Shore crane operation process simulation system and method for port intelligent driving

Abstract

The invention relates to the technical field of port operation simulation software systems, and particularly discloses a quay crane operation process simulation system and method for port intelligent driving. The quay crane operation process simulation system comprises a data interaction module, a quay crane motion simulation module, a service simulation module, a simulation control module, an operation backtracking module, an analysis and optimization module and a simulation display module. The method has the beneficial effects that a complex port operation process is completely mapped to a digital space through fine dynamic modeling and coding, a port manager can visually observe and analyze the whole operation process in a virtual environment through full-process digitization, and potential problems can be found and solved in time; based on the constructed 3D simulation environment, natural environment data and random events are combined to carry out work process simulation, meanwhile, simulation data are fed back to a simulation system to carry out closed-loop control, simulation errors are reduced, and the work process simulation authenticity is improved.

Description

Technical Field

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

[0002] Port automation and intelligence are the inevitable trends in the development of modern ports. Among them, quay cranes, as important loading and unloading equipment, are a key link in realizing port intelligence.

[0003] Intelligent simulation technology is a vital component of intelligent transportation systems. With the rapid development of artificial intelligence and Internet of Things technologies, its application areas are constantly expanding and the technical level is also increasing. This field mainly uses computers to simulate the real-world traffic environment and conduct virtual tests on self-driving cars, thereby testing and optimizing the performance of the self-driving system in different scenarios. For intelligent driving simulation technology in port scenarios, it is necessary to simulate the business processes of the port and pay more attention to the efficiency and risk of each key node of the business process.

[0004] The Chinese patent with application number 202310785993.7 discloses an operation simulation test method and system for unmanned container trucks in ports, the method comprising: L1. Selecting historical production task data in the TOS system, based on the simulation module in the TMC simulation system, making the historical production task data into simulation sample data, and outputting a single task order simulation sample of the TMC simulation system; L2. Based on the single task order simulation sample of the TMC simulation system, taking the simulated container truck as the object, the single task order is disassembled into multiple vehicle task sequences, and the simulated container truck sequence and the corresponding task status sequence are output. This patent needs to be tested based on the existing port unmanned container truck simulation model, and does not provide a model simulation method itself. Moreover, the simulation model is tested only from the perspective of macro business events, and the speed, weight, power and other internal factors of the unmanned container truck itself are not paid attention to. It mainly provides tests for the scheduling strategy adjustment of unmanned container trucks in the port area.

[0005] The Chinese patent application number is 202311798217.7, which discloses a port area scheduling simulation system and method considering precise regulation and control of autonomous driving. The method includes the following steps: S1: Setting a vehicle detector in the simulation system, and the vehicle detector detects vehicles entering the operation point in real time; S2: Setting a vehicle queue for quay crane / yard crane loading and unloading services to simulate a first-come, first-served operation mode; S3: Starting the simulation, and recording the vehicle status and the operation equipment status at the end of each simulation step; S4: Setting or clearing the vehicle path in the simulation system; S4: Setting / clearing the parking line in the simulation system to realize the docking operation simulation; S5: Using the vehicle status and the operation status as input, performing a single-step solution of the model; S7: Using the scheduling algorithm to be evaluated as the input of the simulation system, and performing a joint simulation with the simulation system. This patent is mainly aimed at simulating the transportation scenarios of autonomous vehicles in port areas. It can simulate the precise control functions of vehicle transportation tasks, trajectories and speeds, thereby meeting the scheduling simulation needs of automated container ports and evaluating various scheduling plans. However, this patent only targets the macro-overall operation process level of autonomous vehicles, and does not involve the underlying links such as specific quay crane equipment control, and cannot perform high-precision simulation analysis of the entire process.

[0006] The Chinese patent application number is 202310782045.8, which discloses a multi-agent-based port operation area simulation modeling method, wherein the method includes: collecting port simulation parameters; designing port operation area agents; designing the operation process of the port operation area agents; constructing a port operation area simulation model based on the port simulation parameters and the port operation area agents; and visually outputting the port operation area simulation model. This patent mainly targets the simulation modeling method of operation simulation with port container trucks and containers as the main body. There is no dynamic model, which makes it impossible to perform microscopic simulation and the simulation effect is poor.

[0007] In summary, the existing technology is limited to a single device for operation simulation, and is unable to fully restore the process of collaborative operation of autonomous driving vehicles and quay cranes in combination with actual business. Although there are many space- and time-related algorithms in the existing technology regarding scheduling strategies, they only have relatively macroscopic result outputs and only stay in the instruction scheduling stage. They are not converted into specific parameters to guide or participate in the operation of autonomous driving vehicles and quay cranes. In addition, the existing technologies are all discrete event modeling based on processes, ignoring the uniqueness of entities and their interactions with each other. Summary of the invention

[0008] The present invention proposes a quay crane operation process simulation system and method for port intelligent driving in order to solve the above problems.

[0009] The technical solution of the present invention is achieved in this way:

[0010] The quay crane operation process simulation system for port intelligent driving includes data interaction module, quay crane motion simulation module, business simulation module, simulation control module, operation backtracking module, analysis and optimization module, and simulation display module.

