Port cargo transportation method based on transportation route planning

Abstract

The invention relates to the technical field of intelligent production control, in particular to a port cargo transportation method based on transportation route planning, and the method comprises the steps: obtaining a cargo transportation demand of a port; carrying out preliminary planning of a transportation route based on a port and a cargo transportation destination; loading and unloading characteristics of each port are further considered, and a transport network topological graph is constructed; carrying out optimal configuration through edge computing nodes; the transportation route is dynamically adjusted in combination with real-time weather and ocean conditions, the technical problems that fine optimization cannot be carried out in port cargo transportation planning only through static data analysis, the phenomenon of resource shortage or idling often occurs, and the overall operation efficiency is affected are solved, the purposes of balancing carbon emission minimization and resource utilization rate maximization are achieved, and the economic benefit is improved. The technical effects that berth allocation and operation time windows are optimized, efficient operation of berths and loading and unloading equipment is ensured, waiting time and idle resources are reduced, and the resource utilization efficiency is improved are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent production control, and particularly to a port cargo transportation method based on transportation route planning. Background Art

[0002] With the continuous growth of global trade and the increasing complexity of the supply chain, ports, as key nodes of trade, are facing huge cargo transportation pressures. Conventional port cargo transportation planning relies on static data analysis, resulting in problems such as low transportation efficiency, uneven resource allocation, and high carbon emissions. Especially in the face of changing weather conditions, ocean conditions, and emergencies, fixed routes and loading and unloading plans are often difficult to adapt quickly, affecting the overall logistics efficiency and environmental sustainability.

[0003] In summary, there is a technical problem in the prior art that only through static data analysis, fine optimization cannot be carried out in port cargo transportation planning, and there are often phenomena of resource tension or idleness, affecting the overall operation efficiency. Summary of the Invention

[0004] This application provides a port cargo transportation method based on transportation route planning, aiming to solve the technical problem in the prior art that only through static data analysis, fine optimization cannot be carried out in port cargo transportation planning, and there are often phenomena of resource tension or idleness, affecting the overall operation efficiency.

[0005] In view of the above problems, the technical solution of this application is as follows: This application provides a port cargo transportation method based on transportation route planning, wherein the method includes: obtaining the cargo transportation demands of M output ports, the cargo transportation demands including cargo types, cargo volumes, and cargo weights; planning transportation routes based on the M output ports and N cargo transportation destinations to obtain an initial transportation planning route; obtaining the cargo handling feature set corresponding to the first output port among the M output ports, the features corresponding to the cargo handling feature set including the throughput of the terminal, the number of available berths, and the working efficiency of ship unloaders; based on the cargo handling feature set corresponding to the first output port, traversing the M output ports, and combining with the initial transportation planning route, establishing a transportation route network topology diagram; based on the transportation route network topology diagram, setting P edge computing nodes, uploading the cargo transportation demands, and the P edge computing nodes are optimized and configured with the goal of minimizing carbon emissions and maximizing resource utilization; introducing real-time external conditions, and using the P edge computing nodes to dynamically optimize the initial transportation planning route and adjust port cargo transportation, the real-time external conditions including real-time meteorological conditions and real-time ocean conditions.

[0006] In summary, one or more technical solutions provided in this application solve the technical problem that only through static data analysis, it is impossible to perform fine optimization in port cargo transportation planning, and there are often resource shortages or idle phenomena, which affect the overall operation efficiency. The technical effect of achieving the goal of minimizing carbon emissions and maximizing resource utilization rate, optimizing berth allocation and operation time windows, ensuring the efficient operation of berths and handling equipment, reducing waiting time and idle resources, and improving resource utilization efficiency is realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 This application provides a flowchart of a port cargo transportation method based on transportation route planning; Figure 2 This application provides a flowchart of obtaining an initial transportation planning route in the port cargo transportation method based on transportation route planning. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0008] The following specifically describes this application with reference to the drawings. As Figure 1 shown, this application provides a port cargo transportation method based on transportation route planning, wherein the method includes: S1: Obtain the cargo transportation demands of M output ports, where the cargo transportation demands include cargo types, cargo volumes, and cargo weights; S2: Based on the M output ports and N cargo transportation destinations, perform transportation route planning to obtain an initial transportation planning route; S3: Obtain the cargo handling feature set corresponding to the first output port among the M output ports, and the features corresponding to the cargo handling feature set include the throughput of the terminal, the number of available berths, and the working efficiency of ship unloaders.

