Business simulation system and method for port intelligent driving

Abstract

The invention relates to the technical field of software systems, and particularly discloses a service simulation system and method for port intelligent driving, and the service simulation system comprises a data interaction module, a simulation control module, a service simulation engine and a simulation database. The intelligent port management system has the beneficial effects that historical data are imported into the simulation system for analogue simulation, so that specific historical events can be reset, experience can be summarized conveniently, service scheduling optimization can be performed in a targeted manner, and the iteration speed of the intelligent port management system is increased; future data is imported into the simulation system for analogue simulation, so that the feasibility and correctness of a scheduling plan can be verified, the operation plan can be conveniently changed in time, and the overall operation efficiency of a port is ensured; the real-time data is imported into the simulation system for analogue simulation, the operation result can be predicted in advance by means of the characteristic that the simulation time of the simulation system is shorter than the real business operation time, risks can be found in time, scheduling adjustment is carried out, and the overall operation efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of software systems, and in particular to a business simulation system and method for port intelligent driving. Background Art

[0002] With the continuous development of digitalization and information technology, port digital twin and simulation technology have gradually been applied and promoted, becoming a key tool to improve port operations and management. Digital twin technology provides port managers with a real-time virtual port model, accurately restoring each link of the port, thereby optimizing resource allocation and improving operational efficiency; simulation technology is also used in equipment maintenance and prediction, by real-time monitoring of equipment operation status, predicting potential failures, and reasonably arranging maintenance plans, thereby improving equipment reliability and maintenance efficiency.

[0003] At present, the management of ports mainly uses logistics simulation systems such as port production operation system (TOS), automatic driving fleet management system (FMS), FlexSim, AnyLogic, etc. However, TOS mainly plans and dispatches the overall business and monitors the status of port machinery, and does not consider the real-time transportation status of automatic driving vehicles and abnormal event information. FMS mainly obtains vehicle transportation tasks by docking with the terminal TOS system, and sends the tasks to automatic driving vehicles, which then navigate to the destination to complete the transportation tasks. During the vehicle transportation process, the platform does not comprehensively consider the dynamic changes in the status of various port machinery and business scheduling status of the TOS system, lacks dynamic and real-time comprehensive operation information of various equipment and vehicles in the port area, and often encounters problems such as gantry cranes and other vehicles, vehicle unloading, sudden route closure, and port machinery safety conflicts, and cannot achieve overall optimal and local optimal control of the transportation operation efficiency in the port area. At the same time, the system cannot collaboratively simulate the vehicle conditions between multiple manufacturers, as well as the motion model of non-automatic driving vehicles, and cannot perform path simulation for traffic flow interference caused by manually driven vehicles. Logistics simulation systems such as FlexSim and AnyLogic mainly use discrete event, system dynamics, and agent modeling to perform discrete event simulation. They are mainly used for logistics system operation process simulation, and do not involve the simulation of multiple operation systems, multiple operation equipment, and especially the operation process of intelligent driving vehicles. They focus more on the cargo flow process rather than the transportation process. They cannot simulate the refined operation process, transportation process, and process acceleration, predict the actual operation situation of the next day, and cannot support the operation plan rehearsal. Summary of the invention

[0004] The present invention proposes a business simulation system and method for port intelligent driving in order to solve the above problems.

[0005] The technical solution of the present invention is achieved in this way:

[0006] The business simulation system for port intelligent driving includes data interaction module, simulation control module, business simulation engine and simulation database.

[0007] The data interaction module: connects to the third-party business system and simulation system for joint simulation through a unified external standard protocol;

[0008] The simulation control module is used to control the simulation process and present the simulation process, so that the user can control the simulation process and view the simulation results.

[0009] The business simulation engine includes a model engine, an event engine, a decision engine, and a traffic flow engine.