[0011] The data interaction module is used to coordinate the information flow between the quay crane, berth, intelligent driving vehicle and TOS terminal operating system, ensuring seamless connection and real-time data synchronization between various subsystems, and providing information support for the entire port automation operation;

[0012] The quay crane motion simulation module includes trolley motion simulation, truck motion simulation, and spreader motion simulation.

[0013] The trolley motion simulation is used to simulate the state of the quay crane trolley affected by various factors when it moves horizontally on the bridge. The speed of the quay crane trolley is calculated as follows:

[0014] V1=f v1 (y i ,y f , v i1 , a1, M c , F d , F f , F w ,δ,V t );

[0015] Where: f v1 is the function for calculating the average speed of the car, y i is the initial position, y f is the target position, v i1 is the initial velocity, a1 is the acceleration, M c is the mass of the car, F d is the driving force, F f is the friction force, F w is the wind force, δ is the bridge deflection, V t is lateral vibration;

[0016] The trolley motion simulation is used to simulate the state of the quay crane trolley affected by various factors when it moves linearly on the ground track. The time calculation of the quay crane trolley motion is as follows:

[0017] T t =f d3 (x i , x f ) / f v3 (v i , a3, F d , F f , F w , M m ,θ)

[0018] Where: fd3 Represents the distance calculation function of the movement, x i represents the initial position of the vehicle, x f represents the target position, f v3 Represents the speed calculation function of the vehicle, v i3 represents the initial velocity, a3 acceleration, F d Indicates driving force, F f represents friction, F w Indicates wind force, M m represents the mass of the trolley, θ represents the track slope;

[0019] The spreader motion simulation is used to simulate the motion state of the spreader under the influence of various factors during vertical motion. The spreader motion speed is calculated as follows:

[0020] V2=f v2

【z i , z f , v i2 ,a2,m,F l , F g , k, F b , η d , f c (g, l, φ i ,ω i ,c,W d ,

[0021] F w )】;

[0022] Where: f v2 is the function for calculating the average speed of the spreader, f c is the swing influence function of the spreader, z i is the initial height, z f is the target height, v i2 is the initial speed, a2 is the acceleration, m is the total mass of the sling and cargo, F l is the lifting force, F g is gravity, k is the elasticity of the wire rope, F b is the braking force, η d is the efficiency of the buffer device, g is the acceleration of gravity, l is the length of the sling, φ i is the initial swing angle, ω i is the initial angular velocity, c is the damping coefficient, W d is the cargo weight distribution, F w for wind power;

[0023] The business simulation module is responsible for modeling various entities related to quay crane operations, providing data-driven functions, and simulating the operation instruction interaction, operation rules, and state transfer between various entities;

[0024] The simulation control module is used to control the simulation process, including starting, pausing, stopping, accelerating simulation, and decelerating simulation;

[0025] The operation backtracking module is used to record and store the simulation data of each step, including all information about the operation task status, the movement trajectory of the quay crane, and the vehicle position, so as to facilitate the historical simulation process to be viewed in time and fully reproduce the entire operation process;

[0026] The analysis and optimization module is used to propose optimization suggestions through time efficiency analysis, bottleneck identification, and parameter optimization, and input the optimization parameters into the quay crane motion simulation module for simulation;

[0027] The simulation display module is used to provide data and interface display of simulation tasks, as well as analysis of simulation results, to provide decision-making data support for port operators.

[0028] Furthermore, the data collected by the data interaction module includes environmental data, event data, smart driving vehicle data, and TOS business data. The data interaction module is provided with an event data configuration interface to facilitate the import of event data.

[0029] Furthermore, the business simulation module simulates contents including communication system, positioning and navigation, traffic management, operation coordination, quay crane docking operation, safety control, and unloading interaction.

[0030] The communication system simulation includes:

[0031] Data exchange: simulate the data exchange between the quay crane and the intelligent driving vehicle, as well as the data exchange that requires TOS to transfer;

[0032] Information transmission: simulate the real-time transmission process of data such as operation instructions, location information, status information, etc. Consider data transmission delay and packet loss rate.

[0033] The positioning and navigation simulation includes:

[0034] GNSS positioning: simulates the process of intelligent driving vehicles using the GNSS system to locate themselves, taking into account factors such as GNSS error and signal obstruction;

[0035] Path planning: Based on the lanes and current position of the quay crane operation area, simulate the process of intelligent driving vehicles planning the optimal driving path.

[0036] The traffic management simulation includes:

[0037] Scheduling algorithm: simulates the process of TOS system to dispatch intelligent driving vehicles in real time, taking into account the optimization of operation efficiency and energy consumption;

[0038] Conflict Detection: Simulates the process of detecting and resolving potential conflicts between multiple autonomous vehicles;

[0039] Traffic flow control: simulate the overall control of traffic flow in the quay crane operation area to avoid congestion and deadlock.

[0040] The operation collaborative simulation includes:

[0041] Task allocation: simulate the process of TOS system allocating specific operation tasks to intelligent driving vehicles;

[0042] State synchronization: simulates the process of real-time exchange of operation status information between the quay crane and the intelligent driving vehicle;

[0043] Arrival notification: simulates the process of an intelligent driving vehicle sending a ready signal to the quay crane after arriving at the operating area.

