Port logistics dynamic path planning method and system based on multi-source data fusion

Abstract

The present invention belongs to the field of traffic control, and specifically relates to a method and system for dynamic path planning of port logistics based on multi-source data fusion. The problem solved by the present invention is: how to perform real-time dynamic planning of port logistics paths based on multi-source data under massive real-time data. To solve the above problem, an embodiment of the present invention provides a method for dynamic path planning of port logistics based on multi-source data fusion, including: obtaining port road condition data and task data of external container trucks, and calculating dynamic road weights; performing dynamic path planning on external container trucks based on the dynamic road weights to generate an initial path for each external container truck; monitoring the real-time road conditions of the port and the execution status of the initial path in real time, and adjusting the driving status of the internal container trucks according to the execution status; obtaining the occurrence of dynamic events based on the real-time road conditions, and when a dynamic event occurs, updating the initial path according to the dynamic event to obtain an updated path.

Description

Technical Field

[0001] The present invention relates to the field of traffic control, and in particular to a port logistics dynamic path planning method and system based on multi-source data fusion. Background Art

[0002] With the continuous development of the global trade economy, ports, as important hubs for international trade, play a vital role. The efficiency of their material distribution and management is crucial. Compared with ordinary roads, the road conditions inside ports are more focused on supporting efficient logistics operations and the passage of heavy vehicles, and the safety and environmental factors they face are more complex. In addition, the road environment inside ports is closed, dynamic, and dedicated, and involves a large number of specialized vehicles, equipment, and operating processes. The design logic of ordinary navigation, such as static maps, single vehicles, and low-precision positioning, cannot meet the dynamic, collaborative, high-precision, and safety management requirements of ports. Therefore, a dedicated navigation system is needed inside ports to provide more accurate, efficient, and safer navigation services.

[0003] Port logistics involves multiple types of data, including internal container trucks, external container trucks, and traffic events. The data sources are scattered and the formats are not uniform. Therefore, integrating the data from scattered sources and non-uniform formats to generate comprehensive and real-time data, thereby providing port managers or drivers with dynamic path planning methods, can significantly improve the efficiency and reliability of port logistics, and provide strong support for intelligent management and decision-making of ports. Summary of the Invention

[0004] The problem solved by the present invention is how to perform real-time dynamic planning of port logistics routes based on multi-source data under massive real-time data.

[0005] To solve the above problems, an embodiment of the present invention provides a port logistics dynamic path planning method based on multi-source data fusion. The planning method includes: obtaining the port's road condition data and the task data of external container trucks, and calculating dynamic road weights; performing dynamic path planning on the external container trucks according to the dynamic road weights, and generating an initial path for each external container truck; monitoring the real-time road conditions of the port and the execution status of the initial path in real time, and adjusting the driving status of the internal container trucks according to the execution status; obtaining the occurrence of dynamic events according to the real-time road conditions, and when a dynamic event occurs, updating the initial path according to the dynamic event to obtain an updated path; recalculating the dynamic road weights according to the updated path, and updating the driving status of the internal container trucks according to the changes in the dynamic road weights.

[0006] Compared with the existing technology, the technical effects achieved by adopting this technical solution are as follows: the road weight obtained by obtaining the port road condition data fully considers the changes in the road weight caused by the real-time road conditions of the port; the initial path is obtained by dynamic road weight and the task data of external container trucks, and the differences between external container truck tasks are fully considered, making the path planning more reasonable; by real-time monitoring of the real-time road conditions of the port, it is possible to promptly discover whether there are any emergencies; real-time monitoring of the execution status of the initial path helps to respond to road condition changes in a timely manner according to the real-time road conditions, and reduce transportation delays and confusion caused by sudden changes in road conditions; the acquisition of updated paths helps to uniformly dispatch the affected container trucks, thereby improving the dispatch efficiency; the driving status of the internal container trucks is updated according to the changes in the dynamic road weight, which helps to achieve reasonable scheduling of the container trucks within the port, avoid conflicts and congestion between container trucks, and improve the coordination and smoothness of the internal transportation of the port.

[0007] In one embodiment of the present invention, dynamic path planning is performed on external container trucks based on dynamic road weights to generate an initial path for each external container truck, specifically including: classifying the external container trucks according to their destinations within the port area, and planning the driving paths according to the destinations; when there are multiple driving paths, obtaining the vehicle type and vehicle load corresponding to the external container trucks based on mission data; calculating the startup rate of the external container trucks based on the vehicle type and vehicle load; and allocating the external container trucks to the driving paths based on the startup rate to obtain the initial paths of the external container trucks.

[0008] Compared with the existing technology, the technical effect achieved by adopting this technical solution is: external container trucks are classified according to their destinations in the port area, and the driving routes are planned accordingly, making the driving route planning of external container trucks more targeted. The acquisition of vehicle types and vehicle loads corresponding to external container trucks fully considers the impact of the starting rate of external container trucks during driving, and realizes the reasonable allocation of road resources.