[0010] The model engine is used to simulate the port site according to the port drawings, and to simulate the facilities and equipment according to the movement and operation trajectory of the port facilities and equipment;

[0011] The event engine obtains the event model through training of a large amount of historical port event data, which makes it easy to insert events to interfere with the business simulation process during business simulation;

[0012] The decision engine is used to split the work plan, dynamically adjust the next work plan of each vehicle and the overall work arrangement, and adjust the vehicle conditions of the task according to the predicted time completion progress, perform dynamic planning, and simulate the task distribution rules when achieving maximum efficiency;

[0013] The traffic flow engine is used to simulate the business conditions of manually driven internal container trucks, automatically driven internal container trucks, external container trucks, engineering vehicles, and pedestrians on the port roads. At the same time, according to the mixed traffic of manually driven and automatically driven vehicles in the port area, micro-simulation of mixed traffic of manually driven vehicles and automatically driven vehicles in different proportions can be carried out according to business needs;

[0014] The simulation database includes a model database, a business database, an event database, and a scene database, which provide data support for dynamic models, business models, historical operation data, historical event data, and scene environment data when the engine performs simulation;

[0015] The scenario database includes yard operation scenarios, port intersection scenarios, terminal operation scenarios, station lock operation scenarios, mixed traffic scenarios, and weather environment scenarios.

[0016] The present invention also discloses a business simulation method for port intelligent driving, comprising the following steps:

[0017] Step 1: Loading plan import;

[0018] Step 1.1: Import future data and import the port operation loading plan for the next day;

[0019] Step 1.2: Import historical data, import the loading plan and actual operation status of a certain day or period in history;

[0020] Step 2: Scenario configuration: when future data is imported, select the corresponding environmental model engine for configuration based on the actual predicted weather and environmental information in the future to build a simulation scenario; when historical data is imported, generate the corresponding scenario configuration based on the historical data;

[0021] Step 3: Simulation task establishment and job instruction generation. When future data is imported, the business simulation engine is used to load the plan to split the task and generate the corresponding job instruction set. When historical data is imported, the job instruction is obtained by reading the historical job data in the simulation database.

[0022] Step 4: Instructions are issued. The business simulation engine mobilizes different modules and engines to perform corresponding simulations according to the job instructions.

[0023] Step 5: Simulation control, input simulation task information, perform simulation control according to requirements, and adjust simulation speed;

[0024] Step 6: Facility and equipment operation simulation: According to the simulation task, the manually driven vehicle simulation model, the autonomous driving vehicle simulation model, the vehicle vertical operation interaction simulation model, the traffic event simulation model, and the traffic flow simulation model in the port model are controlled to perform operation simulation according to the operation instructions;

[0025] Step 7: Simulation result output. After the simulation is completed, the result data can be transmitted to different systems through the data interaction module for positive feedback analysis. The simulation results of historical data can be used to review and analyze historical events and provide data reference for optimization. The simulation results of future data can be used to evaluate whether the next day's work plan is reasonable, so as to facilitate timely adjustment of the next day's work plan.

[0026] Furthermore, in step 1, the loading plan importing also includes real-time data importing, and the loading plan of the currently ongoing operation is imported into the simulation system.

[0027] Furthermore, in step 3, the business data of a third-party system may be selected to be accessed to perform task splitting and generate instructions.

[0028] By adopting the above technical scheme, the beneficial effects of the present invention are as follows: by importing historical data into the simulation system for simulation, specific historical events can be reviewed, and it is convenient to summarize experience, carry out targeted business scheduling optimization, and improve the iteration speed of the port intelligent management system; by importing future data into the simulation system for simulation, it can be used to verify the feasibility and correctness of the scheduling plan, which is convenient for timely changing the operation plan and ensuring the overall operation efficiency of the port; by importing real-time data into the simulation system for simulation, the operation results can be predicted in advance by relying on the characteristic that the simulation time of the simulation system is shorter than the actual business operation time, which is convenient for timely discovering risks and making scheduling adjustments, thereby improving the overall operation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0030] Figure 1 It is a business simulation system framework diagram of the present invention;

[0031] Figure 2 It is a diagram of the deployment mode of the business simulation system of the present invention;

[0032] Figure 3 It is a flow chart of the business simulation method of the present invention. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] As shown Figure 1-2 As shown in the figure, the business simulation system for port intelligent driving includes a data interaction module, a simulation control module, a business simulation engine, and a simulation database.

[0035] The data interaction module: connects to the third-party business system and simulation system for joint simulation through a unified external standard protocol. The data interaction module ensures the data consistency and real-time performance between the business simulation engine and the simulation database.

[0036] The simulation control module is used to control the simulation process (start, pause, resume, end, accelerate) and present the simulation process. The simulation process can be presented on the front end in 2D or 3D, which is convenient for users to control the simulation process and view the simulation results. The simulation control module coordinates the work of each engine, and the joint simulation performs joint business simulation by accessing the vehicle simulation capability of the autonomous driving dispatching platform.

