57 results about "Port logistics" patented technology

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 provides a port logistics intelligent simulation and decision optimization platform based on a digital twin technology, and the platform comprises a basic layer which provides calculation, storage, network and basic software support; the resource layer integrates data resources, model resources and algorithm resources and is used for managing port multi-source data, business process models and intelligent algorithms; the digital twinborn layer is used for constructing and maintaining a high-fidelity digital twinborn body synchronous with a physical port and realizing virtual-real mapping, dynamic data fusion and model self-evolution; the simulation layer is used for deducing based on the digital twin and comprises a simulation engine and an event driving and data interaction module; and the application layer is used for providing a user interface, analyzing decision and optimizing functions and supporting scheme simulation, evaluation and closed-loop optimization. According to the method, the high-fidelity digital twinborn body of the port can be constructed, real-time mapping, accurate simulation, prospective prediction and intelligent decision making of the whole process of port logistics are achieved, and a technical closed loop of data acquisition, digital twinborn, intelligent decision making and virtual-real linkage is formed.

The invention discloses a port horizontal transportation tool dynamic intensive scheduling decision method, and relates to the technical field of port logistics transportation management, and the method comprises the steps: firstly collecting port transportation related data in real time, then determining the weight based on the port transportation related data, calculating the priority coefficient of each transportation task, sorting the coefficients from large to small to form a task sequence, and carrying out the scheduling of the task sequence; according to the vehicle state data and the task sequence, constructing a multi-objective optimization function which aims at minimizing the total task completion time, minimizing the total transportation cost and maximizing the vehicle average load rate, and solving and generating an optimal scheduling scheme by adopting an improved genetic algorithm; the task priority is determined, so that emergency and important tasks can be preferentially processed, and the task execution reasonability is improved; a multi-objective optimization function can overall plan efficiency, cost and resource utilization, the problem that traditional scheduling only focuses on a single objective is solved, and the port green and efficient development requirements are met.

The invention discloses a multi-modal port logistics adaptive scheduling system and method. The system comprises a data acquisition module, a semantic alignment module, a situation analysis module, a decision generation module, a multi-objective optimization module, an instruction generation module and an execution monitoring module. Multi-modal data collected by the data collection module generates a unified representation through the semantic alignment module, the situation analysis module calculates KPI and detects abnormity based on the unified representation, the decision generation module generates a scheduling decision according to situation information, the multi-target optimization module optimizes the decision to obtain the scheduling decision, the instruction generation module converts an optimization scheme into an executable instruction, and the instruction generation module sends the executable instruction to the data collection module. And feedback of the monitoring module is executed and returned to the data acquisition layer to update the data source, and meanwhile, a historical corpus is updated for continuous learning of the decision model. According to the method, self-adaptive scheduling of port operation is realized by constructing a port space-time semantic model, fusing multi-source heterogeneous data, performing real-time situation analysis and performing intelligent decision generation.

The invention relates to a port micro-grid energy control strategy considering incomplete information, and the strategy is technically characterized in that the time-of-use electricity price of a power grid, the parameters of a micro-grid operator and the like are set, and a data source is determined; scene sampling is carried out, and uncertain information in micro-grid operation is converted into a computable deterministic scene; establishing a Bayesian-Stackelberg game model under incomplete information by taking a power transaction agent as a main body and a micro-grid operator as a slave body; a follower model constraint is established by considering port berth distribution, and a KKT condition is established for a follower model so that the double-layer game model can be converted into a single-layer optimization model; and solving the model by using a CPLEX solver to obtain an optimal electricity price and an energy control strategy of each scene. According to the method, the uncertainty of port micro-grid information is considered, port operation constraints, berth distribution strategies and the like are comprehensively considered, the economy of micro-grid operation is considered while the port logistics transportation efficiency is ensured, and a port micro-grid energy control strategy considering incomplete information is provided.

The invention discloses a port-adjacent logistics park planning and design method and system, and belongs to the technical field of port logistics planning and engineering informationization, and the method comprises the steps: generating a plurality of sets of port-adjacent logistics park parameterized plane models, and outputting m sets of park planning and design schemes and corresponding unit parameter data sets; calculating the annual workload and the average stockpiling day number of different types of goods, and outputting a goods quantity statistical result; calculating sub-item cost and engineering cost of each plane unit, and calculating other cost and reserve cost of engineering construction so as to obtain and output total investment of the engineering; calculating the sub-item incomes of the storage unit and the storage yard unit, and correcting the sub-item incomes according to the comprehensive influence factors to obtain the expected income of each set of planning and design scheme; and determining the planning design scheme with the minimum cost-to-benefit ratio as an optimal scheme. According to the method, layout generation, cargo volume prediction, cost calculation and income comparison and selection can be completed at the same time in the planning stage, and the problems that traditional planning design is low in efficiency and lacks economical linkage analysis are effectively solved.

