128 results about "Transportation scheduling" patented technology

The invention provides a logistics transportation optimization method and system based on a traffic logistics large model, and the method comprises the steps: carrying out the element extraction through receiving a basic scheduling instruction of a logistics transportation task, generating core element information through semantic scene analysis, and carrying out the transportation scene adaption based on the core element information. Generating a scene influence factor set in combination with a depth feature mining result of the historical transportation scene database; then, calling a pre-trained traffic logistics large model to carry out collaborative path decision on the scene influence factor set, and outputting a candidate path scheme population containing a path topological structure and cost feature distribution through interactive iteration of a path generation unit and a cost evaluation unit in the model; and performing multi-round evolution screening on the candidate path scheme population according to a preset optimization target, determining an optimal path scheme meeting transportation requirements, and generating a transportation scheduling instruction set to be pushed to a transportation execution terminal after conversion. According to the invention, the adaptability, decision-making precision and reliability in the logistics transportation optimization process can be effectively improved.

The invention provides a logistics scheduling path intelligent recommendation method and system based on an AI large model, and a medium, and the method comprises the steps: firstly collecting a transportation path file of a transportation driver, and constructing a driver road familiarity portrait according to the transportation path file; when a new logistics transportation scheduling demand exists, a total scheduling distance is extracted, and then two travel schemes, namely an efficiency travel scheme (including a first predicted total duration) of a theoretical shortest path and an experience travel scheme (including a second predicted total duration) according to driver experience, are determined. And if the second predicted total duration does not exceed the set proportion of the first predicted total duration, setting the experience travel scheme as a final scheduling scheme, finally distributing an optimal driver in combination with a driver road familiarity portrait, and pushing the scheme to a corresponding driver terminal device. The scheduling refers to the optimal theory, the actual experience of the driver is considered, the driver familiar with the route is matched, the decision deviation is reduced, the transportation efficiency and reliability are improved, and intelligent and efficient scheduling is realized.

The invention discloses an urban garbage collection and transportation scheduling method and system based on path optimization, and relates to the technical field of urban garbage collection and transportation and intelligent scheduling optimization, and the method comprises the steps: collecting multi-source original data, carrying out the standardization and time-space registration, and generating the structured observation data of vehicles, roads and collection and transportation points; constructing a dynamic garbage quantity prediction model according to the structured observation data, and predicting garbage increment and liquid level trend of each collection and transportation point; generating a task package and establishing a mapping relation based on a dynamic garbage quantity prediction model result in combination with road passage, vehicle load and time window constraints; and constructing a multi-target path optimization model on the basis of the mapping relation and the constraint, outputting an optimal driving path of the vehicle, and converting a path optimization result into a scheduling instruction. According to the method, the task package is generated on the basis of the dynamic garbage quantity prediction result in combination with road traffic, vehicle load and time window constraints, hierarchical matching of task targets and vehicle resources is achieved, and therefore the reasonability of task allocation and operation feasibility are improved.

The invention relates to the field of combinatorial optimization and artificial intelligence, and discloses a group logistics transportation scheduling method and system based on a role interaction graph neural network, and the method comprises the following steps: S1, dividing agent nodes and position nodes, and generating initial features; s2, iteratively updating node embedding by using a multi-channel attention mechanism of a graph neural network; s3, generating a delivery point distribution probability based on node embedding, and determining an initial distribution scheme; s4, local redistribution optimization is performed on the delivery points with low confidence distribution; and S5, performing parallel path planning on the optimal scheme, and outputting a result for reinforcement learning feedback. In the invention, through modeling of a graph neural network multi-channel attention mechanism on a complex interaction relationship and a synergistic effect of local redistribution optimization and parallel path planning, a second-level generation of a high-quality scheduling scheme is realized, cross-scale scene migration of the model is achieved, and group logistics transportation scheduling efficiency and robustness are improved.

The invention discloses a port and navigation ship traffic transportation scheduling method and system based on big data, and belongs to the technical field of port and navigation traffic, and the method specifically comprises the steps: carrying out the fusion construction of a coverage channel-berth-ship coupling state space; channel microscopic features and ship dynamic response features are excavated, and a channel subsection hydrodynamic disturbance feature set and a ship control response feature set are established; generating an expected energy consumption increment and a steering wear equivalent of each ship in each channel segment based on the two feature sets, and forming a ship-channel coupling cost set; a joint scheduling parameter set is constructed by combining berth space-time constraint and a ship arrival time sequence, a berth allocation scheme and a channel passing time sequence are solved by taking channel disturbance and ship sensitivity matching as targets, and cooperative scheduling is realized. According to the method, channel characteristics and ship response characteristics are quantitatively matched, and port energy consumption and mechanical wear are reduced. Quantitative matching of channel characteristics and ship response characteristics is realized, and port energy consumption and mechanical wear are reduced.

