidance response and congestion sensitivity are extracted, and a walking characteristic model used for simulating the individual motion trend is established. And performing multi-scene simulation under a daily event and an emergency evacuation event by using the walking feature model, calculating performance indexes such as passing time, local congestion degree, key node congestion probability and total evacuation time, and identifying a congestion sensitive area. And further constructing a multi-target evaluation model including traffic efficiency, congestion degree and evacuation performance, and performing global optimization on layout adjustable parameters by adopting an algorithm to obtain a candidate layout scheme set.

The invention relates to a road illegal parking real-time monitoring method based on space-time Transform and congestion context, and belongs to the field of intelligent traffic. The method comprises the steps of obtaining a high-precision track through millimeter wave radar radial speed correction, constructing a congestion context in combination with a lane level / local congestion factor and a cumulative static time clock, outputting a parking probability by EWC-XGBoost after spatio-temporal features are fused through a spatio-temporal Transform, dynamically adjusting a residence time threshold value based on DQN, and finally triggering early warning when a direction angle criterion is met. And meanwhile, a 60-second AES encryption track evidence chain is generated. According to the invention, the real-time monitoring capability with a low false alarm rate is realized through a slow motion false alarm resisting design, a full-scene self-adaptive threshold value, continuous online learning and a backtraceable evidence mechanism.

The invention discloses a real-time road condition-oriented AGV collaborative congestion avoidance path planning method, and relates to the technical field of AGV path planning, and the method comprises the steps: building a global road condition map based on AGV state data, and carrying out the dynamic updating of the global road condition map, and the dynamic updating comprises the weight updating based on an AGV density value and an average speed; based on the updated global road condition map, adopting a path planning algorithm to generate an AGV global optimization cooperative path set; the cooperative path set is converted into a control instruction, dynamic compensation is carried out on the control instruction through load sensing, and a corrected control instruction is obtained and issued to the AGV; according to the method, global road condition dynamic updating is combined with dynamic control of collaborative path optimization and load perception, multi-AGV global flow balance and real-time congestion prevention are achieved, and the problems that under the high load, path conflicts are frequent, local congestion cannot be actively adjusted, and the overall efficiency is reduced due to single AGV optimization are solved.

The invention provides an urban road network traffic congestion state prediction method and system based on deep learning, and relates to the technical field of intelligent traffic control, and the method comprises the steps: obtaining congestion state image data of an urban road network, and carrying out the preprocessing to extract structured road congestion information; constructing a road network topological graph based on the road congestion information, and performing structure sensing state coding on the road network topological graph to generate a structure sensing state vector fusing local congestion features and global topological position information; and inputting the structure perception state vector into an improved deep learning prediction model, performing feature deepening and enhancement through a graph neural network which dynamically adjusts a graph structure and aggregates node information, and finally outputting road network congestion state prediction results of a plurality of time scales in the future. According to the method, accurate prediction of the urban road network congestion state can be realized, and reliable technical support is provided for intelligent traffic management, resource scheduling optimization, travel path planning and the like.

The invention relates to the technical field of traffic flow control, and provides a multi-mode traffic flow control method and system, and the method comprises the steps: obtaining a traffic capacity parameter of a core traffic node based on the preset core traffic node; obtaining an instantaneous personnel entry rate of the core traffic node; calculating a personnel net accumulation rate according to the instantaneous personnel entry rate and the traffic capacity parameters; performing grading and shunting judgment based on the personnel net accumulation rate to obtain a grading judgment result; when the grading judgment result shows that the personnel net accumulation rate reaches a first preset threshold value, generating an information guide shunting instruction to guide the people stream to actively select an alternative traffic mode; and when the grading judgment result shows that the personnel net accumulation rate reaches a second preset threshold value, generating a physical isolation shunting instruction so as to drive a physical isolation device to change a people flow path. The method has the advantages that the cross-mode congestion transmission chain is accurately identified and cut off, and local congestion is prevented from spreading towards systematic paralysis.

