136 results about "Congestion prediction" patented technology

Provided are a method and an apparatus for constructing a road congestion prediction model, a device, a medium, and a product. A road traffic network is defined as a directed weighted graph. Historical dynamic traffic features of each road segment in the road traffic network are obtained as sample data, including recent dynamic traffic features and periodic dynamic traffic features. The sample data is input into a mixture of adaptive graph learners (MAGL) model for learning, and a probability prediction vector is output. The sample data is input into a trend expert model, and a trend distribution vector of a predicted probability of future traffic conditions is output. The periodic dynamic traffic features are fused to determine a periodicity prediction vector. An aggregated logit vector is obtained. An objective function is determined based on the aggregated logit vector. Congestion prediction training is performed to obtain a road congestion prediction model.

The invention discloses a road congestion prediction system and method based on spatial-temporal feature extraction, and belongs to the field of intelligent traffic. The system adopts a layered distributed architecture, and comprises a multi-source data acquisition module, a data preprocessing unit, a double-flow spatio-temporal feature extraction network, a two-stage spatio-temporal attention mechanism module, a congestion prediction model and a result feedback interface. Multi-modal data such as a vehicle-mounted GPS track, checkpoint flow, video monitoring and meteorological data are integrated, a double-flow feature extraction network is constructed by adopting a graph convolutional network and a bidirectional gating circulation unit, and a key space-time region is dynamically focused in combination with a multi-head self-attention and time weighted dot product attention mechanism; and finally, optimizing the generalization ability of the model through a composite loss function. According to the method, a dynamic adaptive learning framework and multi-source data combined modeling mode is adopted for urban road traffic flow characteristics, the space-time precision and the real-time response capability of road network congestion prediction are remarkably improved, and reliable decision support is provided for intelligent traffic control.

The invention discloses an L-shaped conveyor belt path dynamic planning and congestion prediction system, which relates to the technical field of automatic control, and constructs a conveying path node diagram by collecting material weight, speed and spacing information of each node on a conveying path, and calculates a congestion index of each path section; the method comprises the following steps: combining a light-mass material jumping sensitivity model, identifying a high-risk node and marking the high-risk node as a congestion prediction area, constructing a dynamic optimization model based on path risk distribution and a material flow direction, generating and evaluating a plurality of path adjustment strategies, executing an optimal strategy according to priority, and realizing path speed adjustment, standby path switching and feeding rhythm control; through a feedback mechanism, a strategy effect is evaluated in real time, a path state is updated, operation data is synchronized to a historical database, a learnable conveying operation file is formed, the stability and the intelligent response capability of a conveying system can be effectively improved, and the method is suitable for high-speed sorting and automatic packaging scenes.

The invention relates to the technical field of artificial intelligence, and discloses an internet television regulation and control method and device, a storage medium and electronic device.The method comprises the steps that network topology data, real-time performance parameters, user behavior data and environment data of nodes of an internet television are collected in real time through a space-time hypergraph convolutional network, a dynamic hypergraph model is constructed, and the network topology data of the nodes of the internet television, the real-time performance parameters of the nodes of the internet television and the environment data of the nodes of the internet television are obtained; extracting a spatial feature matrix and a time feature matrix of the nodes of the Internet television; performing feature fusion on the spatial feature matrix and the time feature matrix to generate a congestion probability prediction result of each node of the Internet television; and based on a congestion probability prediction result, combining historical flow data, generating a resource allocation optimal strategy by using multi-target Bayesian optimization, and training and updating a congestion prediction model by using a lightweight federated incremental learning framework. The Internet television regulation and control method provided by the invention is further improved in the aspects of network congestion prediction precision, real-time performance, energy consumption and the like.

The invention relates to an intelligent port cargo scheduling method based on the Internet of Things, and the method comprises the steps: collecting cargo state information, equipment position coordinates, operation progress data and environment parameters in real time through Internet of Things sensor nodes disposed on a quay crane, a container truck, a storage yard sling and a cargo carrier, and fusing a port GIS map and a berth plan based on the real-time collected data, and establishing a multi-objective optimization model with maximization of the quay crane-container truck-storage yard cooperative efficiency as an objective and minimization of equipment conflict avoidance and path overlap as constraint conditions, and dynamically activating or sleeping storage yard operation partitions and adjusting the number of container truck marshalling according to the obtained storage yard congestion prediction index and the parking space vacancy rate.