The invention relates to a truck-unmanned aerial vehicle combined transportation scheduling method and system based on a disaster relief scene in the technical field of transportation scheduling. According to the truck-unmanned aerial vehicle combined transportation scheduling method, H trucks and H unmanned aerial vehicles are used for cooperatively transporting materials, the h truck and the h unmanned aerial vehicle form a cooperative group, and the scheduling method of the h cooperative group comprises the steps that the number of rescue points and the positions of the rescue points are determined; according to the number of rescue points, the positions of the rescue points and constraint conditions, generating an initial truck path based on a greedy principle of the shortest distribution distance; generating an initial unmanned aerial vehicle path based on the initial truck path; and optimizing the initial truck path and the initial unmanned aerial vehicle path to obtain an optimal material transportation path. A truck-unmanned aerial vehicle collaborative transportation mechanism is innovatively constructed, the traditional efficiency bottleneck is broken through through a collaborative three-dimensional transportation system of transportation tools, and the emergency material delivery efficiency, reliability and timeliness in a complex catastrophe environment are remarkably improved.

The invention discloses an underground ore transportation path optimization method and system based on deep reinforcement learning, and belongs to the technical field of underground mine transportation scheduling and path planning. According to the method, the dynamic state space is constructed by acquiring the underground mine map, the mine car state data and the transportation task information, and the path optimization decision is performed in combination with the deep reinforcement learning algorithm, so that the mine car path and task allocation can be intelligently selected, the transportation efficiency is improved, the no-load rate is reduced, and the safety and stability of mine car operation are ensured. The system comprises a multi-modal data acquisition module, a state space construction module, an action space definition module, a reward function design module, a deep reinforcement learning model training module and the like, and through real-time monitoring and feedback adjustment functions, paths and task scheduling can be dynamically optimized to adapt to underground complex environment changes. The application of the method in underground ore transportation management verifies the effectiveness and practicability of the method, and the method has certain popularization value in the field of underground mine ore transportation management.

The invention discloses a new energy truck charging path collaborative scheduling optimization system, and relates to the technical field of logistics transportation scheduling, and the system comprises a data fusion processing unit, an AI model calculation unit, and a scheduling decision unit. The data fusion processing unit is used for acquiring vehicle position, residual electric quantity, charging station state and power grid load data and performing integration processing on the data; the AI model calculation unit is used for constructing a dynamic demand prediction model, a multi-objective optimization scheduling model and a power grid-charging station-truck cooperative control model, and calculating truck charging dynamic demand information, optimization information and a control path based on each model; and the scheduling decision unit is used for generating an optimal charging path and a scheduling strategy, receiving feedback results after the vehicle-mounted navigation system and the charging pile control system execute the scheduling instruction, and carrying out visual monitoring on the charging information and the charging station data of each truck. The power grid load is more stable; and meanwhile, the transportation cost is reduced.

The invention discloses a building material transportation scheduling method based on edge calculation, and the method comprises the following steps: S1, deploying an edge sensing terminal, and collecting and standardizing multi-source state data; s2, detecting an abnormal event at an edge node and outputting event information; s3, inputting the event information into a disturbance attention affine transformation unit to generate scheduling input; s4, generating and broadcasting a local scheduling scheme by adopting a distributed asynchronous game mechanism; s5, establishing a multi-order memory pool, recording data and schemes, and retrieving similar scene adjustment parameters; and S6, the scheme and the optimization result are uploaded to the cloud, and the edge node is optimized through strategy return. According to the invention, through edge calculation, multi-source data fusion and a distributed cooperative scheduling mechanism, real-time anomaly perception and adaptive optimization scheduling in a building material transportation process are realized.

The invention belongs to the technical field of food transportation management, and particularly relates to a food end transportation scheduling management system based on big data, which comprises a data fusion and convergence module, a dynamic traffic sensing module, an intelligent order analysis module, a vehicle state evaluation module, a scheduling strategy generation module and a transportation scheduling management end. According to the invention, traffic perception analysis information, order analysis information and vehicle state evaluation information obtained through analysis are sent to a scheduling strategy generation module through a dynamic traffic perception module, an intelligent order analysis module and a vehicle state evaluation module; and the scheduling strategy generation module performs comprehensive analysis based on various types of information to generate an optimal food transportation scheduling strategy, so that optimal configuration of transportation resources is realized, intelligent and refined management of food terminal transportation scheduling is facilitated, the transportation efficiency is comprehensively improved, the transportation cost is reduced, and the food transportation safety is guaranteed.