The invention discloses a wind driven generator cluster power optimization distribution method based on congestion control, and belongs to the technical field of wind driven generator cluster control. According to the method, the power distribution problem of the wind driven generator cluster is solved by using a congestion control thought through the similarity between the power flow and the information flow, so that the optimization solution of the output of each wind driven generator in the wind driven generator cluster is realized. Firstly, a global congestion index reflecting the overall power tracking accuracy of a wind driven generator cluster is constructed, and power tracking of a wind driven generator cluster level is achieved; then, constructing a local congestion index reflecting the operation cost of each wind driven generator, and realizing power optimization distribution of a wind driven generator level, thereby reducing the overall operation cost of a wind driven generator cluster; finally, on the basis of the two congestion indexes, the output power of each wind driven generator is adjusted in a self-adaptive mode, and power optimal distribution is achieved. The method is simple in calculation, low in communication burden, plug-and-play and high in practicability.

The invention discloses a single-node failure recovery method for a remote air-sea cross-domain communication network. The method comprises the following steps of air-sea cross-domain communication network topology modeling, node failure detection and classification, link quality prediction and node failure recovery. According to the method, the recovery efficiency and stability of the air-sea cross-domain communication network after the single node fails are remarkably improved, the prediction average absolute percentage error (MAPE) of the GM-WOA model for the link quality is only 3.46%, which is superior to the traditional ARIMA (8.20%) and ARGM (14.33%), the method adapts to the dynamic change of the network, and under different network moving speeds (2-10m / s), the method has the advantages that the recovery efficiency and stability of the air-sea cross-domain communication network are greatly improved. Compared with a traditional method (non-prediction and one-hop neighbor prediction), the time consumed for recovering link reselection is reduced by 40%-60%, the real-time requirement is met, after the load balancing algorithm is combined, the link load variance of the network is recovered to 0 from 0.675, local congestion is avoided, and the overall transmission efficiency is improved.

The invention provides an unmanned aerial vehicle cluster networking secure communication method, which comprises the following steps: acquiring real-time position information of each node in an unmanned aerial vehicle cluster, and calculating a relative distance between the nodes in combination with a preset communication range threshold; based on the communicable node pair set, constructing a communication topological graph of the unmanned aerial vehicle cluster by adopting a network topology analysis method based on a graph theory, and identifying a possible local congestion area in the network; in a local congestion area, dynamically increasing the interval between the nodes by adjusting the flight path of part of the nodes, and reducing the influence of channel competition and multipath effect; if the node distance is greater than a preset stability threshold value, a relay node is introduced to optimize a communication link, and continuity and reliability of data transmission are ensured; a clustering analysis method in a machine learning algorithm is adopted to perform grouping management on unmanned aerial vehicle clusters, the global configuration of networking intervals is optimized, and the overall performance of the network is improved.

The application discloses a scenic spot tourist navigation method based on a heat map, relates to a scenic spot tourist navigation method, and provides multiple navigation routes for selection for tourists, and generates a heat map by predicting real-time positions of the tourists by using historical data; the predicted heat map is then corrected by a pre-trained convolutional neural network to ensure conformity with actual conditions; when the heat map shows that the crowd density of a certain area is about to exceed a threshold value, the system recommends a navigation route with high uniformity to new tourists to ensure balanced distribution of people flow in the scenic spot. The application solves the problem of possible local congestion of tourists in the scenic spot.

