15 results about "Emergency logistics" patented technology

The invention provides a truck-unmanned aerial vehicle cooperative dynamic scheduling method and system in emergency logistics, and relates to the technical field of unmanned aerial vehicle intelligent scheduling. Constructing a truck-unmanned aerial vehicle cooperative distribution network, wherein each truck is equipped with a cooperative unit comprising a large unmanned aerial vehicle and a small unmanned aerial vehicle; the network comprises warehouses, hub points and demand points; establishing a mixed integer programming model by taking minimization of the total operation cost as a target and taking electric quantity management, service distribution, a time window and collaborative feasibility as constraints; and solving the model by adopting a randomized greedy algorithm, and synchronously deciding a truck path and an unmanned aerial vehicle task in single iteration to obtain a truck path scheme, an unmanned aerial vehicle service distribution scheme and a dynamic electric quantity scheduling scheme. Therefore, the cost optimization and dynamic coordination of emergency logistics scheduling are realized, and the efficiency and feasibility of emergency distribution are effectively improved.

The invention belongs to the technical field of emergency material multimodal transport scheduling, and particularly relates to an emergency material multimodal transport scheduling optimization method considering risk sensitivity, which comprises the following steps of: A, constructing an emergency material multimodal transport network in which a trunk line and a tail end are coordinated, mapping trunk line transport and tail end transport into a multimodal transport network diagram, determining nodes, feasible line sets and parameters thereof; b, establishing an emergency material multimodal transport scheduling optimization model considering risk sensitivity, and establishing a risk-sensitive scheduling optimization model based on an emergency material multimodal transport network and the obtained parameters; and C, generating a model solving result and a scheduling scheme, converting the scheduling optimization model into a solvable form, generating an executable scheduling scheme, verifying the result, and forming a complete technical closed loop. The method has the advantages that the adaptive capacity and robustness of the emergency logistics system under extreme situations such as path blocking are remarkably improved, and the method is suitable for popularization and application. And important decision support is provided for scientifically coping with disaster rescue.

The invention belongs to the technical field of freight unmanned aerial vehicle delivery, and relates to an intelligent cabin door system for an unmanned aerial vehicle and a cabin door structure design method. The system comprises a driving system, a reel system, a curtain plate system, a sliding rail unit and a control unit. The control unit is electrically connected with the driving system; the driving system is arranged on the unmanned aerial vehicle body; the curtain plate system is connected with an unmanned aerial vehicle body through the sliding rail unit, the curtain plate system is connected with the reel system, the driving system is in transmission connection with the reel system through a chain wheel transmission structure, and the driving system drives the reel system to rotate so as to drive the curtain plate system to stretch or retract along the sliding rail unit. Opening and closing of the cabin door are realized. According to the invention, the number of moving parts is greatly reduced, the probability of clamping stagnation and wear failure of a mechanical transmission mechanism is fundamentally reduced, and the operation reliability of a cabin door system in harsh operation scenes such as emergency logistics and disaster rescue is remarkably improved.

The application provides an emergency logistics path optimization method and system based on truck and unmanned aerial vehicle cooperation, belongs to the technical field of logistics path optimization, and comprises the following steps: based on a material center candidate point, a disaster point position and a road network, a main problem is determined, a site of the material center is configured, a number of trucks and unmanned aerial vehicles is configured, a first-stage optimization main problem is optimized to minimize operation cost; meanwhile, truck path reliability of each truck path is calculated according to actual road arc segment damage probability; based on the truck path reliability, the probability that each secondary emergency point needs support is calculated, a sub-problem is constructed, and a second-stage marginal benefit-based tight cut generation strategy is adopted to solve the sub-problem to minimize support scheduling cost; the main problem and the sub-problem constitute a two-stage stochastic programming model, and the total cost minimization of the operation cost and the support scheduling cost is obtained through iterative solving of the programming model to obtain an optimal path. The application improves the solution efficiency of truck and unmanned aerial vehicle cooperative distribution in post-disaster emergency material scheduling.

The application provides an emergency logistics path optimization method and system based on truck and unmanned aerial vehicle cooperation, belongs to the technical field of logistics path optimization, and comprises the following steps: based on a material center candidate point, a disaster point position and a road network, a main problem is determined, a site of the material center is configured, a number of trucks and unmanned aerial vehicles is configured, a first-stage optimization main problem is optimized to minimize operation cost; meanwhile, truck path reliability of each truck path is calculated according to actual road arc segment damage probability; based on the truck path reliability, the probability that each secondary emergency point needs support is calculated, a sub-problem is constructed, and a second-stage marginal benefit-based tight cut generation strategy is adopted to solve the sub-problem to minimize support scheduling cost; the main problem and the sub-problem constitute a two-stage stochastic programming model, and the total cost minimization of the operation cost and the support scheduling cost is obtained through iterative solving of the programming model to obtain an optimal path. The application improves the solution efficiency of truck and unmanned aerial vehicle cooperative distribution in post-disaster emergency material scheduling.