[0009] In one embodiment of the present invention, the real-time road conditions of the port and the execution status of the initial path are monitored in real time, and the driving status of the internal container truck is adjusted according to the execution status, specifically including: real-time monitoring of the running trajectory and running speed of the external container truck; when the running trajectory differs from the initial path, predicting the subsequent path of the external container truck based on the road condition data of the port; obtaining the cruising path of the internal container truck, and when there is mutual interference between the cruising path and the subsequent path, calculating the interference time period when the interference occurs based on the running speed; setting a virtual traffic light for the internal container truck, adjusting the driving status of the internal container truck through the virtual traffic light, and changing the driving position of the internal container truck during the interference time period.

[0010] Compared with existing technologies, this solution achieves the following technical benefits: The tracking and speed of external trucks can be tracked in real time to adapt to changes in their movements, including road conditions and unexpected situations. Interference time periods are calculated based on operating speeds, enabling the foreseeable point and duration of potential conflicts between trucks, allowing for targeted action. The virtual traffic light setup provides clear rules and guidance for internal trucks, enabling them to adjust their driving status in an orderly manner when faced with potential interference.

[0011] In one embodiment of the present invention, a virtual traffic light is set for the internal container truck, and the driving state of the internal container truck is adjusted by the virtual traffic light to change the driving position of the internal container truck during the interference time period, specifically including: obtaining the same section of the cruising path and the subsequent path to obtain the overlapping path; determining the influence coefficient of the internal container truck on the overlapping path according to the traffic density of the overlapping path; when the influence coefficient is greater than or equal to the interference threshold, controlling the driving of the internal container truck by the virtual traffic light to ensure that the internal container truck is located outside the overlapping path during the interference time period; when the influence coefficient is less than the interference threshold, adjusting the initial time for the internal container truck to enter the overlapping path according to the influence coefficient.

[0012] Compared with the existing technology, the technical effect achieved by adopting this technical solution is as follows: the acquisition of overlapping paths can accurately lock the specific areas where internal container trucks and external container trucks may interfere with each other, determine the influence coefficient based on traffic density and compare it with the interference threshold, and provide a reasonable basis for the scheduling decision of internal container trucks. Through real-time monitoring and analysis of container truck driving data, intelligent scheduling and management of internal container trucks are realized, reducing the uncertainty and error of manual intervention.

[0013] In one embodiment of the present invention, the occurrence of dynamic events is obtained based on real-time road conditions. When a dynamic event occurs, the initial path is updated based on the dynamic event to obtain an updated path, which specifically includes: obtaining the occurrence location and event type of the dynamic event based on real-time road conditions, and determining the stagnant area of the dynamic event; obtaining an external container truck that needs to pass through the stagnant area, and determining an adjustable path for the external container truck based on road condition data; selecting a corresponding adjustable path for the external container truck based on mission data to obtain an updated path.

[0014] Compared with existing technologies, the technical effects achieved by adopting this technical solution are: timely discovery and demarcation of congestion areas, and rapid adjustment of the routes of affected container trucks can effectively reduce the negative impact of dynamic events on transportation and enhance the port's ability to respond to various emergencies.

[0015] In one embodiment of the present invention, the dynamic road weight is recalculated according to the updated path, and the driving status of the internal container truck is updated according to the change of the dynamic road weight, specifically including: obtaining the unit weight value of each driving road in each time period; when the unit weight value is greater than the weight threshold, the time period is recorded as a prohibited time period, and when the unit weight value is less than or equal to the weight threshold, the time period is recorded as a passable time period; calculating the unit traffic volume in the passable time period, and adjusting the driving status of the internal container truck according to the unit traffic volume.

[0016] Compared with the existing technology, the technical effect achieved by adopting this technical solution is: by obtaining the unit weight value of each driving road in each time period, it is possible to clearly understand the traffic conditions of the road in different time periods, and based on the comparison between the unit weight value and the weight threshold, the prohibited time period and the passable time period are divided, which provides a clear time reference for the driving planning of internal container trucks, and adjusts the driving status of internal container trucks according to the unit traffic volume, which can ensure the operating efficiency of external container trucks.

[0017] In one embodiment of the present invention, the unit traffic volume within the passable time period is calculated, and the driving status of the internal container trucks is adjusted according to the unit traffic volume, specifically including: obtaining the traffic demand of the internal container trucks within the passable time period, and predicting the theoretical traffic volume of the internal container trucks within the passable time period; when the traffic demand is less than or equal to the theoretical traffic volume, confirming the traffic order of the internal container trucks according to the current positions of the internal container trucks; when the traffic demand is greater than the theoretical traffic volume, determining the internal container trucks that need to pass according to their task priorities, and controlling the internal container trucks that do not need to pass to park nearby.

[0018] Compared with the existing technology, the technical effect achieved by adopting this technical solution is as follows: by obtaining the traffic demand of internal container trucks during the passable period and predicting the theoretical traffic volume, the relationship between the road's carrying capacity and the traffic demand of container trucks can be accurately evaluated. By screening according to task priority, it ensures that container trucks with critical tasks can pass in time and are not hindered by container trucks with other non-critical tasks, thereby improving the overall efficiency and service quality of port operations and ensuring the smooth progress of port production.