[0044] The simulation of the quay crane alignment operation includes:

[0045] Coarse positioning: simulates the initial positioning process of the intelligent driving vehicle after entering the operating area;

[0046] Precise alignment: simulate the process of the quay crane guiding the intelligent driving vehicle to perform precise alignment, considering the application of the visual system and laser ranging system;

[0047] Position correction: simulates the process of detecting position deviation of intelligent driving vehicles and making real-time corrections.

[0048] The safety control simulation includes:

[0049] Anti-collision system: simulates the anti-collision mechanism between the quay crane and the intelligent driving vehicle, including early warning and emergency braking;

[0050] Speed ​​control: simulates the process of dynamically adjusting the speed of the intelligent driving vehicle according to the conditions of the operating area;

[0051] Emergency handling: Simulate the response mechanisms for various emergency situations, such as communication interruption, equipment failure, etc.

[0052] The loading and unloading interaction simulation includes:

[0053] Readiness confirmation: simulates the process of the quay crane confirming that the intelligent driving vehicle is in place and in an operational state;

[0054] Operation instructions: simulate the quay crane sending instructions to the intelligent driving vehicle to start loading and unloading, and the confirmation process of the intelligent driving vehicle;

[0055] Completion notification: After the simulated loading and unloading operation is completed, the quay crane sends a completion signal to the intelligent driving vehicle.

[0056] Furthermore, the job backtracking module can query detailed records of a single job process.

[0057] Furthermore, the job backtracking module can export the backtracking data to facilitate the analysis and optimization module to perform data analysis and process optimization.

[0058] The simulation method of the quay crane operation process for port intelligent driving includes the following steps:

[0059] Step 1: Import simulation basic data, collect various types of information through the data interaction module, and import all equipment data, environment data, business data, and job tasks in TOS into the system;

[0060] Step 2: Create a simulation scene for the quay crane operation area, import port drawings to create a map, define different functional areas, build a static model based on the collected data, and then use programming languages ​​to implement each core business logic, access data, and abstract and dynamically simulate their operation processes;

[0061] Step 3: Create a simulation task.

[0062] 1) Simulation task data preparation: According to TOS data, basic data entry, and rule base configuration, the cargo quantity, ship arrival, and vehicle arrangement information of the port are obtained, and the corresponding data is generated and stored in the precondition database of the simulation task;

[0063] 2) Operation instruction simulation: According to the plan, simulate the operation instruction rules issued by the real TOS system, generate the corresponding operation quality, and send it to the vehicles and port machinery equipment for simulation operation;

[0064] 3) Exception handling method: configure business rules for dealing with abnormal situations, so that abnormal events found during the simulation can be simulated and handled according to the configured rules;

[0065] Step 4: Simulate the operation process of the quay crane. You can choose to perform refined simulation or macro simulation according to the corresponding simulation requirements. Refined simulation is used to analyze the operation process of a given quay crane in detail, and macro simulation is used to simulate the global business operation process.

[0066] Step 5: Output simulation results. Output simulation results, including indicators such as quay crane operation efficiency, ship's time in port, berth utilization, command abnormality rate, failure rate, quay crane operation time, quay crane operation vehicles, container loading and unloading quantity, and terminal lane congestion index. Simultaneously, feed simulation data back to the simulation system for closed-loop control to reduce simulation errors.

[0067] Step 6: Efficiency analysis and optimization: perform feature analysis, model training, and model prediction to obtain efficiency values, then adjust various parameters and continuously simulate in the simulation system to reach the efficiency bottleneck value;

[0068] Step 7: Backtrack the historical process of the simulation operation. Backtrack each operation process based on time and simulation tasks, and decouple time and discrete events through the simulation control module to accelerate the simulation process.

[0069] By adopting the above technical scheme, the beneficial effects of the present invention are as follows: (1) The present invention completely maps the complex port operation process into the digital space through fine dynamic modeling and coding. Through the digitization of the whole process, the port manager can intuitively observe and analyze the entire operation process in a virtual environment, which is conducive to timely discovering and solving potential problems; (2) The present invention provides two methods of fine simulation or macro simulation of the dynamics of quay crane equipment. According to the micro or macro needs, the fine dynamic model or the macro simplified model is used to accelerate the simulation; (3) The present invention simulates the operation process based on the constructed 3D simulation environment combined with natural environment data and random events, and feeds back the simulation data to the simulation system for closed-loop control, thereby reducing the simulation error and improving the authenticity of the operation process simulation; (4) The present invention provides a complete simulation test platform, and R&D personnel can easily integrate various newly developed algorithms into the platform, verify and evaluate their performance in a realistic virtual environment, and verify more efficiently through the acceleration mechanism. This low-cost and high-efficiency iterative optimization model significantly accelerates the time for new algorithms to be put into practice. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] 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.

[0071] Figure 1 It is a system block diagram of the quay crane operation process simulation system of the present invention;

[0072] Figure 2 It is the trolley motion and the sling motion direction diagram of the present invention;

[0073] Figure 3 It is the moving direction diagram of the cart of the present invention;

[0074] Figure 4 It is a flowchart of the implementation of the quay crane operation process simulation system of the present invention;

[0075] Figure 5 It is a specific flow chart of the quay crane operation process simulation method of the present invention. DETAILED DESCRIPTION

[0076] 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.

