Port cargo transportation method based on transportation route planning

Abstract

This invention relates to the field of intelligent production control technology, specifically including a port cargo transportation method based on transportation route planning. The method includes: acquiring port cargo transportation demand; preliminary planning of transportation routes based on ports and cargo transportation destinations; further considering the loading and unloading characteristics of each port to construct a transportation network topology; optimizing configuration through edge computing nodes; and dynamically adjusting transportation routes based on real-time weather and ocean conditions. This solves the technical problem that static data analysis alone cannot perform fine-grained optimization in port cargo transportation planning, often resulting in resource shortages or idle resources, affecting overall operational efficiency. It achieves a balance between minimizing carbon emissions and maximizing resource utilization, optimizes berth allocation and operating time windows, ensures efficient operation of berths and loading / unloading equipment, reduces waiting time and idle resources, and improves resource utilization efficiency.

Description

Technical Field

[0001] This invention relates to the field of intelligent production control technology, specifically to a port cargo transportation method based on transportation route planning. Background Technology

[0002] With the continued growth of global trade and the increasing complexity of supply chains, ports, as key nodes in trade, face enormous pressure in cargo transportation. Conventional port cargo transportation planning relies on static data analysis, leading to problems such as low transportation efficiency, uneven resource allocation, and high carbon emissions. Especially when facing constantly changing weather conditions, ocean conditions, and unforeseen events, fixed routes and loading / unloading plans often struggle to adapt quickly, impacting overall logistics efficiency and environmental sustainability.

[0003] In summary, existing technologies suffer from the problem that relying solely on static data analysis makes it impossible to perform fine-grained optimization in port cargo transportation planning, often resulting in resource shortages or idle resources, which affects overall operational 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 it is impossible to carry out fine optimization in port cargo transportation planning through static data analysis alone, which often leads to resource shortages or idleness and affects the overall operational efficiency.

[0005] In view of the above problems, the technical solution to achieve the present application is as follows:

[0006] This application provides a port cargo transportation method based on transportation route planning. The method includes: obtaining cargo transportation demand from M output ports, where the demand includes cargo type, cargo volume, and cargo weight; planning transportation routes based on the M output ports and N cargo transportation destinations to obtain an initial transportation route plan; obtaining a cargo loading and unloading feature set corresponding to the first output port among the M output ports, where the features include terminal throughput, number of available berths, and unloader efficiency; traversing the M output ports based on the cargo loading and unloading feature set corresponding to the first output port, and establishing a transportation route network topology based on the initial transportation route plan; setting up P edge computing nodes based on the transportation route network topology, uploading the cargo transportation demand, where the P edge computing nodes are optimized with the goal of minimizing carbon emissions and maximizing resource utilization; and introducing real-time external conditions, using the P edge computing nodes to dynamically optimize the initial transportation route plan and adjust port cargo transportation, where the real-time external conditions include real-time weather conditions and real-time ocean conditions.

[0007] In summary, one or more technical solutions provided in this application solve the technical problem that static data analysis alone cannot perform fine-grained optimization in port cargo transportation planning, often resulting in resource shortages or idle resources, which affects overall operational efficiency. They achieve the technical effects of balancing the goal of minimizing carbon emissions and maximizing resource utilization, optimizing berth allocation and operation time windows, ensuring the efficient operation of berths and loading and unloading equipment, reducing waiting time and idle resources, and improving resource utilization efficiency. Attached Figure Description

[0008] Figure 1 This application provides a flowchart illustrating a port cargo transportation method based on transportation route planning;

[0009] Figure 2 This application provides a flowchart illustrating the process of obtaining the initial transportation planning route in a port cargo transportation method based on transportation route planning. Detailed Implementation

[0010] The present application will now be described in detail with reference to the accompanying drawings, such as... Figure 1 As shown, this application provides a port cargo transportation method based on transportation route planning, wherein the method includes:

[0011] S1: Obtain the cargo transportation demand of M output ports, including cargo type, cargo volume and cargo weight; S2: Plan transportation routes based on M output ports and N cargo transportation destinations to obtain the initial transportation planning route; S3: Obtain the cargo loading and unloading feature set corresponding to the first output port among the M output ports, the features corresponding to the cargo loading and unloading feature set include the terminal throughput, the number of available berths and the working efficiency of the unloader.