The invention discloses a general-purpose general cargo wharf typical operation scene stockpiling strategy optimization method, and relates to the technical field of port logistics management, and the method comprises the steps: generating a storage yard global state true phase source data pool through multi-source data perception and fusion processing; carrying out storage yard state entropy calculation and chaos degree quantitative evaluation based on the storage yard global state true phase source data pool to obtain global and partitioned real-time entropy monitoring reports; according to global and partitioned real-time entropy monitoring reports, carrying out adaptive switching decision making to obtain a strategy switching instruction and a new strategy parameter; and according to the strategy switching instruction and the new strategy parameter, carrying out dynamic reconstruction and elastic resource allocation on the virtual stockpiling container to obtain a latest virtual stockpiling container distribution diagram. According to the method, collaborative stockpiling plan generation and operation instruction issuing are performed based on the newest distribution diagram to obtain a plan diagram and an instruction list, decision rules are optimized and updated through closed-loop feedback and entropy learning after execution and monitoring, and self-adaptive optimization of a stockpiling strategy is realized.

The invention discloses a port logistics carbon emission monitoring system based on the Internet of Things and a computer, and the system comprises a multi-modal data collection and transmission module which carries out the full-amount collection of the multi-dimensional data of equipment, sensors and environment in a port area through a hierarchical heterogeneous communication network; the edge intelligent processing and resource scheduling module is deployed at the local of a port, performs priority dynamic allocation and real-time processing on the acquired data, and uploads aggregated data; the anomaly detection and carbon flow modeling module is used for carrying out anomaly detection based on a space-time diagram neural network and constructing an operation chain carbon flow model integrated with a port production operation system; the digital twinning driven monitoring network optimization module is used for constructing and operating a communication-environment digital twinning model and dynamically optimizing the layout of the monitoring network; the accounting decision and intelligent optimization module is used for performing carbon footprint tracing based on a task-level accounting unit and generating a low-carbon optimization strategy; and the visualization and user interaction module is used for providing a visualization display and decision support interface of the carbon emission data.

The invention relates to the field of railway freight automation, and provides an unmanned gantry crane collaborative scheduling method for an intelligent land and port logistics hub, which comprises the following steps: collecting multi-source data of an unmanned gantry crane; constructing a dynamic priority scoring model to determine a to-be-executed optimal task instruction; according to the continuous time sequence state vector of each unmanned gantry crane, generating a prediction trajectory in a future prediction time length, discretizing the prediction trajectory into a multi-dimensional space-time position point sequence, and performing overlap detection on any two unmanned gantry cranes in a future time period to generate a future potential space-time conflict event; and constructing a multi-target cost function to carry out cost value calculation to generate corresponding avoidance control parameters, generating a temporarily exclusive virtual operation corridor, and issuing a control instruction. By introducing artificial intelligence-based multi-source data deep fusion, space-time conflict prediction and dynamic virtual operation corridor control methods, the unmanned gantry crane realizes high-precision perception, high-reliability scheduling and multi-machine cooperative operation in a complex railway operation scene of an intelligent land port logistics hub.

The invention relates to the technical field of port logistics optimization, in particular to an outer container truck arrival operation time planning service and optimization method based on flexible reservation, which comprises the following steps: constructing a demand response service mode based on an outer container truck voluntary participation principle, and dividing outer container trucks into vehicles accepting service and vehicles not accepting service; submitting data information by the vehicles accepting the service, predicting information of the vehicles not accepting the service in combination with historical data, integrating multi-source dynamic data, and constructing a dynamic decision database; the demand response service mode is used for generating a personalized arrival time suggestion for each served vehicle on the basis of the dynamic decision database; and taking the minimum total turnover time of all the external container trucks at the port as a target function, establishing a mathematical optimization model, and realizing collaborative optimization of the system and individual targets. According to the method, on the premise of respecting individual preferences, the minimization of the total turnover time of all the outer container trucks at the port is realized, and finally the collaborative improvement of the overall operation efficiency of the system and the individual satisfaction of the outer container trucks is achieved.

