Port intelligent driving-oriented field bridge operation process simulation system and operation method

Abstract

The invention relates to the technical field of field bridge operation process simulation, in particular to a field bridge operation process simulation system for port intelligent driving and an operation method, and the system comprises the following modules: a modeling layer, a simulation environment, an instruction layer, an application display layer and an AI base layer. The method has the advantages that the high-precision simulation model is constructed to simulate the operation process of the port intelligent driving field bridge in the complex operation environment, the operation process is optimized, the operation efficiency is improved, the operation cost is reduced, and powerful support is provided for research, development, test and verification of the port intelligent driving technology. The advanced simulation technology is utilized to simulate the operation process of the port yard bridge in an intelligent driving mode in an all-around and multi-angle mode, so that the fine management of the operation process is realized. Indexes such as equipment operation states, operation efficiency and energy consumption conditions in different operation scenes are accurately simulated through simulation, and port managers are helped to better understand and master the operation process, so that more scientific and reasonable operation plans and scheduling schemes are formulated.

Description

Technical Field

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

[0002] The simulation technology of the yard crane operation process is based on advanced computer technology, modeling and simulation theory, and a deep understanding of the port operation process. With the rapid development of computer software and hardware technology, the computing power and visualization effect of the simulation system are constantly improving, which provides the possibility for high-precision and high-realistic simulation. At the same time, new modeling technologies and algorithms are constantly emerging, such as deep learning and reinforcement learning, which provide strong technical support for the intelligent and adaptive capabilities of the simulation system.

[0003] In terms of simulation accuracy, the field crane operation process simulation technology has achieved accurate simulation of various factors in the operation process, including equipment performance, operating environment, personnel operation, etc., by continuously optimizing the simulation model and algorithm. This makes the simulation results closer to reality and provides a more reliable basis for decision-making for port managers. At the same time, the simulation efficiency is also constantly improving. Through parallel computing, cloud computing and other technical means, the simulation cycle has been greatly shortened and the simulation efficiency has been improved.

[0004] The application scenarios of the crane operation process simulation technology are constantly expanding, from the initial single application of operation process optimization and equipment performance evaluation to multiple fields such as intelligent scheduling, risk warning, and fault prediction. Through the simulation system, port managers can simulate the operation conditions in different scenarios and evaluate the effects of different strategies, so as to formulate more scientific and reasonable operation plans and scheduling plans. In addition, the simulation system can also be used to train operators to improve their operating skills and ability to respond to emergencies.

[0005] With the continuous development of artificial intelligence technology, the simulation technology of the yard crane operation process is also moving towards intelligence and automation. By integrating intelligent algorithms and sensor technology, the simulation system can realize real-time monitoring and intelligent adjustment of the operation process, improving operation efficiency and safety. At the same time, the simulation system can also achieve seamless connection with other intelligent systems of the port to form a complete port intelligent solution and promote the improvement of the overall intelligent level of the port.

[0006] At present, the existing technology for intelligent driving of port yard crane operation process is mainly through the port tally system (TOS) or equipment control system (ECS).

[0007] However, although the port tally system (TOS) can realize real-time monitoring, intelligent analysis and automatic processing of port logistics data by integrating advanced technical means, such as big data analysis, artificial intelligence, and Internet of Things perception, so as to improve the efficiency and accuracy of port logistics, the port tally system (TOS) cannot quickly generate the operation instructions of the yard bridge in the future day, and cannot meet the needs of accelerated simulation of simulation conditions. In addition, the system is relatively large, and the simulation process will affect the flow of the main business.

[0008] Although the Equipment Control System (ECS) can obtain port operation data in real time, such as equipment status, operation tasks, cargo information, etc., and make intelligent analysis and decisions based on these data, optimize the operation process, and improve operation efficiency, the Equipment Control System (ECS) cannot collaboratively simulate the operation and simulation of the field crane, and cannot deduce the subsequent operation conditions based on the field crane operation process.

