Port container transportation whole-process simulation and space planning quantification system and method under random event deduction

Abstract

The application discloses a random event deduction-based port container transportation whole-process simulation and space planning quantification system and method, which comprises a data acquisition module, a data processing module, a process simulation module, an efficiency quantification index generation module and an efficiency quantification module; the data acquisition module is used for acquiring basic data in port operation; the data processing module is used for processing the basic data; the process simulation module is used for constructing a port container transportation process simulation covering a whole process from ship navigation, ship berthing, shore crane loading and unloading, yard storage and container truck outward transportation based on the processed basic data, and acquiring a simulation result; the efficiency quantification index generation module is used for acquiring efficiency quantification indexes according to container transportation characteristics and port operation experience; and the efficiency quantification module is used for quantifying the simulation result by using the efficiency quantification indexes. The application can comprehensively and objectively measure the current operation efficiency level of the port.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of port planning and port operation management, and particularly relates to a port container transportation full-process simulation and spatial planning quantification system and method under random event deduction. BACKGROUND

[0002] With the continuous growth of global trade, the port container transportation system as the core node of the international logistics chain, its operation efficiency directly affects the overall performance of the supply chain. Port container transportation involves ship arrival, loading and unloading operations, yard management, land transportation and other links, the process is complex and dynamic, and is easily disturbed by random events such as weather, equipment failure, traffic congestion, etc. Traditional port planning and operation management mainly rely on experience decision or static simulation model, which is difficult to accurately simulate the influence of random events on the whole process, resulting in unreasonable resource allocation, frequent congestion, low operation efficiency and other problems.

[0003] In recent years, computer simulation technology has become an important means to optimize port operation. Discrete event simulation (DES) and agent-based modeling (ABM) methods are widely used in port logistics system analysis, which can simulate the flow process of containers in the terminal. However, the existing simulation system has the following limitations: (1) Most models focus on a single link (such as berth allocation or yard scheduling), lack of coordinated simulation of the whole process of "ship-land bridge-yard-container truck-gate"; (2) The modeling of random events is relatively simple, and it is difficult to reflect the complex disturbance under the coupling of multiple factors; (3) The relevance between spatial planning and process simulation is insufficient, and it is difficult to quantify the influence of yard layout, equipment configuration, etc. on system performance.

[0004] In terms of spatial planning, existing researches mostly use geometric modeling or heuristic rules, lacking data-driven quantitative analysis methods. Although some scholars try to combine simulation with optimization algorithms (such as genetic algorithm, reinforcement learning), an integrated decision support system that takes into account dynamic randomness and spatial constraints has not yet been formed. In addition, with the improvement of port automation and digitalization level, the demand for high-precision simulation and real-time decision-making is increasingly urgent, and an intelligent system that can integrate multi-source data, dynamically deduce random events, and quantitatively evaluate spatial planning schemes is urgently needed.

[0005] Therefore, it is of great significance to develop a "port container transportation full-process simulation and spatial planning quantification system under random event deduction", which realizes the fine simulation of port operation process and the quantitative evaluation of planning scheme by integrating dynamic simulation, random process modeling and spatial optimization technology, and improves the port resilience and reduces logistics cost. SUMMARY

[0006] To solve the above technical problems, the application provides a random event deduction-based port container transportation whole-process simulation and space planning quantification system and method, which can bring all-round and revolutionary changes and improvements to port operation.

[0007] The application provides a random event deduction-based port container transportation whole-process simulation and space planning quantification system, which comprises a data acquisition module, a data processing module, a process simulation module, an efficiency quantification index generation module and an efficiency quantification module.

[0008] The data acquisition module is used for acquiring basic data in port operation.

[0009] The data processing module is used for processing the basic data.

[0010] The process simulation module is used for constructing a port container transportation process simulation covering the whole process from ship navigation, ship berthing, shore crane loading and unloading, yard storage and container truck outward transportation based on the processed basic data, and obtaining a simulation result.

[0011] The efficiency quantification index generation module is used for obtaining efficiency quantification indexes according to container transportation characteristics and port operation experience.

[0012] The efficiency quantification module is used for quantifying the simulation result by using the efficiency quantification indexes.

[0013] Optionally, the data acquisition module comprises a ship transportation information unit, a container dispatching unit, a container storage unit and a container truck transportation unit.

[0014] The ship transportation information unit is used for acquiring ship berthing time, ship navigation transportation interval, container wharf operation occupied space range or occupied time and ship occupied berth time.

[0015] The container dispatching unit is used for acquiring container loading and unloading sequence, container yard flow, loading and unloading time and loading and unloading equipment utilization rate.

[0016] The container storage unit is used for acquiring storage time and storage space.