The invention relates to the technical field of port logistics and intelligent management, in particular to a port storage yard intelligent scheduling and visualization method and system based on digital twinning. The method comprises the steps of obtaining real-time multi-source heterogeneous data, evaluating an existence probability coefficient of a suspected unrecorded object, analyzing a dynamic retention probability coefficient of an assembly set, and correcting the existence probability coefficient to generate a scheduling decision result. According to the method, the existence possibility of unrecorded objects is accurately analyzed by combining the migration characteristics and the composite dynamic characteristics of cargoes or equipment, and the overall efficiency of intelligent scheduling and visualization of the harbor storage yard based on the digital twinborn technology is improved.

The application discloses a transport vehicle scheduling method for a smart port, and relates to the field of smart port logistics management.The method comprises the following steps: acquiring attribute information of each freight point and state information of available vehicles at a current time of the port in real time; calculating the urgency of goods to be transported in each freight point based on the attribute information of the freight point; constructing a grid map of the port and generating a basic path length from each available vehicle to each freight point based on a path search algorithm; constructing a multi-dimensional feature vector of each freight point and clustering the multi-dimensional feature vector to obtain a plurality of clustering clusters and calculate a scheduling path length from each available vehicle to each clustering cluster; calculating a scheduling tendency between each clustering cluster and each available vehicle, solving an optimal matching scheme between the available vehicles and the clustering clusters at the current time, and generating a corresponding task instruction to schedule the available vehicles.The application can realize optimal adaptation between the transport vehicles and the transport tasks, and improve the material circulation efficiency.

The invention discloses a port logistics sorting optimization system and method based on digital twinning, and relates to the technical field of logistics transportation, the port logistics sorting optimization system comprises a cabinet and a control screen, and the control screen is arranged in the cabinet; the sliding plate is arranged on the inner wall of the cabinet in a sliding manner; the handle is fixedly mounted on one surface, far away from the cabinet, of the sliding plate; a protection cover is fixedly installed on the surface of the cabinet, a fixing plate is fixedly installed on the face, away from the cabinet, of a sliding plate, a trapezoidal sliding block is slidably installed on the surface of the cabinet, a pulling plate is fixedly installed on the face, away from the cabinet, of the trapezoidal sliding block, and the trapezoidal sliding block is used for limiting the fixing plate. An L-shaped clamping plate is fixedly mounted on the surface of the trapezoidal sliding block, a sliding block is fixedly mounted on the surface of the cabinet, and the control screen is sealed through the sliding plate, so that the situation that the control screen is aged and damaged and normal use is affected due to the fact that the control screen is exposed in air for a long time is prevented.

The application provides a port area collection and distribution automatic driving vehicle and cargo cooperative path planning method and system, and relates to the technical field of intelligent traffic and port logistics. The method comprises setting short-time and long-time storage thresholds, dividing containers into short-time and long-time storage groups, adopting dispersed container trucks and centralized container pickup for fast queuing and port departure for the former, and transferring the latter to a rear collection and distribution center in one time; adopting a multi-agent PPO model to jointly optimize a multi-objective reward function comprising short-time container, long-time container retention penalty, full load rate reward, empty load mileage penalty, yard cost saving reward and delay penalty; combining LSTM traffic prediction to update space-time graph edge weight, and applying return load pre-matching, time window synchronization and safe distance constraint; and realizing second-level to minute-level re-planning through closed-loop online learning. The application effectively reduces yard occupancy rate and storage cost, reduces vehicle empty load ratio, improves collection and distribution efficiency and safety, and has high industrial application value.

This invention belongs to the field of port logistics technology, specifically a cargo handling and identification system for port cargo handling equipment. It includes a video acquisition module that acquires continuous video streams from key port operation locations; an image processing module that performs target detection and character recognition to extract visual features and container number information; a temporal state inference module that uses a Bayesian probability model to infer the temporal state of multiple consecutive frames of identification results, eliminating single-frame random errors; and a logistics topology map construction module that establishes a topological relationship model of the port logistics network to characterize the spatial transfer logic of containers between operational stages. The introduction of the temporal state inference module eliminates errors in single-frame image processing and allows for joint inference of multiple consecutive frames of identification results. By utilizing the identification information of historical frames to correct the identification deviation of the current frame, the accuracy of identification can be improved.