[0077] like Figure 1-Figure 5 As shown in the figure, the quay crane operation process simulation system for port intelligent driving includes a data interaction module, a quay crane motion simulation module, a business simulation module, a simulation control module, an operation backtracking module, an analysis and optimization module, and a simulation display module.

[0078] The data interaction module is used to coordinate the information flow between the quay crane, berth, intelligent driving vehicle and TOS terminal operating system, ensuring seamless connection and real-time data synchronization between various subsystems, and providing information support for the entire port automation operation;

[0079] The data interaction process between each subsystem is as follows:

[0080] 1. Data interaction between TOS and quay crane

[0081] A. Operation instructions are issued:

[0082] TOS generates detailed operation instructions based on the ship loading and unloading plan, and transmits the instructions to the quay crane model. The instruction content includes: container number, target location, operation type (loading / unloading), etc.

[0083] B. Status feedback:

[0084] The quay crane reports its working status to the TOS in real time, including: current position, operation progress, equipment health status, etc. The data is transmitted at a high frequency (such as multiple times per second) to ensure that the TOS has the latest situation.

[0085] C.Exception handling:

[0086] When the quay crane encounters an abnormal situation (such as equipment failure, container damage, etc.), it will immediately send an alarm message to the TOS. After receiving the alarm, the TOS may issue new instructions or activate the emergency plan.

[0087] 2. Data interaction between TOS and berths:

[0088] A. Berth Allocation:

[0089] TOS allocates berths to arriving ships according to the ship schedule. The allocation information includes: ship information, estimated arrival time, designated berth number, etc.

[0090] B. Berth status update:

[0091] The berth management system reports berth occupancy status to TOS in real time. The data includes: current berthed ship information, estimated departure time, berth availability, etc.

[0092] C. Resource Allocation:

[0093] Based on berth usage, TOS dynamically adjusts the allocation of quay cranes and smart driving vehicles.

[0094] 3. Data interaction between TOS and smart driving vehicles:

[0095] A. Task allocation:

[0096] TOS assigns specific tasks to each intelligent driving vehicle based on the overall operation plan. Task information includes: starting position, target position, loading and unloading points, estimated time, etc.

[0097] B. Status Monitoring:

[0098] Smart driving vehicles continuously report their location and other status information to TOS.

[0099] C. Traffic Control:

[0100] TOS coordinates the driving of multiple intelligent driving vehicles by sending control instructions to avoid conflicts.

[0101] The quay crane motion simulation module includes trolley motion simulation, trolley motion simulation, and spreader motion simulation. Each of the trolley motion simulation, trolley motion simulation, and spreader motion simulation has a specific dynamic model.

[0102] The business simulation module is responsible for modeling various entities related to quay crane operations, providing data-driven functions, and simulating the interaction of operation instructions, operation rules, state transfer, etc. between various entities;

[0103] The simulation control module is used to control the simulation process, including the start, pause, stop, acceleration simulation and deceleration simulation of the simulation. The simulation process can be paused by the simulation control module to facilitate viewing of all corresponding implementation status parameters of the quay crane, vehicle, etc. at the time node;

[0104] The operation backtracking module is used to record and store the simulation data of each step, including all information about the operation task status, the movement trajectory of the quay crane, and the vehicle position, so as to facilitate the historical simulation process to be viewed in time and fully reproduce the entire operation process. The operation backtracking module can query the detailed records of a single operation process, and the backtracking data can be exported through the operation backtracking module, so as to facilitate the analysis and optimization module to perform data analysis and process optimization;

[0105] The analysis and optimization module is used to propose optimization suggestions through time efficiency analysis, bottleneck identification, and parameter optimization, and input the optimization parameters into the quay crane motion simulation module for simulation;

[0106] Time efficiency analysis: statistical analysis of operation time consumption and efficiency indicators;

[0107] Bottleneck identification: Identify bottleneck links and key factors that affect efficiency;

[0108] Parameter optimization: Optimize operation parameters based on machine learning and other technologies;

[0109] The simulation display module is used to provide data and interface display of simulation tasks, as well as analysis of simulation results, to provide decision-making data support for port operators.

[0110] In this embodiment, the data collected by the data interaction module includes environmental data, event data, intelligent driving vehicle data, and TOS business data. The data interaction module is provided with an event data configuration interface to facilitate the import of event data. Abnormal events include real traffic abnormalities, equipment abnormalities, weather abnormalities, etc.

[0111] In this embodiment, the trolley motion simulation is used to simulate the state of the quay crane trolley affected by various factors when it moves laterally on the bridge. The factors affecting the quay crane trolley motion include: initial position, target position, initial speed, acceleration, trolley mass, inertia, driving force, friction, wind force, bridge deflection, and lateral vibration. The speed of the quay crane trolley is calculated as follows:

[0112] V1=fv1(yi,yf,vi1,a1,Mc,Fd,Ff,Fw,δ,Vt);

[0113] Where: fv1 is the calculation function of the average speed of the trolley, yi is the initial position, yf is the target position, vi1 is the initial speed, a1 is the acceleration, Mc is the mass of the trolley, Fd is the driving force, Ff is the friction force, Fw is the wind force, δ is the bridge deflection, and Vt is the lateral vibration;