[0009] Based on the purpose of solving the above-mentioned existing technical problems and defects, the present invention proposes a field crane operation process simulation system and operation method for port intelligent driving. Summary of the invention

[0010] The technical problem to be solved by the present invention is to overcome the existing defects, provide a simulation system and operation method for the operation process of a yard crane for intelligent driving in ports, and simulate the operation process of the intelligent driving yard crane in ports in complex operating environments by constructing a high-precision simulation model, so as to optimize the operation process, improve operation efficiency, reduce operating costs, and provide strong support for the research and development, testing and verification of intelligent driving technology in ports. Using advanced simulation technology, the operation process of the port yard crane in the intelligent driving mode is simulated in all directions and from multiple angles to achieve refined management of the operation process. Through simulation, indicators such as equipment operating status, operation efficiency, and energy consumption in different operation scenarios can be accurately simulated to help port managers better understand and master the operation process, thereby formulating more scientific and reasonable operation plans and scheduling plans, which can effectively solve the problems in the background technology:

[0011] The crane operation process simulation system for port intelligent driving includes the following modules: modeling layer, simulation environment, instruction layer, application display layer and AI base layer;

[0012] The modeling layer includes a crane motion model, a crane operation model, and a crane collaborative interaction model, which are used to provide simulation models of various cranes in the port area for simulation operation, and can accurately reflect the collaborative relationship and movement trajectory between the cranes and other equipment in the port;

[0013] The simulation environment layer includes a vehicle coordination model and a port road model, which are used to provide a basic scenario for the field bridge simulation operation, and to construct the vehicle coordination model and the port road model according to the actual port environment data;

[0014] The instruction layer includes TOS port machinery instructions and simulation dispatching instructions, which are used to provide access to field crane operation instructions and provide operation-driven instruction operation data for simulation operation;

[0015] The application display layer includes simulation result display, simulation process acceleration and simulation task control;

[0016] The AI ​​base layer includes field bridge data analysis, field bridge event deduction and prediction, field bridge scheduling rule library and environmental information simulation, and its function is to build a base based on the AI ​​big model.

[0017] The operation method of the field crane operation process simulation system for port intelligent driving includes the following steps:

[0018] Step 1: Data collection and preprocessing:

[0019] (1) Physical parameters of the crane: Maximum lifting capacity (W m ax), maximum lifting height (H m ax) and the maximum span (L m ax) physical parameters;

[0020] (2) Historical operation data: Use data cleaning algorithms to process historical operation data and extract the average operation cycle (T a vg) and mean time between failures (MTBF);

[0021] (3) Collaborative operation data: Use time series analysis algorithms to identify the operation synchronization between collaborative equipment and field cranes, and calculate the collaborative efficiency index (CEI);

[0022] Step 2: Model construction:

[0023] (1) Field crane motion model: The motion model of the field crane is constructed using dynamic equations and mechanical system dynamics software to simulate its dynamic response under different working conditions;

[0024] (2) Field crane operation model: Based on the operation flow chart and state machine model, the operation status and state transition conditions of the field crane are defined, and the operation sequence is optimized using the operation scheduling algorithm;

[0025] (3) Collaborative interaction model of field bridge: Using the multi-agent system (MAS) theory, a collaborative interaction model between the field bridge and other equipment is constructed, and information exchange and collaborative decision-making are achieved through communication protocols and message passing mechanisms.

[0026] (4) Field crane simulation model: Integrate the above-mentioned field crane motion model, field crane operation model and field crane collaborative interaction model, and use discrete event simulation software or a custom simulation engine for simulation;

[0027] Step 3: Issue the operation instruction:

[0028] Design an operation instruction generation algorithm to automatically generate detailed operation instructions including the operation task ID, cargo location, target location and estimated completion time according to the operation plan and resource allocation of the port TOS system;

[0029] Step 4: Simulation job execution:

[0030] (1) Vertical operation simulation of the crane: using the lifting speed function v(t) = at and the height calculation formula h(t) = 0.5a*t 2 Simulate the vertical lifting process;