[0037] The business simulation engine includes a model engine, an event engine, a decision engine, and a traffic flow engine. Multiple business simulation engines can be deployed to handle different simulation tasks, improve simulation speed, and provide support for simulation acceleration and multi-scenario simulation.

[0038] The model engine is used to simulate the port site according to the port drawings. According to the functional attributes of different operation zones of the port, it includes road simulation, yard simulation, shore simulation, engine simulation, etc. At the same time, according to the business attributes corresponding to each scene, the yard can be subdivided into empty container area simulation, heavy container area simulation, refrigerated area simulation, and dangerous goods area simulation; the facilities and equipment are simulated according to the movement conditions and operation trajectories of the port facilities and equipment. The simulation of facilities and equipment includes the simulation of the movement process of port machinery, including the simulation of the movement process of bridge cranes, gantry cranes, forklifts, and reach loaders, and the simulation of the movement process of manually driven vehicles and self-driving vehicles;

[0039] The event engine obtains the event model through training of a large amount of historical port event data. It inserts events into the simulation process according to the periodic distribution of the event model in time, and simulates the environmental interference and other factors of the real port operation process, so as to facilitate the insertion of events to interfere with the business simulation process during business simulation. The event model includes road failure events, customs inspection events, vehicle congestion abnormal events, etc. The application of the event engine can further simulate the factors affecting the operating efficiency of real port business operations;

[0040] The decision engine obtains the original operation plan data by connecting to the external TOS system or importing the loading plan, splits the operation plan according to the operation distribution rules of the port area and the online and operation status of the corresponding facilities and equipment, dynamically adjusts the next operation plan of each vehicle and the overall operation arrangement, and adjusts the vehicle status of the task according to the predicted time completion progress, performs dynamic planning, and simulates the task distribution rules when the maximum efficiency is achieved;

[0041] The traffic flow engine is used to simulate the business conditions of manually driven internal container trucks, automatically driven internal container trucks, external container trucks, engineering vehicles, and pedestrians on the port roads, including the simulation of traffic flow constraints such as lane rules, road rules, pedestrian traffic rules, and engineering vehicle management regulations in the port area. At the same time, according to the mixed traffic of manually driven and automatically driven vehicles in the port area, micro-simulation of mixed traffic scenarios of manually driven vehicles and automatically driven vehicles in different proportions can be performed according to business needs;

[0042] The simulation database includes a model database, a business database, an event database, and a scene database, which provide data support for dynamic models, business models, historical operation data, historical event data, and scene environment data when the engine performs simulation;

[0043] The scenario database includes yard operation scenarios, port intersection scenarios, terminal operation scenarios, lock station operation scenarios, mixed traffic scenarios, and weather environment scenarios;

[0044] Yard operation scenario: describes the interaction process between the vehicle and the yard crane or forklift / reach stacker during loading and unloading operations in the yard;

[0045] Port intersection scene: describes the port traffic convergence points such as port three-way intersections, crossroads, and approach bridge intersections, which are used to simulate the operation of mixed traffic flows in intersection scenes;

[0046] Terminal operation scenario: describes the interaction between vehicles and cranes during loading and unloading operations at the terminal, including crane alignment, dock surface queuing, sorting, and other operations;

[0047] Lock station operation scenario: describes the scenario of the vehicle interacting with the loading and unloading lock station during the loading and unloading lock operation, simulating the actual lock station operation lane planning and efficiency;

[0048] Mixed traffic scenario: describes the traffic conditions when autonomous vehicles and manually driven vehicles are simultaneously performing mixed operations in the port area, including common traffic scenarios such as lane changing, overtaking, congestion, and queuing;

[0049] Weather environment scenario: Describe the impact of rain, snow, and strong winds on equipment, facilities, and vehicles.

[0050] In this application, the facility and equipment simulation models all adopt simplified models. According to the decomposition of the work tasks by the decision engine, the facility and equipment models are described as simplified models based on the work tasks, the vehicle model is simplified to time efficiency models such as lane movement and parking operations, and the port machinery model is simplified to the lateral movement of the boom and the longitudinal movement of the spreader.

[0051] In this embodiment, the business simulation engine also includes a control engine, which mainly simulates the action operation command control of the equipment according to the simulation model of the facility equipment, including vehicle driving, braking, overtaking, lane changing, turning; bridge crane's boom movement, hoist movement, hoist grabbing and other control commands. When the control engine receives the task command issued by the decision engine, it converts the corresponding operation task command into a control command to drive the facility equipment simulation engine for simulation, thereby simulating the time efficiency difference of different operation nodes and different operation processes in the entire operation process.

