108 results about "Port logistics" patented technology

The invention discloses an intelligent port cargo scheduling system and method based on the Internet of Things, relates to the technical field of intelligent port logistics scheduling, and realizes dynamic adaptive adjustment of a port scheduling strategy by constructing a full-chain linkage mechanism of real-time sensing, intelligent analysis, dynamic decision, accurate execution and closed-loop feedback. The system can automatically expand or contract the boundary of the storage yard buffer area according to the congestion index of the core operation area and ship tide prediction, and synchronously adjust the AGV and the yard bridge scheduling rule, in the peak tide period, the AGV preferentially guarantees the rapid transfer of cargoes in the core area, the efficiency loss caused by long-distance transportation is avoided, and the transportation efficiency is improved. In the low ebb backflow period, goods in the elastic storage area are actively moved to the core preparation area in advance, layout is conducted in advance for the follow-up operation peak, the dynamic scheduling mode can effectively cope with severe fluctuation of the port operation amount, all the operation links are kept efficient and cooperative all the time, and the adaptability and response capacity of the port in the complex operation environment are greatly improved.

The invention provides a container shipping stowage method and system based on a multi-stage optimization algorithm, is applied to the technical field of operational research and port logistics, and decomposes a complex stowage process into a plurality of optimization stages based on a four-stage mixed integer programming method. The method comprises the following steps: firstly splicing 20-foot boxes into 40-foot equivalent boxes, quickly locking a shipping space by taking the minimum box area-ship-shellfish connection number and weight deviation as targets, then eliminating vertical overweight and transverse unbalance layer by layer, and finally finely adjusting the shipping space through local neighborhood search, thereby reducing the box turnover rate and improving the dispatching efficiency. Therefore, higher efficiency, lower conflict rate and better process matching degree of container space distribution are realized, the adaptability and efficiency of actual wharf operation are remarkably improved while the safety of a ship structure is guaranteed, and intelligent, systematized and efficient development of a port operation system is promoted.

The invention discloses an operation method and device for a dry bulk cargo shipping wharf and electronic equipment, and relates to the technical field of port logistics, and the method comprises the steps: carrying out the unit grid processing of a bulk cargo storage yard, and updating the storage yard state of each storage yard basic unit in real time through laser point cloud scanning, according to the state of the storage yard and the working state of each unloading device, executing a wharf unloading operation process on the storage yard basic unit; according to the state of the storage yard and the idle state of each ship loader, a material taking operation process is executed on the stacked goods of the storage yard basic unit; and dynamically adjusting stockpiling and material taking operation states of the storage yard basic units by adopting a pre-constructed simulation model framework of the dry bulk cargo shipping wharf, and adjusting wharf berth distribution and storage yard cargo stockpiling and taking strategies. The technical problems that the real-time state of the storage yard cannot be reflected in real time and the scheduling response speed is reduced due to the fact that a dry bulk cargo shipping wharf operation management system only depends on static resource management in the prior art are solved.

The invention belongs to the field of traffic control, particularly relates to a port logistics dynamic path planning method and system based on multi-source data fusion, and aims to solve the problem of how to dynamically plan a port logistics path in real time according to multi-source data under massive real-time data. The embodiment of the invention provides a port logistics dynamic path planning method based on multi-source data fusion, and the method comprises the steps: obtaining the road condition data of a port and the task data of an external container truck, and calculating a dynamic road weight; performing dynamic path planning on the external container trucks according to the dynamic road weights, and generating an initial path of each external container truck; the real-time road condition of the port and the execution state of the initial path are monitored in real time, and the driving state of the internal container truck is adjusted according to the execution state; and according to the real-time road condition, obtaining the occurrence condition of the dynamic event, and when the dynamic event occurs, updating the initial path according to the dynamic event to obtain an updated path.

The invention belongs to the technical field of power system optimization scheduling, and discloses a mathematical model dual-drive-based port intelligent scheduling method, which comprises the following steps of inputting real-time operation data of a port logistics system and an energy system; on the basis of a deep reinforcement learning model, operation data is adopted for training, a state-action-reward mapping relation is established, and a data-driven preliminary scheduling strategy is generated; based on the operation data, constructing a model-driven traffic distribution-user balance optimization model, and obtaining an energy pricing strategy capable of minimizing the operation cost; constructing a multi-agent collaborative decision framework to coordinate a preliminary scheduling strategy and a traffic distribution-user equilibrium optimization model, and realizing iterative optimization of dual-drive strategy collaboration; dynamically selecting an optimal sub-heuristic strategy by adopting a dual deep Q network structure based on an iterative optimization result; the problems of insufficient strategy generalization ability and poor dynamic environment adaptability in the prior art are solved.