The invention discloses an intelligent bulk commodity freight logistics management method and system, and belongs to the technical field of logistics management, and the method comprises the steps: obtaining the multi-dimensional information of a transportation task, and generating a task semantic tag set; collecting state data and performance behaviors of the transportation units to construct a resource feature vector set; constructing a multi-model scheduling system including transportation risks, node passing trends and loading and unloading waiting prediction, and generating candidate scheduling strategies; performing game optimization on the strategy set in a dynamic environment through a reinforcement learning optimizer, and outputting an optimal scheduling strategy; deploying a continuous monitoring mechanism at edge nodes, and realizing adaptive reconfiguration of paths and resources according to abnormal scores; the intelligent level and the response capability of transportation scheduling can be remarkably improved, the delay risk is reduced, the resource utilization rate is improved, and the method is suitable for a bulk commodity multi-path multi-node combined transportation scene.

The present invention relates to the technical field of tunnel construction transportation scheduling, and specifically to a data fusion-based intelligent scheduling decision-making system for tunnel construction. The present invention uses a work surface demand prediction module to collect data in real time and, in combination with historical curves and schedules, predict concrete demand. This data is then combined with the concrete supply of the mixing station within a future time window to evaluate the supply and demand balance index of the mixing station. When the supply and demand balance index deviates from a threshold, a multi-factor combination is used to determine and generate a feed priority. A set of schedulable work surfaces is screened based on the feed priority and concrete demand of each work surface. Based on the last concrete transport time of all schedulable work surfaces and the concrete loading time of the mixing station, a transport scheduling target is dynamically generated for all concrete transport vehicles to be loaded at the mixing station. This provides precise decision-making support for intelligent scheduling of tunnel construction, improves transport efficiency and construction continuity, and enhances the flexibility and adaptability of transport scheduling.

The invention relates to an SAAS-based logistics intelligent scheduling method, device and equipment and a storage medium, and the method comprises the steps: receiving and analyzing an external logistics service request through an API interface of a cloud SAAS platform, and generating a logistics transportation task; carrying out resource matching and path optimization on the logistics transportation task based on real-time multivariate data gathered by the cloud SAAS platform, and generating a transportation scheduling task; and issuing the transportation scheduling task to a transportation terminal according to the cloud SAAS platform, carrying out polymer networking positioning data in real time, and updating a transportation state. According to the method, transport capacity resource allocation can be dynamically optimized, and the utilization efficiency of transport tools is effectively improved.

The invention belongs to the technical field of logistics transportation and container loading optimization, and particularly relates to an intelligent container loading system and optimization method, which comprises a data acquisition module, a data processing module, a priority distribution module, a transportation scheduling module and a visual operation interface. The data acquisition module acquires container size, cargo information, transport ship parameters and customer demand four-element data, the data is transmitted to the data processing module, and the data processing module adopts a layering-backfilling collaborative double-layer chromosome adaptive algorithm to maximize the space utilization rate. The priority distribution module is used for carrying out priority ranking on containers and guiding the loading and unloading sequence, the transportation scheduling module is used for dynamically planning port berth resources and carrying out berth distribution based on the ship size and the container priority average value, and the visual operation interface is used for providing 3D loading simulation and real-time animation guidance. The method supports multi-angle checking of cargo placement conditions, performs interactive adjustment, converts a complex three-dimensional problem into a multi-layer two-dimensional problem through a three-section process of layering, backfilling and merging, and remarkably improves the space utilization rate by using a layering-backfilling collaborative double-layer chromosome adaptive algorithm.

The invention relates to the technical field of intelligent transportation, in particular to a public transportation passenger and freight co-transportation scheduling system based on big data and the Internet of Things. Comprising data collection and processing, scheduling system architecture, path optimization and dynamic adjustment, and user interface and system management. Wherein the data collection and processing is used for the process of collecting and processing bus operation data by the system and is the basis of the system; the data collection and processing comprises passenger flow data, cargo flow data, vehicle state data and environment data; the scheduling system architecture is used for realizing comprehensive monitoring, real-time scheduling and resource optimization of bus passenger and freight co-transportation; according to the invention, by integrating real-time passenger flow data, cargo flow data and vehicle state information and utilizing big data analysis, an Internet of Things technology and an artificial intelligence algorithm, co-scheduling of buses and freight, path optimization and maximum utilization of resources are realized.