[0017] The container truck transportation unit is used for acquiring dispatching time, dispatching route and transportation time.

[0018] Optionally, processing the basic data comprises cleaning, preprocessing and storing the basic data.

[0019] Optionally, the process simulation module comprises a ship navigation and arrival unit, a shore crane loading and unloading operation unit, a yard storage and container truck outward transportation unit and a ship departure unit.

[0020] The ship navigation and arrival unit is configured to randomly allocate the ship to arrive at the port based on a ship navigation uncertainty determination.

[0021] The shore crane loading and unloading operation unit is configured to unload the containers from the ship to arrive at the port and load the containers to the ship.

[0022] The yard storage and truck outward transportation unit is configured to cope with temporarily adjusted container storage requirements based on the uncertainty of the ship navigation, and dispatches based on reservations and truck locations and states.

[0023] The ship departure unit is configured to make the ship depart from the berth and the port after determining that the waterway is available and the hydro-meteorological conditions meet the requirements, and wait for the next round of allocation.

[0024] Optionally, the random allocation of the ship to arrive at the port based on the ship navigation uncertainty determination comprises:

[0025] A simulated duration meteorological and hydrological process is randomly generated using historical wind, wave, current, and tidal data as a model judgment boundary condition, a route is dynamically adjusted by an intelligent algorithm, a state of different tidal levels, current speeds, wind speeds, and visibility at the arrival time is predicted, an impact on berthing is evaluated in advance, a berthing plan is adjusted, a berth and operation resource is re-planned, a ship expected arrival time is determined, and the ship to arrive at the port is randomly allocated.

[0026] Optionally, the unloading of the containers from the ship to arrive at the port comprises:

[0027] A shore crane unloading task sequence is generated, the system optimizes the unloading sequence of the containers according to the destinations, weights, and types of the containers, and assigns a shore crane to start unloading the containers according to a shore crane load condition.

[0028] The loading of the containers to the ship to arrive at the port comprises:

[0029] A loading operation starts, the system generates a task according to a ship stowage plan, considers power and path optimization, an AGV travels to a designated container pickup location in the yard, and picks up corresponding containers from a yard crane.

[0030] The AGV travels to the front of the wharf, a shore crane loads the containers, a ship stowage and container type are pre-judged and adjusted, and a loading sequence is optimized.

[0031] If a storm or other severe weather occurs, a transportation route is dynamically adjusted according to a random generation, or non-urgent tasks are temporarily suspended.

[0032] Optionally, the efficiency quantitative indicators comprise a front operation capacity, a horizontal transportation capacity, a yard operation capacity, a collection and distribution capacity, a green energy saving indicator, and a port service level.

[0033] The application also provides a random event deduction port container transportation whole-process simulation and space planning quantification method, comprising:

[0034] Collecting basic data in port operation;

[0035] Processing the basic data to obtain processed data;

[0036] Simulating the processed data to obtain simulation results;

[0037] Obtaining container transportation characteristics and port operation experience data, and obtaining efficiency quantification indexes according to the container transportation characteristics and the port operation experience data;

[0038] Quantifying the simulation results by using the efficiency quantification indexes to obtain quantification results.

[0039] Optionally, the basic data comprises ship transportation data, container transportation data, container storage data and container truck transportation data.

[0040] Optionally, obtaining efficiency quantification indexes according to container transportation characteristics and port operation experience data comprises:

[0041] Extracting key information from the container transportation characteristics and the port operation experience data, analyzing the key information, determining an evaluation index system, and generating efficiency quantification indexes.

[0042] Compared with the prior art, the application has the following advantages and technical effects:

[0043] The application makes in-depth research on a port container operation whole-process digital twin considering random factors and an efficiency quantification index generation system, aims to bring all-round and revolutionary changes and improvements to port operation. Deeply relying on the leading digital twin technology, the application considers the complex relationship from the overall layout planning of the port to the fine division and collaborative operation of each functional area with a systematic thinking. Meanwhile, the natural environmental random factors such as tides and weather, the economic field random variables such as AGV and internal card energy supplement random processes and trade situation fluctuations are fully taken into account, so as to create a complete and highly visual port container operation whole-process digital twin model. The model accurately replicates the whole life cycle of the port operation process, from the moment when the ship enters the port anchorage to the final completion of the container loading and unloading and the transportation off the port by the truck, and each subtle link can be accurately mapped in the virtual model. Through the construction of an efficient data interaction link, the virtual model can quickly respond to the dynamic changes of the physical system, provide the port managers with an intuitive operation panoramic display as if they were on the scene, and greatly improve the timeliness, accuracy and scientificity of the management decision.