[0019] In one embodiment of the present invention, the present invention also provides a planning system, and the port logistics dynamic path planning method recorded in the above embodiment is applied to the planning system. The planning system includes: a data acquisition module, the data acquisition module is used to collect road condition data and task data of external container trucks; a data processing module, the data processing module is used to fuse road condition data and task data, and calculate dynamic road weights; a path planning module, the path planning module is used to generate initial paths and updated paths; a road monitoring module, the road monitoring module is used to monitor real-time road conditions. The planning system has all the technical features of the above-mentioned port logistics dynamic path planning method, which will not be repeated here one by one. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings to be used in describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive efforts.

[0021] Figure 1 This is one of the flow charts of the port logistics dynamic path planning method of the present invention;

[0022] Figure 2 This is the second flow chart of the port logistics dynamic path planning method of the present invention;

[0023] Figure 3 This is the third flow chart of the port logistics dynamic path planning method of the present invention;

[0024] Figure 4 This is the fourth flow chart of the port logistics dynamic path planning method of the present invention;

[0025] Figure 5 A schematic diagram of the planning system of the present invention;

[0026] Description of reference numerals:

[0027] 100 - planning system; 110 - data acquisition module; 120 - data processing module; 130 - path planning module; 140 - road monitoring module. DETAILED DESCRIPTION

[0028] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0029] [First embodiment]

[0030] See also Figure 1 In a specific embodiment, the present invention provides a port logistics dynamic path planning method based on multi-source data fusion, the planning method comprising:

[0031] S100, obtaining port road condition data and external container truck mission data, and calculating dynamic road weights;

[0032] S200, performing dynamic path planning for external container trucks based on dynamic road weights to generate an initial path for each external container truck;

[0033] S300: monitor the real-time traffic conditions of the port and the execution status of the initial route in real time, and adjust the driving status of the internal container trucks according to the execution status;

[0034] S400: Acquire the occurrence of dynamic events according to real-time traffic conditions. When a dynamic event occurs, update the initial path according to the dynamic event to obtain an updated path.

[0035] S500: Recalculate the dynamic road weight according to the updated path, and update the driving status of the internal container truck according to the change of the dynamic road weight.

[0036] In step S100, under normal circumstances, the road network layout within the port is relatively complex and may include multiple docks, warehouses, and yards. These different functional areas are connected by a complex road system, which can easily lead to traffic congestion or navigation difficulties. In order to ensure the transportation efficiency of external container trucks, it is necessary to perform real-time dynamic planning on the paths of external container trucks. When performing real-time dynamic planning on the paths of external container trucks, it is usually necessary to obtain the task data of the external container trucks and the real-time road conditions within the port. The task data includes but is not limited to task type, cargo information, vehicle type, reservation and scheduling information, operation area, entry and exit time, and task priority, etc., which can be obtained through the port management information system, container operation scheduling system, reservation system, and data sharing between logistics companies and ports. The port's road condition data usually includes traffic flow, lane information, and traffic events, etc., which can be obtained through sensors and monitoring equipment, data sharing platforms, traffic management systems, and manual inspections.

[0037] Generally speaking, lane information usually includes road conditions, road maintenance, lane width, and lane type. Traffic events generally refer to traffic congestion and traffic accidents. The calculation of dynamic road weights requires comprehensive consideration of static road attributes such as lane width, road conditions, and lane type, and dynamic traffic conditions such as traffic flow, congestion, and accidents. These can be obtained through a variety of calculation methods.

[0038] Taking the weighted average method as an example, the dynamic road weight formula is:

[0039] ;

[0040] ;

[0041] ;

[0042] .

[0043] in, is the traffic event weight, is the event occurrence judgment value, is the event impact coefficient, which can be optimized through fuzzy comprehensive evaluation or grey neural network model. is the traffic flow weight, is the current traffic flow of the road section, is the road capacity, is the lane information weight, is the weight of lane information, is the influence coefficient of lane information, which can be determined by analytic hierarchy process or rough set theory. is the dynamic road weight, is the basic weight.

[0044] Fixed weights can be directly assigned based on the road surface flatness, road damage, road slipperiness, and whether there are obstacles. For example, the road conditions can be divided into four levels: excellent, good, medium, and poor, with weights corresponding to 0.1, 0.5, 1, and 1.5, respectively.

[0045] Generally speaking, the basic weights can be preset according to the functional importance of the lanes in the port. For example, the main roads, such as the roads connecting the dock and the yard, are set to 0.2, the secondary roads, such as the yard ring roads, are set to 0.5, the loading and unloading area channels are set to 0.8, and the temporary channels, such as construction detours, are set to 1.2. Lane types can also be divided into dedicated lanes and mixed lanes based on whether the lanes are dedicated lanes. For example, the weight of dedicated truck lanes is set to 1, and the weight of mixed lanes is set to 1.5.