[0012] Known as water-to-water transport, this refers to the transfer of goods between large and small ships, including transshipment operations within ports or at sea, where goods are transferred from small boats to large ships, or vice versa, to accommodate different navigation conditions or to deliver goods directly to small ports near the final destination; water-to-land transport refers to the continuation of the land journey of goods after they are unloaded from ships, via road or rail transport, for example, using trucks to transport containers from ports to inland warehouses or directly to customers, or transferring goods to more distant areas via dedicated rail lines; multimodal transport combines two or more modes of transport, such as water and rail (water-rail intermodal transport) or water and road transport, to achieve broader logistics coverage, for example, using ships to transport train carriages across the sea, connecting different rail networks.

[0013] Based on this, this application conducts an in-depth analysis of the loading and unloading capacity of each export port, including terminal throughput, berth resources, and loading and unloading machinery efficiency. The constructed transportation network topology can more accurately simulate actual transportation scenarios. The introduced edge computing node technology not only minimizes carbon emissions and effectively reduces the environmental burden during transportation, but also maximizes resource utilization, ensures the efficient operation of berths and loading and unloading equipment, and reduces waiting time and idle resources. Furthermore, by dynamically monitoring real-time weather and ocean conditions, the transportation routes are flexibly adjusted to enhance adaptability to external changes and ensure the timely completion rate of transportation tasks.

[0014] Specifically, information on cargo transportation needs is collected from cargo owners or logistics companies through various channels (such as email, online platforms, telephone, etc.); the collected information is then categorized and organized to ensure that the cargo transportation needs of each port include key information such as cargo type, cargo volume, and cargo weight; the collected data is verified to ensure accuracy and completeness, and to avoid errors in subsequent planning.

[0015] Based on the known port locations and cargo destinations, preliminary transportation route planning is prepared; using professional route planning software or algorithms, such as GIS systems, factors such as waterways, water depth, and traffic flow are considered to generate preliminary transportation routes; and one or more initial transportation planning routes are obtained through algorithm calculation.

[0016] Randomly select the first output port from M output ports; collect detailed data of the first output port, including the throughput of the terminal, the number of currently available berths, and the working efficiency of the unloader; organize the collected data into a feature set, and the cargo loading and unloading feature set corresponding to the first output port will be used for subsequent transportation network topology construction.

[0017] Traverse the M output ports, and perform the same feature collection and analysis on the remaining M-1 output ports to obtain the cargo loading and unloading feature set corresponding to the first output port, the cargo loading and unloading feature set corresponding to the second output port, ..., the cargo loading and unloading feature set corresponding to the Mth output port. According to the initial transportation planning route, add nodes and edges to the topology graph. The feature information and route information of each port are integrated into the topology graph to form a complete transportation network graph and named the transportation route network topology graph. Based on the actual situation and feedback from the network graph, optimize and adjust the topology graph to ensure its practicality and accuracy.

[0018] S4: Based on the cargo loading and unloading feature set corresponding to the first output port, traverse the M output ports and establish a transportation route network topology map in conjunction with the initial transportation planning route. S5: Based on the transportation route network topology map, set up P edge computing nodes and upload the cargo transportation demand. 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 port cargo transportation. The real-time external conditions include real-time weather conditions and real-time ocean conditions.

[0019] Select P nodes at key locations (such as ports and transit stations) in the transportation route network topology as deployment locations for edge computing nodes; configure the necessary hardware devices for the P edge computing nodes, such as high-performance servers, storage devices, and network devices, to handle large amounts of real-time data and complex computing tasks; deploy edge computing platforms and related applications on the nodes, which will be responsible for receiving and processing data from different sources and executing corresponding optimization algorithms.