[0009] It is known that water-to-water transfer means the transfer of cargo between large ships and small ships, including lightering operations carried out inside the port or in the open sea, that is, transferring cargo from a small ship to a large ship or vice versa to adapt to the navigation conditions of different waterways or directly delivering the cargo to a small port near the final destination; water-to-land transfer means that after the cargo is unloaded from the ship, its land journey continues through road transportation or rail transportation. For example, using trucks to transport containers from the port to inland warehouses or directly delivering them to customers, or transferring the cargo to a farther area through a dedicated railway line; multimodal transportation combines two or more transportation methods, such as water transportation and rail transportation (water-rail intermodal transportation) or water transportation and road transportation, to achieve a wider logistics coverage. For example, using ships to transport train carriages across the sea to connect different railway networks.

[0010] Based on this, the present application deeply analyzes the handling capabilities of each output port, including terminal throughput, berth resources, and the efficiency of handling machinery, etc. The constructed transportation network topology map can more precisely simulate the actual transportation scenario. The introduced edge computing node technology not only realizes the minimization of carbon emissions, effectively reduces the environmental burden during transportation, but also maximizes the resource utilization rate, ensures the efficient operation of berths and handling equipment, reduces waiting time and idle resources. Then, by dynamically monitoring real-time meteorological and ocean conditions, the transportation route is flexibly adjusted to enhance the adaptability to external changes and ensure the on-time completion rate of transportation tasks.

[0011] Specifically, collect cargo transportation demand information from shippers or logistics companies through various channels (such as email, online platforms, phone calls, etc.); classify and organize the collected demand information to ensure that the cargo transportation demand for each port includes key information such as cargo type, cargo volume, and cargo weight; verify the collected data to ensure accuracy and completeness, and avoid errors in subsequent planning.

[0012] Based on the known port locations and cargo transportation destinations, prepare for preliminary transportation route planning; use professional route planning software or algorithms, such as GIS systems, consider factors such as waterways, water depth, and traffic flow, to generate preliminary transportation routes; obtain one or more initial transportation planning routes through algorithm calculation.

[0013] Randomly select the first output port from the M output ports; collect detailed data of the first output port, including the throughput of the terminal, the current number of available berths, and the working efficiency of the ship unloader; organize the collected data into a feature set, and the cargo handling feature set corresponding to the first output port will be used for subsequent construction of the transportation network topology map.

[0014] Traverse the M output ports, perform the same feature collection and analysis on the remaining M - 1 output ports, obtain the cargo handling feature set corresponding to the first output port, the cargo handling feature set corresponding to the second output port,..., the cargo handling feature set corresponding to the Mth output port, and add nodes and edges to the topology map according to the initial transportation planning route; integrate the feature information and route information of each port into the topology map to form a complete transportation network diagram and name it the transportation route network topology map; optimize and adjust the topology map according to the actual situation and the feedback of the network diagram to ensure its practicality and accuracy.

[0015] S4: Based on the set of cargo handling characteristics corresponding to the first output port, traverse the M output ports, and combine with the initial transportation planning route to establish a transportation route network topology diagram: S5: Based on the transportation route network topology diagram, set P edge computing nodes, upload the cargo transportation demand, and the P edge computing nodes are optimized and configured with the goal of minimizing carbon emissions and maximizing resource utilization: S6: Introduce real-time external conditions, and use the P edge computing nodes to dynamically optimize the initial transportation planning route and adjust the port cargo transportation. The real-time external conditions include real-time meteorological conditions and real-time ocean conditions.

[0016] Select P nodes at key positions (such as ports, transfer stations, etc.) in the transportation route network topology diagram as the deployment locations of the edge computing nodes; configure necessary hardware devices for the P edge computing nodes, such as high-performance servers, storage devices, network devices, etc., which can handle a large amount of real-time data and complex computing tasks; deploy the edge computing platform and related application programs on the nodes, so as to be responsible for receiving and processing data from different sources and executing corresponding optimization algorithms.