[0046] Road maintenance can generally be assigned weights based on the degree of obstruction to traffic. For example, the weight of normal traffic can be set to 0, the weight of one-side closure can be set to 1, and the weight of complete closure can be set to infinity, forcing a detour.

[0047] The calculation formula of the lane width weight is as follows:

[0048] ;

[0049] in, is the actual width of the current road, The maximum width of the internal roads of the port.

[0050] It should be noted that the higher the traffic priority, the lower the weight.

[0051] For example, the dynamic road weight of the road condition of a certain port road is 0.55, and the dynamic road weight of another road is 0.6. Then, the road with a dynamic road weight of 0.55 has a higher traffic priority than the road with a dynamic road weight of 0.6.

[0052] Taking the method based on traffic zoning as an example, the dynamic road weight formula is:

[0053] ;

[0054] Among them, T is the actual travel time, is the free flow travel time, and Parameters adjusted according to actual conditions. is the current traffic flow of the road section, is the road capacity.

[0055] For example, suppose the free flow travel time of a road is 10 minutes, the traffic volume is 120 vehicles / hour, and the road capacity is 150 vehicles / hour. The model parameters are =0.15, =4, then the actual travel time of this road is approximately: T=10×(1+0.15×0.7776)≈11.16 minutes, then it takes about 11.16 minutes to pass this road, that is, the dynamic road weight of this road is 11.16. If the dynamic road weight of another road is 12, then the road with a dynamic road weight of 11.16 has a higher priority than the road with a dynamic road weight of 12.

[0056] In step S200, the dynamic road weights reveal the traffic efficiency and priority under the current traffic environment. Generally, container trucks in ports are usually large in size and slow in speed. If they mix with other vehicles, it may cause traffic congestion and inefficiency. Therefore, in many large ports, external container trucks usually have dedicated channels or dedicated routes. Therefore, when performing dynamic path planning for external container trucks, the dedicated lanes for external container trucks need to be screened out first.

[0057] Secondly, the selected routes can be determined through the mission data of external container trucks. Among them, the mission types include: loading and unloading tasks, container transshipment tasks, and customs inspection tasks. The vehicle types of external container trucks can be divided according to their purpose, such as flatbed container trucks, container trucks, van container trucks, and refrigerated container trucks.

[0058] In step S300, under normal circumstances, the external container truck driver may choose a driving route different from the initial route due to his driving habits. Therefore, the external container truck usually has two situations: following the route and not following the route. In addition, due to the complexity of the internal routes of the port, there may be situations where the internal container trucks and external container trucks cause interference. Therefore, it is necessary to monitor the real-time road conditions of the port and the execution status of the initial route through detection equipment, and adjust the driving status of the internal container trucks according to the road priority of the internal container trucks.

[0059] For example, generally speaking, the priority of external container trucks is usually higher than that of internal container trucks. Therefore, when internal container trucks interfere with the driving of external container trucks, virtual traffic lights are generated for the internal container trucks to ensure the smooth passage of external container trucks.

[0060] It should be noted that when an internal container truck has an urgent task and interferes with the driving of an external container truck, instructions can be issued to the external container truck, for example, a voice prompt can be issued to remind the external container truck, thereby ensuring the smooth passage of the internal container truck.

[0061] In steps S400 and S500, generally speaking, dynamic events refer to situations such as traffic congestion or bottlenecks, sudden traffic accidents or obstacles, and emergency scheduling or priority transportation that occur in the routes of external container trucks. Normally, when a dynamic event occurs, it is necessary to uniformly schedule the affected container trucks based on the impact range of the dynamic event, the total number of vehicles that need to change routes, and the traffic conditions of the updated routes, so as to avoid traffic congestion and long-term stagnation.

[0062] By obtaining the port road condition data to obtain the road weight, the changes in the port's real-time road conditions on the road weight are fully considered. The initial path is obtained by the dynamic road weight and the task data of the external container truck, and the differences between the external container truck tasks are fully considered, making the path planning more reasonable. By monitoring the real-time road conditions of the port in real time, it is possible to detect whether there are any emergencies in a timely manner. Real-time monitoring of the execution status of the initial path helps to respond to changes in road conditions in a timely manner according to the real-time road conditions, and reduce transportation delays and confusion caused by sudden changes in road conditions. The acquisition of updated paths helps to uniformly dispatch the affected container trucks, thereby improving the dispatch efficiency. The driving status of the internal container trucks is updated according to the changes in the dynamic road weight, which helps to achieve reasonable scheduling of the container trucks within the port, avoid conflicts and congestion between container trucks, and improve the coordination and smoothness of the internal transportation of the port.

[0063] [Second embodiment]

[0064] See also Figure 2 In a specific embodiment, dynamic path planning is performed on external container trucks based on dynamic road weights to generate an initial path for each external container truck, specifically including:

[0065] S210. Classify external container trucks according to their destinations within the port area, and plan driving routes based on the destinations;

[0066] S220: When there are multiple driving routes, obtain the vehicle type and vehicle load corresponding to the external container truck according to the mission data;

[0067] S230: Calculate the startup rate of the external container truck according to the vehicle type and vehicle load, assign the external container truck to a driving path according to the startup rate, and obtain an initial path of the external container truck.