[0044] To realize the scientific evaluation and continuous optimization of port operation efficiency, the application uses advanced methods of multi-disciplinary cross-fusion such as operations research and statistics to deeply analyze the key processes and core links in port operations. On this basis, a set of quantitative evaluation index system of container comprehensive transportation efficiency for high-quality port operation is creatively put forward. Not only can it comprehensively and objectively measure the current operation efficiency level of the port, but also can find potential problems through data analysis, providing clear and explicit direction for the subsequent optimization and upgrading of port container operations.

[0045] Based on the above self-developed digital twin model and quantitative evaluation system, the application can provide a high-quality, scientific and reasonable and perfect transportation system planning scheme for port construction. It helps to accurately identify the short board and restrictive elements in port container transportation, realize the visual display of port ship-container-car collaborative scheduling, effectively optimize the utilization of port shoreline resources, significantly enhance the spatial function coordination within the port boundary, so as to achieve the optimal allocation of port resources and maximize the system efficiency of the port. BRIEF DESCRIPTION OF DRAWINGS

[0046] The accompanying drawings, which form a part of this application, are included to provide a further understanding of the application, and are incorporated herein for purposes of illustration. The embodiments of the present application, and the explanations given thereto, do not constitute any limitation of the present application, but serve as a specific description of the present application. In the drawings:

[0047] Figure 1 is a random event deduction under the port container transportation full-process simulation and spatial planning quantification system structure diagram of the embodiment of the application;

[0048] Figure 2 is a simulation logic diagram of the digital twin system of the whole process of port container operation of the embodiment of the application;

[0049] Figure 3 is a simulation process diagram of the embodiment of the application;

[0050] Figure 4 is a simulation result diagram of the embodiment of the application;

[0051] Figure 5 is a statistical diagram of different factors causing delay of ships of the embodiment of the application. DETAILED DESCRIPTION

[0052] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0053] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown.

[0054] Embodiment one:

[0055] The embodiment provides a port container transportation whole-process simulation and space planning quantification system under random event deduction, as shown in the figure, comprising a data acquisition module, a data processing module, a process simulation module, an efficiency quantification index generation module and an efficiency quantification module. Figure 1 The data acquisition module is used for acquiring basic data in port operation.

[0056] The data acquisition module is used for acquiring basic data in port operation.

[0057] The data processing module is used for processing the basic data.

[0058] The process simulation module is used for constructing a port container transportation process simulation covering the whole process from ship navigation, ship berthing, shore crane loading and unloading, yard storage and container truck outward transportation based on the processed basic data, and acquiring simulation results.

[0059] The efficiency quantification index generation module is used for acquiring efficiency quantification indexes according to container transportation characteristics and port operation experience.

[0060] The efficiency quantification module is used for quantifying the simulation results by using the efficiency quantification indexes.

[0061] Specifically, the main content of the embodiment includes two aspects: one is to construct a virtual model corresponding to the physical system of port container ship navigation operation considering random variables based on digital twinning technology, and to make the virtual model reflect the state and behavior of the physical system in real time through data interaction. Remote sensing image, chart, AIS data, port operation statistical data and the like are used to provide data and information input for the digital twinning model, so as to realize digital mapping of the whole process of port operation. The second is to establish a scientific and reasonable efficiency quantification index system, and to use operations research, statistics and the like to consider the actual flexible berthing of container berths, and to quantitatively evaluate the key links and processes in port operation in a hierarchical and dimensional manner. In the process of system development and index generation, a data transmission network and a data updating mechanism are established to ensure the timeliness and reliability of the data.

[0062] Further, the data acquisition module comprises a ship transportation information unit, a container transportation unit, a container storage unit and a container truck transportation unit.

[0063] Ship transportation information unit, for collecting ship waiting time, ship navigation transportation interval, container terminal operation occupied space range or occupied time and ship occupied berth time;

[0064] Container dispatching unit, for collecting container loading and unloading sequence, container yard flow, loading and unloading time and loading and unloading equipment utilization rate;

[0065] Container storage unit, for collecting storage time and storage space;

[0066] Container truck transportation unit, for collecting dispatching time, dispatching route and transportation time.

[0067] Specifically, various data in port operation are collected, including AIS data, ship basic information, tide data, weather data, container information, container truck operation data, etc. The collected data are cleaned, pretreated and stored to ensure the accuracy and integrity of the data, providing reliable data support for subsequent modeling and analysis.

[0068] 1. Calibration research scope:

[0069] Prepare a coordinate-based remote sensing image base map to frame the research scope of the container navigation operation whole process digital twin system, and determine the coordinate system as the national 2000 coordinate system.

[0070] 2. Extracting port infrastructure information and refining ship transportation structure and rules:

[0071] Ship waiting time statistical analysis judgment logic:

[0072] Speed: The speed data of the ship for this voyage are all greater than 0.5 knots, indicating that it does not stay here.