[0017] Upload the collected cargo transportation demand data (including cargo types, volumes, weights, etc.) to the edge computing nodes; clean, integrate, and format the data on the nodes to ensure the accuracy and consistency of the data; store the processed data in the local storage or cloud storage of the nodes for subsequent analysis and optimization use.

[0018] Clarify the optimization goals, that is, to minimize carbon emissions and maximize resource utilization; select appropriate optimization algorithms (such as genetic algorithms, etc.) to achieve the goals; based on the transportation route network topology diagram and cargo transportation demand data, consider factors such as carbon emissions, resource utilization, and real-time external conditions of different paths; through continuous iteration and optimization, find the optimal solution or approximate optimal solution that meets the goals, and output the optimization results to the corresponding system.

[0019] Then, collect real-time external condition data through channels such as sensors and weather stations, such as wind speed, wind direction, wave height, tide, etc.; fuse the real-time data with the cargo transportation demand and the transportation route network topology diagram to form a more comprehensive data basis; based on the real-time data and the optimization model, dynamically optimize the initial transportation planning route, including adjusting the transportation path, changing the transportation mode (such as changing one section from sea transportation to land transportation), adjusting the transportation time, etc.; transmit the optimized decision results to relevant personnel so that they can take corresponding actions to execute the decision; monitor the execution process in real time and collect the execution result data, and feedback and adjust the optimization model according to the changes in the execution results and real-time data to achieve more accurate dynamic optimization.

[0020] Through the above steps, it is ensured that while meeting the cargo transportation demand, the transportation plan minimizes carbon emissions as much as possible and improves resource utilization. At the same time, by introducing real-time external conditions for dynamic optimization, the flexibility and adaptability of the transportation plan are further enhanced.

[0021] Furthermore, as Figure 2 shown, based on M output ports and N cargo transportation destinations, a transportation route plan is made to obtain an initial transportation plan route. The method of this application includes: Adopt the ARIMA model, combine historical transportation instances, and extract transportation route features: Based on M output ports and N cargo transportation destinations, combine the transportation route features, and synchronously optimize the berth allocation to obtain the candidate distribution of berth arrangements: Based on the candidate distribution of berth arrangements, perform iterative optimization of the transportation route to obtain the initial transportation plan route.

[0022] Collect historical transportation instance data over a past period, including information such as output ports, cargo transportation destinations, transportation routes, transportation times, cargo types, cargo volumes, cargo weights, etc.; Clean and organize the collected data, remove duplicate, incorrect, or invalid data to ensure the quality and accuracy of the data; According to the characteristics of the transportation route, select key factors affecting transportation efficiency and cost as features, such as transportation distance, transportation time, port throughput, cargo type, etc.: Use ARIMA (Autoregressive Integrated Moving Average Model) to model the transportation route features. The ARIMA model can capture the autocorrelation and trend of time series data, thereby predicting future transportation route features; Through the prediction results of the ARIMA model, extract features that have an important impact on transportation route planning, such as the prediction of peak hours and the expected changes in transportation costs.

[0023] According to information such as cargo transportation demand, cargo type, and volume, predict the number and time of berths required at each output port; Evaluate the berth resources at each output port, including the number, type, location, etc. of available berths, as well as the working efficiency and service quality of the berths; Combine the transportation route features and the berth resource evaluation results, and use optimization algorithms (such as genetic algorithms, simulated annealing algorithms, etc.) to optimize the berth allocation. The goal is to maximize the utilization rate of berths while ensuring the timely loading, unloading, and transportation of cargo; After optimization calculations, obtain multiple candidate berth arrangement plans, and these plans are different in terms of berth utilization rate, transportation time, and cost.

[0024] Evaluate the candidate solutions for each berth arrangement, considering its impact on the transportation route, such as transportation distance, transportation time, etc.; based on the evaluation results, iteratively optimize the transportation route; shorten the transportation time or improve the transportation efficiency by adjusting the starting point, ending point, ports along the way, etc. of the transportation route; compare the results of each iterative optimization with the previous results, and select the optimal or near-optimal transportation route as the initial transportation planning route; output the initial transportation planning route to the corresponding system or platform for subsequent execution and monitoring.