[0068] In step S210, generally, multiple container truck driving routes are set up inside the port, and these driving routes lead to different cargo yards or terminal areas. Therefore, it is necessary to first preliminarily screen out suitable driving routes based on the destinations of external container trucks, and then select the initial path based on the size of the road dynamic weights of the preliminarily screened suitable driving routes.

[0069] In steps S220 and S230, when only one driving path is available based on the dynamic road weight and the destination of the external container truck, this driving path is the initial path. When there are multiple driving paths available, in order to avoid the external container trucks uniformly selecting the same path or the transportation efficiency of the driving path being reduced due to the different operating speeds of the external container trucks and other trucks on the driving path, it is necessary to uniformly schedule the driving paths of the external container trucks.

[0070] Generally speaking, different types of container trucks, such as heavy-duty, medium-duty, and light-duty trucks, differ in load capacity and size, which will affect the vehicle's driving speed, acceleration capability, and ability to negotiate curves. For example, a heavy-duty truck may require more time to accelerate and maintain a slower speed, which may affect traffic flow. In addition, the same model of container truck has different weights when empty and fully loaded. Therefore, the driving route of the external container truck needs to be planned according to the vehicle type and vehicle load.

[0071] For example, assuming that the average speed of a fully loaded medium-sized container truck in the port is 15 kilometers per hour, and when it is empty, its average speed is 20 kilometers per hour. The distance from this medium-sized container truck to the container collection area in the port after entering the terminal is 2 kilometers. Then, when it is fully loaded, it takes about 6 minutes to get from the terminal to the container collection area in the port area, and when it is empty, it takes 8 minutes to get from the terminal to the container collection area in the port area. Therefore, when planning the driving route, external container trucks with the same load capacity will be given priority to be assigned to the same driving route.

[0072] In step S230, the startup rate is generally affected by the vehicle type and vehicle load, and can be simply obtained by the following formula:

[0073] ;

[0074] Where F is the traction force, is the rolling resistance, is the air resistance, is the total weight of the external container truck, The startup rate of the external set card.

[0075] It can be seen from the above formula that for the same external container truck, its starting speed is different when it is fully loaded and empty.

[0076] In addition, lanes with faster starting speeds will have smoother traffic flow, while vehicles with slower starting speeds are likely to affect the overall traffic flow and may cause delays for subsequent vehicles. Therefore, when planning roads for external container trucks, in order to improve the overall transportation efficiency within the port, it is necessary to obtain the maximum speed limit and average speed on each driving path, arrange vehicles with higher starting speeds on driving paths with higher average speeds, and arrange vehicles with lower starting speeds on driving paths with lower average speeds.

[0077] External container trucks are classified according to their destinations within the port area and their driving routes are planned accordingly, making the route planning of external container trucks more targeted. The acquisition of vehicle types and vehicle loads corresponding to external container trucks fully considers the impact of the starting rate of external container trucks during driving, thereby achieving a reasonable allocation of road resources.

[0078] [Third embodiment]

[0079] See also Figure 3 In a specific embodiment, the real-time traffic conditions of the port and the execution status of the initial route are monitored in real time, and the driving status of the internal container trucks is adjusted according to the execution status, specifically including:

[0080] S310, real-time monitoring of the running track and running speed of the external container truck;

[0081] S320: When the running trajectory differs from the initial path, predict the subsequent path of the external container truck based on the port's road condition data;

[0082] S330: Obtain the cruising path of the internal container truck. When there is interference between the cruising path and the subsequent path, calculate the interference time period according to the running speed.

[0083] S340: Setting a virtual traffic light for the internal container truck, adjusting the driving state of the internal container truck through the virtual traffic light, and changing the driving position of the internal container truck during the interference time period.

[0084] In step S320, generally speaking, the destination of the external container truck can be obtained through the mission data, and the paths that the external container truck can travel can be known through the port's road condition data. When the running trajectory differs from the initial path, the subsequent path of the external container truck can be predicted based on the external container truck's mission data, the port's road condition data and the current driving path.

[0085] In step S330 and step S340, under normal circumstances, when there is mutual interference between the cruising path and the subsequent path, the interference time period during which the interference occurs is calculated based on the running speed of the external container truck on the subsequent path, the traffic length of the external container truck that is interfered with, and the length of the interference section. After obtaining the specific range of the interference time period, the interference of the internal container truck on the external container truck is judged based on the cruising path of the internal container truck, and the passage mode of the internal container truck is controlled based on the virtual traffic light. A virtual red light can be displayed for the internal container truck in an open area to stop the internal container truck from running and continue to execute the cruising path after the interference time period ends. Alternatively, a virtual street light can be set when road conditions permit to allow the internal container truck to quickly pass through some intersections and pass through the intersection before the interference time period begins, thereby avoiding interference between the cruising path and the subsequent path. Because the internal container trucks in the port area all use assisted driving technology, the virtual traffic light is more effective for the internal container trucks, but it does not play a due role for the external container trucks.