The invention provides a container surface defect detection method based on multi-scale feature fusion, and the method comprises the steps: obtaining a first-resolution image of the surface of a container, carrying out the data preprocessing of the first-resolution image, and obtaining a preprocessed image; a lightweight backbone network is adopted to extract basic features of the preprocessed image; performing multi-scale feature fusion on the basic features to obtain fused features; an adaptive attention module is adopted to extract defect candidate region features of the fusion features so as to suppress background interference of container surface defects; detecting different scale defects of the defect candidate region features by adopting a multi-branch detection head to obtain multi-scale features; and performing post-processing on the multi-scale features to obtain a defect detection result. According to the method, high-precision detection of full-scale defects can be achieved, meanwhile, the model calculation complexity and parameter quantity are remarkably reduced, the method is suitable for industrial-grade application such as port logistics, and the requirements for high precision, high speed and high stability of container surface defect detection are met.

The invention discloses an unmanned aerial vehicle flight path analysis method and system for port logistics and an unmanned aerial vehicle. The method relates to the technical field of autonomous navigation, and comprises the following steps: collecting and processing flight path analysis data of an unmanned aerial vehicle, and analyzing, optimizing and adjusting the data. According to the method, the unmanned aerial vehicle flight path analysis data is collected and processed, the unmanned aerial vehicle flight path analysis data is analyzed, the navigation precision accuracy evaluation value and the unmanned aerial vehicle dynamic obstacle avoidance evaluation value are obtained, comprehensive analysis and optimization adjustment are carried out, the real-time performance of the unmanned aerial vehicle flight path analysis method for port logistics is improved, and the accuracy of port logistics is improved. The problem that in the prior art, an unmanned aerial vehicle flight path analysis method for port logistics is insufficient in real-time performance is solved.

The invention belongs to the technical field of power system optimization scheduling, and discloses an intelligent port logistics energy scheduling method based on multi-modal digital twinning, and the method specifically comprises the following steps: obtaining collected multi-modal perception data of a port operation scene; processing the multi-modal perception data through an evolutionary reinforcement learning-based target detection algorithm, and obtaining optimized target detection information in combination with a spatial semantic attention mechanism; performing multi-modal fusion on the target detection information and the multi-modal perception data to generate a port global situation semantic vector; constructing a multi-granularity digital twin model based on the semantic vector, and performing simulation prediction; generating a cooperative scheduling strategy of the logistics and energy system according to the simulation result; according to the method, the problems of insufficient multi-modal perception fusion, lack of logistics energy collaborative optimization and insufficient simulation modeling precision in the prior art are effectively solved by combining a target detection algorithm based on evolutionary reinforcement learning with a spatial semantic attention mechanism.

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 container loading and unloading positioning system and method based on visual identification, and relates to the technical field of port logistics automation. The method comprises the following steps: acquiring a container surface image through an image acquisition device on a sling, and detecting an initial angular point through a deep learning model; when the angular points are missing or the confidence coefficient is insufficient, edge features are extracted, and sub-pixel coordinates are predicted through a reconstruction network so as to complement the angular points; sorting the complete angular point set to form closed-loop vectors, and calculating cross products of adjacent vectors to verify the topological effectiveness; and when the topology is effective, through coordinate conversion and pose optimization, outputting three-dimensional coordinates of the angular points in a world coordinate system, and further determining the position of the container and feeding back the position to a lifting appliance control system to guide loading and unloading. The system correspondingly comprises an image acquisition and corner detection module, a corner completion module, a corner sorting and cross product calculation module, a topology verification and coordinate conversion module and a positioning feedback module. The problem of inaccurate positioning caused by lifting tool shaking, stacking deviation and the like is solved, and the precision and efficiency of container loading and unloading are improved.

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 relates to the field of port logistics, and discloses an automatic port crane container hoisting positioning method, which specifically comprises the following steps: acquiring data by adopting various sensors such as a laser radar, a visual sensor and an inertial measurement unit, processing and fusing the data by utilizing algorithms such as Kalman filtering and extended Kalman filtering, and integrating the advantages of each sensor to obtain a positioning result; in combination with a target recognition algorithm based on deep learning, information such as the position of a container is accurately recognized from images collected by a visual sensor, and meanwhile, in combination with a real-time dynamic compensation and intelligent path planning algorithm, the problems that traditional manual operation and an existing automation technology are not high enough in positioning precision in container hoisting and positioning, and the positioning precision is low are solved. The requirement of high-precision operation cannot be met; the adaptability is poor, different port environments and container types cannot be compatible, and the operation cost is high.