[0073] Status: According to whether the ship state is IN, it is determined whether it wants to enter the port, so as to exclude the ship data passing through.

[0074] Ship navigation transportation interval statistical analysis judgment logic:

[0075] The observation section is along the width direction of the channel, and according to the set two observation points (one point includes a longitude value and a latitude value), a observation section is determined, and the ship spacing calculation is the spacing rule of the front and rear two ships passing through the observation section.

[0076] Container terminal operation occupied space range / occupied time statistical analysis judgment logic:

[0077] The ship is in the state of entering the port, and is in the range of the turning water area to determine the berthing, and can be used for loading and unloading containers. The ship is in the state of leaving the port, and the initial speed is gradually increased to 1 knot, which is determined as unberthing, and the subsequent connection ship port flow process.

[0078] Ship occupancy berth time statistical analysis judgment logic:

[0079] Speed: The ship's speed is between 0-0.5 knots.

[0080] Status: The ship is in the berth water area of the dock basin.

[0081] 3. Extract the container handling rules:

[0082] Container loading and unloading sequence analysis and judgment logic: According to the ship's loading and unloading plan and the stowage position information of the container on the ship, the loading and unloading sequence is determined. First, according to the stowage plan of the ship, the loading and unloading priority of different compartments of the container is determined, generally following the principles of loading after unloading, light and heavy matching, and avoiding reverse stacking. At the same time, combined with the actual operation process of the port, such as the operation habits and efficiency of the shore-based handling equipment, the loading and unloading sequence is further optimized.

[0083] Container yard flow analysis and judgment logic: According to the time of the container entering the yard, the ship it belongs to, the destination, and the layout planning of the yard, the flow rule is analyzed. When the container is unloaded from the ship, it will be allocated to different yard areas according to its attributes (such as ordinary dry cargo box, refrigerated box, dangerous goods box, etc.). At the same time, considering the storage strategy of the yard, such as first-in first-out, concentrated stacking according to destination, etc., the moving path and residence time of the container in the yard are observed.

[0084] Loading and unloading time analysis and judgment logic: According to the berthing time and unberthing time of the ship, combined with the operation efficiency of the handling equipment, the loading and unloading time of each container is analyzed. Through statistical analysis, the average loading and unloading time of different types of ships (such as large container ships and small branch line ships) can be obtained.

[0085] Loading and unloading equipment utilization rate calculation logic: Analyze the use of terminal handling equipment (such as shore cranes and yard cranes) to determine whether the equipment is in an efficient operation state. By optimizing equipment scheduling, reducing idle time of equipment, and improving overall operation efficiency.

[0086] 4. Extract the container storage rules:

[0087] Storage time analysis and judgment logic: According to the residence time of the container in the yard, the turnover rate of the container is analyzed. By analyzing the storage time of containers of different consignors and different destinations, the yard layout is optimized to reduce storage time.

[0088] Storage space utilization rate calculation logic: Analyze the space utilization rate of the yard to determine whether the yard is reasonably utilized. By optimizing the storage strategy, reducing congestion in the yard, and improving the efficiency of storage.

[0089] 5. Extract the container truck transportation rules:

[0090] Scheduling time analysis and determination logic: Based on the loading and unloading time and storage time of containers, the scheduling time of trucks is analyzed. Through optimizing the scheduling strategy, the waiting time of trucks is reduced, and the scheduling efficiency is improved.

[0091] Scheduling route analysis and determination logic: Based on the layout of the port and the traffic conditions of the hinterland, the transportation route of the truck is analyzed. Through optimizing the route selection, the transportation distance and time are reduced, and the transportation cost is reduced.

[0092] Transportation time analysis and determination logic: Based on the transportation records of the truck, the transportation time of different time periods and different routes is analyzed. Through statistical analysis, the transportation time law of peak period and congested section is obtained

[0093] Further, the processing of the basic data includes cleaning, preprocessing and storing the basic data.

[0094] Further, the process simulation module includes: ship navigation and arrival unit, shore crane loading and unloading unit, yard storage and truck delivery unit and ship departure unit;

[0095] The ship navigation and arrival unit is used to randomly allocate the arrival of ships based on the uncertainty of ship navigation;

[0096] The shore crane loading and unloading unit is used to unload the containers of the arrived ships and load the ships;

[0097] The yard storage and truck delivery unit is used to cope with the temporary adjustment of container storage demand based on the uncertainty of ship navigation, and to schedule according to the reservation and truck location and state;

[0098] The ship departure unit is used to make the ship leave the berth and port after determining that the channel is available and the hydro-meteorological conditions meet the requirements, and to wait for the next round of allocation.