The invention provides a hybrid vehicle energy management system and method based on a traffic state, a storage medium and a computer program product, and the system comprises the steps: constructing a congestion prediction model based on a deep learning model, the congestion prediction is used for predicting the traffic flow and the driving speed of a future time period according to the historical traffic flow data and the historical driving speed data; calculating a first congestion index according to the predicted traffic flow in the future period; calculating a second congestion index according to the predicted driving vehicle speed sequence of the future time period; performing weighted calculation on the first congestion index and the second congestion index to obtain a comprehensive traffic congestion index in a future time period; traffic jam types are divided according to the interval where the comprehensive traffic jam index is located; and determining an energy management mode and / or a target SOC of the hybrid vehicle based on the traffic jam type. According to the invention, multi-source data are collected and analyzed in real time, congestion is accurately quantified by using a deep learning algorithm, an energy distribution strategy of vehicles is dynamically adjusted, and the energy utilization efficiency is improved.

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 specifically relates to a smart parking lot traffic management system based on traffic Internet of Things, and relates to the technical field of smart traffic and Internet of Things. A data processing and fusion module; a congestion prediction module; a prediction optimization module; and an induction information generation and release module. According to the invention, through multi-dimensional data acquisition, fusion and model optimization, accurate pre-judgment and dynamic adaptation of parking lot congestion are realized; the congestion prediction module constructs a multi-dimensional feature system of stagnation degree, unit average delay degree and occupancy degree, covers core inducements of vehicle retention, parking efficiency, resource mismatch and the like, combines historical data optimization factors and k value dynamic adjustment, and enables the prediction model to continuously adapt to changes of parking lot vehicle types, user habits and the like. Passive response of congestion treatment is upgraded to active prevention, invalid wandering of vehicles and lane congestion are reduced, the parking lot passing efficiency and the parking space turnover rate are remarkably improved, and the operation cost is reduced.

The invention discloses a traffic jam prediction management method and system based on artificial intelligence, and a medium, and relates to the technical field of intelligent traffic systems, and the method comprises the steps: collecting multi-source traffic data to construct a space-time traffic data set, carrying out the traffic state classification based on the space-time traffic data set, and generating a multistage jam probability distribution diagram; performing congestion prediction in combination with the multi-stage congestion probability distribution diagram and the real-time traffic event data, and determining a congestion evolution path; and carrying out traffic management according to the congestion evolution path, generating a dynamic dispersion instruction set, and sending the dynamic dispersion instruction set to traffic control equipment of the target road section to carry out traffic congestion management. The technical problems that a traditional traffic management means cannot adapt to complex and changeable traffic conditions, and accurate congestion prediction and efficient management are difficult to achieve are solved, accurate prediction of traffic congestion is achieved, a dynamic dispersion strategy is efficiently generated and executed according to the real-time traffic conditions, and the traffic congestion prediction efficiency is improved. Therefore, the traffic management efficiency and the road traffic capacity are improved.

The invention relates to the technical field of port logistics intelligent analysis, in particular to an inter-port congestion propagation inference method based on Granger causal relationship and reserve pool calculation, which comprises the following steps: constructing a multi-directed container ship transportation network according to AIS ship trajectory data based on an L-space modeling method; quantizing the congestion degree of the port by using the average waiting time of the port as a core index; calculating network features of the container ship transportation network; constructing an initial candidate port set; a machine learning prediction model based on the congestion propagation relation between the ports and reservoir calculation is constructed, and the congestion degree of each port in the next time step is predicted; designing a greedy iterative algorithm based on a Granger causality idea, and optimizing a congestion propagation relation inference result of each port based on a congestion prediction error; and constructing a congestion degree prediction model, and taking the congestion propagation relationship as input to realize prediction of the port congestion degree. According to the invention, congestion propagation between ports can be accurately deduced.