[0025] Through the above steps, comprehensively considering multiple factors such as the output port, cargo transportation destination, transportation route characteristics, and berth resources, plan and optimize the transportation route, so as to obtain the initial transportation planning route, which can improve the transportation efficiency and optimize the utilization of berths.

[0026] Furthermore, the P edge computing nodes are optimized and configured with the goal of minimizing carbon emissions. The method of this application includes: Construct a carbon footprint model, and the carbon footprint model includes a path carbon emission assessment channel: obtain the basic information of the ship's power and fuel efficiency, and through the path carbon emission assessment channel, combine the transportation distance and cargo volume to calculate the carbon emissions corresponding to each path: perform carbon emission constraints based on the carbon emissions corresponding to each path, and select the first optimal path set with the minimum carbon emissions.

[0027] Clarify the factors that need to be considered in the carbon footprint model, such as ship type, fuel type, transportation distance, cargo volume, etc.; design a carbon footprint model framework including a path carbon emission assessment channel, and the path carbon emission assessment channel will be used to calculate the carbon emissions under different transportation paths; set necessary parameters in the carbon footprint model, such as the fuel efficiency of the ship and the carbon emission factor per unit of fuel.

[0028] Collect the basic information of the ship's power from ship manufacturers, shipping companies or relevant databases, such as ship type, power, fuel type, etc.; calculate the fuel efficiency of different ships according to the basic information of the ship's power, combined with actual operation data or experimental data; verify the collected data and the calculated fuel efficiency to ensure the accuracy and reliability of the data.

[0029] Define all possible transportation paths according to the combination of M output ports and N cargo transportation destinations; input parameters such as the transportation distance, cargo volume, and fuel efficiency of each path into the path carbon emission assessment channel of the carbon footprint model; use the algorithms or formulas in the path carbon emission assessment channel to calculate the carbon emissions corresponding to each path, which usually involves multiplying parameters such as transportation distance, cargo volume, and fuel efficiency by the carbon emission factor per unit of fuel; record the carbon emissions of each path calculated for subsequent analysis and comparison.

[0030] According to the actual needs and carbon emission reduction targets, set the constraint conditions for carbon emissions. For example, a threshold for the maximum allowable carbon emissions can be set; according to the calculation results of carbon emissions and the set constraint conditions, filter out the paths that meet the constraint conditions; among the paths that meet the constraint conditions, select the path with the minimum carbon emissions as the first optimal path. If there are multiple paths with the same and minimum carbon emissions, they can form the first optimal path set; output the selected optimal path to the corresponding system for subsequent execution.

[0031] Through the above steps, based on P edge computing nodes, with the goal of minimizing carbon emissions, optimize the configuration of transportation routes, which can effectively reduce the carbon emissions during transportation and achieve green and low-carbon logistics transportation.

[0032] Furthermore, the P edge computing nodes are optimized and configured with the goal of maximizing resource utilization. The method of this application includes: Predict the berth demand based on the initial transportation planning route to obtain the berth demand prediction sequence; based on the berth demand prediction sequence, combine the efficiency of the loading and unloading machines to set the operation time window; based on the operation time window corresponding to the berth demand prediction sequence, perform resource utilization constraint, and select the second optimal path set that maximizes resource utilization.

[0033] Collect relevant information of the initial transportation planning route, including cargo type, transportation volume, estimated arrival time, etc.; according to the characteristics of the cargo (such as volume, weight, whether special treatment is required) and the estimated arrival time, analyze the berth demand of different ships; use historical data, machine learning algorithms (such as time series analysis, neural networks, etc.) or other prediction models to predict the berth demand and obtain the berth demand prediction sequence, which will include the demand situation of different berths in different time periods.

[0034] Evaluate the working efficiency of different loading and unloading machines in the port, including loading and unloading speed, failure rate, maintenance cycle, etc.; according to the berth demand prediction sequence and the efficiency of the loading and unloading machines, plan a reasonable operation time window for each berth and loading and unloading machine. The operation time window should not only meet the berth demand but also ensure the efficient utilization of the loading and unloading machines; according to the time window plan, schedule the resources in the port and arrange appropriate loading and unloading machines to operate at the corresponding berths.