[0099] Specifically, as shown in Figures 2-5 , the "container" of the container ship is taken as the smallest intelligent agent, and the whole process of port container transportation process simulation covering ship navigation, ship berthing, shore crane loading and unloading, yard storage, truck delivery, etc. is constructed, fully considering the uncertainty and randomness of meteorological and hydrological conditions, berths, trucks, cranes, etc. The simulation model determination logic is as follows:

[0100] I. Ship navigation and arrival

[0101] (I) Ship navigation uncertainty determination

[0102] The historical wind, wave, current, and tidal data are used to randomly generate simulated meteorological and hydrological processes for a certain period of time as the model's boundary conditions. The route is dynamically adjusted by an intelligent algorithm, which predicts the state of different tidal levels, flow rates, wind speeds, and visibility at the time of arrival, assesses the impact on berthing in advance, adjusts the berthing plan, and considers the channel and anchorage idle state to re-plan the berth and operation resources, determine the expected arrival time of the ship, and start the shipping operation with the ship.

[0103] (II) Random allocation of ship arrival

[0104] Dynamic berth evaluation: The system automatically allocates berthing based on real-time berth status (loading and unloading, equipment condition, remaining space), ship type (large trunk ship, small branch ship), cargo capacity (number and weight of containers), and task urgency, as well as the monitoring of shore crane loading and unloading models and the maintenance plan of transport equipment such as trucks and AGVs. If the berth is empty, the berth assignment application is opened for the ship, and the ship begins to berth. If the berth is not empty, it enters the anchorage for berthing.

[0105] Flexible berthing adjustment: Based on real-time tidal and ship draft data, dynamically adjust the berthing angle and position. For berth occupancy changes caused by weather or other ship operation delays, trigger the re-allocation mechanism in time to adjust the ship originally planned to berth at a certain berth to a standby berth that has just been vacated and is in good condition. When ships are combined for berthing, large and small ships are reasonably matched to improve berth utilization. Flexible berthing adjusts the berthing position and angle based on real-time ship draft and tides to ensure safe and efficient berthing.

[0106] II. Shore crane loading and unloading operations:

[0107] (I) Unloading process:

[0108] Shore crane unloading task sequence generation: The system optimizes the unloading sequence based on the destination, weight, and type (refrigerated container, dangerous goods container) of the container. The shore crane is assigned to start unloading based on the overall load condition.

[0109] Dynamic truck scheduling: Simulate the truck transportation process with the shortest distance, and immediately dispatch a backup truck when traffic congestion or insufficient power / oil is detected. Re-plan the AGV travel path to an area with an idle truck to avoid disruptions in operations.

[0110] (II) Loading process:

[0111] The loading operation starts, and the system generates tasks based on the ship's loading plan (taking into account the ship's balance and stability). Considering power and path optimization, the AGV is empty and drives to the designated container location in the yard, where the corresponding yard crane retrieves the container. Then, the AGV is loaded and drives to the wharf, where the shore crane loads the container. Considering the ship's loading, the container's category is predicted and adjusted, and the loading sequence is optimized. If there is heavy rain or other bad weather, the actual dynamic transportation route is randomly generated, or non-urgent tasks are suspended, and the transportation safety is ensured when the weather improves.

[0112] Three, yard storage and truck delivery:

[0113] (I) Yard storage:

[0114] Considering the uncertainty of truck and ship operations, a certain proportion of flexible container locations (such as 10%) are reserved to respond to temporary adjustments in container storage needs. Considering the storage needs of different types of containers, dangerous and oversized containers are stored separately, and the layout is optimized according to the turnover frequency (fast turnover containers near the exit), destination (same port containers are concentrated), and type (refrigerated containers are temperature controlled, and dangerous goods containers are monitored separately). According to the yard reservation algorithm, the shore crane loading and unloading process is linked.

[0115] The priority weight matrix rule is referenced, and the higher the cumulative priority index, the higher the priority:

[0116] Refrigerated container urgency 0.35

[0117] Dangerous goods isolation coefficient 0.25

[0118] Transit container timeliness 0.20

[0119] Heavy container balance requirement 0.15

[0120] Device energy consumption constraint 0.05

[0121] (II) Truck delivery:

[0122] After receiving the consignee's reservation container instruction, the reservation is dispatched according to the truck location and state (idle, energy state). The empty truck drives from the gate to the yard, and the truck is refueled or charged (scheduled in advance according to the task plan to avoid peak hours). Then the truck arrives at the container location, and the truck departs from the port.

[0123] Four, ship departure:

[0124] After the loading operation is completed, the ship departs and leaves the port when the waterway is available and the hydrological and meteorological conditions meet the requirements. At this time, the berth is vacated, and the next round of allocation is waiting.