[0052] like Figure 3 As shown, the business simulation method for port intelligent driving includes the following steps:

[0053] Step 1: Loading plan import;

[0054] Step 1.1: Import future data, import the port operation loading plan for the next day, including berthing information, container information, operation equipment scheduling information, etc.

[0055] Step 1.2, historical data import, import the loading plan and actual operation status of a certain day or a certain period of time in history. In addition to the information contained in the loading plan, it also includes the completion time of the port instruction, equipment operation task information, equipment historical operation location and other process data;

[0056] Step 1.3: Real-time data import: import the current operation loading plan into the simulation system, output the simulation results in real time, identify the current operation safety risks, support global business transportation scheduling, and improve real-time operation efficiency and overall operation safety;

[0057] Step 2: Scenario configuration. When future data is imported, the corresponding environmental model engine is selected for configuration according to the actual predicted weather and environmental information in the future to build a simulation scenario. When historical data is imported, the corresponding scenario configuration is generated according to the historical data, including the corresponding map information, road information, vehicle information, port machinery information, ship information, etc. When real-time data is imported, the corresponding scenario configuration is generated according to the real-time environmental data, completely simulating the current real port operation environment, and providing a scenario basis for predicting the operation task situation in the next 1-2 cycles.

[0058] Step 3: Simulation task establishment and job instruction generation. When future data is imported, the business simulation engine loads the plan to split the task and generate the corresponding job instruction set. When historical data is imported, the job instructions are obtained by reading the historical job data in the simulation database. When real-time data is imported, the control instructions are obtained by connecting to the TOS system.

[0059] Step 4: Instructions are issued. The business simulation engine mobilizes different modules and engines to perform corresponding simulations according to the job instructions.

[0060] Step 5: Simulation control, input simulation task information, perform simulation control according to requirements, adjust simulation speed, and face historical data and future data: retrieve the corresponding simulation model and engine in the simulation database according to the scenario information, and perform simulation according to the instructions split by the data interaction system;

[0061] Step 6: Facility and equipment operation simulation: According to the simulation task, the manually driven vehicle simulation model, the autonomous driving vehicle simulation model, the vehicle vertical operation interaction simulation model, the traffic event simulation model, and the traffic flow simulation model in the port model are controlled to perform operation simulation according to the operation instructions;

[0062] Step 7: Output of simulation results. After the simulation is completed, the result data can be transmitted to different systems through the data interaction module for positive and negative feedback analysis. The simulation results of historical data can be used to review and analyze historical events and provide data reference for optimization. For example, the review results are imported and fed back to the simulation engine and autonomous driving manufacturers, which not only adjusts the simulation accuracy of the simulation engine through positive feedback, but also provides simulation data support for autonomous driving manufacturers to iterate intelligent driving algorithms. The simulation results of future data are fed back to the port operation control system, which can be used to evaluate whether the operation plan arrangement for the next day is reasonable, so as to facilitate timely adjustment of the operation plan for the next day. By optimizing and expanding the system configuration, the simulation time of real-time data is ensured to be 1-2 beats faster than the actual business operation time. The simulation results of real-time data are fed back to the loading plan and TOS system in real time, providing basis data for real-time operation plan adjustment, avoiding abnormal situations such as congestion and operation plan timeout, and adjusting the plan in time to improve the efficiency of operation completion.

[0063] Offline simulation is used to import historical data and future data. Historical data is reviewed or the rationality of future operation plans is evaluated through historical or future port loading plan data. Online closed-loop simulation is used to import real-time data. For port operation plans, offline simulation is used to simulate the next day's operation plan, and port machinery and vehicles are arranged in advance to ensure the overall operation efficiency of the port. For smart driving vehicles, the business simulation system can be used to verify the port business process and test the adaptability of the site environment before actually entering the port, thereby accelerating the development and debugging efficiency. At the same time, the specified historical operation process is reviewed to realize scenario analysis, and rectification support is provided to improve the iteration speed of the smart driving algorithm. For real-time port operation scheduling, short-term prediction is made through online closed-loop simulation of the simulation system to achieve real-time global optimal scheduling of the entire port, improve overall operation efficiency, and identify port safety risks.