[0035] Define evaluation metrics for resource utilization, such as berth utilization rate, loader / unloader utilization rate, etc. Evaluate the resource utilization rate under the current path planning through actual operation data or simulation data. Set the constraint conditions for resource utilization rate according to the evaluation results of resource utilization rate and actual requirements. The constraint conditions include the minimum resource utilization rate threshold, maximum idle time, etc. On the premise of meeting the resource utilization rate constraint conditions, optimize the initial transportation planning route. The optimization goal is to maximize the resource utilization rate, that is, to minimize the idle time and inefficient utilization of resources as much as possible.

[0036] Through optimization calculation, select the path that meets the resource utilization rate constraint conditions and has the highest resource utilization rate as the second optimal path. If there are multiple paths with the same and highest resource utilization rate, form a second optimal path set. Output the selected second optimal path or second optimal path set to the corresponding system for subsequent execution and monitoring. Through the above steps, P edge computing nodes can be optimized and configured with the goal of maximizing resource utilization rate, so as to ensure the effective utilization of port resources and improve the efficiency of logistics transportation.

[0037] Furthermore, in combination with the loader / unloader efficiency, set the operation time window. The method of this application further includes: Through the discrete point analysis method, simulate the operation process of the loader / unloader and identify the bottleneck periods: Based on the bottleneck periods, balance and optimize the operation time window.

[0038] Collect the historical operation data of the loader / unloader, including the start time, end time, operation volume, loader / unloader model, etc. of each operation; clean and sort the collected data, remove outliers and incorrect data to ensure the accuracy and reliability of the data; represent the operation data in the form of discrete points, and each discrete point represents the start or end of an operation. By analyzing the discrete points, identify the operation volume and operation efficiency of the loader / unloader in different time periods.

[0039] Based on the results of discrete point analysis, use simulation software or mathematical models to simulate the operation process of the loader / unloader. During the simulation process, it is necessary to consider the performance parameters of the loader / unloader (such as loading / unloading speed, maximum load, etc.), the limitations of the operation environment (such as weather, traffic conditions, etc.), and the characteristics of the goods (such as volume, weight, shape, etc.).

[0040] In the simulated operation process of the loader / unloader, identify the time periods with low operation efficiency or large operation volume. The time periods with low operation efficiency or large operation volume are called bottleneck periods; conduct in-depth analysis on the identified bottleneck periods to find out the reasons for the bottlenecks. Commonly, they include insufficient loader / unloader performance, poor operation environment, complex goods characteristics, etc.; quantitatively evaluate the operation efficiency and operation volume of the bottleneck periods to determine their impact on the entire operation process.

[0041] According to the analysis results of the bottleneck period, adjust the operation time window. By extending or shortening the length of some time windows, balance the operation volume and operation efficiency in different time periods. When adjusting the time window, consider the allocation of resources. For example, increase the number of loading and unloading machines or improve the performance of loading and unloading machines during the bottleneck period to relieve the operation pressure. Develop an optimization plan, including the specific adjustment plan of the time window, the resource allocation plan, and the corresponding implementation plan. Evaluate the developed optimization plan to ensure that the plan can effectively solve the bottleneck problem and improve the efficiency of the entire operation process.

[0042] According to the optimization plan, adjust the operation time window and allocate the corresponding resources. During the implementation of the plan, conduct real-time monitoring to ensure that all measures are effectively implemented. At the same time, collect new operation data for continuous optimization of the plan. Through the above steps, combined with the efficiency of the loading and unloading machine, set a reasonable operation time window, identify the bottleneck period through the discrete point analysis method, and balance and optimize the operation time window, which helps to improve the operation efficiency of the loading and unloading machine, reduce resource waste, and enhance the efficiency of the entire logistics transportation system.

[0043] Furthermore, the method of the present application further includes: Identify the multimodal transportation demand, where the multimodal transportation demand includes the transfer time: based on the first optimal path set for minimizing carbon emissions and the second optimal path set for maximizing resource utilization, optimize the node connection time in combination with the multimodal transportation demand, and determine P edge computing nodes corresponding to the seamless connection between sea transportation and land transportation.