[0020] The collected cargo transportation demand data (including cargo type, volume, weight, etc.) is uploaded to the edge computing node; the data is cleaned, integrated, and formatted on the node to ensure its accuracy and consistency; the processed data is stored in the node's local storage or cloud storage for subsequent analysis and optimization.

[0021] The optimization objectives are clearly defined, namely, minimizing carbon emissions and maximizing resource utilization. Appropriate optimization algorithms (such as genetic algorithms) are selected to achieve the objectives. Based on the transportation route network topology and cargo transportation demand data, factors such as carbon emissions, resource utilization, and real-time external conditions of different routes are considered. Through continuous iteration and optimization, the optimal solution or near-optimal solution that meets the objectives is found, and the optimization results are output to the corresponding system.

[0022] Then, real-time external condition data, such as wind speed, wind direction, wave height, and tides, are collected through sensors, weather stations, and other channels. This real-time data is then integrated with cargo transportation demand and the transportation route network topology to form a more comprehensive data foundation. Based on the real-time data and optimization models, the initial transportation planning routes are dynamically optimized, including adjusting transportation paths, changing transportation modes (such as changing one segment from sea to land), and adjusting transportation times. The optimized decision results are then communicated to relevant personnel to facilitate appropriate actions. The execution process is monitored in real-time, and execution result data is collected. Based on changes in execution results and real-time data, the optimization model is fed back and adjusted to achieve more precise dynamic optimization.

[0023] By following the steps above, we can ensure that transportation planning meets the needs of freight transportation while minimizing carbon emissions and improving resource utilization. Furthermore, by introducing real-time external conditions for dynamic optimization, we can further enhance the flexibility and adaptability of transportation planning.

[0024] Furthermore, such as Figure 2 As shown, based on M output ports and N cargo transportation destinations, transportation route planning is performed to obtain the initial transportation route. The method in this application includes:

[0025] Using the ARIMA model and combining historical transportation examples, transportation route features are extracted: Based on M output ports and N cargo transportation destinations, berth allocation optimization is performed simultaneously, and a candidate distribution of berth arrangements is obtained; based on the candidate distribution of berth arrangements, iterative optimization of transportation routes is performed to obtain the initial transportation planning route.

[0026] Collect historical transportation instance data over a period of time, including information such as output port, cargo destination, transportation route, transportation time, cargo type, cargo volume, and cargo weight; clean and organize the collected data, removing duplicate, erroneous, or invalid data to ensure data quality and accuracy; select key factors affecting transportation efficiency and cost as features based on the characteristics of the transportation routes, such as transportation distance, transportation time, port throughput, and cargo type; use ARIMA (Autoregressive Integral Moving Average) model to model the characteristics of the transportation routes. The ARIMA model can capture the autocorrelation and trend of time series data, thereby predicting future transportation route characteristics; extract features that have an important impact on transportation route planning based on the prediction results of the ARIMA model, such as the prediction of peak hours and expected changes in transportation costs.

[0027] Based on information such as cargo transportation demand, cargo type, and volume, the required number of berths and time for each output port are predicted. The berth resources of each output port are assessed, including the number, type, and location of available berths, as well as their operational efficiency and service quality. Combining the characteristics of the transportation route and the berth resource assessment results, optimization algorithms (such as genetic algorithms and simulated annealing algorithms) are used to optimize berth allocation, aiming to maximize berth utilization while ensuring timely loading, unloading, and transportation of cargo. After optimization calculations, multiple candidate berth arrangements are obtained, which differ in berth utilization, transportation time, and cost.

[0028] Each berth allocation candidate is evaluated, taking into account its impact on the transportation route, such as transportation distance and time. Based on the evaluation results, the transportation route is iteratively optimized. By adjusting the starting point, ending point, and ports along the route, transportation time is shortened or transportation efficiency is improved. The results of each iteration are compared with the previous results, and the optimal or near-optimal transportation route is selected as the initial transportation planning route. The initial transportation planning route is output to the corresponding system or platform for subsequent execution and monitoring.