The present application relates to heavy load AGV cooperative control technical field, especially in a kind of heavy load AGV group's cooperative control method, the motion state parameter of each heavy load AGV is collected to construct motion state vector, local traffic congestion parameter is predicted, and then local congestion index is calculated and dynamic coordination trigger threshold is determined;Distributed speed coordination is triggered based on local congestion index and dynamic coordination trigger threshold, to determine the reference coordination speed of each heavy load AGV;The reference coordination speed is passed to relevant heavy load AGV, to generate transmission coordination speed;Based on ground state parameter, the motion state vector of each heavy load AGV and transmission coordination speed, the traffic optimization sorting of each heavy load AGV is determined, to modify transmission coordination speed and determine modified coordination speed;Based on the modified coordination speed of each heavy load AGV, the timing and number of subsequent local traffic congestion are predicted, to determine the effective duration of coordination control.The present application improves the running efficiency and safety of heavy load AGV group in complex traffic environment.

This invention provides a path planning method based on the DYPIBT algorithm, which can effectively solve the problem of path collisions between agents in narrow and busy terrain, reducing ineffective agent movements and chain congestion. It dynamically calculates priorities based on four dimensions: task urgency, remaining distance, local congestion coefficient, and unique ID, ensuring that each agent has a different priority at the same time step. For each location on the map, it performs predictive backtracking judgment, anticipating the next congestion risk before the agent moves and triggering backtracking in advance to reduce ineffective agent movements. To address the path conflict problem, it designs a high-priority priority rule and a priority inheritance rule. After a high-priority agent occupies the target location, a low-priority agent inherits the priority of the high-priority agent, giving agents that have already yielded the right-of-way an opportunity to gain priority in the next path selection, thus reducing ineffective movements for individual agents.

The invention relates to the technical field of circuit board production, and discloses a circuit board production tracking management method and device, equipment and a storage medium, and the method comprises the steps: obtaining the circuit board identity information and fault detection information of a current detection site corresponding to a target circuit board, executing the equipment shutdown processing based on the fault detection information, constructing a candidate recheck option set, and carrying out the detection of the target circuit board. And based on the real-time operation data of each candidate site and the equipment shutdown maintenance information, executing comprehensive cost evaluation to determine an optimal target recheck option, further driving the target detection site to execute recheck on the target circuit board according to the circuit board identity information, and updating the real-time operation data of each detection site at the same time. Therefore, a fusion mechanism that candidate station queue prediction, comprehensive cost evaluation and recheck and maintenance information jointly participate in equipment restart triggering is introduced, the perspectiveness of a recheck decision is improved, it can be avoided that local congestion is expanded into whole-line beat decline, and the production line recovery efficiency and the timeliness of quality control are improved.

The embodiment of the invention discloses a warehouse elevator scheduling method, computer equipment and a computer readable storage medium. The warehouse elevator scheduling method comprises the following steps: constructing a scheduling algorithm to process received task information so as to determine a reservable object; determining a reservation mode according to the task information, and obtaining a reservation form; processing the reservation form according to the reservation mode so as to determine a target object from the reservable objects; and generating and outputting a scheduling queue based on the target object. Therefore, the reservable object can be determined firstly through the scheduling algorithm, so that the workload of the multiple elevators in the warehouse is balanced, the load balance of the elevators is optimized, local congestion and resource idleness are avoided, and the overall scheduling efficiency is improved. The real-time state and resource occupation condition of each connection point can be dynamically reflected by using the reservation form, channel resources are reserved in advance in combination with a pre-locking mechanism, dynamic reservation scheduling and path cooperative control of the elevator are realized, and the response speed and stability of the system are remarkably improved.

The application discloses a kind of AGV cooperative path planning methods for real-time traffic, it is related to AGV path planning technical field, comprising: based on AGV state data constructs global traffic map, and global traffic map is dynamically updated, the dynamic updating includes the weight update based on AGV density value and average speed;Based on the global traffic map after updating, using path planning algorithm generates AGV global optimization cooperative path set;Cooperative path set is converted into control instruction, and control instruction is dynamically compensated by load sensing, and corrected control instruction is obtained and issued to AGV;The application utilizes global traffic dynamic updating to combine cooperative path optimization and the dynamic control of load sensing, realizes the global traffic balance of multiple AGVs and real-time congestion prevention, solves the problem that path conflict is frequent under high load, local congestion cannot be actively adjusted and the overall efficiency is reduced by single AGV optimization.