[0044] Clarify the multimodal transportation demand, including the starting point and destination, cargo type, transportation volume, estimated arrival time, etc.; determine a reasonable transfer time range according to the characteristics and transportation requirements of the cargo; based on the first optimal path set and the second optimal path set obtained by minimizing carbon emissions and maximizing resource utilization; analyze the key indicators such as transfer time, carbon emissions, and resource utilization of the paths in the first optimal path set and the second optimal path set.

[0045] In the multimodal transportation path, identify key transfer nodes such as ports, railway stations, freight stations, etc.; based on the multimodal transportation demand and path characteristics, optimize the connection time of the nodes to ensure that the goods can be efficiently and smoothly switched from one transportation mode to another during the transfer process; use mathematical programming or optimization algorithms (such as the DEA method, etc.) to accurately calculate the node connection time; consider factors such as the operation time of the loading and unloading machine, the arrival and departure time of ships or trains, etc., to ensure that the goods can arrive at the next transfer node on time.

[0046] Select P edge computing nodes that can achieve seamless sea-land transportation connection according to the optimized node connection time; ensure that the P edge computing nodes have sufficient processing power, storage capacity, and communication capabilities to meet the needs of multimodal transportation.

[0047] In the actual application process, it also includes configuring and deploying P edge computing nodes according to the determined plan to ensure that they can work as expected; through a real-time monitoring system, monitoring and recording the operating status of the nodes and the cargo transfer situation in real time; continuously optimizing the node connection time, resource utilization rate, etc. according to the monitoring data to ensure the efficient operation of the multimodal transportation system.

[0048] Through the above steps, combined with the multimodal transportation requirements, the optimal path set, and the optimization of the node connection time, determine the P edge computing nodes corresponding to the seamless sea-land transportation connection, which helps to improve the efficiency and reliability of multimodal transportation, reduce carbon emissions, and achieve green and efficient logistics transportation.

[0049] Furthermore, using the P edge computing nodes to dynamically optimize the initial transportation planning route, the method of this application also includes: Connect the P edge computing nodes, and based on the rule engine, determine the emergency special requirements: through the emergency special requirements, establish a priority decision matrix and adjust the priorities: use the shortest path algorithm for emergency response and adjust the operation plan.

[0050] Ensure that the P edge computing nodes can communicate efficiently and stably through the network, which involves configuring VPN, setting security firewall rules, ensuring data transmission encryption, etc.; establish a data synchronization mechanism between the nodes to ensure that each node can obtain key information such as the latest transportation plan, operation status, and resource utilization in real time.

[0051] According to the business requirements, define a series of rules to identify emergency special requirements, including the nature of the goods (such as dangerous goods, perishable goods), the timeliness requirements of transportation, the priority of customers, etc.; use the real-time data processing ability of the edge computing nodes to monitor and analyze various data during the transportation process to detect situations that meet the emergency special requirements rules in a timely manner; once a situation that meets the emergency special requirements rules is detected, the rule engine will trigger the corresponding processing process.

[0052] Construct a priority decision matrix according to the nature and urgency of the emergency special requirements. The priority decision matrix will consider multiple factors, such as the importance of the goods, the urgency of transportation, the status of available resources, etc.; dynamically adjust the priorities of each transportation task according to the results of the decision matrix. Ensure that the emergency special requirements can be processed preferentially while minimizing the impact on other normal transportation tasks.

[0053] When an emergency special need is triggered, use the shortest path algorithm (such as Dijkstra algorithm or A* algorithm) to quickly calculate the optimal path from the current location to the destination. The corresponding path should take into account multiple factors such as transfer time, resource utilization rate, carbon emissions, etc.; according to the calculated optimal path, dynamically adjust the original operation plan, including reallocating loading and unloading machine resources, adjusting the departure time of ships or trains, optimizing the loading sequence of goods, etc.

[0054] In the actual application process, it also includes, according to the adjusted operation plan, quickly implementing emergency response measures to ensure the timely and accurate execution of the new plan; during the emergency response process, through edge computing nodes for real-time monitoring to ensure the effective implementation of various measures, and at the same time, collecting feedback data for continuous optimization of the plan.

[0055] Through the above steps, using P edge computing nodes to dynamically optimize the initial transportation planning route, especially for quickly responding to emergency special needs, helps to improve the flexibility and efficiency of logistics transportation, and ensures that the goods transportation in emergency situations can be processed in a timely and effective manner.