[0029] By taking into account multiple factors such as the port of departure, cargo destination, transportation route characteristics, and berth resources, the transportation route is planned and optimized to obtain an initial transportation route, thereby improving transportation efficiency and optimizing berth utilization.

[0030] Furthermore, the P edge computing nodes are optimally configured with the goal of minimizing carbon emissions. The method of this application includes:

[0031] A carbon footprint model is constructed, which includes a path carbon emission assessment channel: acquiring basic ship power information and fuel efficiency, calculating the carbon emissions corresponding to each path through the path carbon emission assessment channel, combined with transportation distance and cargo capacity; applying carbon emission constraints based on the carbon emissions corresponding to each path, and selecting the first optimal path set that minimizes carbon emissions.

[0032] Identify the factors that the carbon footprint model needs to consider, such as vessel type, fuel type, transport distance, and cargo capacity; design a carbon footprint model framework that includes a route carbon emission assessment channel, which will be used to calculate carbon emissions under different transport routes; and set necessary parameters in the carbon footprint model, such as vessel fuel efficiency and carbon emission factor per unit of fuel.

[0033] Collect basic power information of ships, such as ship type, power, and fuel type, from ship manufacturers, shipping companies, or relevant databases; calculate the fuel efficiency of different ships based on the basic power information and actual operating data or experimental data; verify the collected data and calculated fuel efficiency to ensure the accuracy and reliability of the data.

[0034] Based on the combination of M outbound ports and N cargo transportation destinations, all possible transportation routes are defined. Parameters such as transportation distance, cargo capacity, and vessel fuel efficiency for each route are input into the route carbon emission assessment channel of the carbon footprint model. Using the algorithm or formula in the route carbon emission assessment channel, the carbon emissions corresponding to each route are calculated, which typically involves multiplying parameters such as transportation distance, cargo capacity, and fuel efficiency by the carbon emission factor per unit of fuel. The calculated carbon emissions for each route are recorded for subsequent analysis and comparison.

[0035] Based on actual needs and carbon emission reduction targets, set constraints on carbon emissions, such as a maximum allowable carbon emission threshold. Based on the calculated carbon emissions and the set constraints, select paths that meet the constraints. Among these paths, select the path with the lowest carbon emissions as the first optimal path. If multiple paths have the same minimum carbon emissions, they can form a set of first optimal paths. Output the selected optimal paths to the corresponding systems for subsequent execution.

[0036] By following the steps above, and optimizing the configuration of transportation routes based on P edge computing nodes with the goal of minimizing carbon emissions, it is possible to effectively reduce carbon emissions during transportation and achieve green and low-carbon logistics transportation.

[0037] Furthermore, the P edge computing nodes are optimally configured with the goal of maximizing resource utilization. The method of this application includes:

[0038] Based on the initial transportation planning route, predict berth demand and obtain a berth demand prediction sequence. Based on the berth demand prediction sequence and the loading and unloading efficiency, set an operation time window. Based on the operation time window corresponding to the berth demand prediction sequence, impose resource utilization constraints and select the second optimal path set that maximizes resource utilization.

[0039] Collect relevant information on the initial transportation planning route, including cargo type, transportation volume, and estimated arrival time; analyze the demand for berths for different vessels based on cargo characteristics (such as volume, weight, and whether special handling is required) and estimated arrival time; and predict berth demand using historical data, machine learning algorithms (such as time series analysis, neural networks, etc.) or other predictive models to obtain a berth demand prediction sequence, which will include the demand for different berths in different time periods.

[0040] Assess the operational efficiency of different loading and unloading machines within the port, including loading and unloading speed, failure rate, and maintenance cycle; based on the berth demand forecast sequence and loading and unloading machine efficiency, plan reasonable operating time windows for each berth and loading and unloading machine. The operating time windows should both meet berth demand and ensure efficient utilization of loading and unloading machines; based on the time window plan, schedule resources within the port and arrange suitable loading and unloading machines to the corresponding berths for operation.