The invention discloses an urban traffic big data congestion prediction method based on deep learning, and the method comprises the following steps: collecting multi-source traffic data in an urban traffic operation process, and constructing a standardized space-time traffic data set; respectively inputting the prediction results into an improved Autoformer model and an improved GKAN model, and respectively generating a first prediction result and a second prediction result; calculating a numerical difference between the first prediction result and the second prediction result, and generating an inconsistency score; executing weighted fusion operation to generate a fusion prediction result; calculating an error value between the fusion prediction result and the observation data, and generating candidate prediction results according to the error value and a preset error threshold value; and comparing the disturbance score with a preset disturbance threshold value to generate a final prediction result. The method realizes joint modeling of model prediction consistency and traffic disturbance state, and has the technical advantages of high prediction precision, strong adaptability and excellent error control capability.

The invention discloses a storage robot cluster management system and method, and belongs to the technical field of robot scheduling. The system comprises a central management server and an autonomous mobile robot cluster. The central management server comprises a congestion prediction and global path planning module; the congestion prediction and global path planning module comprises a congestion prediction sub-module and an A * path planning sub-module with space-time cost; the autonomous mobile robot is internally provided with a multi-sensor sensing and communication module and a local decision module, and the off-line centralized training platform is used for optimizing the local decision module in each autonomous mobile robot through a centralized evaluation network with global information in a simulation environment. The invention provides a hybrid control architecture integrating global path planning, dynamic task allocation and local real-time decision based on multi-agent reinforcement learning, and efficient and smooth cluster collaborative operation can be realized.

The invention discloses a circulation processing method and system based on digital process congestion degree analysis, and relates to the technical field of digital process management. The method comprises the following steps: constructing a network model containing nodes and an association relationship, and representing a task flow path and a weight; real-time data such as to-be-processed task queues, equipment states and environment parameters are collected in a multi-source mode and subjected to cleaning standardization processing; based on static load, dynamic change, equipment reliability and environmental interference characteristics, predicting a future congestion index through an LSTM sequential network; setting a dynamic threshold by combining node importance and a real-time state, and matching a strategy mapping table to generate adjustment strategies such as task shunting and resource redistribution; feedback is monitored after execution, and model parameters are optimized. The system comprises a process modeling module, a data acquisition module, a congestion prediction module, a strategy generation module and an execution feedback module. Through full-process intelligent management, the congestion risk is avoided, and the improvement efficiency and the resource utilization rate are improved.

The invention relates to the technical field of computer network communication and intelligent control, and discloses a switch configuration method and system based on AI adaptive congestion control, and the method comprises the steps: collecting the multi-dimensional feature data of a network in real time through a data plane programmable pipeline of a switch, and transmitting the multi-dimensional feature data to an AI intelligent decision engine through an encryption channel; based on the multi-dimensional feature data, executing congestion prediction, strategy generation and interpretability analysis through an AI intelligent decision engine to obtain a congestion control regulation and control strategy; converting the congestion control regulation and control strategy into an executable switch configuration command, and issuing the executable switch configuration command to switch hardware to dynamically adjust congestion control parameters of the switch; and acquiring the network performance index after configuration execution, calculating the profit value of the congestion control strategy, and updating the model weight of the AI intelligent decision engine based on the profit value. According to the invention, the adaptability of the network to dynamic loads and complex scenes is effectively improved, delay and packet loss are reduced, and the operation and maintenance complexity is reduced.

The invention discloses a multi-level linkage ramp control method and related equipment based on multi-source data fusion, and the method comprises the steps: carrying out the clustering analysis of target multi-source traffic trajectory data, and obtaining travel trajectory feature data; on the basis of the travel track feature data, performing weight calibration on the on-bridge ramp of the expressway to obtain on-bridge ramp control priority data; performing short-time prediction on the current traffic flow data to obtain a short-time flow prediction result; performing congestion prediction according to the short-time flow prediction result and the dynamic bearing capacity of the expressway to obtain a congestion section prediction result; and according to the short-time flow prediction result, the congestion section prediction result and the on-bridge ramp management and control priority data, carrying out ramp control on the predicted on-bridge ramps of the expressway congestion section by adopting a layered management and control strategy. The method can achieve the precise dynamic sorting of the turn-off priorities of the ramps, balances the load of the road network, avoids the secondary congestion, improves the overall traffic efficiency of the road network, and can be widely applied to the technical field of traffic control.