The application discloses a kind of based on the naming data network hop-by-hop congestion control method of forwarding path number, including router congestion detection and router congestion control;Router calculates the congestion degree of local router by means of active queue management method.Routers determine local congestion state and update the congestion information of data packet.Downstream adjacent router is based on the number of forwarding path and executes multipath forwarding strategy or interest packet sending rate adjustment strategy.When router executes multipath forwarding strategy, the router calculates the shunt proportion of interest packet flow of current interface according to the congestion information of data packet and forwards flow through current or relatively suboptimal interface.When router executes interest packet sending rate adjustment strategy, the router adjusts the interest packet sending rate of same name prefix according to the congestion information of data packet.The application proposes two router control strategies for the characteristics of multiple-source multipath transmission of named data network, to cope with single-path flow and multi-path flow in network.

The invention belongs to the technical field of building emergency evacuation path planning, and particularly provides an intelligent building-oriented emergency evacuation path dynamic planning method and related equipment thereof, and the method mainly comprises the steps: obtaining building real-time monitoring data, carrying out the preprocessing of the real-time monitoring data, and obtaining building security situation data; performing threat diffusion simulation and region division on the building security situation data to obtain safety region division data; and performing personnel cluster analysis and social force model prediction based on the building security situation data to obtain personnel evacuation behavior prediction data and evacuation bottleneck early warning data. According to the invention, accurate adaptation of the evacuation path with the on-site threat diffusion trend and the real-time change of personnel flow is realized, personnel are prevented from falling into an unavoided threat area, local congestion is reduced, and the overall evacuation time consumption is effectively shortened.

This invention belongs to the field of tourism exhibition hall crowd control technology, specifically involving a tourism digital simulation system and method. By real-time monitoring of visitor gathering status in various exhibition areas, combined with analysis of the impact of new visitors on crowd gathering and prediction of their distribution ratio in each area, a dynamic simulation model of future crowd gathering trends is constructed using tourism digital simulation technology. Based on the simulation results of this model, the system can provide exhibition area recommendations for new visitors, optimize visitor flow distribution in a timely manner, effectively alleviate local congestion, improve space utilization and visitor comfort, thereby achieving refined, intelligent, and forward-looking crowd management in the exhibition hall, enhancing operational efficiency and visitor experience quality.

The invention relates to an elevator collaborative scheduling method and system based on man-machine transport capacity balance, and the method comprises the steps: receiving and analyzing a robot task state packet, and determining the task urgency degree, the task timeliness and the key resource state; building passenger transport state data are collected in real time, and current passenger flow pressure data and elevator system state data are analyzed; carrying out transport capacity balance and scheduling cost decision making, and calculating comprehensive weighted cost of different scheduling strategies based on a robot task state packet and building passenger transport state data; and according to the strategy of the lowest comprehensive weighting cost, differentiated two-way instructions are output, and control instructions are sent to the robot terminal and the elevator control system. According to the scheme provided by the invention, the elevator transport capacity can be dynamically and elastically distributed between the requirements of people and robots, transport capacity waste or local congestion is avoided, the overall vertical transport efficiency of a building is improved, and the problem that the passenger transport service quality is reduced due to improper scheduling of the robots is relieved.

The invention discloses a traffic signal control method based on regional hierarchical multi-agent reinforcement learning, and belongs to the technical field of intelligent traffic systems. According to the method, a target traffic network is divided into a plurality of areas, each area is configured with a management layer agent, each intersection in the area is configured with a control layer agent, and a double-layer layered architecture is constructed; the management layer intelligent agent periodically generates a regional control target based on regional joint state observation, and the target remains unchanged in a plurality of bottom layer control periods; the control layer intelligent agent collects local state observation in each control period, receives the regional control target, dynamically corrects the target based on a local congestion state, and outputs a signal lamp phase control action; the control layer and the management layer respectively calculate level rewards, empirical data are stored in an empirical playback buffer area, and an offline strategy training mode is combined with a double-Q network, Huber loss, delay update and an action entropy regularization mechanism to collaboratively optimize two layers of strategy functions.