The invention relates to a port multi-vehicle parallel scheduling optimization method considering storage yard priorities, and belongs to the field of port logistics optimization. The method comprises the following steps: carrying out initialization setting on ant colony algorithm parameters, genetic algorithm parameters, path calculation parameters and storage yard priorities; establishing a fitness function according to the transportation time, the oil consumption and the storage yard priority, and establishing a corresponding constraint condition to obtain a problem model; searching a path through an ant colony algorithm on the premise of combining the piling priority influence to obtain a path set; adopting a genetic algorithm, taking the obtained path set as an initial population, and obtaining an optimal solution after various mutation operations; and according to a calculation result of the optimal path, determining a specific driving path, a loading and unloading task and a time table of each vehicle. According to the method, the problems of insufficient path planning and insufficient multi-objective optimization in existing port logistics can be effectively solved, the port logistics efficiency is improved, and the energy consumption is reduced.

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 container unattended weighing method, relates to the field of intelligent management of port logistics, and provides a whole-process unattended solution aiming at the defects that in the prior art, a container number depends on manual verification, container identification equipment is high in cost, a service type needs to be judged manually, and weighing data is tampered easily. The core of the method comprises the steps that a wharf operating system pre-binds a license plate number, and a loading and unloading equipment (gantry crane, reach stacker and stacker) equipment terminal confirms a three-level binding relation from a box number to a specified vehicle number and an operation number; when a vehicle is loaded on the weighbridge, the weighbridge automatically calls binding data through license plate recognition, and container entity recognition requirements are avoided; automatically judging a service type according to a vehicle moving line; and associating double weighing data based on three-level mapping, and calculating net weight in real time to generate a tamper-proof operation record. According to the method, unmanned operation in the whole weighing process is realized, the efficiency is improved, the hardware cost is reduced, the operation data has complete traceability, and key technical support is provided for harbor intelligence.

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 provides an intelligent warehouse integrated management and control system for port logistics, and relates to the technical field of port warehouse management, and the system comprises a graph module which is configured to be used for constructing an operation relation knowledge graph based on the entity relation of a port warehouse, the nodes of the knowledge graph represent warehouse operation elements, and the edges of the knowledge graph represent the incidence relation between the warehouse operation elements; the analysis module is connected with the graph module and is configured to be used for monitoring the node state of each node in the knowledge graph and reasoning the potential influence of the abnormal node state on the global operation plan by analyzing the association relationship between the nodes when the abnormal node state is monitored; and the strategy generation module is connected with the analysis module and is configured to be used for generating a target coping strategy according to the potential influence and outputting a corresponding management and control instruction. According to the invention, the risk that local abnormity is not disposed in time and is evolved into global operation interruption is avoided, so that stable execution of a port logistics overall operation plan is guaranteed, and the throughput efficiency and operation toughness of a warehouse are improved.

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 discloses a port integrated energy system cooperative scheduling method and device, and relates to the technical field of power system optimization scheduling, and the method comprises the steps: receiving equipment operation parameters of a port logistics system and an integrated energy system and ship operation related data, the method comprises the following steps of: inputting equipment operation parameters of a port logistics system and an integrated energy system and ship operation related data into a pre-established port integrated energy system collaborative scheduling model fusing hydrogen energy and electric energy, and modeling uncertainty by utilizing a Markov chain according to a multi-stage stochastic optimization theory; based on a random dual dynamic integer programming algorithm, solving the port integrated energy system collaborative scheduling model fusing the hydrogen energy and the electric energy, and outputting to obtain an optimal collaborative scheduling result of the port integrated energy system under the multi-source uncertainty condition; and the operation flexibility and stability of the energy system are enhanced.

The invention discloses an intelligent scheduling method for wharf and port resources, and belongs to the technical field of port logistics optimization. An improved CSA algorithm is adopted, the problem of efficiency bottleneck of a traditional scheduling mode in a complex scene is solved, and the method comprises the steps of collecting a data set, setting constraint conditions, establishing the data set and a distribution matrix, establishing a multi-target fitness function, establishing a door machine scheduling rule, establishing a berth-door machine cooperative scheduling model and solving to obtain a scheduling scheme. The method mainly aims at minimizing the total waiting time of the ship and the equipment balance utilization rate, and meanwhile optimizes the berth utilization rate and the portal crane utilization rate. By constructing a multi-target scheduling model, a parking space allocation and portal crane scheduling scheme is dynamically planned, and efficient utilization of resources is ensured. The method has the advantages of optimizing the resource utilization rate and the like, and can be widely applied to the field of modern port logistics and management.