The invention discloses a logistics intelligent vehicle distribution method with a real-time decision-making capability, and relates to the technical field of logistics transportation, and the method comprises the steps: receiving and analyzing transportation plan data, automatically merging transportation plans meeting merging conditions, and extracting the key information of the merged transportation plans; collecting driver data; jointly forming a project-level real-time matching rule R4 based on the dynamic matching rule R1, the static matching rule R2 and the enterprise customization rule R3; inputting the transportation plan data and the driver data into a matching model, performing multiple rounds of screening and priority ranking according to the project-level real-time matching rule R4, and outputting an optimal vehicle and cargo matching result; and a transportation task is automatically generated according to the vehicle and cargo matching result, and monitoring and early warning are carried out on abnormal conditions based on real-time data in the transportation process. Precise matching of goods and vehicles is achieved, and the automation level, matching accuracy and exception handling timeliness of logistics transportation scheduling are improved.

The invention discloses a transportation hub transport capacity scheduling system based on big data, and particularly relates to the technical field of traffic scheduling, which comprises a data aggregation and sensing module, an early warning and triggering module, a scheduling scheme simulation and decision module and an instruction distribution and execution monitoring module. The system collects subway, bus, taxi, online car-hailing and passenger flow data in real time through a multi-source interface and a sensor, and a unified transport capacity diagram is generated after cleaning and anomaly detection; carrying out transport capacity gap identification and early warning based on a multi-index coupling risk analysis model; a plurality of scheduling schemes are generated and evaluated through a microscopic simulation model integrated with passenger behavior prediction, and an optimal scheme is recommended; finally, the scheme is converted into a specific instruction to be distributed and executed, and real-time monitoring and feedback are performed to form closed-loop management. According to the invention, the transformation from passive response to active intervention and from experience decision to scientific scheduling is realized, and the transport capacity cooperation efficiency and passenger evacuation capability of the transportation hub are significantly improved.

The utility model discloses a multi-intelligence collaborative material automatic transportation scheduling platform, which relates to the technical field of material scheduling platforms, and comprises a working table, a device groove is arranged in the working table, a condensation box is arranged on the inner wall of the device groove, a cooling cavity is arranged in the condensation box, a water pump is further arranged in the condensation box, and the water pump is arranged in the cooling cavity. The water outlet end of the water pump is connected with a condensation pipe, the condensation pipe is arranged around the inner wall of the device groove, the other end of the condensation pipe is connected with the device groove and extends into the cooling cavity, the water inlet end of the water pump is connected with a water inlet pipe, the water inlet pipe extends into the cooling cavity, and vent holes are formed in the inner top face of the device groove. According to the utility model, air in the device groove is cooled through cold and heat exchange of air near the condensation pipe, so that the purpose of heat dissipation is achieved, electronic elements in the device groove are protected, and the service life of the electronic elements is prolonged.

The invention relates to an AGV scheduling method, system and device based on a laboratory environment and a medium, and relates to the technical field of transportation scheduling, and the method comprises the steps that a plurality of elevator selection nodes and elevator reservation nodes in an optimal path planned to a sample unloading point are marked, and the elevator reservation node is the last elevator selection node of the path; when the AGV dolly arrives at one elevator selection node, calculating and obtaining a risk assessment value of each elevator based on the property of the loaded object and the real-time parameters of the elevator environment sensor, outputting a target elevator selection signal with the minimum risk assessment value, and re-planning an optimal path to the target elevator; and when the elevator arrives at the elevator reservation node, an instruction is sent to the elevator control system to lock the target elevator time period resources. The AGV can be dispatched according to the environment in the elevator in the laboratory, and the influence of the elevator environment on loaded objects is reduced.

This invention discloses a supply chain management method and system based on adaptive optimization using traceable containers. The method collects multi-dimensional supply chain data in real time using standardized traceable containers, including inbound volume, outbound volume, inventory, transportation location, environmental parameters, and carbon emission estimates; calculates data volatility and reliability to determine the importance of data collection time; integrates target smoothing parameters and uses reinforcement learning to correct predicted demand; generates multimodal transport schemes using K-D tree matching and the dragonfly algorithm; optimizes production and transportation scheduling based on simulated annealing algorithm, embedding carbon footprint constraints to minimize total green costs; and forms an adaptive closed loop through container feedback to improve system robustness. This method achieves real-time tracking, adaptive optimization, and sustainable development of the supply chain, and is suitable for complex and ever-changing supply chain environments.