[0041] Define evaluation indicators for resource utilization, such as berth utilization and loading / unloading machine utilization. Evaluate the resource utilization under the current route planning using actual operating data or simulation data. Based on the evaluation results and actual needs, set constraints for resource utilization, including minimum resource utilization thresholds and maximum idle time. Under the premise of meeting the resource utilization constraints, optimize the initial transportation planning route. The optimization goal is to maximize resource utilization, that is, to minimize idle time and inefficient use of resources.

[0042] Through optimized calculations, the path with the highest resource utilization rate that satisfies the resource utilization constraint is selected as the second optimal path. If multiple paths have the same maximum resource utilization rate, they are combined into a set of second optimal paths. The selected second optimal path or set of second optimal paths is output to the corresponding system for subsequent execution and monitoring. Through the above steps, P edge computing nodes can be optimally configured with the goal of maximizing resource utilization, thereby ensuring the effective use of port resources and improving the efficiency of logistics transportation.

[0043] Furthermore, by setting an operating time window based on the efficiency of the loading and unloading machines, the method of this application also includes:

[0044] By using discrete point analysis, the operation process of the loading and unloading machine is simulated to identify bottleneck periods. Based on the bottleneck periods, the operation time window is balanced and optimized.

[0045] Collect historical operation data of the loading and unloading machine, including the start time, end time, workload, and model of the loading and unloading machine for each operation; clean and organize the collected data to remove outliers and erroneous data to ensure the accuracy and reliability of the data; represent the operation data in the form of discrete points, with each discrete point representing the start or end of an operation, and identify the workload and efficiency of the loading and unloading machine in different time periods by analyzing the discrete points.

[0046] Based on the results of discrete point analysis, simulation software or mathematical models are used to simulate the operation process of loading and unloading machines. During the simulation, the performance parameters of loading and unloading machines (such as loading and unloading speed, maximum load, etc.), the limitations of the operating environment (such as weather, traffic conditions, etc.) and the characteristics of the goods (such as volume, weight, shape, etc.) need to be considered.

[0047] In the simulated loading and unloading operation process, time periods with low operating efficiency or high operating volume are identified, which are called bottleneck periods. The identified bottleneck periods are analyzed in depth to find out the causes of the bottlenecks. Common causes include insufficient performance of the loading and unloading machine, harsh operating environment, and complex cargo characteristics. The operating efficiency and operating volume of the bottleneck periods are quantitatively evaluated to determine their impact on the entire operation process.

[0048] Based on the analysis of bottleneck periods, the operation time windows are adjusted by extending or shortening certain time windows to balance the workload and efficiency in different time periods. While adjusting the time windows, resource allocation is considered; for example, increasing the number of loading / unloading machines or improving their performance during bottleneck periods to alleviate operational pressure. Optimization plans are developed, including specific adjustments to the time windows, resource allocation plans, and corresponding implementation schedules. The developed optimization plans are evaluated to ensure they effectively solve the bottleneck problem and improve the efficiency of the entire operation process.

[0049] According to the optimization plan, the operation time window is adjusted and corresponding resources are allocated. During the implementation of the plan, real-time monitoring is conducted to ensure the effective execution of all measures. Simultaneously, new operation data is collected to continuously optimize the plan. Through these steps, combined with the efficiency of the loading and unloading machines, a reasonable operation time window is set, and bottleneck periods are identified using discrete point analysis. Balancing and optimizing the operation time window helps improve the efficiency of the loading and unloading machines, reduces resource waste, and enhances the efficiency of the entire logistics and transportation system.

[0050] Furthermore, the method of this application also includes:

[0051] Identify multimodal transport demand, which includes transshipment time: Based on the first optimal path set that minimizes carbon emissions and the second optimal path set that maximizes resource utilization, optimize node connection time in combination with the multimodal transport demand, and determine P edge computing nodes corresponding to seamless sea-land transport connection.

