77 results about "Transportation scheduling" patented technology

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 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 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 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 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.

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 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 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.

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.

The embodiment of the invention provides an order processing method and device, equipment and a storage medium. The method comprises the following steps: receiving at least one order to be processed; determining a transportation scheduling result of the at least one order, wherein the transportation scheduling result at least indicates a transportation timeliness requirement and / or a transportation tool corresponding to the at least one order; determining a target order set corresponding to the at least one order based on the order information corresponding to the at least one order, the transportation scheduling result and a predetermined screening rule; and based on each order in the target order set, a picking task for the target goods corresponding to each order in the target order set is generated, and the picking task indicates execution of a picking operation for the target goods. Therefore, the optimized order processing scheme is generated through intervention of an intelligent algorithm in combination with the transportation scheduling result, so that warehouse operation can be arranged more accurately, and the cooperation efficiency of the whole warehousing and logistics process is improved.

The invention discloses a mine transportation scheduling method and system based on a hybrid genetic algorithm, and belongs to the technical field of intelligent optimization scheduling. The method comprises the following steps: S100, inputting mine tunnel network, facility and equipment parameters; s200, a Dijkstra algorithm and breadth-first search are combined, and an optimal path set containing a shortest path and a secondary short path is generated for each pair of ore unloading fields and mining sites so as to disperse traffic flows; s300, establishing a multi-objective optimization model for minimizing waste time and fuel consumption and maximizing transportation volume; s400, a non-dominated sorting genetic algorithm is adopted for solving, the core of the non-dominated sorting genetic algorithm is that a special triple coding mode is adopted, an execution vehicle is allocated for each transportation task, a going route is selected for each transportation task, a return route is selected for each transportation task, and complex constraints are fused into a chromosome structure; s500, performing dynamic simulation on each scheduling scheme, updating the vehicle state and speed based on the real-time road congestion degree, and accurately evaluating the performance of the scheme; s600, selecting a final scheme from the Pareto optimal solution set by using a multi-attribute decision-making method; and S700, when a fault occurs, automatically re-evaluating and allocating tasks. According to the method, the problems of single path, inaccurate model and uncomfortable coding of a traditional method are effectively solved, and high efficiency, energy conservation and high robustness of vehicle scheduling in a complex mine tunnel network are realized.

The invention belongs to the technical field of intelligent mine transportation scheduling, and particularly relates to an underground trackless rubber-tyred vehicle intelligent scheduling method based on complex path clustering, which comprises the following steps: designing a dynamic weight mechanism in combination with coal mine underground path multi-dimensional geographic information, and implementing path clustering by adopting a density-based spatial clustering method DBSCAN; performing cluster optimization on noise points of a clustering result according to road conditions, establishing a cluster priority mechanism, and forming path clusters with similar traffic characteristics and priorities; according to the method, the material weight of an auxiliary material work order and the maximum load of the trackless rubber-tyred vehicle are comprehensively considered, a priority path cluster is fused, an intra-cluster and inter-cluster double-layer path planning strategy is provided, a planned driving path of the trackless rubber-tyred vehicle is given, and a scheduling instruction is sent out. The method can effectively solve the problems that a traditional scheduling method depends on artificial experience, cannot dynamically respond to a complex underground environment and is difficult to balance multi-target conflicts, the transportation efficiency is remarkably improved, the operation cost is reduced, and the punctuality of a production plan is guaranteed.

The invention relates to the technical field of distribution service, in particular to a multimodal transport logistics collaborative management and control system, which comprises a task triggering coordination module, a handover sequence constraint module, a transport rhythm monitoring module, a state gap complementation module and a label propulsion adjustment module. According to the method, through sequential comparison of task path segments and node time, in combination with task state updating conditions, scheduling differentiation step items are extracted, a task waiting list with control effectiveness is generated, handover promotion after previous uncompleted tasks is blocked according to a state undertaking sequence, rhythm breaking path segments are identified, and state blank fields are complemented. And the continuous conduction direction of the tail-section label is adjusted according to the path capacity, so that task state evolution and path section propulsion are kept in a consistent rhythm, the phenomena of node conflict, task suspension and information disjunction in a transportation chain are reduced, and a transmission system with continuous rhythm and coordinated time sequence is promoted to be formed between path sections. And the conduction stability of transportation scheduling and the smooth cohesion of tasks among the nodes are enhanced.