[0086] Obtaining the trajectory and speed of external trucks allows for timely response to changes in their travel, adapting to route changes caused by factors such as road conditions and emergencies. By calculating interference time periods based on operating speed, it is possible to predict the points and timing of potential conflicts between trucks, allowing for targeted action. The virtual traffic light setup provides clear rules and guidance for internal trucks, enabling them to adjust their driving status in an orderly manner when faced with potential interference.

[0087] [Fourth embodiment]

[0088] In a specific embodiment, a virtual traffic light is set for the internal container truck, and the driving state of the internal container truck is adjusted by the virtual traffic light to change the driving position of the internal container truck during the interference period, specifically including:

[0089] S341, obtaining the common sections of the cruise path and the subsequent path to obtain an overlapping path;

[0090] S342. Determine the influence coefficient of the internal container truck on the overlapping path based on the traffic density of the overlapping path;

[0091] S343. When the influence coefficient is greater than or equal to the interference threshold, the internal container trucks are controlled by using virtual traffic lights to ensure that the internal container trucks are located outside the overlapping path during the interference period.

[0092] S344: When the influence coefficient is less than the interference threshold, the initial time for the internal container truck to enter the overlapping path is adjusted according to the influence coefficient.

[0093] In steps S341 and S342, under normal circumstances, the internal container truck and the external container truck may have the same driving section. The traffic density is directly related to the smoothness of traffic flow, traffic capacity, delays, etc. When the traffic density is too high, the vehicle's driving speed will be reduced, thereby affecting transportation efficiency.

[0094] Based on the analysis of historical traffic flow data, we can obtain the threshold of traffic density, which is recorded as the density threshold. The traffic density threshold represents the upper limit of the number of vehicles per unit length on the road. When the flow density exceeds this value, traffic begins to be significantly disturbed, resulting in a decrease in vehicle speed and traffic efficiency, which may eventually lead to traffic congestion.

[0095] The influence coefficient of the internal container truck on the overlapping path can be determined based on the traffic density of the internal container truck and the traffic density of the overlapping path. It can usually be obtained by the following formula:

[0096] ;

[0097] in, is the influence coefficient, is the traffic density of the overlapping paths, is the internal truck traffic density, is the density threshold, usually, The larger the internal container truck is, the greater its influence on the overlapping path will be.

[0098] In steps S343 and S344, generally, the interference threshold may be set according to the urgency of the task of the external container truck.

[0099] For example, when the interference threshold is 1 and the influence coefficient is 1.1, since 1.1 is greater than 1, it is necessary to set a virtual traffic light to ensure that the internal trucks do not enter the overlapping path during the interference period.

[0100] In step S344, when the influence coefficient is less than the interference threshold, it means that the traffic on the road has not reached the level of hindering the normal operation of the external container truck. Therefore, the initial time for the internal container truck to enter the overlapping path can be adjusted according to the size of the influence coefficient. The specific method is as follows:

[0101] The first method is to increase the interval time for internal container trucks to enter the overlapping route. For example, internal container trucks can enter the overlapping route in batches to avoid a large number of container trucks entering at the same time.

[0102] The second method: Through real-time traffic, observe the changes in traffic flow and density, and adjust the initial time of the container trucks in time. For example, if it is found that the traffic volume gradually increases in certain periods, the time for internal container trucks to enter the overlapping path can be adjusted in advance to avoid excessive aggregation.

[0103] The acquisition of overlapping paths can accurately identify specific areas where internal and external container trucks may interfere with each other. The impact coefficient is determined by traffic density and compared with the interference threshold, providing a reasonable basis for scheduling decisions of internal container trucks. Through real-time monitoring and analysis of container truck driving data, intelligent scheduling and management of internal container trucks is achieved, reducing the uncertainty and errors of manual intervention.

[0104] [Fifth embodiment]

[0105] See also Figure 4 In a specific embodiment, the occurrence of a dynamic event is obtained according to the real-time traffic conditions. When a dynamic event occurs, the initial path is updated according to the dynamic event to obtain an updated path, which specifically includes:

[0106] S410: Obtain the location and type of a dynamic event according to real-time traffic conditions, and determine a traffic stagnation area of the dynamic event;

[0107] S420: Obtain an external container truck that needs to pass through the traffic congestion area, and determine an adjustable path for the external container truck based on road condition data;

[0108] S430: Select a corresponding adjustable path for the external container truck according to the task data to obtain an updated path.

[0109] In step S410, generally speaking, whether the driving path of the external container truck needs to be dynamically adjusted can be predicted based on the event type of the dynamic event. For example, when traffic congestion occurs on the current driving section, when the predicted congestion duration is less than the time it takes for the external container truck to reach the congested section, there is no need to update the initial path.