[0031] (2) Collaborative operation simulation: The collaborative algorithm is used to adjust the operation time window of the field crane and other equipment to ensure the smooth progress of the collaborative operation. The collaborative efficiency formula CE = actual completion time / theoretical shortest completion time is used to evaluate the collaborative effect;

[0032] (3) Adjustment of operation sequence: Use the operation optimization algorithm to adjust the operation sequence in real time to cope with emergencies and optimize overall operation efficiency;

[0033] Step 5: Information reporting and feedback:

[0034] Design information reporting protocols and data structures to report the status information, operation progress, and abnormal conditions of the yard crane to the port TOS system in real time, and use feedback control algorithms to fine-tune the simulation model based on the reported information to ensure that the simulation results are close to the actual situation;

[0035] Step 6: Simulation operation completed:

[0036] When all scheduled tasks have completed simulation, collect all the data during the simulation and prepare for result evaluation;

[0037] Step 7: Result evaluation:

[0038] Use statistical analysis methods to evaluate simulation results, compare with actual operation data, calculate simulation accuracy and error range, and use performance evaluation indicators to quantify simulation effects;

[0039] Step 8: Model optimization:

[0040] Based on the evaluation results, the deficiencies and potential optimization points in the simulation model are identified, and the model parameters are adjusted and optimized using the parameter optimization algorithm. The simulation test, result evaluation and optimization work are repeated to form a closed-loop optimization process until the expected simulation effect and accuracy are achieved.

[0041] As a preferred technical solution of the present invention, step one and step two are the preparation stage, step three, step four and step five are the simulation test stage, and step six, step seven and step eight are the result evaluation and optimization stage.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] 1. Improve port operation efficiency: Through simulation methods, the operation process of port yard cranes can be accurately simulated to help port managers better understand and optimize the operation process. This optimization can reduce unnecessary waiting time and operational conflicts, thereby improving the overall operation efficiency of the port.

[0044] 2. Reduce operational risks: The simulation system can simulate various potential operational risks, such as equipment failure, traffic congestion, etc., so that port managers can verify risk identification and response strategies in a virtual environment. This helps to reduce risks and improve safety and stability in actual operations.

[0045] 3. Support decision-making: Based on the data analysis of simulation results, port managers can make more scientific decisions, such as equipment configuration, job scheduling, etc. This data-based decision-making process can reduce the uncertainty caused by subjective judgment and improve the effectiveness and accuracy of decision-making.

[0046] 4. Promote technological innovation: The application of simulation methods provides strong support for the research and development of port intelligent driving technology. By simulating the operation process in different scenarios, R&D personnel can have a deeper understanding of technical requirements and application scenarios, thereby promoting continuous innovation and optimization of technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a structural block diagram of the simulation system of the present invention.

[0048] Figure 2 The flowchart of the operation method of the simulation system of the present invention. DETAILED DESCRIPTION

[0049] 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.

[0050] See also Figure 1-2 The present invention provides a technical solution: a field crane operation process simulation system for port intelligent driving, including the following modules: a modeling layer, a simulation environment, an instruction layer, an application display layer and an AI base layer.

[0051] The modeling layer includes the yard crane motion model, the yard crane operation model and the yard crane collaborative interaction model, which is used to provide simulation models of various types of yard cranes in the port area for simulation operation, and can accurately reflect the collaborative relationship and movement trajectory between the yard cranes and other equipment in the port.

[0052] Specifically, the field crane motion model accurately simulates the motion state of the field crane during operation based on the principles of physics and mechanical characteristics, including lifting, translation, rotation and other actions, to ensure that the simulation results can truly reflect the actual operation of the field crane. The field crane operation model simulates the specific behavior of the field crane during operations such as loading and unloading cargo and moving positions according to the port's operating procedures and specifications. Consider a variety of factors, such as cargo weight, size, shape and operating environment, to ensure the accuracy and reliability of the simulation results. The field crane collaborative interaction model simulates the collaborative operation process between multiple field cranes, including information sharing, task allocation, conflict resolution, etc. Ensure that the field cranes in the simulation environment can work together efficiently and improve the operating efficiency of the entire port. Simulate the interaction process between the field crane and other equipment (such as container trucks, rail cranes, etc.) or systems (such as TOS). Consider a variety of interaction methods and protocols to ensure that the field crane in the simulation environment can seamlessly connect with other equipment or systems to achieve real-time information sharing and accurate execution of instructions.