The invention discloses an expressway congestion prediction method, device and equipment, which are used for more accurately predicting the congestion of an expressway. The method comprises the following steps: acquiring historical data of highway detection points, including license plate data and travel data of vehicles; determining the travel speed of each vehicle under the spatial granularity according to the historical data of the highway detection points; the space granularity is the distance between two adjacent detection points; according to the travel speed of each vehicle under the spatial granularity, determining a vehicle average speed change diagram under each spatial granularity in each adjacent equal-length time interval; in the vehicle average speed change diagram, based on a congestion speed threshold value, extracting a speed change characteristic segment before congestion; acquiring the real-time vehicle average speed of the target location of the expressway; generating a real-time vehicle average speed change diagram corresponding to adjacent equal-length time intervals; and matching the real-time vehicle average speed change graph with the speed change characteristic segment before congestion, and determining a congestion risk coefficient of the target location.

The invention discloses a hydraulic engineering management and control system and method based on digital twinning, and relates to the technical field of digital twinning, and the method comprises the steps: firstly collecting and preprocessing channel condition data in real time, then comprehensively processing the data to obtain a sediment deposition factor and a congestion prediction factor, and analyzing whether sediment deposition exists according to the sediment deposition factor and the congestion prediction factor; if yes, whether the channel is congested or not is predicted according to the latter, if congestion is predicted, a congestion early warning is given out, and if no deposition exists or congestion does not exist, the process is ended correspondingly or prediction continues. The method combines flow velocity and flow direction abnormal parameters to form sediment deposition factors, bypasses underwater direct detection bottlenecks by means of digital twin modeling and simulation algorithms, improves sediment deposition judgment accuracy through multi-dimensional analysis, constructs congestion prediction factors based on a standard and actual water flow difference value and the flow direction abnormal parameters, and performs threshold exceeding early warning. The blocking risk is recognized in advance, and time is won for hydraulic engineering maintenance regulation and control.

The invention provides a highway operation and service intelligent system, which comprises a road section platform, an area platform and a road network platform, and is characterized in that the road section platform is used for collecting and summarizing various sensing data of a corresponding highway section based on an ETC system to obtain system business data; road network situation historical statistics, road network situation real-time monitoring and road network situation future prediction are carried out according to the ETC data of the corresponding expressway section and the adjacent expressway road network; and long and short time traffic flow prediction, congestion prediction, holiday and festival traffic flow prediction and traffic flow prediction in a severe weather scene aiming at the corresponding expressway section are realized from multiple angles. According to the invention, the emergency disposal response speed and the intelligent degree of expressway operation and service can be improved, the timeliness rate, the accuracy rate and the correct rate of detection of various traffic and meteorological events can be improved, and the requirements of cooperative monitoring and linkage emergency disposal of low-time-delay traffic safety events are met.

The invention discloses a UWB-based market shopping guide system and method, and relates to the field of intelligent shopping guide, and the system comprises a positioning module, an acquisition fusion module, a model construction module, a shopping guide path generation module, an interference compensation construction module and a credibility evaluation module. According to the method, the problem of insufficient precision of WiFi and Bluetooth methods is effectively solved by combining a time difference arrival and arrival angle combined positioning mode of UWB. A five-level space mapping index is established, direct correspondence between a customer target and an actual position is achieved, and the commodity retrieval and navigation accuracy of the shopping guide system is improved. The machine learning model is used for modeling the residence time, the access frequency and the promotion interaction behaviors of the customers, and the preference distribution of the customers is dynamically calculated, so that personalized recommendation and interest area navigation are provided in the shopping guide process. A congestion prediction model and a customer behavior prediction mechanism are introduced in a path planning process, a high-flow area is dynamically avoided, an optimal shopping path is generated, and shopping guide efficiency and comfort are improved.