[0052] Define multimodal transport needs, including origin and destination, cargo type, transport volume, and estimated arrival time; determine a reasonable transit time range based on cargo characteristics and transport requirements; derive a first optimal route set and a second optimal route set based on minimizing carbon emissions and maximizing resource utilization; analyze key indicators such as transit time, carbon emissions, and resource utilization of the routes in the first and second optimal route sets.

[0053] In multimodal transport routes, key transshipment nodes, such as ports, railway stations, and freight stations, are identified. Based on multimodal transport demands and route characteristics, the connection time between nodes is optimized to ensure that goods can be efficiently and smoothly switched from one mode of transport to another during transshipment. Mathematical programming or optimization algorithms (such as DEA methods) are used to accurately calculate the connection time between nodes. Factors such as the operation time of loading and unloading machines and the arrival and departure times of ships or trains are considered to ensure that goods can arrive at the next transshipment node on time.

[0054] Based on the optimized node connection time, select P edge computing nodes that can achieve seamless integration of sea and land transportation; ensure that the P edge computing nodes have sufficient processing power, storage capacity and communication capabilities to meet the needs of multimodal transport.

[0055] In practical applications, this also includes configuring and deploying P edge computing nodes according to a defined plan to ensure they can work as expected; monitoring and recording the node operation status and cargo transfer status in real time through a real-time monitoring system; and continuously optimizing node connection time and resource utilization based on monitoring data to ensure the efficient operation of the multimodal transport system.

[0056] By taking the above steps, combining the needs of multimodal transport, the optimal set of routes, and the optimization of node connection time, we can determine P edge computing nodes corresponding to the seamless connection between sea and land transport. This will help improve the efficiency and reliability of multimodal transport, reduce carbon emissions, and achieve green and efficient logistics transportation.

[0057] Furthermore, by utilizing the P edge computing nodes to dynamically optimize the initial transportation planning route, the method of this application also includes:

[0058] Connect the P edge computing nodes, and based on the rule engine, determine the urgent and special needs: establish a priority decision matrix and adjust the priorities based on the urgent and special needs: use the shortest path algorithm for emergency response and adjust the work plan.

[0059] Ensuring efficient and stable communication between P edge computing nodes via the network involves configuring VPNs, setting security firewall rules, and ensuring encrypted data transmission. A data synchronization mechanism is established between nodes to ensure that each node can obtain the latest key information such as transportation plans, operational status, and resource utilization in real time.

[0060] Based on business needs, a series of rules are defined to identify urgent and special needs, including the nature of the goods (such as dangerous goods and perishable goods), the timeliness requirements of transportation, customer priorities, etc.; the real-time data processing capabilities of edge computing nodes are used to monitor and analyze various data during transportation to promptly identify situations that meet the urgent and special needs rules; once a situation that meets the urgent and special needs rules is detected, the rule engine will trigger the corresponding processing flow.

[0061] Based on the nature and urgency of urgent and special needs, a priority decision matrix is ​​constructed. This matrix considers multiple factors, such as the importance of the goods, the urgency of transportation, and the availability of resources. The priority of each transportation task is dynamically adjusted based on the results of the decision matrix. This ensures that urgent and special needs are handled with priority, while minimizing the impact on other normal transportation tasks.

[0062] When an emergency or special need is triggered, the shortest path algorithm (such as Dijkstra's algorithm or A* algorithm) is used to quickly calculate the optimal path from the current location to the destination. The corresponding path should take into account multiple factors such as transit time, resource utilization, and carbon emissions. Based on the calculated optimal path, the original work plan is dynamically adjusted, including reallocating loading and unloading machine resources, adjusting the departure time of ships or trains, and optimizing the loading sequence of goods.

[0063] In practical applications, this also includes rapidly implementing emergency response measures according to the adjusted work plan to ensure timely and accurate execution of the new plan; during the emergency response process, real-time monitoring is conducted through edge computing nodes to ensure the effective implementation of various measures, while collecting feedback data to continuously optimize the solution.

[0064] By using the above steps, the initial transportation planning route is dynamically optimized using P edge computing nodes, especially for rapid response to urgent and special needs. This helps improve the flexibility and efficiency of logistics transportation and ensures that cargo transportation in emergency situations can be handled in a timely and effective manner.