[0053] The simulation environment layer includes a vehicle coordination model and a port road model, which are used to provide the basic scenario for the field crane simulation operation. The vehicle coordination model and the port road model are constructed according to the actual port environment data.

[0054] Specifically, the vehicle coordination model simulates the coordinated operation between various vehicles in the port (including self-driving yard cranes, container trucks, etc.), ensuring that they can operate efficiently according to the predetermined path and speed to avoid collisions and congestion. The port road model accurately restores the port's road layout, traffic signs, obstacles and other information to ensure that vehicles in the simulation environment can truly follow the port's traffic rules and simulate driving conditions under actual road conditions.

[0055] The instruction layer includes TOS port machinery instructions and simulation scheduling instructions, which are used to provide access to yard crane operation instructions and provide operation-driven instruction operation data for simulation operation.

[0056] Specifically, the instruction layer serves as a bridge between the simulation environment and actual operations, receiving port machinery instructions and simulation scheduling instructions from TOS, and guiding the field crane in the simulation environment to operate according to these instructions. In addition, it also has a prediction function, which can predict future operation trends and potential problems based on the current status and instructions.

[0057] The application display layer includes simulation result display, simulation process acceleration and simulation task control.

[0058] Specifically, the simulation task control module is responsible for setting the goals, parameters and constraints of the simulation task to ensure that the simulation process can proceed according to the predetermined plan. At the same time, it can also monitor the execution status of the simulation task and adjust the simulation parameters in time to meet different test requirements. The simulation process acceleration module is to improve the simulation efficiency and use advanced algorithms and technologies to accelerate the simulation process so that large-scale simulation tests can be completed in a shorter time. The simulation result display module intuitively displays the data and information in the simulation process in the form of charts, animations, etc., which is convenient for users to understand and analyze the simulation results. At the same time, it also supports the export of simulation reports for subsequent analysis and decision-making.

[0059] The AI ​​base layer includes field bridge data analysis, field bridge event simulation and prediction, field bridge scheduling rule base and environmental information simulation. Its function is to build a base based on the AI ​​big model.

[0060] Specifically, the field crane data analysis module conducts in-depth mining and analysis of the field crane operation data during the simulation process to extract valuable information and rules. It helps users understand the field crane's operating performance, efficiency bottlenecks and potential problems. The field crane event simulation and prediction module is based on historical data and current status, and can predict field crane operation events (such as failures, congestion, etc.) that may occur in the future, and give corresponding countermeasures and suggestions. It helps port companies prepare in advance and reduce operational risks. The field crane scheduling rule base module formulates and optimizes the field crane scheduling rules according to the port's operational needs and resource conditions. Consider a variety of factors, such as operation priority, equipment status, traffic conditions, etc., to ensure that the field crane can complete its operating tasks efficiently and orderly.

[0061] The operation method of the field crane operation process simulation system for port intelligent driving includes the following steps:

[0062] Step 1: Data collection and preprocessing:

[0063] (1) Physical parameters of the crane: Maximum lifting capacity (W m ax), maximum lifting height (Hm ax) and the maximum span (L m ax) and other physical parameters;

[0064] (2) Historical operation data: Use data cleaning algorithms (such as missing value filling, outlier detection and removal) to process historical operation data and extract key indicators such as average operation cycle (T a vg) and mean time between failures (MTBF), etc.