[0064] In this embodiment, the business types simulated in step 5 include loading, unloading, and container transfer, which are correspondingly divided into the following operation processes:

[0065] The vehicle drives to the yard: the vehicle triggers the simulation from the starting position and drives to the task-specified yard to queue up and wait for the operation; two simulation databases, port intersection scenarios and mixed traffic scenarios, are called. Depending on the vehicle, the manual driving vehicle simulation engine or the automatic driving vehicle simulation engine is selected for motion simulation. The main interference factors for the efficiency of this process are the vehicle's own driving and the interference of other traffic participants. The vehicle adopts refined dynamic simulation and the traffic flow simulation engine to simulate the traffic flow of the driving path, and completes the simulation of the operation completion time within the current operation node of the vehicle. At the same time, in order to better simulate the operation scenarios under real business, the traffic event simulation engine is used to simulate traffic events such as congestion and road closure. Here, the time equivalent model is used for accelerated simulation, and the time impact caused by the traffic event is imported into the simulation process for time correction.

[0066] Yard loading and unloading operations: After the vehicle arrives at the site, the vehicle vertical operation interaction simulation engine is triggered, and the yard operation simulation scenario database is called to simulate the operation interaction process between the vehicle and the yard crane. This process itself focuses on the vertical operation efficiency of the vehicle: using a simplified model, the horizontal movement model of the yard crane and the vertical movement model of the spreader are equivalent to a time model to output the simulation results of the overall operation efficiency.

[0067] Lock station loading and unloading operations: When the vehicle drives to the lock station, the vehicle interacts with the lock station and calls the vehicle vertical operation interaction engine to simulate the loading and unloading process of the lock pin. Here, the vehicle alignment time and the time spent on manual operations are abstracted into a fluctuation model within a time limit through historical data, and the operation time of the current operation node is output.

[0068] Terminal loading and unloading operations: After the vehicle arrives at the port, the vertical operation interaction simulation engine is triggered, and the yard operation simulation scenario database is called to simulate the operation interaction process between the vehicle and the bridge crane. This process itself focuses on the vertical operation efficiency of the vehicle: using a simplified model, the horizontal movement model of the bridge crane and the vertical movement model of the spreader are equivalent to a time model to output the simulation results of the overall operation efficiency.

[0069] In this embodiment, in step 6, different engines are responsible for simulating different operation nodes in the overall port operation process, and the corresponding engine joint simulation is performed according to the information input by the simulation control in step 5:

[0070] Manually driven vehicle simulation model: responsible for the road driving conditions of manually driven vehicles during the operation process and the overall operation simulation: corresponding to the port business flow, it is mainly used in the simulation process of horizontal vehicle transportation. As the core participant of the port business, it adopts a refined model of manually driven vehicles, including detailed power models, motion models, and lateral scheduling in conjunction with the traffic flow engine.

[0071] Autonomous driving vehicle simulation model: responsible for the road driving conditions of autonomous driving vehicles during operation and the overall operation simulation; corresponding to the port business flow, it is mainly used in the simulation process of intelligent vehicle horizontal transportation. As a core participant in the port business, it adopts a refined model of autonomous driving vehicles, including detailed power models, motion models, path planning models, fleet scheduling decision models; and is used in conjunction with the traffic flow engine for lateral scheduling.

[0072] Vehicle vertical operation interaction simulation model: simulates the operation of vehicles interacting with port machinery equipment in vertical operation; including manual driving vehicle and yard crane vertical interaction operation model, automatic driving vehicle and bridge crane vertical interaction operation model, manual driving vehicle and bridge crane vertical interaction operation model, automatic driving vehicle and bridge crane vertical interaction operation model, manual driving vehicle and lock station vertical interaction operation model, automatic driving vehicle and lock station vertical interaction operation model; this simulation is mainly for vertical operation, vertical operation interference is less, considering the overall simulation efficiency. The above model adopts a simplified model: the motion model of port machinery equipment is simplified into a time model, and the operation time is the output result of the simulation engine.

[0073] Traffic event simulation model: responsible for simulating traffic events that may affect operation efficiency, such as road closures and customs inspections, based on historical data deduction in the overall operation plan; the engine constructs an event distribution model based on historical data, and distributes interference events in the operation process of the business flow according to the overall plan. The interference caused by traffic events on the business is simplified to interference with the completion time of the business. Time influencing factors are distributed in the operation nodes of each business flow for simulation output.