The invention discloses a transportation path optimization method and device considering port cascade failure risk, and belongs to the technical field of port logistics and transportation network optimization. In order to solve the problems that in existing transportation path planning, systematic risks of a port are insufficiently considered, and chain reaction is easily caused by single-point failure, the invention provides a comprehensive evaluation and optimization mechanism, a port transportation network is represented by constructing a weighted directed graph model, and the port bearing capacity, the historical failure probability and multi-dimensional external risk factors are integrated, so that the port transportation path planning efficiency is improved. And quantitative measurement and calculation of the port cascade failure risk are realized. On the basis, a multi-objective optimization function is designed, the minimum product of time and cost is used as an optimization objective, meanwhile, the constraint full-path failure risk probability is minimum, and the optimal transportation path is solved through algorithms such as gradient descent. By dynamically adjusting the transportation volume distribution, high-risk nodes are effectively avoided, and the network robustness and the transportation reliability are improved. The method can be widely applied to scenes such as international sea transportation and container dispatching, the transportation cost and the interruption risk are remarkably reduced, and the emergency handling capacity is enhanced.

The embodiment of the invention provides a double-box operation method, device and equipment based on adjacent box postures and a medium, and relates to the technical field of port logistics. The method comprises the following steps: acquiring pose information of at least one existing container adjacent to the position of a container to be placed, and taking the pose information as an adjacent container pose; according to the posture of the adjacent container, the placing posture of the to-be-placed container is adjusted; determining that the adjusted posture of the to-be-placed container and the adjusted posture of the adjacent container meet preset double-container grabbing conditions; and determining that the to-be-placed position of the to-be-placed container is located in a preset safety area, and executing container placing operation. According to the method, the problem that the container cannot be aligned with the adjacent container under the condition that it is guaranteed that the container meets the position requirement of the container in double-container operation in the related technology is effectively solved.

The application provides a kind of pan-port area collection and distribution automatic driving vehicle formation task scheduling method and system, it is related to intelligent transportation system and port logistics technical field.The method includes system initialization and data acquisition to construct pan-port area digital twin model and global state space;Based on K-means clustering, task grouping and preliminary allocation are completed;Fusion multi-agent deep reinforcement learning MADRL model and auction mechanism realize vehicle collaborative bidding and decision optimization;Based on the action sequence output by auction, dynamic formation and path planning are carried out;Based on the gate reservation decision output by MADRL model, gate intelligent allocation and execution are carried out;Through performance evaluation and safety guarantee by executing monitoring and adaptive optimization.The application effectively promotes vehicle collaborative formation, realizes peak shaving in peak period, significantly improves the efficiency and safety of pan-port area collection and distribution, reduces energy consumption and carbon emissions, and is suitable for large container port pan-port area scene.

The invention relates to the technical field of logistics robots, and particularly discloses a navigation control method and system for a port logistics intelligent robot, and the method comprises the steps: collecting port environment data through multi-sensor fusion, and constructing a dynamically updated three-dimensional environment model; an optimal path is planned by adopting an improved A * algorithm, and real-time adjustment is carried out according to environmental changes; controlling the robot to execute a motion instruction according to the planned path to realize autonomous navigation; a multi-level interaction mechanism is set, when the robot encounters a preset complex scene, a remote operator is automatically requested for intervention, and the operator sends a control instruction through an interaction interface; navigation parameters are continuously optimized according to the motion trail of the robot and environment feedback. According to the invention, a dynamic three-dimensional environment model is constructed through multi-sensor fusion, and dynamic obstacles and road surface condition changes of a port can be reflected in real time; autonomous decision and manual intervention are balanced through a three-level interaction mechanism, the remote control delay is low in a complex scene, and the risk of full-autonomous navigation failure is avoided.

This invention relates to the field of port logistics technology, specifically to a parallel collaborative intelligent yard crane scheduling method and system. The method includes the following steps: integrating coordinate parameters to generate a scheduling interference topology graph; constructing a concurrent task queue and establishing operational status data; mapping a three-dimensional envelope surface to calculate the intersection volume difference; mapping the volume difference to a braking level and injecting it into the motor to output an avoidance command; and deriving and superimposing a compensation current based on the sway angle acceleration and the trolley acceleration. In this invention, the three-dimensional anti-collision logic is mapped to a graph structure priority topology stripping to ensure concurrent tasks are immune to spatial deadlock conflicts. The volume derivative is used to dynamically predict the multi-machine displacement overlap rate to achieve flexible trolley coasting and avoid physical collision losses from rigid brakes. The feedforward compensation current is extracted by combining the spatial cross-product nonlinear coupling deviation to compensate for off-center load swaying potential energy from the electrical level and ensure the smoothness of the crane handling process.