This invention discloses a method and system for intelligent scheduling of logistics vehicles based on big data, belonging to the field of logistics big data scheduling technology. It includes establishing a logistics transportation scheduling big data warehouse, collecting and storing raw data such as historical orders, vehicle files, real-time traffic, weather, and warehouse operation status; standardizing, cleaning, and integrating the data to form a multi-dimensional scheduling decision feature set; identifying high-frequency delivery areas and frequently congested road sections through spatiotemporal clustering algorithms; generating regional heat maps and road network risk layers; dynamically correcting the road network risk layers by combining real-time traffic and weather information to obtain a real-time road network status layer; and performing multi-objective programming operations by integrating orders to be scheduled, available vehicle pools, regional heat maps, and real-time road network status layers to generate a preliminary scheduling scheme. This invention can accurately present the spatiotemporal distribution characteristics of orders and road networks, dynamically adapt to the real-time transportation environment, and improve the multi-dimensional data support and computational adaptability of scheduling decisions.

This invention relates to the field of logistics transportation scheduling and loading optimization technology, and discloses a robust two-stage cyclic pickup loading and route optimization method and system, including: acquiring planned data and real-time status data; based on the planned data, solving a preset planned stage model to generate a master plan; in the execution stage, based on the real-time status data, performing execution verification on the master plan; when the verification finds that packing is infeasible or results in a late return to the factory, triggering the solution of a preset minimum repair model to generate an executable repair solution; outputting the repair solution or the verified feasible master plan as the final scheduling solution. This invention significantly improves the execution reliability and economy of the cyclic pickup scheme by constructing a two-stage collaborative optimization closed loop of planning and execution, simultaneously integrating three-dimensional packing feasibility verification, opportunity cost quantification of reserved buffer space, and timeliness control based on robust optimization in route planning.

The invention provides a container transportation scheduling method and device, a terminal and a medium, and relates to the technical field of intelligent logistics scheduling. The method comprises the steps that a container transportation request set containing trailer operation information is obtained, and the trailer operation information comprises a first identifier and a second identifier which indicate whether a tractor carries a trailer with the matched size to a goods taking point or not and whether the trailer is left at a delivery point or not after delivery; based on the set, constructing a transportation scheduling planning model with the goal of maximizing the net income, and solving by using a preset heuristic algorithm to obtain an ideal transportation scheduling scheme; simulating trailer resource dynamic consumption based on the initial scheme, determining a shortage request list, and generating a trailer repositioning task set according to the shortage request list; based on the task set, constructing a scheduling model taking minimization of total trailer relocation cost as a target and solving the scheduling model to obtain a trailer relocation scheduling scheme; and generating a container transportation and trailer resource scheduling scheme based on the two schemes. According to the invention, dynamic balance of trailer resources and maneuverability of a scheduling scheme are ensured.

The present application relates to the field of prefabricated component transportation scheduling of assembly type building, and particularly discloses a dynamic transportation scheduling optimization method for prefabricated components with interference of assembly type building, which designs an improved NSGA-II algorithm combining a simulated annealing mechanism and an adaptive strategy; for the multi-objective optimization problem of dynamic transportation scheduling of prefabricated components with interference, the present application combs three types of interference events, i.e., emergency orders, order modifications and vehicle transportation time changes, which occur frequently in the transportation process, and formulates dynamic rescheduling strategies for different disturbance types; in combination with a rolling horizon optimization method, the continuous dynamic scheduling problem is converted into a series of static sub-problems, and then static optimization schemes are adopted in each static process to realize dynamic optimization.

The invention relates to the technical field of intelligent logistics transportation scheduling, and discloses an intelligent express transportation scheduling method and system, and the method comprises the steps: constructing a transportation stage feature vector sequence; executing stage time consumption benchmark prediction; constructing a transportation stage deviation value and an accumulated deviation index based on the actual time consumption and the predicted time consumption; early abnormal transportation state judgment is executed in combination with the current transportation state characteristics; outputting a corresponding transportation abnormity risk score; and executing transportation scheduling control. The technical problem that in the prior art, post judgment is carried out mainly depending on result indexes such as whether transportation is overtime or not, and it is difficult to recognize potential transportation anomalies in advance in a multi-distribution-center and multi-trunk-line vehicle transportation scene is solved. Due to the fact that a time consumption benchmark prediction and structured deviation construction mechanism based on the transport stage context is introduced, early recognition of the transport rhythm deviation trend is achieved, and the situation that scheduling intervention is triggered after abnormal delay is diffused to a full link is avoided.