[0125] Record the data of this operation process to provide a basis for subsequent optimization.

[0126] More specifically, the setting mode of the conventional parameter setting sub-module is shown in Table 1:

[0127] Table 1

[0128]

[0129]

[0130] Wherein ① String type data must use English double quotation marks; ② The "brackets" in the parameter value need to be English brackets.

[0131] Random factor parameterization:

[0132] Random setting of berthing mode: support random setting of whether the ship needs to turn around when berthing and leaving, provide multiple mode selection: in, out, in and out, random and no. Among them, "in" represents the need to turn around when berthing, "out" represents the need to turn around when leaving, "in and out" represents the need to turn around when berthing and leaving, "random" represents whether to turn around when berthing and leaving is randomly determined, "no" represents that the ship does not need to turn around when berthing and leaving. By flexibly setting the berthing mode, the ship scheduling strategy under different port operation scenarios is simulated, and the port resource utilization efficiency is optimized.

[0133] Random setting of multiple route selection: support setting of route selection probability, set the probability value to 0.5, which means that the probability of selecting this route for a ship of "a service ship type" from "a certain port area 1" to "general bulk cargo area" is 50%. By simulating the selection probability of different routes, the utilization rate, congestion and transportation efficiency of the route are analyzed, which provides data support for route planning and resource allocation.

[0134] Random setting of ship arrival: support random setting of ship arrival, and can set the flow of ships of different types (such as container ships, bulk carriers) and tonnages (such as 5000 tons, 10000 tons). The arrival of ships can choose to set three kinds of random distribution, namely normal distribution, Poisson distribution and uniform distribution, combined with the input of the number of each type of ship arriving at each port area per year to simulate. By simulating different ship arrival rules, optimizing port resource scheduling and operation plan, reducing ship waiting time, and improving port throughput capacity.

[0135] Random setting of AGV and inner card energy supplement process as Figure 4The random setting of supporting AGV (Automatic Guided Vehicle) and inner truck energy supplement process is shown. It includes random setting of charging and refueling time: simulating the time consumption of AGV and inner truck in energy supplement process, considering the type of equipment, energy type (electricity or fuel) and capacity limit of supplement facility. Maintenance random event setting: simulating the failure and maintenance events that may occur during the operation of AGV and inner truck, including failure probability, maintenance time and resource occupation. By simulating energy supplement and maintenance random events, the scheduling strategy of AGV and inner truck is optimized, the equipment downtime is reduced, and the port operation efficiency is improved.

[0136] Further, the efficiency quantification indexes include: front operation capacity, horizontal transportation capacity, yard operation capacity, collection and distribution capacity, green energy saving index and port service level.

[0137] Specifically, a container port is a complex system. In order to evaluate the space planning scheme of port water area and land area, an evaluation index system corresponding thereto needs to be established for quantitative analysis.

[0138] Firstly, combined with the characteristics of container transportation and port operation experience, combined with the key information extracted from the collected data, referring to the industry standard "Green Port Grade Evaluation Guide", through data mining, theoretical analysis, expert consultation and other methods, the evaluation index system is determined, and each efficiency quantification index is generated, including ship navigation dimension, shore crane loading and unloading dimension, horizontal transportation dimension, yard operation dimension, collection and distribution dimension, green energy efficiency index and comprehensive service level dimension, a total of 6 first-level indexes and 13 second-level indexes. The efficiency quantification index output to Excel is automatically calculated by the simulation results of the digital twin system, and is displayed in the form of charts and tables in the statistical result view. The proposed different dimension indexes are as follows:

[0139] For the container water area operation capacity, the queuing phenomenon of arriving port waiting container ships in the terminal berth system is generally visible. How to estimate the service quality and efficiency of a terminal berth operation system, how to estimate the ship stay time and waiting time in port are the key problems to be considered, so the berth utilization rate, ship operation efficiency and ship service level are selected to evaluate it.

[0140] For the front operation capacity of the terminal, the operation area of this part is mainly between the berth wall along the terminal shoreline and the terminal yard. The shore crane is generally selected as the main loading and unloading machinery, and the shore crane loading and unloading efficiency, shore crane utilization rate and shore crane service level are selected to evaluate it.

[0141] For horizontal transportation capacity, considering the service objects and operation modes and characteristics of horizontal transportation equipment within the scope of land, the horizontal transportation equipment operation efficiency, horizontal transportation average turnaround time, and horizontal transportation service level are selected to evaluate it.

[0142] For container yard operation capacity, considering the operation equipment and content within the yard area, the yard bridge operation efficiency and yard service level are selected to evaluate it.

[0143] For container collection and distribution capacity, mainly focusing on the flow of vehicles entering and leaving the port and the operation content of the gate area, the gate passage utilization rate and gate service level are selected to evaluate it.

