CN-121998528-A - Cargo loading and unloading efficiency optimization method and system in public water intermodal transportation process

CN121998528ACN 121998528 ACN121998528 ACN 121998528ACN-121998528-A

Abstract

The application discloses a cargo handling efficiency optimizing method and system in the process of public water intermodal transportation, relating to the technical field of logistics management, by integrating real-time running information of the truck and status data of dock loading and unloading equipment, an optimal allocation plan is generated, road transportation dynamic information and dock loading and unloading capacity data can be deeply integrated, the problem of unbalance of resource matching is solved, and the overall operation efficiency of the public water intermodal transportation hub is improved.

Inventors

LIANG BO
LI PENGYAN
FU QINGHUA
LIU QIANG
LI XIANG
YANG WENXUAN
ZHANG XINGKAI
LUO TIANTIAN
LIU BIN
DING YINHUA
Gong Xingcai

Assignees

四川长虹民生物流股份有限公司

Dates

Publication Date: 20260508
Application Date: 20260122

Claims (10)

1. The cargo handling efficiency optimization method in the process of intermodal transportation of public water is characterized by comprising the following steps: Acquiring real-time running information data of a truck and real-time state data of dock loading and unloading equipment, and processing the real-time running information data of the truck to generate predicted arrival time data and delay risk level data; processing according to the real-time state data of the dock loading and unloading equipment to generate a data list of future operable time intervals; matching is carried out based on the predicted arrival time data and the data list of the future operable time interval, and preliminary cargo loading and unloading task allocation plan data is generated; Adjusting the preliminary cargo loading and unloading task allocation plan data according to the delay risk level data to generate final optimized cargo loading and unloading task allocation plan data; And generating and issuing truck dispatching instruction data and loading and unloading equipment operation instruction data according to the finally optimized cargo loading and unloading task allocation plan data.
2. The method for optimizing cargo handling efficiency in a public water intermodal transportation process according to claim 1, wherein the real-time operation information data of the truck comprises current position data reported by a vehicle-mounted terminal and planned route traffic congestion data acquired from a traffic information platform, and the risk delay grade data comprises high risk delay flag data and low risk delay flag data; The step of processing the real-time running information data of the truck to generate predicted arrival time data and delay risk level data comprises the following steps: According to the current position data and the traffic jam data, combining electronic map path information, and calculating to obtain basic estimated arrival time data; comparing the basic estimated arrival time data with a predicted deviation dynamic threshold value to obtain estimated deviation data, if the estimated deviation data exceeds the predicted deviation dynamic threshold value, combining vehicle history delay record data to generate the high delay risk flag data, otherwise, generating the low delay risk flag data; When the high delay risk flag data is generated, starting a correction calculation flow, generating time correction data based on the real-time running information data of the truck, adding the time correction data with the basic estimated arrival time data, and generating the predicted arrival time data; and when the low delay risk flag data is generated, the basic estimated arrival time data is directly used as the estimated arrival time data.
3. The method of optimizing cargo handling efficiency in a intermodal process as set forth in claim 2, wherein the method further includes: the method comprises the steps of calling a sample data set of actual passing time of a truck in the same road section in the same period of history from a history database; Calculating the average value and standard deviation of the actual transit time sample data set of the truck, and calculating to obtain basic fluctuation range data based on the average value and standard deviation; Acquiring real-time weather early warning data and dock gate queuing vehicle quantity data; and according to the early warning level of the real-time weather early warning data and the difference value of the quantity data of the vehicles queued at the wharf gate exceeding a preset reference value, correspondingly expanding the basic fluctuation range data to generate the predicted deviation dynamic threshold.
4. The method of optimizing cargo handling efficiency in a intermodal transportation process as set forth in claim 2, wherein when generating the high risk of delay flag data, initiating a corrective calculation process, generating time correction data based on the truck real-time running information data includes: When the high delay risk sign data is generated, starting a correction calculation flow, and collecting wharf workload data in the current time period; Integrating the real-time running information data of the truck with the wharf workload data to construct feature vector data to be corrected; and inputting the feature vector data to be corrected into a pre-trained time prediction correction model to output the time correction data.
5. The method of optimizing cargo handling efficiency in a intermodal transportation process as set forth in claim 1 wherein the step of generating preliminary cargo handling mission allocation plan data based on matching the predicted arrival time data and the list of future operational time interval data includes: Screening a candidate loading and unloading equipment time interval sub-list capable of executing the current cargo loading and unloading task from the future operable time interval data list according to cargo type matching rules; According to the sequence of the predicted arrival time data, sequentially matching each target truck with the corresponding sub-list of the candidate loading and unloading equipment time intervals, and distributing target operable time interval data meeting the operation duration requirement and having the earliest starting time for each truck; And recording the corresponding relation between all trucks and the corresponding target operable time interval data and the corresponding loading and unloading equipment to form the preliminary cargo loading and unloading task allocation plan data.
6. The method of optimizing cargo handling efficiency in a intermodal process as set forth in claim 5, wherein when the target operable time interval data meeting the condition cannot be matched for the current truck, the method further includes: from the tasks allocated with the target task time interval data, determining task pair data to be exchanged with the latest task duration in task pairs interchangeable with equipment; exchanging equipment allocation schemes of two tasks in the task pair to be exchanged to release available target operable time interval data for a current truck; And carrying out conflict verification on all task allocation schemes after the equipment exchange so as to ensure that no new time conflict or equipment use conflict exists.
7. The method of optimizing cargo handling efficiency in a intermodal transportation process as set forth in claim 1 wherein the step of adjusting the preliminary cargo handling mission allocation plan data based on the delayed risk level data to generate final optimized cargo handling mission allocation plan data includes: identifying all core task item data to be protected associated with the high risk delay flag data from the preliminary cargo handling task allocation plan data according to the risk delay level data; For each core task item data to be protected, inserting protection buffer time data before the allocated planned operation starting time; checking whether the time data inserted into the protection buffer area conflicts with a preamble task on the same loading and unloading equipment or not, if so, compressing the time data of the protection buffer area or adjusting the time interval of the preamble task; And summarizing all the task item data without conflict, and generating the final optimized cargo loading and unloading task allocation plan data.
8. The method of optimizing cargo handling efficiency in a intermodal process as set forth in claim 7, wherein prior to the step of adjusting the preliminary cargo handling mission allocation plan data based on the delay risk level data to generate final optimized cargo handling mission allocation plan data, the method further includes: Acquiring downstream urgency assessment data associated with a current dock load to be loaded; Distinguishing relevant task items in the preliminary cargo loading and unloading task allocation plan data according to the downstream urgent evaluation data so as to acquire high downstream urgent task item data and common task item data; the high downstream urgent task item data is marked as core task item data to be protected.
9. The method of optimizing cargo handling efficiency in a intermodal transportation process as set forth in claim 1, wherein the step of generating truck dispatch instruction data based on the final optimized cargo handling mission allocation plan data includes: Extracting planned arrival time data of the target truck and target loading and unloading equipment position data based on the final optimized cargo loading and unloading task allocation plan data; Combining the real-time electronic map data of the wharf field and the on-site traffic flow data, and planning optimal on-site path data from a wharf entrance to the position data of the target loading and unloading equipment for the target truck; According to the planned arrival time data and the current time, reversely calculating suggested running speed sequence data of the truck on each segment of the optimal in-field path data; And packaging the optimal in-field path data and the suggested driving speed sequence data to generate the truck dispatching instruction data.
10. A cargo handling efficiency optimization system in a intermodal process, characterized in that the cargo handling efficiency optimization system in a intermodal process comprises a memory, a processor and a cargo handling efficiency optimization program in a intermodal process stored on the memory and operable on the processor, the cargo handling efficiency optimization program in a intermodal process configured to implement the steps of the cargo handling efficiency optimization method in a intermodal process of any one of claims 1 to 9.