[0065] Furthermore, the method of this application also includes:

[0066] The aforementioned urgent and special needs include the type and quantity of materials: a priority decision matrix is ​​established based on the urgency of the materials.

[0067] The types of supplies should be classified in detail. For example, supplies can be divided into life-saving materials, medical supplies, critical components, food and water, etc. Different types of supplies have different importance in emergency situations. The demand and available quantity of each type of supply should be assessed to determine the degree of scarcity. The quantity of supplies will directly affect their priority.

[0068] Based on the type and quantity of supplies and the specific context (such as disaster relief, epidemic prevention and control, etc.), formulate standards for assessing the urgency of supplies, including the importance of supplies to relief or recovery work, the substitutability of supplies, and the impact of shortages on the deterioration of the situation; based on the above standards, score the urgency of each type of supply. The score can be a quantitative indicator, such as 1-10, where 10 points represents the highest urgency.

[0069] Construct a two-dimensional matrix with material type as the row and urgency score as the column. Each cell represents the priority of a material under a certain urgency score. Assign a value to each cell according to the material type and urgency score to indicate the priority of the material. The priority can be a relatively fixed numerical value or level (such as high, medium, low).

[0070] For example, the priority decision matrix P is an A×B matrix, where A represents different types of materials and B represents different levels of material quantity; each element P in the matrix... ij This represents the priority score for the i-th type of material and the j-th quantity of material; correspondingly, , The weight used to characterize the urgency of the i-th type of material The weight used to characterize the urgency of the j-th type of material.

[0071] As circumstances change, the type, quality, and urgency of supplies will also change. Therefore, it is necessary to update the data in real time and reassess the priority of supplies. When faced with urgent and special needs, the priority decision matrix can be consulted to quickly determine the priority of various supplies, thereby making reasonable resource allocation and scheduling decisions. Feedback data should be collected in practical applications, including the accuracy of decisions and the efficiency of supply scheduling. Based on the feedback data, the priority decision matrix should be adjusted and optimized to improve its accuracy and effectiveness in practical applications.

[0072] Through the above steps, an effective priority decision matrix is ​​established based on the urgency of materials to address urgent and special needs, including the type and quantity of materials. This provides rapid and accurate decision support in emergency situations, ensuring that resources can be allocated and utilized rationally and efficiently.

[0073] In summary, the beneficial effects of the embodiments of this application are:

[0074] 1. By introducing real-time external condition monitoring and edge computing nodes, dynamic optimization and adjustment of transportation routes are realized, which improves adaptability to unforeseen circumstances, reduces waiting time and transfer time, reduces the risk of transportation delays, and improves the overall efficiency of cargo transportation.

[0075] 2. By employing a carbon emission minimization strategy, a carbon footprint model and berth demand forecasting sequence were constructed, achieving a balance between minimizing carbon emissions and maximizing resource utilization. This optimized resource allocation and reduced carbon emissions, mitigating environmental impact and meeting the requirements of sustainable development.

[0076] 3. By optimizing node connection time, seamless connection between sea and land transportation has been achieved, shortening transshipment time, improving the overall efficiency of multimodal transport, and enhancing the quality of logistics services. In the face of emergencies and special needs, the rule engine and priority decision matrix can be used to quickly respond to urgent and special needs, automatically adjust operation plans and priorities, enhance the flexibility and speed of decision-making, and improve the stability and reliability of the transportation system.

[0077] 4. By identifying multimodal transport demand, including transshipment time, and based on a first optimal path set that minimizes carbon emissions and a second optimal path set that maximizes resource utilization, and by optimizing node connection times in conjunction with multimodal transport demand, P edge computing nodes corresponding to seamless sea-land transport integration are determined. Combining multimodal transport demand, the optimal path set, and node connection time optimization to determine the P edge computing nodes corresponding to seamless sea-land transport integration helps improve the efficiency and reliability of multimodal transport, reduce carbon emissions, and achieve green and efficient logistics transportation.