[0144] For the green energy-saving level of containers, based on the main content of green port evaluation, considering the reform trend of port operation mode, the wharf energy consumption and cost, and ecological environment indicators are selected to evaluate it.

[0145] For the service level of containers, the port service efficiency and container throughput are selected to evaluate it.

[0146] Table 2

[0147]

[0148]

[0149] Further, the digital twin model and the efficiency quantification index generation module are integrated into a unified system platform to realize real-time interaction and sharing of data. Through the visual interface, intuitive and convenient operation and decision support tools are provided for port managers, so that they can monitor the port operation state in real time and optimize the scheduling decision.

[0150] Embodiment Two:

[0151] The embodiment also provides a method for simulating the whole process of port container transportation under random events and quantifying space planning, including:

[0152] Collecting basic data in port operation;

[0153] Processing the basic data to obtain processed data;

[0154] Simulating the processed data to obtain simulation results;

[0155] Obtaining container transportation characteristics and port operation experience data, and obtaining efficiency quantification indexes according to the container transportation characteristics and port operation experience data;

[0156] Quantifying the simulation results by using the efficiency quantification indexes to obtain quantification results.

[0157] Further, the basic data includes: ship transportation data, container transportation data, container storage data and container truck transportation data.

[0158] Further, according to the container transportation characteristics and the port operation experience data, the efficiency quantitative index is obtained, including:

[0159] The key information is extracted from the container transportation characteristics and the port operation experience data, the key information is analyzed, the evaluation index system is determined, and the efficiency quantitative index is generated.

[0160] The embodiments one to two will be described in detail below with reference to the accompanying drawings:

[0161] Step one: collection and processing of full-process heterogeneous data, collection and arrangement of relevant information of terminal operation, statistics, analysis and calculation, for modeling and modeling of digital twin model.

[0162] Calibration research scope and infrastructure information: water boundary, track subsection, route, container berth sea side, anchorage (detailed), anchorage (simplified) and port anchorage, container berth land side, gate, road section, road network relationship, container yard, container yard (special) and the like. Container storage related information: loading and unloading process, fixed facilities, shore crane type library, yard crane type library, AGV, truck, ship container information, container storage period, ship schedule, reservation system (external truck), ship container capacity (without ship schedule), ship fuel typical (without ship schedule) and the like.

[0163] Refine the ship transportation structure and rules, and further determine the main information of port operation. According to the statistical analysis, the container ship arrival proportion and average operation time within one week are obtained according to the ship grade, as shown in Table 3:

[0164] Table 3

[0165]

[0166] Refine the port ship in and out of port navigation rules, and determine the team in port, in port area selection probability, night navigation rules, safety distance, in and out of port priority, intersection water area, meeting and overtaking rules one by one.

[0167] Refine the rules of container storage, transfer and transportation.

[0168] Some loading and unloading process and scheduling rules are shown in Table 4:

[0169] Table 4

[0170]

[0171]

[0172] Part of the ship loading and unloading example rules are shown in Table 5:

[0173] Table 5

[0174]

[0175] Container stacking rules are shown in Table 6:

[0176] Table 6

[0177]

[0178] Step two: build a digital twin model and perform model checking and verification.

[0179] Generalize the above information and data, parameterize the settings, and perform simulation and model checking after modeling. First, experts in the field check each agent and its behavior defined in the simulation model one by one to ensure that it meets the expectations.

[0180] Second, the system platform tracks the entire simulation process and compares the simulation results with the actual situation, and adjusts as necessary to ensure its accuracy.

[0181] Finally, ensure that all agents and their behaviors can be included in the simulation model in the correct way. Through these checking work, the reliability of the research results can be ensured, and the foundation for further research work is laid.

[0182] As shown in Table 7, according to the comparison between the simulation results and the actual situation, the simulation port annual throughput can reach the actual design long-term annual throughput, and the simulation results of each tonnage container ship operation time differ from the actual value by less than 5%, indicating that the model can generally correctly reflect the actual situation of the port operation.

[0183] Table 7

[0184]

[0185] Step three: system integration and application, automatic output of statistical indicators.

[0186] The simulation port operation one-week energy consumption index statistics are shown in Table 8:

[0187] Table 8

[0188]

[0189] The simulation port operation one-week cost index statistics are shown in Table 9:

[0190] Table 9

[0191]

[0192]

[0193] The above merely provides the preferred embodiment of the application, and the protection scope of the application is not limited thereto. Any modification or replacement within the technical scope disclosed by the application should be covered within the protection scope of the application. Therefore, the protection scope of the application should be subject to the protection scope of the claims.