Description

Cargo loading and unloading efficiency optimization method and system in public water intermodal transportation process Technical Field The application relates to the technical field of logistics management, in particular to a cargo handling efficiency optimization method and system in a public water intermodal transportation process. Background Public water intermodal transportation is used as a key link of a modern logistics system, and the transportation structure can be effectively optimized and the social logistics cost can be reduced by integrating the flexibility of road transportation and the economy of water transportation. In the actual operation process, when goods are loaded and unloaded from a highway to a waterway or from the waterway to the highway at a port or a inland dock, the bottleneck of cooperative efficiency is faced. The method is characterized in that available operation window information of the loading and unloading equipment cannot be acquired in real time by an arrival truck, so that long-time queuing and waiting are performed outside a dock gate or an operation area, so that transportation vehicles are idle, excessive fuel consumption and additional carbon emission are increased, and meanwhile, due to uneven arrival time distribution or unsmooth information transmission of the truck, the phenomenon that operation idleness and overcrowding are alternated frequently occurs in high-value loading and unloading equipment such as a quay crane and a portal crane arranged at the dock, the utilization rate of the equipment is low, and the overall throughput capacity of the dock cannot reach the design level. The prior art scheme has obvious limitation, mainly focuses on independent dispatching optimization of loading and unloading equipment in a wharf or independent planning of road transportation paths, and cannot consider the real-time running state of a dynamic on-road truck and the available capacity of the loading and unloading equipment of the wharf as a unified whole to carry out overall coordination. The processing mode of the fracturing causes that the system lacks the real-time fusion capability of multi-source data such as truck positions, traffic jam conditions, equipment states and the like, and cannot realize accurate arrival time prediction and delay risk assessment, so that a collaborative scheduling scheme which takes resource waiting time minimization and operation efficiency maximization into consideration is difficult to generate. The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present application and is not intended to represent an admission that the foregoing is prior art. Disclosure of Invention The application mainly aims to provide a cargo handling efficiency optimization method and system in the process of intermodal transportation of public water, and aims to improve the overall operation efficiency of the intermodal transportation junction of public water. In order to achieve the above object, the present application provides a method for optimizing cargo handling efficiency in a combined transportation process, the method comprising: Acquiring real-time running information data of a truck and real-time state data of dock loading and unloading equipment, and processing the real-time running information data of the truck to generate predicted arrival time data and delay risk level data; processing according to the real-time state data of the dock loading and unloading equipment to generate a data list of future operable time intervals; matching is carried out based on the predicted arrival time data and the data list of the future operable time interval, and preliminary cargo loading and unloading task allocation plan data is generated; Adjusting the preliminary cargo loading and unloading task allocation plan data according to the delay risk level data to generate final optimized cargo loading and unloading task allocation plan data; And generating and issuing truck dispatching instruction data and loading and unloading equipment operation instruction data according to the finally optimized cargo loading and unloading task allocation plan data. In an embodiment, the real-time running information data of the truck comprises current position data reported by a vehicle-mounted terminal and planned route traffic jam data obtained from a traffic information platform, wherein the delay risk level data comprises high delay risk flag data and low delay risk flag data; The step of processing the real-time running information data of the truck to generate predicted arrival time data and delay risk level data comprises the following steps: According to the current position data and the traffic jam data, combining electronic map path information, and calculating to obtain basic estimated arrival time data; comparing the basic estimated arrival time data with a predicted deviation dynamic threshold value to