The application provides a marine mobile micro-grid group logistics and energy collaborative scheduling method and system, comprising the following steps: constructing a fleet logistics deployment model based on a multi-layer discrete space-time network; constructing a multi-objective ship energy management model; combining the fleet logistics deployment model and the multi-objective ship energy management model to construct a scheduling model based on the multi-layer discrete space-time network, optimizing the scheduling model based on the multi-layer discrete space-time network, and realizing the distribution of the ship sailing link and the optimized deployment of the energy equipment under the premise of meeting the logistics demand of each port; the fleet logistics deployment model is used for realizing the logistics cluster scheduling of the all-electric ship and the distribution of the sailing space-time link based on the marine discrete space-time network; and the multi-objective ship energy management model is used for minimizing the ship energy system operation cost and carbon emission under the premise of guaranteeing the logistics demand of each port and the on-time arrival of the ship, thereby effectively realizing the double optimization of energy utilization and carbon emission.

The application discloses a kind of transport path optimization method and device considering port cascading failure risk, belong to port logistics and transport network optimization technical field.For the problem that insufficient consideration is given to port systemic risk in existing transport path planning, and chain reaction is easily caused by single point failure, the application proposes a comprehensive evaluation and optimization mechanism, by constructing a weighted directed graph model to represent the port transport network, integrating the port carrying capacity, historical failure probability and multi-dimensional external risk factors, the quantitative calculation of port cascading failure risk is realized.On this basis, a multi-objective optimization function is designed, with the minimum time and cost product as the optimization goal, while constraining the lowest probability of full-path failure risk, and using gradient descent algorithm to solve the optimal transport path.By dynamically adjusting the transport volume distribution, effectively avoiding high-risk nodes, the network robustness and transport reliability are improved.The application method can be widely applied to international sea transport, container scheduling and other scenarios, significantly reducing transport cost and interruption risk, and enhancing the ability to respond to emergencies.

The invention, which relates to the technical field of port logistics efficiency prediction, discloses an efficiency prediction system for cooperation of a port-approaching industry and port logistics, comprising the following steps: collecting the residence time of goods of the port-approaching industry at a port, and collecting the turnover time of a truck for transporting the goods of the port-approaching industry at the port; obtaining cargo detention time data and truck turnover time data; performing hierarchical calculation processing to obtain average retention data and average turnover data; respectively analyzing and correcting to obtain reference retention turnover data; constructing an efficiency prediction model based on the reference detention turnover data, and predicting the average detention time of the goods; the method is used for solving the problem that when an existing port logistics efficiency prediction technology analyzes and predicts the detention time of goods of port-approaching industries at a port, a prediction model cannot be established according to the historical detention time of the goods at the port, and the detention time of the goods at the port cannot be objectively and stably predicted.

The invention relates to the technical field of logistics management, and discloses a container demand prediction and scheduling optimization method and system for port logistics, and the method comprises the steps: carrying out the calibration and merging processing of the historical demand data and real-time operation data of a port container, and obtaining a preprocessing data set; performing data cleaning on the preprocessed data set to obtain a standardized data set; performing time sequence pattern recognition analysis on the standardized data set to obtain a comprehensive prediction result; determining an initial priority of the scheduling task based on the comprehensive prediction result, adjusting the initial priority according to the port real-time operation environment parameters, and generating an initial scheduling scheme; acquiring operation state data, and performing comparative analysis on the operation state data and an expected state of the initial scheduling scheme to obtain state deviation information; performing iterative optimization adjustment on the initial scheduling scheme based on the state deviation information to obtain a target scheduling scheme; according to the invention, the accuracy of container demand prediction and scheduling optimization for port logistics can be improved.

The invention relates to the technical field of port logistics optimization, in particular to a service mode design and optimization method for outer container truck port abutting operation time planning, which comprises the following steps: constructing a demand response service mode for outer container truck port abutting operation time planning based on a service autonomous selection principle, and dynamically integrating demand information; the port abutting operation time scheme of the outer container truck receiving the demand response service is optimized; constructing a mathematical optimization model according to an optimization target for minimizing the average turnover time of all service accepting vehicles in port operation; and outputting the port abutting operation time optimization scheme of each service accepting vehicle by using the demand response service mode. According to the method, minimization of the average turnover time of the serviced outer container trucks is taken as an optimization target, the overall operation efficiency of the port and the single operation efficiency of the individual outer container trucks are effectively and collaboratively optimized, the turnover time of the outer container trucks in the port is obviously shortened while peak congestion of the port is intelligently avoided, and the working efficiency of the outer container trucks in the port is improved. And finally, the synergistic interaction of the port collecting and distributing system main body is realized.