[0065] (3) Collaborative operation data: Use time series analysis algorithms to identify the operational synchronization between collaborative equipment (such as container trucks) and yard cranes, and calculate the collaborative efficiency index (CEI);

[0066] Step 2: Model construction:

[0067] (1) Field bridge motion model: The field bridge motion model is constructed using dynamic equations (such as Newton's second law F=ma) and mechanical system dynamics software (such as ADAMS) to simulate its dynamic response under different working conditions;

[0068] (2) Crane operation model: Based on the operation flow chart and state machine model, define the operation state (such as idle, lifting, translation, rotation and lowering) and state transition conditions of the crane, and use the operation scheduling algorithm (such as genetic algorithm, simulated annealing algorithm) to optimize the operation sequence;

[0069] (3) Collaborative interaction model of the field bridge: Using the multi-agent system (MAS) theory, a collaborative interaction model between the field bridge and other equipment is constructed, and information exchange and collaborative decision-making are achieved through communication protocols (such as TCP / IP) and message passing mechanisms.

[0070] (4) Field crane simulation model: Integrate the above-mentioned field crane motion model, field crane operation model and field crane collaborative interaction model, and use discrete event simulation software (such as Arena, Flexsim) or a custom simulation engine for simulation;

[0071] Step 3: Issue the operation instruction:

[0072] Design an operation instruction generation algorithm to automatically generate detailed operation instructions such as operation task ID, cargo location, target location and estimated completion time according to the operation plan and resource allocation of the port TOS system;

[0073] Step 4: Simulation job execution:

[0074] (1) Vertical operation simulation of the crane: Use the lifting speed function v(t) = at (a is the acceleration) and the height calculation formula h(t) = 0.5a*t 2 (Ignore air resistance) Simulate the vertical lifting process and consider the effect of load changes on speed and acceleration;

[0075] (2) Collaborative operation simulation: The operation time windows of the field crane and other equipment are adjusted through collaborative algorithms (such as priority-based scheduling algorithms) to ensure the smooth progress of collaborative operations. The collaborative efficiency formula CE = actual completion time / theoretical shortest completion time is used to evaluate the collaborative effect.

[0076] (3) Adjustment of job sequence: Use job optimization algorithms (such as dynamic programming and heuristic search) to adjust the job sequence in real time to cope with emergencies and optimize overall job efficiency;

[0077] Step 5: Information reporting and feedback:

[0078] Design information reporting protocols and data structures to report the status information, operation progress, and abnormal conditions of the yard crane to the port TOS system in real time, and use feedback control algorithms (such as PID control) to fine-tune the simulation model according to the reported information to ensure that the simulation results are close to the actual situation;

[0079] Step 6: Simulation operation completed:

[0080] When all scheduled tasks have completed simulation, collect all the data during the simulation and prepare for result evaluation;

[0081] Step 7: Result evaluation:

[0082] Use statistical analysis methods (such as mean, variance, and confidence interval) to evaluate simulation results, compare with actual operation data, calculate simulation accuracy and error range, and use performance evaluation indicators (such as percentage of operating efficiency improvement and percentage of energy consumption reduction) to quantify simulation effects;

[0083] Step 8: Model optimization:

[0084] According to the evaluation results, the deficiencies and potential optimization points in the simulation model are identified, and the model parameters are adjusted and optimized using parameter optimization algorithms (such as gradient descent method and particle swarm optimization algorithm). The simulation test, result evaluation and optimization work are repeated to form a closed-loop optimization process until the expected simulation effect and accuracy are achieved.

[0085] Specifically, step one and step two are the preparation stage, step three, step four and step five are the simulation test stage, and step six, step seven and step eight are the result evaluation and optimization stage.