[0114] The trolley motion simulation is used to simulate the state of the quay crane trolley affected by various factors when it moves linearly on the ground track. The factors affecting the quay crane trolley motion include: initial position, target position, initial speed, driving force, friction, wind force, trolley mass, inertia, track slope. The time calculation of the quay crane trolley motion is as follows:

[0115] Tt=fd3(xi,xf) / fv3(vi,a3,Fd,Ff,Fw,Mm,θ)

[0116] Where: fd3 represents the distance calculation function, xi represents the initial position of the vehicle, xf represents the target position, fv3 represents the speed calculation function of the vehicle, vi3 represents the initial speed, a3 represents the acceleration, Fd represents the driving force, Ff represents the friction force, Fw represents the wind force, Mm represents the vehicle mass, and θ represents the track slope;

[0117] The sling motion simulation is used to simulate the motion state of the sling after being affected by various factors during vertical motion. The motion of the sling is mainly to study vertical motion, and is affected by basic factors such as initial height, target height, initial speed, acceleration, total mass of sling and cargo, lifting force, inertia, gravity, wire rope elasticity, braking force, etc. At the same time, it is also affected by the swing of the sling during vertical motion. The factors affecting the vertical motion of the sling include: initial height, target height, initial speed, acceleration, total mass of sling and cargo, lifting force, gravity, wire rope elasticity, braking force, buffer device efficiency, swing amplitude, and the factors affecting the swing motion of the sling include: gravity acceleration, sling length, initial swing angle and angular velocity, damping coefficient, cargo weight distribution, wind force, and the speed of the sling motion is calculated as follows:

[0118] V2=fv2 [zi, zf, vi2, a2, m, Fl, Fg, k, Fb, eta, fc (g, l, φi, ωi, c, Wd, Fw)];

[0119] Wherein: fv2 is the calculation function of the average speed of the spreader, fc is the influence function of the spreader swing, zi is the initial height, zf is the target height, vi2 is the initial speed, a2 is the acceleration, m is the total mass of the spreader and the cargo, Fl is the lifting force, Fg is the gravity, k is the elasticity of the wire rope, Fb is the braking force, ηd is the efficiency of the buffer device, g is the acceleration of gravity, l is the length of the spreader, φi is the initial swing angle, ωi is the initial angular velocity, c is the damping coefficient, Wd is the cargo weight distribution, and Fw is the wind force.

[0120] In this embodiment, the business simulation module simulates the contents including communication system, positioning and navigation, traffic management, operation coordination, quay crane docking operation, safety control, and unloading interaction.

[0121] The communication system simulation includes:

[0122] Data exchange: simulate the data exchange between the quay crane and the intelligent driving vehicle, as well as the data exchange that requires TOS to transfer;

[0123] Information transmission: simulate the real-time transmission process of data such as operation instructions, location information, status information, etc. Consider data transmission delay and packet loss rate.

[0124] The positioning and navigation simulation includes:

[0125] GNSS positioning: simulates the process of intelligent driving vehicles using the GNSS system to locate themselves, taking into account factors such as GNSS error and signal obstruction;

[0126] Path planning: Based on the lanes and current position of the quay crane operation area, simulate the process of intelligent driving vehicles planning the optimal driving path.

[0127] The traffic management simulation includes:

[0128] Scheduling algorithm: simulates the process of TOS system to dispatch intelligent driving vehicles in real time, taking into account the optimization of operation efficiency and energy consumption;

[0129] Conflict Detection: Simulates the process of detecting and resolving potential conflicts between multiple autonomous vehicles;

[0130] Traffic flow control: simulate the overall control of traffic flow in the quay crane operation area to avoid congestion and deadlock.

[0131] The operation collaborative simulation includes:

[0132] Task allocation: simulate the process of TOS system allocating specific operation tasks to intelligent driving vehicles;

[0133] State synchronization: simulates the process of real-time exchange of operation status information between the quay crane and the intelligent driving vehicle;

[0134] Arrival notification: simulates the process of an intelligent driving vehicle sending a ready signal to the quay crane after arriving at the operating area.

[0135] The simulation of the quay crane alignment operation includes:

[0136] Coarse positioning: simulates the initial positioning process of the intelligent driving vehicle after entering the operating area;

[0137] Precise alignment: simulate the process of the quay crane guiding the intelligent driving vehicle to perform precise alignment, considering the application of the visual system and laser ranging system;

[0138] Position correction: simulates the process of detecting position deviation of intelligent driving vehicles and making real-time corrections.

[0139] The safety control simulation includes:

[0140] Anti-collision system: simulates the anti-collision mechanism between the quay crane and the intelligent driving vehicle, including early warning and emergency braking;

[0141] Speed ​​control: simulates the process of dynamically adjusting the speed of the intelligent driving vehicle according to the conditions of the operating area;

[0142] Emergency handling: Simulate the response mechanisms for various emergency situations, such as communication interruption, equipment failure, etc.

[0143] The loading and unloading interaction simulation includes:

[0144] Readiness confirmation: simulates the process of the quay crane confirming that the intelligent driving vehicle is in place and in an operational state;

[0145] Operation instructions: simulate the quay crane sending instructions to the intelligent driving vehicle to start loading and unloading, and the confirmation process of the intelligent driving vehicle;

[0146] Completion notification: After the simulated loading and unloading operation is completed, the quay crane sends a completion signal to the intelligent driving vehicle.