[0056] Furthermore, the method of this application also includes: The emergency special needs include the type of materials and the quantity of materials: according to the urgency of the materials, establish the priority decision matrix.

[0057] Classify the type of materials in detail. For example, materials can be divided into life-saving materials, medical materials, critical components, food and water, etc.; the importance of different types of materials is different in emergency situations; evaluate the demand and existing quantity of each material to determine the shortage degree of the materials, and the quantity of materials will directly affect its priority.

[0058] According to the type of materials, the quantity of materials and the specific situation (such as disaster relief, epidemic prevention and control, etc.), formulate the criteria for evaluating the urgency of materials, including the importance of materials for rescue or recovery work, the substitutability of materials, the impact of out-of-stock on the deterioration of the situation, etc.; according to the above criteria, score the urgency of each material, and the score can be a quantitative index, such as 1-10 points, where 10 points represents the highest urgency.

[0059] Taking the type of materials as rows and the urgency score as columns, construct a two-dimensional matrix. Among them, each cell represents the priority of a material under a certain urgency score; according to the type of materials and the urgency score, assign a value to each cell to represent the priority of the material, and the priority can be a relatively fixed value or level (such as high, medium, low).

[0060] Exemplarily, the priority decision matrix P is an A×B matrix, where A is different types of materials and B is different levels of material quantities; each element P in the matrix ij represents the priority score under the i-th type of material and the j-th material quantity; correspondingly, , is used to characterize the urgency weight of the i-th type of material, is used to characterize the urgency weight of the j-th type of material.

[0061] As the situation changes, the type, quality, and urgency of materials will all change. Therefore, it is necessary to update the data in real time and re-evaluate the priority of materials; when facing urgent and special needs, the priority decision matrix can be consulted to quickly determine the priority of various materials, so as to make reasonable resource allocation and scheduling decisions; in practical applications, feedback data is collected, including the accuracy of decisions, the efficiency of material scheduling, etc.; according to the feedback data, the priority decision matrix is adjusted and optimized to improve its accuracy and effectiveness in practical applications.

[0062] Through the above steps, for urgent and special needs including material types and material quantities, an effective priority decision matrix is established according to material urgency, providing fast and accurate decision support in emergency situations, and ensuring that resources can be reasonably and efficiently allocated and utilized.

[0063] In summary, the beneficial effects of the embodiments of the present application are as follows: 1. By introducing real-time external condition monitoring and edge computing nodes, the dynamic optimization and adjustment of transportation routes are realized, the adaptability to unforeseen situations is improved, the waiting time and transfer time are reduced, the risk of transportation delays is lowered, and the overall efficiency of cargo transportation is enhanced.

[0064] 2. Through the carbon emission minimization strategy, a carbon footprint model and a berth demand prediction sequence are constructed, achieving a balance between carbon emission minimization and resource utilization maximization, optimizing resource allocation and reducing carbon emissions, mitigating the impact on the environment, and meeting the requirements of sustainable development.

[0065] 3. By optimizing the node connection time, seamless connection between sea and land transportation is achieved, the transfer time is shortened, the overall efficiency of multimodal transportation is improved, the quality of logistics services is enhanced. In the face of emergencies and special needs, using the rule engine and the priority decision matrix, it can quickly respond to urgent and special needs, automatically adjust the operation plan and priority, enhance the flexibility and response speed of decision-making, and improve the stability and reliability of the transportation system.

[0066] 4. By adopting the recognition of multimodal transport demands, which include transfer time; based on the first optimal path set for minimizing carbon emissions and the second optimal path set for maximizing resource utilization, the node connection time is optimized in combination with the multimodal transport demands to determine P edge computing nodes corresponding to the seamless connection between sea and land transport. Determining P edge computing nodes corresponding to the seamless connection between sea and land transport by combining multimodal transport demands, the optimal path set, and the optimization of node connection time helps improve the efficiency and reliability of multimodal transport, reduce carbon emissions, and achieve green and efficient logistics transportation.

[0067] In summary, any step can be stored as computer instructions or programs in an unrestricted computer memory and can be called and recognized by an unrestricted computer processor, without any further limitations here.