[0074] Traffic flow simulation model: responsible for simulating the interaction between different vehicles on the road, including path intersection, priority passage, overtaking by using other lanes, etc.; the engine simulates the mixed traffic of manually driven vehicles and autonomous driving vehicles. Simulate the mixed traffic conditions of the port business process, and allocate different traffic mixing ratios according to needs; at the same time, introduce the external fleet interference model, simulate the operation of the external fleet in the port area in a unified manner, and perform refined simulation to obtain the mixed traffic flow simulation of vehicles at different ratios. The simulation results of the engine enable port operators to adjust the road rules and traffic rules of the port area to improve the overall operation efficiency of the port area.

[0075] In this embodiment, in step 3, the business data of a third-party system may be selected to access the task splitting and generating instructions.

[0076] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. The business simulation system for port intelligent driving is characterized by: Including data interaction module, simulation control module, business simulation engine, simulation database, The data interaction module: connects to the third-party business system and simulation system for joint simulation through a unified external standard protocol; The simulation control module is used to control the simulation process and present the simulation process, so that the user can control the simulation process and view the simulation results. The business simulation engine includes a model engine, an event engine, a decision engine, and a traffic flow engine. The model engine is used to simulate the port site according to the port drawings, and to simulate the facilities and equipment according to the movement and operation trajectory of the port facilities and equipment; The event engine obtains the event model through training of a large amount of historical port event data, which makes it easy to insert events to interfere with the business simulation process during business simulation; The decision engine is used to split the work plan, dynamically adjust the next work plan of each vehicle and the overall work arrangement, and adjust the vehicle conditions of the task according to the predicted time completion progress, perform dynamic planning, and simulate the task distribution rules when achieving maximum efficiency; The traffic flow engine is used to simulate the business conditions of manually driven internal container trucks, automatically driven internal container trucks, external container trucks, engineering vehicles, and pedestrians on the port roads. At the same time, according to the mixed traffic of manually driven and automatically driven vehicles in the port area, micro-simulation of mixed traffic of manually driven vehicles and automatically driven vehicles in different proportions can be carried out according to business needs; The simulation database includes a model database, a business database, an event database, and a scene database, which provide data support for dynamic models, business models, historical operation data, historical event data, and scene environment data when the engine performs simulation; The scenario database includes yard operation scenarios, port intersection scenarios, terminal operation scenarios, station lock operation scenarios, mixed traffic scenarios, and weather environment scenarios.

2. A business simulation method for port intelligent driving, characterized by: The following steps are involved: Step 1: Loading plan import; Step 1.1: Import future data and import the port operation loading plan for the next day; Step 1.2: Import historical data, import the loading plan and actual operation status of a certain day or period in history; Step 2: Scenario configuration: when future data is imported, select the corresponding environmental model engine for configuration based on the actual predicted weather and environmental information in the future to build a simulation scenario; when historical data is imported, generate the corresponding scenario configuration based on the historical data; Step 3: Simulation task establishment and job instruction generation. When future data is imported, the business simulation engine is used to load the plan to split the task and generate the corresponding job instruction set. When historical data is imported, the job instruction is obtained by reading the historical job data in the simulation database. Step 4: Instructions are issued. The business simulation engine mobilizes different modules and engines to perform corresponding simulations according to the job instructions. Step 5: Simulation control, input simulation task information, perform simulation control according to requirements, and adjust simulation speed; Step 6: Facility and equipment operation simulation: According to the simulation task, the manually driven vehicle simulation model, the autonomous driving vehicle simulation model, the vehicle vertical operation interaction simulation model, the traffic event simulation model, and the traffic flow simulation model in the port model are controlled to perform operation simulation according to the operation instructions; Step 7: Simulation result output. After the simulation is completed, the result data can be transmitted to different systems through the data interaction module for positive feedback analysis. The simulation results of historical data can be used to review and analyze historical events and provide data reference for optimization. The simulation results of future data can be used to evaluate whether the next day's work plan is reasonable, so as to facilitate timely adjustment of the next day's work plan.

3. The business simulation method for port intelligent driving according to claim 2 is characterized in that: In the step 1, the loading plan import also includes real-time data import, which imports the loading plan of the current operation into the simulation system.

4. The business simulation method for port intelligent driving according to claim 2 is characterized in that: In step 3, the business data of a third-party system may be accessed to perform task splitting and generate instructions.

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