[0147] The simulation method of the quay crane operation process for port intelligent driving includes the following steps:

[0148] Step 1: Import the basic simulation data. Through the data interaction module, collect various types of information, and import all the equipment data, environmental data, business data, and operation tasks in TOS into the system. Equipment data includes the number and status of quay cranes, and control parameters related to the dynamic model, the number of intelligent driving vehicles, vehicle speed, control parameters and other equipment data, and quay crane operation events; environmental data includes the collection of natural environment data such as wind speed and weather; business data includes the collection of operation task information, vehicle dispatch plan data, etc.; the operation tasks in TOS include loading and unloading tasks, ship information, container lists, berth allocation and other information, which are the business data source of this module;

[0149] Step 2: Create a simulation scene for the quay crane operation area, import port drawings to create a map, define different functional areas, such as the quay crane operation area, intelligent driving vehicle driving path, loading and unloading points, waiting areas, etc., establish a static model based on the collected data, and then implement each core business logic through programming language, access data, and realize abstraction and dynamic simulation of their operation processes;

[0150] Step 3: Create a simulation task.

[0151] 1) Simulation task data preparation: According to TOS data, basic data entry, and rule base configuration, the cargo quantity, ship arrival, and vehicle arrangement information of the port are obtained, and the corresponding data is generated and stored in the precondition database of the simulation task;

[0152] 2) Operation instruction simulation: According to the plan, simulate the operation instruction rules issued by the real TOS system, generate the corresponding operation quality, and send it to the vehicles and port machinery equipment for simulation operation;

[0153] 3) Exception handling method: Configure business rules for dealing with abnormal situations, so that abnormal events found during the simulation can be simulated and handled according to the configured rules, such as the following rules:

[0154] A. Quay crane and vehicle alignment retry mechanism

[0155] Set a timeout (such as N minutes) for the alignment between the quay crane and the vehicle. If the intelligent driving vehicle cannot complete the alignment within the specified time, immediately check whether the status data and environmental data of the equipment are normal. If normal, try to re-identify the alignment again. If more than N retries fail, an alarm is triggered, the alignment is stopped, and the alignment failure is reported, requesting manual intervention before recovery.

[0156] B. Cascading Failure Handling Mechanism

[0157] When a quay crane fails, it will be reported to the central dispatching system, the affected operation scope will be analyzed, all related operations will be suspended, and attempts will be made to transfer the suspended operations to backup equipment resources. If there are not enough backup resources, all operations in the quay crane area will be completely suspended and resumed after manual intervention.

[0158] C. Fault handling simulation mechanism simulating human intervention

[0159] The faults are divided into different levels according to their severity, and the corresponding fault recovery time of each level increases gradually;

[0160] Step 4: Simulate the operation process of the quay crane. You can choose to perform refined simulation or macro simulation according to the corresponding simulation requirements. Refined simulation is used to analyze the operation process of a given quay crane in detail, and macro simulation is used to simulate the global business operation process.

[0161] Step 5: Output simulation results. Output simulation results, including indicators such as quay crane operation efficiency, ship's time in port, berth utilization, command abnormality rate, failure rate, quay crane operation time, quay crane operation vehicles, container loading and unloading quantity, and terminal lane congestion index. Simultaneously, feed simulation data back to the simulation system for closed-loop control to reduce simulation errors.

[0162] Step 6: Efficiency analysis and optimization: perform feature analysis, model training, and model prediction to obtain efficiency values, then adjust various parameters and continuously simulate in the simulation system to reach the efficiency bottleneck value;

[0163] Step 7: Backtrack the history of the simulation operation. Backtrack each operation process based on time and simulation tasks, and decouple time and discrete events through the simulation control module to accelerate the simulation process.

[0164] Time decoupling is to separate the simulation logic time from the actual running time, and introduce a controllable time acceleration factor, such as 1x (real time), 2x (double the speed), 4x, 16x, etc. During the acceleration process, a single quay crane is taken as the center to simplify the operation process of the vehicles and services related to the quay crane, or time is used as a condition to simplify the operation process of multiple quay cranes and the vehicles and services related to the quay cranes at the same time;

[0165] The key event is to discretize the continuous motion process into a series of key events using discrete event simulation. For example, when accelerating at the starting point and the end point, the insignificant events in the middle can be skipped and only the key events can be triggered.

[0166] In this embodiment, part of the simulation process in step 2 is as follows:

[0167] 1. Quay crane business simulation

[0168] A. Create a new quay crane entity, add basic attributes, such as operating efficiency, failure rate, maximum lifting weight, etc., add variables, such as current position, current task, energy consumption, trolley moving speed, trolley position, trolley moving speed, trolley position, spreader height, spreader lifting speed, etc.;

[0169] B. Define different states of the quay crane and their transition conditions, such as idle, working, faulty, etc.;

[0170] C. Create the operation process functions of the quay crane, such as receiving tasks, moving, loading and unloading, etc. Each step can be associated with the corresponding animation;

[0171] D. Dynamic simulation: the dynamic model can be selected according to the simulation precision. When a given quay crane operation process needs to be analyzed in detail, the moving speed and acceleration of the trolley and carriage of the quay crane should conform to the mechanical dynamics of the quay crane during simulation. For the global business operation process, a simplified dynamic model can be used to perform equivalent operation time of the trolley and carriage during simulation.