[0078] In summary, any step can be stored as a computer instruction or program in an unrestricted computer memory and can be called and recognized by an unrestricted computer processor; no further restrictions are imposed here.

[0079] Furthermore, the above technical solutions only embody the preferred technical solutions of the embodiments of this application. Any changes that those skilled in the art may make to certain parts of these solutions embody the novel principles of the embodiments of this application. Obviously, those skilled in the art can make various modifications and variations to this application without departing from the scope of this application.

Claims

1. A port cargo transportation method based on transportation route planning, characterized in that, The method includes: Obtain the cargo transportation demand of M output ports, wherein the cargo transportation demand includes cargo type, cargo volume and cargo weight; Based on M output ports and N cargo transportation destinations, a transportation route is planned to obtain the initial transportation route plan. Obtain the cargo loading and unloading feature set corresponding to the first output port among the M output ports. The features corresponding to the cargo loading and unloading feature set include the terminal throughput, the number of available berths, and the working efficiency of the unloader. Based on the cargo loading and unloading feature set corresponding to the first output port, the M output ports are traversed, and a transportation route network topology is established in conjunction with the initial transportation planning route. Based on the transportation route network topology, P edge computing nodes are set up to upload the cargo transportation demand. The P edge computing nodes are optimized with the goal of minimizing carbon emissions and maximizing resource utilization. By introducing real-time external conditions and utilizing the P edge computing nodes, the initial transportation planning route is dynamically optimized and port cargo transportation is adjusted. The real-time external conditions include real-time weather conditions and real-time ocean conditions.

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

3. The port cargo transportation method based on transportation route planning as described in claim 2, characterized in that, The P edge computing nodes are optimized with the goal of minimizing carbon emissions, and the method includes: Construct a carbon footprint model, which includes pathway carbon emission assessment channels; Obtain basic information on ship power and fuel efficiency, and calculate the carbon emissions corresponding to each route by combining the transportation distance and cargo volume through the route carbon emission assessment channel. Based on the carbon emissions corresponding to each path, carbon emission constraints are applied, and the first optimal path set that minimizes carbon emissions is selected.

4. The port cargo transportation method based on transportation route planning as described in claim 3, characterized in that, The P edge computing nodes are optimized and configured with the goal of maximizing resource utilization. The method includes: Based on the initial transportation planning route, predict berth demand and obtain a berth demand prediction sequence; Based on the berth demand forecast sequence and combined with the loading and unloading machine efficiency, the operation time window is set; Based on the operational time window corresponding to the berth demand prediction sequence, resource utilization constraints are applied to select the second optimal path set that maximizes resource utilization.

5. The port cargo transportation method based on transportation route planning as described in claim 4, characterized in that, The method further includes setting an operation time window based on the efficiency of the loading and unloading machines, and also includes: The operation process of the loading and unloading machine is simulated using discrete point analysis to identify bottleneck periods; Based on the bottleneck period, the operation time window is balanced and optimized.

6. The port cargo transportation method based on transportation route planning as described in claim 4, characterized in that, The method includes: Identify multimodal transport needs, including transit time; Based on the first optimal path set that minimizes carbon emissions and the second optimal path set that maximizes resource utilization, and combined with the optimization of node connection time for the multimodal transport demand, P edge computing nodes corresponding to seamless sea-land transport connection are determined.

7. The port cargo transportation method based on transportation route planning as described in claim 1, characterized in that, The method further includes dynamically optimizing the initial transportation planning route using the P edge computing nodes. Connect the P edge computing nodes and determine urgent and special needs based on the rule engine; Based on the aforementioned urgent and special needs, a priority decision matrix is ​​established and priorities are adjusted; Employ the shortest path algorithm for emergency response and adjust the work plan.

8. The port cargo transportation method based on transportation route planning as described in claim 7, characterized in that, The aforementioned emergency special needs include the type and quantity of supplies; Based on the urgency of the resources, establish the aforementioned priority decision matrix.

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