Claims

1. A system for simulating and quantifying the whole process of port container transportation under random events and spatial planning, characterized in that, The application relates to a container port efficiency quantitative analysis system. The system comprises a data collection module, a data processing module, a process simulation module, an efficiency quantitative index generation module and an efficiency quantitative module. The data collection module is used for collecting basic data in port operation. The data collection module comprises a ship transportation information unit, a container transportation unit, a container storage unit and a container truck transportation unit. The ship transportation information unit is used for collecting ship berthing time, ship transportation interval, container wharf operation space range or occupied time and ship berth occupation time. The container transportation unit is used for collecting container loading and unloading sequence, container yard flow, loading and unloading time and loading and unloading equipment utilization rate. The container storage unit is used for collecting storage time and storage space. The container truck transportation unit is used for collecting scheduling time, scheduling route and transportation time. The data processing module is used for processing the basic data. The process simulation module is used for constructing a container port transportation process simulation covering the whole process of ship navigation, ship berthing, shore crane loading and unloading, yard storage and container truck outward transportation based on the processed basic data, and obtaining a simulation result. The process simulation module comprises a ship navigation and arrival unit, a shore crane loading and unloading operation unit, a yard storage and container truck outward transportation unit and a ship departure unit. The ship navigation and arrival unit is used for randomly allocating ship arrival based on ship navigation uncertainty determination. The shore crane loading and unloading operation unit is used for unloading containers and loading ships for the arrived ships. The yard storage and container truck outward transportation unit is used for coping with temporarily adjusted container storage demand based on the uncertainty of ship navigation, and scheduling according to reservation and container truck position and state. The ship departure unit is used for making the ship depart from the berth and the port after judging that the waterway is available and the hydro-meteorological condition meets the requirements, and waiting for the next round of allocation. Randomly allocating ship arrival based on ship navigation uncertainty determination comprises the following steps: a simulated weather and hydrology process with a simulation duration is randomly generated by using historical wind, wave, current and tide data as model judgment boundary conditions, a route is dynamically adjusted by an intelligent algorithm, the state of different tide levels, flow rates, wind speeds and visibility at the arrival time is predicted, the influence on berthing is evaluated in advance, the berthing plan is adjusted, the idle state of the waterway and anchorage is considered, the berth and operation resource are re-planned, the expected arrival time of the ship is determined, and the ship arrival is randomly allocated; unloading containers for the arrived ships comprises the following steps: a ship unloading task sequence is generated, the unloading sequence of containers is optimized according to the destination, weight and type of the containers, and the shore crane is assigned to start unloading containers according to the shore crane load condition; loading ships for the arrived ships comprises the following steps: loading operation starts, a task is generated according to the ship loading plan, the AGV is driven to the designated container position in the yard, the corresponding yard crane is used to pick up the container, the AGV is driven to the wharf front, the shore crane is used to load the container, the loading sequence is optimized according to the ship loading plan and container type, and the loading sequence is adjusted according to the random generation of the actual dynamic adjustment transportation route or the suspension of non-emergency tasks in the case of heavy rain and bad weather. ​ The efficiency quantification index generation module is configured to obtain efficiency quantification indexes according to container transportation characteristics and port operation experience. The efficiency quantification module is configured to quantify the simulation result by using the efficiency quantification indexes.

2. The random event deduction system according to claim 1, wherein, The processing of the basic data includes cleaning, preprocessing and storing of the basic data. 3.The random event deduction system for simulating and quantifying the whole process of port container transportation and spatial planning according to claim 1, wherein, The efficiency quantification indexes include leading-edge operation capacity, horizontal transportation capacity, yard operation capacity, collection and distribution capacity, green energy-saving index and port service level.

4. A method for simulating the entire process of port container transportation and quantifying spatial planning under random event deduction is applied to the system described in claim 1, characterized in that: The method comprises the following steps: collecting basic data in port operation; processing the basic data to obtain processed data; simulating the processed data to obtain a simulation result; obtaining container transportation characteristics and port operation experience data, and obtaining efficiency quantification indexes according to the container transportation characteristics and the port operation experience data; quantifying the simulation result by using the efficiency quantification indexes to obtain a quantification result.

5. The random event deduction method for simulating and quantifying the whole process of port container transportation and spatial planning according to claim 4, characterized in that, The basic data includes ship transportation data, container dispatching data, container storage data and container truck transportation data.

6. The random event deduction method for simulating and quantifying the whole process of port container transportation and spatial planning according to claim 4, characterized in that, According to the container transportation characteristics and the port operation experience data, the efficiency quantification indexes are obtained by: extracting key information from the container transportation characteristics and the port operation experience data, analyzing the key information, determining an evaluation index system, and generating efficiency quantification indexes.

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