[0172] 2. Intelligent Driving Vehicle Physical Simulation

[0173] Create an entity for the smart driving vehicle and set properties such as speed, power, load status, trunk size, direction, vehicle position, etc.

[0174] 3. Berth Business Simulation

[0175] A. Create a new entity for the berth and define the properties of the berth, including berth number, location, length, water depth, current status, berthed ships, remaining operation time, and historical usage records;

[0176] B. Define the behavior of ship arrival processing and operation progress update through functions;

[0177] C. Define the berth status change function and record the timestamp every time the status changes.

[0178] 4. Dockside Lane Modeling and Simulation

[0179] A. The dock lane is a key channel connecting the berth and the yard. Its traffic flow needs to be precisely controlled. Create an entity and define the attributes including lane number (id), lane type, length, current number of vehicles, maximum capacity, current average speed, and congestion level.

[0180] B. Define the behaviors of vehicles entering and leaving lanes, updating vehicle congestion levels, and traffic control through functions.

[0181] In this embodiment, in step 4, in order to take into account the needs of macroscopic, mesoscopic and microscopic simulations, different dynamic models are used to accelerate the simulation. Microscopic simulation adopts refined simulation, and when accelerated simulation is needed, macroscopic simulation is adopted.

[0182] in:

[0183] 1. The refined simulation process is as follows:

[0184] 1.1. Ship berthing: After the ship arrives, it notifies the relevant quay crane. After receiving the assignment notification from TOS, the quay crane moves to the designated position according to the actual mechanical dynamic characteristics, accurately calculates the moving speed and acceleration of the large and small vehicles, and waits for the intelligent driving vehicle to arrive;

[0185] 1.2. At the same time, after receiving the TOS task instruction, the intelligent driving vehicle simulates the actual driving path and speed and goes to the quay crane operation waiting area;

[0186] 1.3. After the intelligent driving vehicle arrives at the working parking space, it simulates the actual signal transmission delay and sends a ready signal to the quay crane. The quay crane aligns through precise lidar or visual calibration models, taking into account possible errors and correction processes;

[0187] 1.4. After the quay crane confirms that the intelligent driving vehicle is in place, it accurately simulates the movement of the trolley and the spreader, taking into account the precise extraction time T of containers in different positions (rows, columns, layers);

[0188] T=f(col i ,row i ,heighti ,Mb i )+tw(id,time),

[0189] Among them, f() is the lifting time mapping function for lifting the container at the specified position, col i row is the row on the ship where the container is hoisted. i The height of the ship where the container is hoisted i Mb is the layer on the ship where the container is hoisted. i is the weight of the hoisted container; tw() is the human interference time of the bridge crane operator, which is related to the personnel id and operation time time.

[0190] 1.5. After the quay crane completes loading and unloading, it simulates its own status inspection process (hanger height, trolley position, etc.) in detail, and simulates actual signal transmission to send a signal to the intelligent driving vehicle that it can leave.

[0191] 1.6. After receiving the signal, the intelligent driving vehicle accurately simulates its own status inspection process and drives away from the quay crane operation parking space according to the actual acceleration and speed curve.

[0192] 1.7. After the intelligent driving vehicle leaves, the quay crane accurately simulates the update process of the operation parking space status and simulates the actual data transmission delay to report the completion of the operation to TOS.

[0193] 2. Macro simulation process

[0194] 2.1. Ship berthing: After the ship arrives, the average berthing time is used for simplified simulation and the relevant TOS system is notified. After the quay crane receives the TOS notification, it uses the equivalent time to calculate the process of the quay crane moving to the designated position and waits for the intelligent driving vehicle to arrive;

[0195] 2.2. After TOS notifies the intelligent driving vehicle that the quay crane is ready and the operation parking space is free, the intelligent driving vehicle uses the average driving time model to simplify the process of simulating the intelligent driving vehicle going to the operation parking space;

[0196] 2.3. After the intelligent driving vehicle arrives at the working parking space, it uses a fixed signal transmission time to simulate the process of sending a ready signal to the quay crane. The quay crane uses a simplified alignment model and ignores possible errors and correction processes.

[0197] 2.4. After the quay crane confirms that the intelligent driving vehicle is in place, it uses the average operation time model to simplify the movement of the simulation trolley and spreader, without considering the specific extraction time differences of containers at different locations;

[0198] 2.5. After the quay crane completes loading and unloading, it uses a fixed inspection time to simulate its own status inspection process, and uses a fixed signal transmission time to send a departure signal to the intelligent driving vehicle;

[0199] 2.6. After receiving the signal, the intelligent driving vehicle uses a fixed inspection time to simulate its own status inspection process, and uses the average driving time to simulate the process of leaving the quay crane operation parking space;

[0200] 2.7. After the intelligent driving vehicle leaves, the quay crane uses a fixed status update time to simulate the operation parking space status update process, and uses a fixed data transmission time to simulate the process of reporting the completion of the operation to TOS.