The invention is suitable for the technical field of traffic management, and provides an urban traffic dynamic command method and system based on digital twinning. According to the method, the high-quality data set is generated through direct multi-source data acquisition, restoration and integration; hierarchical dynamic storage is carried out, and block chain security management is carried out; carrying out stored multi-source data acquisition and processing, and constructing a real-time dynamic traffic twin model; based on the real-time dynamic traffic twinborn model, decision optimization of traffic control is carried out, evaluation feedback is carried out through edge calculation, and model parameters of the real-time dynamic traffic twinborn model are corrected; a three-level data storage system is adopted, a data management center is constructed, and data life cycle management is carried out. According to the method, the real-time dynamic traffic twin model can be constructed by fusing multi-source data, traffic flow simulation, congestion prediction and dynamic optimization are realized, a scientific basis is provided for smart city traffic management, traffic congestion is effectively relieved, and the urban traffic efficiency is improved.

The invention discloses a channel congestion prediction method and device, a model training method and device, equipment and a medium. The model training method comprises the following steps: constructing a first model and a second model; taking the training set as input to respectively obtain a first prediction set output by the first model and a second prediction set output by the second model; respectively constructing a first loss function and a second loss function based on the first prediction set, the second prediction set and the channel parameters of the to-be-predicted channel; and respectively training the first model and the second model by adopting the first loss function and the second loss function to obtain a convergent first model and a convergent second model. According to the method, the ship congestion degree is determined based on the prediction result of the first model and the prediction result of the second model, the prediction accuracy and stability can be effectively improved, and the method is fully suitable for channel congestion prediction in different scenes.

The invention discloses a channel scheduling method and a storage medium, and the method comprises the steps: building a network flow-discretization ship kinematics model based on channel parameters which comprise ship lock parameters and ship channel parameters; first real-time data of the ship arriving at the ship lock is obtained in real time, a queuing strategy is obtained, and the first real-time data comprises ship arrival time, departure port information and destination port information; generating a ship lock scheduling plan based on the network flow-discrete ship kinematics model and a queuing strategy; and if the ship leaves the ship lock, generating a ship lane scheduling plan based on the network flow-discretization ship kinematics model. According to the method, the channel topology is abstracted into the network flow model with capacity constraint, and discretization simulation is carried out on ship microcosmic motion in a superposition manner, so that multi-scale coupling of macroscopic scheduling and microcosmic behaviors can be realized, the channel congestion prediction error rate is reduced, and the scheduling efficiency is improved.

The invention discloses a road congestion prediction system and method based on spatial-temporal feature extraction, and belongs to the field of intelligent traffic. The system adopts a layered distributed architecture, and comprises a multi-source data acquisition module, a data preprocessing unit, a double-flow spatio-temporal feature extraction network, a two-stage spatio-temporal attention mechanism module, a congestion prediction model and a result feedback interface. Multi-modal data such as a vehicle-mounted GPS track, checkpoint flow, video monitoring and meteorological data are integrated, a double-flow feature extraction network is constructed by adopting a graph convolutional network and a bidirectional gating circulation unit, and a key space-time region is dynamically focused in combination with a multi-head self-attention and time weighted dot product attention mechanism; and finally, optimizing the generalization ability of the model through a composite loss function. According to the method, a dynamic adaptive learning framework and multi-source data combined modeling mode is adopted for urban road traffic flow characteristics, the space-time precision and the real-time response capability of road network congestion prediction are remarkably improved, and reliable decision support is provided for intelligent traffic control.

This invention belongs to the field of computer simulation of crowd evacuation, and proposes a crowd congestion prediction method and system based on a spatiotemporal fusion neural network. The method includes: acquiring scene information and crowd state information of crowd movement; constructing a knowledge graph based on the scene information and crowd state information, and preprocessing the knowledge graph; inputting the preprocessed knowledge graph into a graph neural network to obtain a spatial feature matrix; inputting the preprocessed knowledge graph, spatial feature matrix, and crowd state information into the spatiotemporal fusion neural network to predict the movement speed of crowds within a region, and predicting the crowd congestion situation within the region based on the crowd movement speed. The knowledge graph-based construction method of this invention can more comprehensively store heterogeneous information related to crowd movement. Furthermore, using a knowledge graph to represent scene information and store crowd movement state attributes provides relatively complete input information for the next step of the neural network to mine the spatiotemporal correlation of crowd movement.