This invention addresses the challenge of complex scheduling tasks in industrial manufacturing, where multiple tasks exist simultaneously with coupled constraints, leading to high optimization difficulty. It proposes an intelligent multi-task optimization method based on constraint coupling strength indices. This method aims to minimize the number of vehicles used and the total transportation cost. First, a knowledge graph network is constructed to store task information and constraints. Then, a constraint coupling strength index is calculated based on the relationship between constraints and decision variables. Subsequently, during the multi-task differential evolution search, candidate solutions are grouped according to constraint coupling strength, and differentiated cross-tabulation, mutation, and cross-task knowledge transfer strategies are employed to guide the search process towards efficient convergence. This method effectively characterizes complex constraint structures, enhances multi-task collaborative optimization capabilities, and provides an efficient and feasible optimization solution for transportation scheduling in industrial manufacturing.

The invention discloses a hazardous chemical substance logistics transportation scheduling method based on multiple data sources, and relates to the technical field of hazardous chemical substance logistics transportation, and the method comprises the following steps: collecting static basic data and dynamic real-time data through a multi-source data collection device; cleaning and filtering the collected multi-source data, and judging and processing abnormal values; a preliminary transportation path is generated by analyzing the collected data, an optimal transportation path is analyzed by using the road transportation cost, and a standby path storage module and a path risk real-time monitoring mechanism are set to monitor the selected transportation path; analyzing the risk change of the road condition by using the collected real-time data, and adjusting a risk monitoring threshold value in real time according to the change of the risk condition; the enhanced monitoring mode is set to analyze and judge the transportation risk change condition, and the delayed monitoring mode is set, so that the data acquisition time interval is shortened in the enhanced monitoring mode, the risk judgment time can be shortened, and the accuracy of the risk judgment result is improved.

The invention discloses an intelligent dispatching method and system for an underground mining truck fleet, and relates to the technical field of underground mine transportation dispatching. The method comprises the following steps: constructing an underground mine transportation system scene model, and obtaining a topological structure of an underground mine; designing an observation space, and obtaining a physical state and a logic state of each vehicle and a global task state; a graph attention network is adopted as a feature extractor for reinforcement learning, and collaborative features used for inputting a strategy network are extracted according to observation space state data; a strategy network is constructed based on a reinforcement learning algorithm of a continuous action space, and an intelligent agent obtains current collaborative features at each time step and outputs continuous actions; a shaping reward function inspired by a dynamic window method is designed, intelligent agent learning is guided, efficiency and safety are balanced, and vehicle optimization scheduling is achieved. According to the invention, the vehicle can execute smooth and fine dynamic control, the transportation efficiency is effectively improved, and conflicts are reduced.

The invention belongs to the technical field of traffic transportation scheduling and emergency management, and particularly relates to an emergency rescue system and a scheduling method based on an expressway network structure and detouring characteristics. According to the emergency rescue scheduling method, firstly, digital modeling is carried out on an expressway network, then optimization configuration is carried out on rescue resources of a stationary point, and then the emergency rescue is carried out. And then accident response and scheduling decision making are carried out by establishing a path calculation model, so that the accident emergency response time is shortened, the accuracy and efficiency of rescue scheduling are improved, resource waste or resource shortage in the stationary point is avoided by optimizing the configuration of the rescue vehicles in the stationary point, and the rescue efficiency is improved.

The invention relates to the technical field of railway heavy-load transportation scheduling, and discloses a single-line bidirectional railway heavy-load train virtual marshalling scheduling optimization method, which comprises the following steps: step 1, determining a scheduling scene and system composition of a single-line bidirectional railway heavy-load train: the system comprises a collection and distribution system, a distribution system and a transportation system; a marshalling station of the gathering and distributing system gathers full-load unit trains from a loading station, and the full-load unit trains are grouped into a virtual marshalling combined train capable of being dynamically degrouped through a train-to-train communication technology. Virtual train marshalling is achieved through the train-to-train communication technology, dynamic demarshalling and marshalling are supported, and long-time waiting of traditional mechanically-connected trains during meeting at an intermediate station is avoided; in combination with management and control of a mixed integer linear programming model on multi-dimensional constraints such as driving intervals and meeting conflicts, the total travel time is reduced by 36.56% compared with a scheduling scheme without virtual marshalling based on actual data verification of the Huangwan heavy haul railway in China, and transportation delay caused by the meeting conflicts is effectively relieved.

