84 results about "Local congestion" patented technology

The invention discloses a digital content tracing system based on a block chain technology, and particularly relates to the technical field of block chains. The tampering risk of the digital content in the distributed storage process is evaluated by analyzing the multi-node data synchronization behavior; the transaction overstock risk judgment module evaluates the transaction overstock and local congestion hidden danger according to the transaction verification rate in the propagation process; and the reliability evaluation module judges the reliability of the digital content tracing chain in combination with the tampering risk and the local congestion hidden danger. The abnormal risk assessment module analyzes operation behavior abnormity through the intelligent contract log, and the conflict possibility assessment module detects consistency deviation of circulation records; and the potential risk assessment module synthesizes the multi-dimensional risk, generates a potential risk assessment result of the tracing chain, and determines whether to trigger an on-chain responsibility node tracing verification mechanism, so that potential hidden dangers in the tracing chain can be comprehensively identified, and an efficient and reliable solution is provided for block chain digital content tracing.

The invention provides a multi-node collaborative airspace control method and device for urban low-altitude traffic. The method comprises the following steps: acquiring sensing data of a real-time sensing system of an urban low-altitude aircraft body and an urban airspace infrastructure; assigning task scheduling sorting, and determining a local congestion coefficient and a path starting and ending point pair of a current aircraft assignment task; constructing a path cost function to optimize the initial spline path, and generating a path control point sequence and a path total cost; generating a final path of the aircraft during actual execution based on dynamic path adjustment of distributed cooperative control; and mapping the final path to the urban airspace, generating a complete scheduling sequence of the aircraft in the urban airspace, and sending the complete scheduling sequence to an aircraft control system for execution. Under the background that the urban low-altitude airspace is increasingly complex, cooperation among the aircrafts can be guaranteed, the utilization efficiency of airspace resources is improved, meanwhile, flight safety is guaranteed, and therefore a foundation is laid for intelligent development of a low-altitude traffic system.

The invention provides an optical communication flow dynamic wavelength allocation optimization method and system, computer equipment and a storage medium, flow monitoring modules are arranged at nodes of an optical communication network, flow data of different service types at the nodes are acquired, a central control unit acquires existing wavelength resource information in the optical communication network, and the optimal wavelength allocation of the optical communication flow is realized. Evaluating whether the current wavelength resource allocation is reasonable; the central control unit sends the generated dynamic wavelength allocation mechanism to each node of the optical communication network, and each node adjusts the existing wavelength allocation through the received mechanism; and continuing to collect data of different services at each node, transmitting the collected data to the central control unit for evaluation, and if the preset target is not reached, executing the above steps again. The method has the advantages of balancing the resource distribution of the whole network, avoiding local congestion, improving the overall resource utilization rate, remarkably reducing the problems of packet loss rate and delay exceeding caused by signal crosstalk, reducing the probability of fragment generation in the future and avoiding single-point congestion.

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 intelligent logistics, in particular to a canteen people flow intelligent scheduling and dish recommendation system, which comprises a people flow monitoring module used for collecting people flow data of each region of a canteen in real time; the dish database is used for storing dish components, tastes, nutrition data and historical sales volume; the central processing unit is used for dynamically analyzing the data and generating a scheduling strategy; the user terminal is used for receiving the recommendation information and displaying a navigation path; according to the invention, the multi-mode sensing network is used for monitoring the distribution of people flow in the canteen in real time, the dynamic queuing model and the collaborative filtering algorithm are combined, the optimal distribution of window resources and personalized dish recommendation are realized, the average waiting time is reduced, the dining order in the peak period is ensured through the digital twin technology and the emergency response mechanism, and the dining efficiency is improved. The operation efficiency and the service quality of the canteen are integrally improved, and meanwhile, the dining path selection of the user is optimized through AR navigation, and the problem of local congestion is relieved.

The invention discloses an intelligent logistics transport capacity digital reservation management method based on big data, relates to the technical field of logistics management, and provides an intelligent logistics transport capacity digital reservation management method based on a big data platform. Through real-time acquisition, cleaning and fusion of vehicle access data and statistics of the reservation proportion of each time period, the problems of data updating lag and serious information island in a traditional method are effectively solved; data collected in real time can reflect the dynamic state of vehicles in a park in time, a self-adaptive reservation distribution mechanism is adopted, historical vehicle data and real-time feedback are utilized, the operation duration of reserved vehicles and non-reserved vehicles is compared and analyzed, a traffic flow pressure index is introduced, and dynamic adjustment of a reservation window and a reservation proportion is achieved; by balancing the operation time of the two types of vehicles, the risk of local congestion caused by concentrated reservation vehicles in peak hours is obviously reduced.

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 invention provides a communication method and device based on a global load, a storage medium and electronic equipment. When the method is applied to a first leaf switch in the leaf-ridge topology network, the leaf-ridge topology network further comprises a plurality of ridge switches and a second leaf switch. In the method, congestion information of a plurality of first ports in a first leaf switch is acquired, and the plurality of first ports are used for being in communication connection with a plurality of ridge switches; receiving state information from each ridge switch; wherein the state information comprises congestion information of a second port in the corresponding ridge switch, and each ridge switch is in communication connection with the second leaf switch through the second port of the ridge switch; and determining an optimal sending port of the target information from the plurality of first ports according to the congestion information of each first port and the congestion information of the second port in the corresponding ridge switch. Therefore, when the sending port is selected, the load information in the global range is combined, and the communication quality problem caused by local congestion can be remarkably reduced.

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 discloses a charging station load charge allocation method based on integer programming. The method comprises the following steps: data acquisition: acquiring historical charging data of an electric vehicle user; establishing an integer programming optimization model; optimization solution: adopting a branch and bound method or a cutting plane method to solve the integer programming problem; charging strategy generation: outputting an optimal distribution scheme xij, and generating a charging station i most suitable for selection of the jth electric vehicle; and power grid-traffic collaborative optimization: in combination with real-time traffic data, a charging station recommendation strategy is adjusted, local congestion is avoided, and power grid load distribution is optimized. The method has the beneficial effects that the data value is deeply mined, multi-factor optimization is comprehensively considered, power grid and traffic collaborative optimization is realized, and the result robustness is enhanced.

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.

Local Congestion Mitigation re-routes traffic around a congested network segment without regard for end point destinations of the re-routed traffic. By inhibiting consideration of end point destinations, determinations of alternate routes are simplified relative to techniques that compute new end-to-end routes for traffic when a congested segment is encountered. These methods therefore remain relatively efficient as a network grows in size and network routes within the network become increasingly complex.

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 relates to the field of campus intelligent systems, and discloses a campus intelligent pick-up and drop-off management method, which comprises the following steps of: obtaining real-time observation data of a target vehicle in a parking lot; updating a belief state of the traffic state in the parking lot based on the real-time observation data and a preset system dynamic model, wherein the system dynamic model is coupled with a traffic flow model used for simulating vehicle microscopic behaviors; and based on the updated belief state, adopting a partially observable Markov decision process optimization algorithm to decide a target adjacent berthing position and a preliminary guide path for the target vehicle. According to the method, a partial observable Markov decision process and cellular automaton microscopic traffic flow simulation are fused, and belief state updating is carried out in combination with a particle filter, so that dynamic highly-intelligent prediction and decision of the parking lot are realized; and the problems that the adaptability is insufficient in a complex dynamic environment, and local congestion or uneven resource allocation is easily caused are effectively solved.

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.