The invention provides a multi-vehicle cooperative scheduling method and system for mobile robots, and relates to the technical field of intelligent storage scheduling, and the method comprises the steps: obtaining the real-time states and environment perception data of a plurality of mobile robots, carrying out the fusion processing, and combining with a pre-stored map to generate a global dynamic map; then identifying the key bottleneck region and acquiring feature data of the key bottleneck region, and performing three-dimensional modeling by using digital twinning to generate congestion prediction data; then dynamically distributing network bandwidth according to the congestion prediction data, and planning a conflict-free cooperative motion trajectory for each mobile robot based on the transmitted data to form an initial trajectory set; conflict detection and decoupling optimization are carried out on the set through a mixed integer programming model, and an optimized track set is generated; and finally, performing consensus verification on the set, generating a scheduling instruction block, analyzing a path instruction from the scheduling instruction block, and issuing the path instruction to each mobile robot for execution. According to the method, the cooperative operation efficiency and safety of the multiple mobile robots in a complex dynamic environment are improved.

A system is provided.SOLUTION: A system comprising: means for receiving visit destination information input by a user; means for acquiring past congestion data from a database based on the visit destination information; means for acquiring weather data of a current day from an external weather information service based on the visit destination information; means for performing congestion prediction based on the past congestion data and the weather data of the current day; and means for providing a result of the congestion prediction to the user.SELECTED DRAWING: Figure 1

The invention provides a port congestion prediction method and system, electronic equipment and a storage medium. The port congestion prediction method comprises the steps of obtaining a ship schedule and AIS data of each ship in the ship schedule; constructing a ship arrival number prediction curve according to the ship schedule and the AIS data of each ship; harbor information of the target harbor is obtained, and the harbor information comprises the ship carrying capacity processed by one berth in unit time, the average time needed by the ships to complete loading and unloading operation on the berth, the number of the ships processed by one berth in unit time and the number of the ships scheduled in unit time; constructing a processing capacity curve of the target port according to the port information; and predicting the congestion condition of the target port according to the ship arrival number prediction curve and the processing capacity curve. According to the method, the ship arrival number prediction curve and the port processing capacity curve are established respectively, then the relation between the two curves is compared, and the congestion condition is determined, so that the accuracy of the prediction result is improved.

The application discloses a 4G three-card single-waiting method and system applied to a monitoring camera, belongs to the technical field of SIM card communication, and specifically comprises the following steps: a multi-card virtual bearing layer is constructed; the multi-card virtual bearing layer is based on historical signal tracks of three SIM cards, environmental electromagnetic disturbance trends and base station congestion prediction results of a target area, link stability scores of the SIM cards are calculated in real time, a target SIM card currently used preferentially is automatically determined according to the scores, video streams collected by the monitoring camera are divided into continuous small segments, each segment is attached to error correction coding and card segment binding identification, and when it is detected that the link stability score of the target SIM card decreases to below a threshold value, the next video segment is switched to a standby SIM card with the highest score to be output; the application can maintain the continuity of video backhaul under complex electromagnetic environments, cross-region mobile scenes and base station congestion changes, and reduce the probability of interruption of monitoring pictures.

An energy operation support device for supporting energy operation performed by a plurality of consumers, said device comprising: a congestion prediction unit for predicting a distribution state of power in a power network; and a plan creation unit for creating, on the basis of the prediction by the congestion prediction unit, an energy operation plan including transaction plans of storable energy among the plurality of consumers, said storable energy being generated by power usage.

The invention relates to the technical field of storage intelligent sorting, and discloses an unmanned storage yard storage intelligent sorting method and system, and the method comprises the steps: obtaining storage yard data, collecting cargo data and scheduling data, reversely deducing a target path of a to-be-sorted task by taking a target sorting port of the to-be-sorted task as an end point, generating a blocking link according to the cargo data, and carrying out the blocking link; establishing a sorting sequence; acquiring historical data, and establishing and training a congestion prediction model according to the historical data; executing a sorting sequence, updating cargo data and scheduling data in real time, calling a congestion prediction model to predict congestion risks, and performing delayed release control according to the congestion risks until the sorting sequence is completed; and in the execution process of the sorting sequence, distributing a target cache bit for the blocking link and generating a transfer action, and writing the transfer action into a corresponding position in the sorting sequence. According to the method, the sorting throughput and the on-time completion rate can be improved on the whole, the invalid carrying and waiting time is shortened, and the operation stability of the unmanned storage yard is improved.