The application discloses an inner-cross type tire crane instruction scheduling method based on multi-dimensional dynamic priority and predictive scheduling, which comprises the following steps: step S1, candidate instruction set generation and hard constraint filtering; step S2, opportunity overtaking window evaluation; step S3, nonlinear dynamic sorting of regular instructions; and step S4, instruction issuing and rescheduling. The application quantifies and decides whether to execute the overtaking instruction which can bring higher net benefit through the opportunity overtaking window evaluation, so as to improve the work efficiency. The application introduces the instruction waiting time factor and the maximum tolerance threshold with exponential growth characteristics, so as to ensure that the priority of the instruction waiting for a long time can be systematically improved, and the scheduling fairness can be ensured. In addition, the local congestion factor is used to reduce the priority of the instruction in the congestion area, so as to relieve the traffic bottleneck.

The application discloses an unmanned park vehicle remote control scheduling method and system, and belongs to the field of route planning and remote control. By constructing a park space signal strength matrix and adopting an optimized A algorithm to fuse communication quality and vehicle density for path planning, a communication delay and a virtual distance penalty term are calculated according to node signal strength, and are used for an actual cost function; an impedance coefficient is calculated according to regional vehicle density, and is used for a heuristic cost function. Finally, an optimal scheduling path that avoids signal blind areas and balances road network load is generated, effectively reducing remote control delay risk, relieving local congestion, and improving unmanned vehicle operation safety and efficiency.

The invention relates to the technical field of traffic control, in particular to an arterial traffic green wave band dynamic adjustment method and system, and the method comprises the steps: constructing an arterial traffic data multi-source recognition network, and collecting and transmitting real-time arterial traffic data to a signal control center; constructing a single-intersection control scheme of each intersection on the trunk line based on the real-time trunk line traffic data; based on each single intersection control scheme, a trunk line coordination control scheme is constructed by taking a trunk line as an axis and taking districts as a network; according to the multi-source identification network, carrying out real-time judgment on emergencies, and according to the emergencies, constructing a predictive emergency scheduling scheme; according to multi-source real-time trunk traffic data, on the basis of actual traffic scene suitability, trunk green wave signals can be adaptively adjusted, intersection traffic flow changes are accurately matched, green wave control is effectively formed, temporary congestion scenes can be quickly responded through real-time emergency judgment and predictive scheduling, local congestion is prevented from being diffused to other intersections of a trunk, and the traffic safety of the trunk is improved. And the road passing efficiency is improved.

The embodiment of the invention provides a network slice routing method and device, equipment, a storage medium and a product. The method comprises the following steps: analyzing a received slicing request, and determining a target slice and resource demand information of the target slice; determining a target path and resource allocation information according to the resource demand information and the network state information; the target slice is subjected to routing transmission according to the resource allocation information and the target path, so that the differentiated requirements of the multi-granularity FlexE slice can be accurately responded, the management load of the multi-granularity slice is effectively balanced, and local congestion and resource idleness are avoided; meanwhile, paths and resource allocation information are dynamically determined based on real-time data, dynamic changes of service traffic can be flexibly adapted, deterministic time delay of slice end-to-end transmission is guaranteed, reasonable resource allocation and efficient routing of the multi-granularity FlexE slices are finally achieved, and the overall resource utilization rate, service quality and operation reliability of the network are improved.