[0201] By integrating multi-source heterogeneous data in the port, the full interactive operation process of intelligent driving quay crane operations is simulated, and a micro-simulation model of intelligent driving vehicles in the port quay crane operation area is constructed. At the same time, the properties, control, and operation logic of multiple entities of intelligent driving vehicles and quay crane equipment are simulated. By combining refined micro-models with macro-simplified models, it is compatible with micro-, meso-, and macro-simulation requirements, supports accelerated simulation, and deduces future operating conditions, providing decision support for the overall port plan design and efficiency optimization.

[0202] Components not described in detail herein are prior art.

[0203] 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 quay crane operation process simulation system for port intelligent driving is characterized by: It includes data interaction module, quay crane motion simulation module, business simulation module, simulation control module, operation backtracking module, analysis and optimization module, and simulation display module. The data interaction module is used to coordinate the information flow between the quay crane, berth, intelligent driving vehicle and TOS terminal operating system, ensuring seamless connection and real-time data synchronization between various subsystems, and providing information support for the entire port automation operation; The quay crane motion simulation module includes trolley motion simulation, trolley motion simulation, and spreader motion simulation. The trolley motion simulation is used to simulate the state of the quay crane trolley affected by various factors when it moves horizontally on the bridge frame; the trolley motion simulation is used to simulate the state of the quay crane trolley affected by various factors when it moves linearly on the ground track; the spreader motion simulation is used to simulate the motion state of the spreader affected by various factors when it moves vertically; The business simulation module is responsible for modeling various entities related to quay crane operations, providing data-driven functions, and simulating the operation instruction interaction, operation rules, and state transfer between various entities; The simulation control module is used to control the simulation process, including starting, pausing, stopping, accelerating simulation, and decelerating simulation; The operation backtracking module is used to record and store the simulation data of each step, including all information about the operation task status, the movement trajectory of the quay crane, and the vehicle position, so as to facilitate the historical simulation process to be viewed in time and fully reproduce the entire operation process; The analysis and optimization module is used to propose optimization suggestions through time efficiency analysis, bottleneck identification, and parameter optimization, and input the optimization parameters into the quay crane motion simulation module for simulation; The simulation display module is used to provide data and interface display of simulation tasks, as well as analysis of simulation results, to provide decision-making data support for port operators.

2. The system and method for quay crane operation process simulation for port intelligent driving according to claim 1 is characterized by: The data collected by the data interaction module includes environmental data, event data, smart driving vehicle data, and TOS business data. The data interaction module is provided with an event data configuration interface to facilitate the import of event data.

3. The system and method for quay crane operation process simulation for port intelligent driving according to claim 1 is characterized by: The business simulation module simulates the contents including communication system, positioning and navigation, traffic management, operation coordination, quay crane docking operation, safety control, and unloading interaction.

4. The system and method for quay crane operation process simulation for port intelligent driving according to claim 1 is characterized by: The job backtracking module can query the detailed records of a single job process.

5. The system and method for quay crane operation process simulation for port intelligent driving according to claim 1 is characterized by: The job backtracking module can be used to export the backtracking data, which is convenient for the analysis and optimization module to perform data analysis and process optimization.

6. A method for simulating a quay crane operation process for intelligent port driving using the quay crane operation process simulation system for intelligent port driving according to any one of claims 1 to 5, characterized in that: The following steps are involved: Step 1: Import simulation basic data, collect various types of information through the data interaction module, and import all equipment data, environment data, business data, and job tasks in TOS into the system; Step 2: Create a simulation scene for the quay crane operation area, import port drawings to create a map, define different functional areas, build a static model based on the collected data, and then use programming languages ​​to implement each core business logic, access data, and abstract and dynamically simulate their operation processes; Step 3: Create a simulation task. 1) Simulation task data preparation: According to TOS data, basic data entry, and rule base configuration, the cargo quantity, ship arrival, and vehicle arrangement information of the port are obtained, and the corresponding data is generated and stored in the precondition database of the simulation task; 2) Operation instruction simulation: According to the plan, simulate the operation instruction rules issued by the real TOS system, generate the corresponding operation quality, and send it to the vehicles and port machinery equipment for simulation operation; 3) Exception handling method: configure business rules for dealing with abnormal situations, so that abnormal events found during the simulation can be simulated and handled according to the configured rules; Step 4: Simulate the operation process of the quay crane. You can choose to perform refined simulation or macro simulation according to the corresponding simulation requirements. Refined simulation is used to analyze the operation process of a given quay crane in detail, and macro simulation is used to simulate the global business operation process. Step 5: Output simulation results. Output simulation results, including indicators such as quay crane operation efficiency, ship's time in port, berth utilization, command abnormality rate, failure rate, quay crane operation time, quay crane operation vehicles, container loading and unloading quantity, and terminal lane congestion index. Simultaneously, feed simulation data back to the simulation system for closed-loop control to reduce simulation errors. Step 6: Efficiency analysis and optimization: perform feature analysis, model training, and model prediction to obtain efficiency values, then adjust various parameters and continuously simulate in the simulation system to reach the efficiency bottleneck value; Step 7: Backtrack the historical process of the simulation operation. Backtrack each operation process based on time and simulation tasks, and decouple time and discrete events through the simulation control module to accelerate the simulation process.

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