The invention relates to the technical field of cargo transportation scheduling, in particular to an intelligent vehicle dispatching method and system based on cargo transportation, and the method comprises the steps: firstly, integrating delivery orders, vehicles and warehouse geographic information, automatically recognizing and merging abnormal orders of the same contact person at different addresses, and forming a unified delivery task; secondly, dynamically evaluating the daily transport capacity load based on historical data, and intelligently recommending an alternative delivery date for an order exceeding a threshold value; and finally, screening orders according to region limitation, taking the order with the maximum weight as a path anchor point, performing real-time path planning in combination with the vehicle region proficiency and the loading capacity, and generating an optimal vehicle dispatching scheme after ensuring that constraint conditions such as a working time window are met. Through intelligent data integration, dynamic load evaluation and adaptive path planning, automation and precision of cargo transportation and vehicle dispatching are realized, the distribution efficiency and the resource utilization rate are effectively improved, the manual dispatching cost and the error risk are reduced, and the flexibility and practicability of the system are enhanced.

This application discloses an artificial intelligence-based dynamic scheduling method and system for logistics transportation, relating to the field of artificial intelligence technology. It addresses the technical problem that existing technologies often rely on fixed rules based on experience or simple optimization algorithms, which become rigid in complex and dynamic real-world environments, unable to make forward-looking and global trade-offs, leading to low efficiency in transportation scheduling methods. The method obtains a dynamic scheduling knowledge graph by processing and integrating multimodal transportation data; optimizes the dynamic scheduling knowledge graph to obtain an optimized scheduling instruction set, and constructs a dynamic scheduling knowledge graph that transforms fragmented static information into a semantically related global dynamic relationship network, providing a panoramic and structured situational awareness foundation for decision-making. Based on this knowledge graph, rolling optimization scheduling is performed using an artificial intelligence model, enabling the learning of complex relationships, prediction of dynamic changes, and global trade-offs and adaptive decision-making, thereby improving the efficiency of transportation scheduling methods.

The invention discloses a logistics management method and system based on data collaboration, and relates to the field of logistics management, and the system comprises a sensing module which is used for building a dynamic association sensing link between a cargo body and a cabin refrigeration environment, synchronously collecting the multi-dimensional state data of the cargo body and the cabin refrigeration environment, and marking association attributes; the fusion module is used for constructing a railway cold chain cross-link data interaction channel, receiving the sensing data and carrying out collaborative association and dynamic fusion on the sensing data, the transportation scheduling dynamic data and the cabin body equipment operation data; through collaborative association and dynamic fusion of multiple types of data, data integrity, timeliness and accuracy are improved, meanwhile, potential risks of the refrigeration environment are accurately recognized and reasonably graded based on the fused data, the adaptability regulation and control scheme is generated in combination with the real-time transportation working condition, the refrigeration environment rapidly returns to the preset range, and optimal energy consumption is considered.

The application provides an AI large model-based logistics scheduling path intelligent recommendation method, system and medium, which comprises the following steps: firstly, collecting the transportation path archives of transportation drivers, and constructing a driver road familiarity portrait based on the archives; when there is a new logistics transportation scheduling demand, extracting the total scheduling distance, and then determining two travel schemes, namely, an efficiency travel scheme of the shortest theoretical path (containing a first estimated total time length) and an experience travel scheme based on the driver experience (containing a second estimated total time length); if the second estimated total time length is not more than a set proportion of the first estimated total time length, the experience travel scheme is determined as the final scheduling scheme; finally, the optimal driver is distributed in combination with the driver road familiarity portrait, and the scheme is pushed to the corresponding driver terminal device. This allows the scheduling to refer to the theoretical optimum and take into account the actual experience of the driver, matches the driver familiar with the route, reduces the decision bias, improves the transportation efficiency and reliability, and realizes intelligent and efficient scheduling.

The invention discloses a railway freight logistics intelligent scheduling method and system, and relates to the technical field of railway transportation scheduling, and the method comprises the steps: constructing a directed graph, calculating the shortest path for each freight logistics unit, defining the variation of the freight flow, and updating the freight flow; calculating the capacitance power ratio of the coordination power to the equivalent capacitance, judging the section state, searching an alternative path set, calculating the cargo flow shunted to the alternative paths, discretizing and correcting the shunted section flow by using an Euler method, screening the alternative paths of which the power headroom is greater than 0, and marking the alternative paths as a candidate merging pair set. According to the method, the stability of cargo flow change and the dynamic stability of a scheduling system are enhanced through the combination of a heat conduction equation and a mass conservation principle, and the traffic safety and the anti-congestion capability of a railway freight network are improved through the combination of coordination power and equivalent capacitance calculation and a congestion risk shunting mechanism.