[0110] In step S430, in order to prevent a large number of external container trucks from merging into the same driving route, it is necessary to uniformly deploy the affected external container trucks based on the destination, the traffic density threshold of the adjustable route, the current traffic density of the adjustable route, and the average speed of the adjustable route, so as to obtain an updated route for the external container trucks.

[0111] For example, the calculation method of the updated path obtained by the multi-objective path allocation model is as follows:

[0112] ;

[0113] in, For the The current traffic density of the alternative routes, for The maximum traffic density of the alternative path, For the The estimated travel time of the vehicle, is a weight parameter that can be adjusted dynamically according to demand , When it is smaller, the total travel time dominates. When is larger, congestion balance is more important.

[0114] It should be noted that when the mission priority of the external container truck is higher, a driving route that enables the external container truck to reach the destination in the shortest time is selected.

[0115] For example, suppose the travel time of section A is =10 minutes, maximum traffic density The current traffic density is 50 vehicles / km. The travel time of Section B is 30 vehicles / km. =15 minutes, maximum traffic density The current traffic density is 100 vehicles / km. The number of vehicles affected is 50 vehicles / km, and the total number of external container trucks affected is 100. The number of vehicles allocated to section A is recorded as x, and the number of vehicles allocated to section B is recorded as 100-x. When balanced allocation is required, = , we can get x=30, then, is equal to 1.2, so the total cost = 1350+ ×1.2, when making extreme allocation, if all walks are in section A, then the total cost = 1000+ ×2.6, if you walk all the way to section B, then the total cost = 1500+ ×1.5, finally we know that when When it is less than 100, all roads will be taken along Road A. When it is greater than 300, all roads will be taken along Road B. When the value is greater than or equal to 100 and less than or equal to 300, the distribution is balanced.

[0116] Timely discovery and demarcation of congestion areas and rapid adjustment of the routes of affected container trucks can effectively reduce the negative impact of dynamic events on transportation and enhance the port's ability to respond to various emergencies. Directed planning of adjustable routes based on mission data can make the updated routes more representative of the current traffic conditions in the port and more reasonable.

[0117] [Sixth embodiment]

[0118] In a specific embodiment, recalculating the dynamic road weight according to the updated path and updating the driving status of the internal container truck according to the change of the dynamic road weight specifically includes:

[0119] S510: Obtaining a unit weight value of each driving road in each time period;

[0120] S520: When the unit weight value is greater than the weight threshold, the time period is recorded as a prohibited time period; when the unit weight value is less than or equal to the weight threshold, the time period is recorded as a passable time period;

[0121] S530: Calculate the unit traffic volume within the passable period, and adjust the driving status of the internal container trucks according to the unit traffic volume.

[0122] In step S510, the unit weight value of each driving road in each time period can be obtained based on the driving route updated by the external container truck and the average speed of the external container truck. Generally speaking, the unit weight value refers to the traffic density of each driving road.

[0123] In step S520, under normal circumstances, the weight threshold can be set according to the actual situation. For example, in order to ensure the rapid passage of external container trucks, the weight threshold can be set slightly smaller. When it is necessary to ensure the overall transportation efficiency, the weight threshold can be set slightly larger. In addition, the maximum weight threshold cannot exceed the traffic density threshold.

[0124] By obtaining the unit weight value of each driving road in each time period, we can clearly understand the traffic conditions of the roads in different time periods. According to the comparison between the unit weight value and the weight threshold, we can divide the prohibited time period and the passable time period, which provides a clear time reference for the driving planning of internal container trucks. By adjusting the driving status of internal container trucks according to the unit traffic volume, the operating efficiency of external container trucks can be guaranteed.

[0125] [Seventh embodiment]

[0126] In a specific embodiment, calculating the unit traffic volume within the passable period and adjusting the driving state of the internal container truck according to the unit traffic volume specifically includes:

[0127] S531. Obtain the traffic demand of internal container trucks during the passable period, and predict the theoretical traffic volume of internal container trucks during the passable period;

[0128] S532: When the required traffic volume is less than or equal to the theoretical traffic volume, the traffic order of the internal container trucks is determined according to the current positions of the internal container trucks;

[0129] S533. When the traffic demand is greater than the theoretical traffic volume, the internal container trucks that need to pass are determined according to their task priorities, and the internal container trucks that do not need to pass are controlled to park nearby.

[0130] In steps S531 to S533, under normal circumstances, the operation of internal container trucks needs to be reserved in advance. The passable road section and passable time period of the internal container truck can be obtained through the reservation data. The passage demand of the internal container truck in the passable time period is obtained based on the passable road section and passable time period of the internal container truck. According to the traffic density and traffic density threshold of the driving road in the passable time period, the theoretical traffic volume of the internal container truck in the passable time period can be predicted. When the theoretical traffic volume is sufficient, the internal container truck determines the arrival order of the internal container truck according to the current position arrival time. When there are more than two internal container trucks arriving at similar times, the arrival times can be fine-tuned through virtual traffic lights to ensure that the internal container trucks will not affect each other and increase traffic efficiency. When the theoretical traffic volume is insufficient, the internal container trucks will stop working as much as possible to ensure that the external container trucks can pass smoothly. When the traffic situation in the port area improves, the internal container trucks will be controlled to start running. In this state, except for some internal container trucks that will affect the work of external container trucks, the other internal container trucks are parked nearby on the roadside and will not affect the external container trucks traveling on the road.