The present disclosure provides a transportation scheduling method and apparatus for a vertical warehouse. The transportation scheduling method for the vertical warehouse comprises: according to task information of a task to be executed, determining a target unit to be transported; according to article attribute information in the target unit to be transported, screening at least one candidate aisle in the vertical warehouse conforming to the article attribute information; according to distribution information of the at least one candidate aisle in multiple storage levels and current level information of the target unit, screening at least one target storage level from the multiple storage levels; according to current position information of the target unit and storage position information of the candidate aisle in the at least one target storage level, determining a target storage position; scheduling a transportation device to transport the target unit to the target storage position for storage. By means of screening a candidate aisle, and screening a target storage level according to the candidate aisle, a target storage position is determined, and the storage position is reasonably planned, improving storage position utilization and transportation scheduling efficiency.

The invention relates to the technical field of intelligent logistics scheduling, and discloses a cargo transportation scheduling method and system based on artificial intelligence. The method comprises the following steps: performing standardized formatting preprocessing on original transportation task data to obtain standard task description data; task space-time coding is executed, and task space-time coding data containing time sensitivity weights and path conflict marks are generated; inputting the coded data into a trained deep neural network scheduling optimization model, and outputting a group of preliminary scheduling schemes; inputting the initial scheme into a multi-scheme game decision module, and selecting an optimal scheduling scheme based on a preset game rule and a real-time road condition information flow; and performing dynamic environment deduction on the optimal scheme, and generating an execution deduction path with a time label and a resource label. According to the method, constraint perception is enhanced through space-time coding, dynamic preferential selection is realized through game decision, and the accuracy and adaptability of cargo transportation scheduling are improved.

Embodiments of the present application provide a dike breach plugging method and system, electronic equipment and storage medium, belonging to the technical field of automatic rescue. The method reconstructs a three-dimensional flow field model according to the collected water surface flow data of the breach area; extracts target features from the three-dimensional flow field model to obtain a flow field feature matrix; optimizes the first target function as the optimization target, and performs decision optimization according to the flow field feature matrix, the regional throwing state data and the available material data to obtain an optimal throwing scheme, wherein the first target function represents at least one of the minimum flow rate energy or the minimum material loss; the second target function is used as the optimization target, and the optimal throwing scheme and the transportation resource data are used for transportation decision optimization to obtain an optimal transportation scheduling scheme, and the optimal transportation scheduling scheme is used for transportation resource scheduling, and the second target function represents the maximum transportation efficiency. The application can improve the efficiency of dike breach plugging.

The application discloses an intelligent supply chain logistics transportation management method and system, relates to the transportation management technical field, and comprises the following steps: collecting multi-source data in a transportation process; creating a digital identity for each freight unit; planning a transportation path and generating a transportation scheduling instruction based on the multi-source data and the digital identity; performing real-time positioning and track tracking on a transportation vehicle executing the transportation scheduling instruction, and monitoring freight state data associated with the digital identity in real time, and generating early warning information when freight state abnormalities or path deviations occur; generating a response instruction for processing the freight state abnormalities or the path deviations, and notifying relevant cooperative parties among a consignor party, a consignee party and a driver party of the early warning information to obtain abnormal processing records; and recording and performing performance evaluation according to transportation whole-process data, so as to be used for optimizing decision parameters of subsequent transportation. The application can provide customers with reliable logistics services with high timeliness, low cost and high transparency.

The application relates to the field of logistics transportation and discloses a multi-distribution center-oriented unmanned vehicle logistics transportation scheduling method and system, which comprises the following steps: constructing a logistics transportation scheduling model; the logistics transportation scheduling model is a model for describing the delivery tasks of vehicles of multiple distribution centers to multiple customers within a preset time range; calculating the attribution between customers, establishing a customer group of the logistics transportation scheduling model according to the attribution; calculating the intimacy between the customer group and the distribution center, and distributing the customer group to the distribution center according to the intimacy; and solving the logistics transportation scheduling model for completing the customer group distribution by using an improved ant colony algorithm to obtain an optimal distribution path of logistics transportation scheduling. The matching relationship between the customer group and the distribution center is improved, and the improved ant colony algorithm is introduced, so that the practicability of the model can be further improved, and the efficiency and quality of logistics transportation scheduling are improved.