[0068] Furthermore, the above technical solutions only represent the preferred technical solutions of the technical solutions of the embodiments of the present application. Some possible changes made by those skilled in the art to some parts thereof all reflect the principles of the novel embodiments of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application.

Claims

1. A port cargo transportation method based on transportation route planning, characterized in that: The method comprises: Obtaining cargo transportation requirements of M output ports, where the cargo transportation requirements include cargo type, cargo volume, and cargo weight; Carry out transportation route planning based on M output ports and N cargo transportation destinations, and obtain the initial transportation planning route; Obtaining a cargo handling feature set corresponding to a first output port among the M output ports, wherein features corresponding to the cargo handling feature set include throughput of a terminal, number of available berths, and working efficiency of a ship unloader; Based on the cargo handling feature set corresponding to the first output port, traverse the M output ports and establish a transportation route network topology diagram in combination with the initial transportation planning route; Based on the transportation route network topology diagram, P edge computing nodes are set, and the cargo transportation demand is uploaded. The P edge computing nodes are optimized with the goal of minimizing carbon emissions and maximizing resource utilization; Real-time external conditions are introduced, and the P edge computing nodes are used to dynamically optimize the initial transportation planning route and adjust the port cargo transportation. The real-time external conditions include real-time meteorological conditions and real-time ocean conditions.

2. The port cargo transportation method based on transportation route planning according to claim 1, characterized in that: Based on M output ports and N cargo transportation destinations, transportation route planning is performed to obtain an initial transportation planning route, and the method includes: The ARIMA model is used to extract the characteristics of transportation routes in combination with historical transportation examples; Based on M output ports and N cargo transportation destinations, combined with the characteristics of transportation routes, berth allocation optimization is performed simultaneously to obtain the candidate distribution of berth arrangements; Based on the candidate distribution of berth arrangements, iterative optimization of the transportation route is performed to obtain an initial transportation planning route.

3. The port cargo transportation method based on transportation route planning as claimed in claim 2, characterized in that: The P edge computing nodes are optimally configured with the goal of minimizing carbon emissions, and the method includes: Constructing a carbon footprint model, wherein the carbon footprint model includes a path carbon emission assessment channel; Obtain basic information on ship power and fuel efficiency, and calculate the carbon emissions corresponding to each route through the carbon emission assessment channel of the route, combined with the transportation distance and cargo capacity; Carbon emission constraints are imposed based on the carbon emissions corresponding to the various paths, and a first optimal path set that minimizes carbon emissions is selected.

4. The port cargo transportation method based on transportation route planning as claimed in claim 3 is characterized in that: The P edge computing nodes are optimally configured with the goal of maximizing resource utilization, and the method includes: Predicting berth demand based on the initial transportation planning route, and obtaining a berth demand prediction sequence; Based on the berth demand forecast sequence and combined with the efficiency of the loader and unloader, an operation time window is set; Resource utilization constraints are imposed based on the operation time window corresponding to the berth demand forecast sequence, and a second optimal path set that maximizes resource utilization is selected.

5. The port cargo transportation method based on transportation route planning as claimed in claim 4, characterized in that: In combination with the efficiency of the loader and unloader, an operation time window is set, and the method further comprises: By using the discrete point analysis method, the operation process of the loader is simulated to identify the bottleneck period; Based on the bottleneck period, the balance of the operation time window is optimized.

6. The port cargo transportation method based on transportation route planning as claimed in claim 4, characterized in that: The method comprises: identifying intermodal transportation requirements, including transit times; Based on the first optimal path set that minimizes carbon emissions and the second optimal path set that maximizes resource utilization, the node connection time is optimized in combination with the multimodal transport demand, and the P edge computing nodes corresponding to the seamless connection between sea and land transport are determined.

7. The port cargo transportation method based on transportation route planning as claimed in claim 1, characterized in that: Using the P edge computing nodes, dynamically optimizing the initial transportation planning route, the method further includes: Connect the P edge computing nodes and determine urgent special needs based on the rule engine; Establish a priority decision matrix and adjust priorities based on the urgent special needs; Use the shortest path algorithm for emergency response and adjust the operation plan.

8. The port cargo transportation method based on transportation route planning according to claim 7 is characterized in that: The emergency special needs include the type and quantity of supplies; According to the urgency of materials, the priority decision matrix is ​​established.

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