The invention discloses an incomplete constraint robot formation-inclusion control method based on preset performance. The method comprises the following steps: selecting a navigator and a follower; constructing a communication network, and ensuring that each follower at least has an information interaction path with one navigator; constructing a nominal formation, so that the followers are uniformly distributed in a convex hull formed by the navigator; calculating the weight of each communication edge; the navigator adopts a distributed preset performance formation control algorithm, and a tracking error is constrained by introducing a monotone management boundary function; the follower adopts a preset performance including control algorithm, and position errors of the follower, the navigator and the neighbors are restrained within a preset performance boundary. By designing the nominal formation and the weight on each communication edge, on one hand, followers are uniformly distributed in a convex hull formed by a navigator, local congestion is prevented, and the collision risk is reduced, and on the other hand, the error can be always maintained in a preset range, and the formation deviation is reduced.

This invention relates to the field of educational resource management technology and discloses an information management method based on an academic affairs management system, including: collecting atomic logs to generate an academic affairs event sequence; comparing frequency distributions to calculate state deviation values; constructing a resource association logic graph and determining business pressure characteristics; calculating the conflict intensity coefficient of each node to identify bottleneck nodes; determining resource constraint release paths and updating node topology relationships based on logical dependency boundaries. This invention effectively avoids the risk of local congestion in static threshold judgment by establishing a mapping mechanism between business load and topology conflict, improves the allocation accuracy of teaching resources in a high-concurrency environment, and achieves stable self-healing of the business architecture by using closed-loop monitoring logic.

The invention relates to the technical field of smart tourism and intelligent scheduling, and discloses a command and scheduling system for standardized construction of a tourist attraction. The system comprises a data acquisition module, a data analysis and evaluation module, a command and dispatch decision and execution module and a visual display and information release module. According to the invention, through the full-process closed-loop design of multi-source data perception acquisition, single threshold, comprehensive evaluation, intelligent scheduling and visual display, real-time control and active risk prevention and control of the operation situation of the scenic spot are realized, so that the safety, order and tourist experience of the operation of the scenic spot are improved; based on the road passing cost and the multi-target path planning strategy, candidate paths with the shortest time, the shortest distance, the highest comfort degree and the like are generated, tourists can be effectively distributed, local congestion can be relieved, diversified travel choices can be provided for the tourists, and the tourism experience can be improved.

The application discloses a logistics optimization method and system based on an intelligent algorithm, relates to the technical field of logistics and supply chain management, and comprises the following steps: S1, acquiring dynamic traffic event information and real-time traffic flow distribution data of a logistics distribution network; the dynamic traffic event information comprises a traffic accident position, a road control range and a time constraint condition. The application reversely generates traffic diffusion model parameters based on the dynamic traffic event information, converts the chain effect of the event on the surrounding road network into quantifiable dynamic indexes, combines real-time traffic flow data and congestion intensity evaluation, provides multi-dimensional risk evaluation basis for distribution path planning, accurately identifies key road sections affected by the event, avoids high-risk road sections through dynamic fusion of diffusion parameters and weight evaluation, significantly improves the timeliness of distribution and the balance of global resource allocation of the logistics network, sensitively responds to road network resource fluctuations in a high-dynamic traffic scene, and reduces local congestion risks.

The application discloses a time slot scheduling system and method, wherein the system comprises a time slot management module and a weighted polling scheduler; the time slot management module is used for determining the data interaction path delay between a master device and a slave device based on the time sequence delay of a hardware unit in the data transmission path between the master device and the slave device; the data interaction path delay between the master device and each slave device is used for performing the arbitration of the weighted polling scheduling authority, generating a weighted polling request scheduling signal based on the arbitration result, and sending the weighted polling request scheduling signal to the weighted polling scheduler; the weighted polling scheduler is used for polling and selecting the slave device command queue participating in the weighted polling scheduling, and determining the arbitrated slave device command queue; the application can dynamically control the scheduling time slot of the slave device based on the write command length and the data interaction path delay between the master device and the slave device, and inhibit the local congestion caused by the continuous data flow injection into the on-chip interconnection network.