A lightweight port comprehensive energy system distributed optimization method, constructs and trains the port logistics predictor through the historical operation data, models the port logistics-energy system distributed optimization problem, and uses the output of the trained port logistics predictor as the initial solution of the local sub-problem of the logistics system in the optimization problem model, solves the port logistics-energy system distributed optimization problem model through the ADMM algorithm iteration, and obtains the globally optimal joint scheduling scheme. The application uses a convolutional neural network to predict the initial solution of the logistics system sub-problem, reduces the solving time of the MIQP sub-problem, and guarantees the convergence of the global optimal solution of the system, which can significantly improve the efficiency of the port energy and logistics system scheduling, realize the lightweight and intelligent solving process, and adapt to the efficient management demand of large-scale port comprehensive energy system.

The present invention belongs to the field of traffic control, and specifically relates to a method and system for dynamic path planning of port logistics based on multi-source data fusion. The problem solved by the present invention is: how to perform real-time dynamic planning of port logistics paths based on multi-source data under massive real-time data. To solve the above problem, an embodiment of the present invention provides a method for dynamic path planning of port logistics based on multi-source data fusion, including: obtaining port road condition data and task data of external container trucks, and calculating dynamic road weights; performing dynamic path planning on external container trucks based on the dynamic road weights to generate an initial path for each external container truck; monitoring the real-time road conditions of the port and the execution status of the initial path in real time, and adjusting the driving status of the internal container trucks according to the execution status; obtaining the occurrence of dynamic events based on the real-time road conditions, and when a dynamic event occurs, updating the initial path according to the dynamic event to obtain an updated path.

The invention discloses a port logistics and urban industry linkage supply chain optimization model construction method, and relates to the technical field of supply chain collaborative optimization, and the method comprises the steps: collecting port logistics data, urban industry data and urban consumption data, carrying out the cleaning and fusion processing, and forming a port logistics and urban industry supply chain data lake; based on the urban consumption data in the port logistics and urban industry supply chain data lake, consumption demand prediction is carried out and is inversely converted into a supply chain demand instruction set for industrial production and port operation; and according to the supply chain demand instruction set and the real-time data in the port logistics and urban industry supply chain data lake, calculating and monitoring the port logistics and urban industry linkage performance index. According to the method, iterative optimization of the model is realized by executing feedback, a closed-loop collaborative decision of data driving, causal sensing and continuous evolution is formed, and the accuracy, dynamic nature and overall efficiency of port and industry linkage are effectively improved.

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 discloses an intelligent grocery tallying system, and relates to the field of port logistics automation. Comprising a cloud server which is internally provided with a data management module and a central control module; the intelligent trolley cluster is composed of a plurality of unmanned intelligent trolleys, and video monitoring modules used for collecting video information are carried on the intelligent trolleys; the intelligent management terminal is installed in intelligent equipment of a manager, and the manager can operate and manage the system through the intelligent management terminal; the wireless transmission network is used for communication connection between the intelligent trolleys, between the intelligent trolleys and the cloud server, and between the cloud server and the wireless transmission network; and a system management module. According to the invention, automatic identification tallying is carried out through video monitoring and cargo identification identification, manual adjustment is combined with a tallying staff, the error probability is greatly reduced, the video monitoring can be traced, and automatic intelligent cooperation and dynamic adjustment functions are realized through taking the intelligent trolley as a carrier.

The application discloses a kind of sea port comprehensive energy system whole process logistics-energy joint scheduling method and system, it is related to sea port comprehensive energy system technical field, including: constructing sea port comprehensive energy system model and sea port logistics system model;With the minimum comprehensive energy system cost and ship average in port length as optimization goal, according to ship arrival information, improved multi-objective differential evolution algorithm is used to solve optimization goal, obtain multiple Pareto solution, according to analytic hierarchy process combination entropy weight method to multiple Pareto solution is authorized, to in multiple Pareto solution set screening optimal solution, obtain including ship berth allocation and each logistics equipment operating state logistics scheduling plan, and the output of each energy equipment, to complete logistics-energy joint scheduling.In improving logistics scheduling efficiency, reduce the energy consumption and carbon emission of sea port comprehensive energy system.