The invention discloses an automatic positioning system of an unattended screw ship unloader, which comprises an image processing module, a data processing and analyzing module, a positioning and navigation module and an execution control module, and is characterized in that the image processing module is used for acquiring and processing real-time images of goods in a cabin; the data processing and analysis module is used for receiving and analyzing the preprocessed image and sending an image analysis result to the positioning and navigation module; the positioning and navigation module is used for generating a control instruction of the screw ship unloader according to the received image analysis result and sending the control instruction to the execution control module; and the execution control module is used for receiving the control instruction and controlling actions of a mechanical arm, a walking mechanism and a spiral unloading device of the spiral ship unloader according to the control instruction. According to the system, the operation efficiency is remarkably improved, the labor cost is reduced, the operation safety is enhanced, and powerful technical support is provided for automatic and intelligent development of port logistics.

The application provides a high-robustness port logistics-energy coupling scheduling method and system, relates to the field of port scheduling, and solves the technical problem of low logistics efficiency, poor power grid stability and low economic benefit caused by the uncertainty of deep coupling of port logistics and energy system. The method comprises the following steps: constructing a logistics system model and an energy system model of the port; based on the logistics system model and the energy system model, constructing an energy balance constraint condition, coupling the logistics system model and the energy system model; and based on the logistics-energy coupling model, obtaining state variables and energy variables of the port; based on the cost corresponding to the state variables, establishing a logistics cost objective function, and based on the cost corresponding to the energy variables, constructing an energy cost objective function. The application is used in the process of port logistics-energy coupling scheduling.

The invention provides a constrained multi-objective collaborative optimization method, system, device and medium, and belongs to the technical field of port logistics, the method comprises the following steps: firstly defining constraints such as total transportation mileage, time efficiency, cost minimization objective function, capacity, time window and the like, initializing main and auxiliary populations and setting parameters; environment change is detected by comparing the abrupt change of the inter-generation target value; performing three-dimensional analysis on change intensity from a target spatial form, decision variable distribution and population diversity; calling strategies such as multi-knee-point guidance, knowledge memory and diversity injection according to the change type to generate a response solution; combining repair response solutions, reconstructing main and auxiliary populations, and updating elite archives; and cooperating the main population with the auxiliary population in stages, and outputting a solution set approaching the dynamic Pareto frontier after the main population is evolved. According to the method, mileage and aging targets are balanced through multi-target modeling and multi-population cooperation, and feasibility and diversity of solutions are guaranteed; and finally, the solution set approaches the dynamic Pareto leading edge, so that the scheduling efficiency and robustness are improved.

The invention relates to the technical field of port logistics tallying automation, in particular to an unmanned intelligent tallying system and method based on a gantry crane. Comprising a multi-source fusion positioning sensing and processing unit; a container identity identification and container number analysis unit; a whole-process tallying operation management and control unit; and an edge computing and cloud cooperative control unit. According to the method, an accurate triggering basis is provided for container number collection by means of multi-source fusion positioning, the dynamic operation scene of the gantry crane is adapted, and the interference of the operation environment on container number collection and recognition is reduced. By constructing a multi-level container identity verification mechanism, dynamic binding and consistency verification of a container number, a trailer identifier and a wharf operation plan are achieved, and the high-precision identity verification requirement in a dynamic scene is met. Meanwhile, edge computing and cloud collaborative architecture are adopted to guarantee stable operation of the system, and remote monitoring, data traceability and self-learning optimization design are combined, so that the adaptability and practicability of tallying operation automation are improved.

The invention discloses a ship-yard-vehicle-machine cooperative scheduling optimization method for a dry bulk cargo port, and relates to the technical field of port logistics scheduling or the field of intelligent traffic systems, and the method comprises the following steps: S1, obtaining the shipping date, business order and operation plan data of a to-be-arrived ship, and carrying out the data preprocessing; and S2, on the basis of the preprocessed data, predicting a shipping mode, a shipping period and a stack average shipping period of the pre-arrived goods through a combined prediction model, and generating ship loading and unloading operation data. The core of the method is to construct a full-process optimization system which takes data prediction as a drive and multi-module intelligent collaboration as a venation; comprising but not limited to a prospective dispatching behavior prediction method based on time sequence prediction, a collaborative allocation mechanism of berth and yard resources, a mechanical dispatching and reshipment dispatching strategy based on multi-objective optimization, and an automobile transportation departure dispatching method.