[0086] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. The crane operation process simulation system for port intelligent driving is characterized by: Includes the following modules: modeling layer, simulation environment, instruction layer, application display layer and AI base layer; The modeling layer includes a crane motion model, a crane operation model, and a crane collaborative interaction model, which are used to provide simulation models of various cranes in the port area for simulation operation, and can accurately reflect the collaborative relationship and movement trajectory between the cranes and other equipment in the port; The simulation environment layer includes a vehicle coordination model and a port road model, which are used to provide a basic scenario for the field bridge simulation operation, and to construct the vehicle coordination model and the port road model according to the actual port environment data; The instruction layer includes TOS port machinery instructions and simulation dispatching instructions, which are used to provide access to field crane operation instructions and provide operation-driven instruction operation data for simulation operation; The application display layer includes simulation result display, simulation process acceleration and simulation task control; The AI ​​base layer includes field bridge data analysis, field bridge event deduction and prediction, field bridge scheduling rule base and environmental information simulation, and its function is to build a base based on the AI ​​big model.

2. The operating method of the field crane operation process simulation system for port intelligent driving according to claim 1 is characterized by: The following steps are involved: Step 1: Data collection and preprocessing: (1) Physical parameters of the crane: collect the physical parameters of the maximum lifting weight, maximum lifting height and maximum span; (2) Historical operation data: Use data cleaning algorithms to process historical operation data and extract indicator data such as average operation cycle and failure interval time; (3) Collaborative operation data: Use time series analysis algorithms to identify the operation synchronization between collaborative equipment and field cranes, and calculate the collaborative efficiency index; Step 2: Model construction: (1) Field crane motion model: The motion model of the field crane is constructed using dynamic equations and mechanical system dynamics software to simulate its dynamic response under different working conditions; (2) Field crane operation model: Based on the operation flow chart and state machine model, the operation status and state transition conditions of the field crane are defined, and the operation sequence is optimized using the operation scheduling algorithm; (3) Field bridge collaborative interaction model: Using the multi-agent system theory, a collaborative interaction model between the field bridge and other equipment is constructed, and information exchange and collaborative decision-making are achieved through communication protocols and message passing mechanisms. (4) Field crane simulation model: Integrate the above-mentioned field crane motion model, field crane operation model and field crane collaborative interaction model, and use discrete event simulation software or a custom simulation engine for simulation; Step 3: Issue the operation instruction: Design an operation instruction generation algorithm to automatically generate detailed operation instructions including the operation task ID, cargo location, target location and estimated completion time according to the operation plan and resource allocation of the port TOS system; Step 4: Simulation job execution: (1) Vertical operation simulation of the crane: using the lifting speed function v(t) = at and the height calculation formula h(t) = 0.5a*t 2 Simulate the vertical lifting process; (2) Collaborative operation simulation: The collaborative algorithm is used to adjust the operation time window of the field crane and other equipment to ensure the smooth progress of the collaborative operation. The collaborative efficiency formula CE = actual completion time / theoretical shortest completion time is used to evaluate the collaborative effect; (3) Adjustment of operation sequence: Use the operation optimization algorithm to adjust the operation sequence in real time to cope with emergencies and optimize overall operation efficiency; Step 5: Information reporting and feedback: Design information reporting protocols and data structures to report the status information, operation progress, and abnormal conditions of the yard crane to the port TOS system in real time, and use feedback control algorithms to fine-tune the simulation model based on the reported information to ensure that the simulation results are close to the actual situation; Step 6: Simulation operation completed: When all scheduled tasks have completed simulation, collect all the data during the simulation and prepare for result evaluation; Step 7: Result evaluation: Use statistical analysis methods to evaluate simulation results, compare with actual operation data, calculate simulation accuracy and error range, and use performance evaluation indicators to quantify simulation effects; Step 8: Model optimization: Based on the evaluation results, the deficiencies and potential optimization points in the simulation model are identified, and the model parameters are adjusted and optimized using the parameter optimization algorithm. The simulation test, result evaluation and optimization work are repeated to form a closed-loop optimization process until the expected simulation effect and accuracy are achieved.

3. The operating method of the field crane operation process simulation system for port intelligent driving according to claim 2 is characterized by: The steps 1 and 2 are the preparation stage, the steps 3, 4 and 5 are the simulation test stage, and the steps 6, 7 and 8 are the result evaluation and optimization stage.

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