[0131] By obtaining the traffic demand of internal container trucks during the passable period and predicting the theoretical traffic volume, we can accurately evaluate the relationship between the road's carrying capacity and the traffic demand of container trucks. By screening according to task priority, we ensure that container trucks with critical tasks can pass in a timely manner and are not hindered by container trucks with other non-critical tasks, thereby improving the overall efficiency and service quality of port operations and ensuring the smooth progress of port production.

[0132] [Eighth embodiment]

[0133] See also Figure 5 The present invention also provides a planning system 100. The port logistics dynamic path planning method described in the above embodiment is applied to the planning system 100. The planning system 100 includes: a data acquisition module 110, which is used to collect road condition data and task data of external container trucks; a data processing module 120, which is used to integrate the road condition data and task data and calculate dynamic road weights; a path planning module 130, which is used to generate an initial path and an updated path; and a road monitoring module 140, which is used to monitor real-time road conditions. The planning system has all the technical features of the above-mentioned port logistics dynamic path planning method, which will not be described in detail here.

[0134] Although the present invention is disclosed as above, the present invention is not limited thereto. Any person skilled in the art may make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention shall be based on the scope defined by the claims.

Claims

1. A port logistics dynamic path planning method based on multi-source data fusion, characterized by: The planning method includes: Obtain port traffic data and external container truck mission data, and calculate dynamic road weights; Classify the external container trucks according to their destinations within the port area, and plan driving routes based on the destinations; When there are multiple driving routes, obtaining the vehicle type and vehicle load corresponding to the external container truck according to the mission data; Calculating a starting rate of the external container truck according to the vehicle type and the vehicle load; Allocating the external container truck to the driving path according to the starting rate to obtain an initial path of the external container truck; Real-time monitoring of the running track and running speed of the external container truck; When the running trajectory differs from the initial path, predicting a subsequent path of the external container truck based on the road condition data of the port; Obtaining a cruising path of an internal container truck, and when there is mutual interference between the cruising path and the subsequent path, calculating an interference time period in which the interference occurs according to the running speed; Obtaining the same section of the cruise path and the subsequent path to obtain an overlapping path; Determining an influence coefficient of the internal container truck on the overlapping path according to the traffic density of the overlapping path; When the influence coefficient is greater than or equal to the interference threshold, the internal container truck is controlled to travel by a virtual traffic light to ensure that the internal container truck is outside the overlapping path during the interference time period; When the influence coefficient is less than the interference threshold, adjusting the initial time for the internal container truck to enter the overlapping path according to the influence coefficient; Acquiring the occurrence of a dynamic event according to real-time traffic conditions, and when the dynamic event occurs, updating the initial path according to the dynamic event to obtain an updated path; The dynamic road weight is recalculated according to the updated path, and the driving state of the internal container truck is updated according to the change of the dynamic road weight.

2. The planning method according to claim 1, characterized in that: include: Obtaining the occurrence location and event type of the dynamic event according to the real-time traffic conditions, and determining the stagnant area of the dynamic event; Acquire the external container truck that needs to pass through the stagnant area, and determine an adjustable path for the external container truck based on the road condition data; The corresponding adjustable path is selected for the external container truck according to the task data to obtain the updated path.

3. The planning method according to claim 2, characterized in that: include: Obtain the unit weight value of each driving road in each time period; When the unit weight value is greater than the weight threshold, the time period is recorded as a prohibited time period; when the unit weight value is less than or equal to the weight threshold, the time period is recorded as a passable time period; The unit traffic volume within the passable time period is calculated, and the driving state of the internal container truck is adjusted according to the unit traffic volume.

4. The planning method according to claim 3, characterized in that: include: Obtaining the traffic demand of the internal container truck during the passable period, and predicting the theoretical traffic volume of the internal container truck during the passable period; When the passage demand is less than or equal to the theoretical passage volume, determining the passage order of the internal container trucks according to the current positions of the internal container trucks; When the traffic demand is greater than the theoretical traffic volume, the internal container trucks that need to pass are determined according to their task priorities, and the internal container trucks that do not need to pass are controlled to park nearby.

5. A planning system, characterized in that: The port logistics dynamic path planning method according to any one of claims 1 to 4 is applied to the planning system, and the planning system comprises: A data acquisition module, the data acquisition module is used to collect the road condition data and the mission data of the external container truck; A data processing module, the data processing module is used to fuse the road condition data and the task data, and calculate the dynamic road weight; A path planning module, the path planning module is used to generate the initial path and the updated path; A road monitoring module is used to monitor the real-time road conditions.

Images