The application provides a truck-UAV collaborative dynamic scheduling method and system in emergency logistics, and relates to the technical field of UAV intelligent scheduling. A truck-UAV collaborative distribution network is constructed, each truck is equipped with a collaborative unit containing a large UAV and a small UAV; the network contains warehouses, hub points and demand points; a mixed integer programming model is established with the minimum total operating cost as the target, and the power management, service allocation, time window and collaborative feasibility as the constraints; a randomized greedy algorithm is used to solve the model, and the truck path and UAV task are decided synchronously in a single iteration to obtain the truck path scheme, UAV service allocation scheme and dynamic power scheduling scheme. Thus, the cost optimization and dynamic collaboration of emergency logistics scheduling are realized, and the efficiency and feasibility of emergency distribution are effectively improved.

The integrated optimization method for emergency logistics hub location, vehicle allocation and task scheduling belongs to the technical field of operational optimization of emergency logistics. Firstly, for the emergency logistics network, the first, second and third level division and basic parameters of the material distribution node are determined. Secondly, the decision variables, constraint conditions and objective functions of the three-layer decision of hub location, vehicle allocation and task scheduling are designed, and a three-layer integrated optimization model is established. Thirdly, an integrated coding strategy is designed, and the three-layer decision variables are uniformly represented in the same coding structure. Finally, based on the three-layer integrated optimization model, a VNS-SA hybrid heuristic algorithm is used to solve the optimal hub location, vehicle allocation and task scheduling scheme. The present application is suitable for the emergency material distribution scene under large-scale emergencies, and can realize the balanced allocation of transportation resources while ensuring the efficiency of material distribution, and provide a reasonable integrated optimization scheme for the emergency logistics system.

The invention discloses a modular management method, system, equipment and product based on intelligent storage warehouse-in and warehouse-out, and relates to the technical field of intelligent storage and emergency logistics. The method comprises the steps of firstly analyzing a disaster situation text into a disaster situation vector through a natural language processing model, then mapping the vector into at least one fire-fighting function module identifier based on a mapping rule, and then determining an initial fire-fighting equipment set according to the identifier, and generating a complete fire-fighting equipment set with comprehensive optimal internal function and space through a community discovery algorithm by combining an equipment association graph with fire-fighting equipment as nodes and function association degree and goods allocation proximity weighted value as edge weights, and then generating a simulated ex-warehouse task in a storage digital twin model based on equipment real-time goods allocation. And a multi-agent reinforcement learning algorithm is utilized to plan a global conflict-free collaborative goods taking instruction set for a plurality of AGVs, and finally, when simulation verification is passed, an instruction is issued to an AGV controller for execution, so that the fire emergency response efficiency can be greatly improved.

The invention relates to the technical field of emergency logistics engineering and intelligent traffic control, and relates to a dynamic disturbance-oriented emergency material multimodal transport intelligent scheduling method, which comprises the following steps of: A, constructing a multimodal transport dynamic space-time network based on physical and logic mapping, and opening up a data space in a memory by a processor based on a time extension graph technology; b, building an emergency material multimodal transport dynamic scheduling optimization model under a rolling time domain framework, building a continuous dynamic scheduling model based on the rolling time domain framework, and decomposing long-period global optimization into a series of short-period sub-problems which are mutually associated; c, designing a double-layer decision-making framework according to a self-adaptive large-field search algorithm fused with reinforcement learning under a rolling time domain framework: processing dynamic disturbance by adopting the rolling time domain framework on an outer layer, and designing a self-adaptive large-field search algorithm fused with a Q learning mechanism on an inner layer; the method has the advantage of self-adaptive evolution capability with higher and higher disaster evolution decision speed and higher and higher quality.

This invention discloses an emergency logistics route planning method and system based on edge-enhanced graph attention, relating to the field of emergency logistics route planning. The method includes: dividing road segments into multiple micro-segments and representing them based on micro-traffic flow data and topological connectivity data; constructing a segment association graph for each road segment, with segments as nodes and traffic flow interaction relationships as edges; fusing conflict information using a graph convolutional network layer to update segment features; subsequently, calculating risk indicators for the road segments based on the segment feature set and dynamically optimizing edges with abnormal indicators; finally, determining the computational granularity mode of each edge in the graph attention network based on the optimized indicators, and modulating the attention mechanism accordingly to generate the final emergency logistics route. This invention achieves refined perception and dynamic modeling of risk heterogeneity within road segments, improving the reliability and timeliness of emergency logistics route planning.

The invention relates to a national logistics hub emergency function layout double-layer optimization method considering demand uncertainty, and belongs to the field of emergency logistics planning. The method comprises the following steps: firstly, constructing a national logistics hub-emergency material collection and distribution center-demand point three-level network; a bilevel planning model is established, the upper layer takes the minimization of the total cost of the system as a target, the lower layer takes the maximization of the demand satisfaction rate as a target, and the demand uncertainty is processed by a triangular fuzzy number; converting the model into a deterministic form through an expected value operator, converting the double-layer model into a single-layer nonlinear model by using a KKT condition, and linearizing the single-layer nonlinear model into a mixed integer linear programming model through a large M method and a binary variable; and finally, outputting an optimal scheme by using a standard mathematical programming solver. The method solves the problems of mismatching of decision levels, weak uncertainty response and low solving efficiency in the prior art, realizes balance of economic cost and rescue effect, and is suitable for rapid planning of post-disaster emergency logistics.

This invention relates to the field of emergency logistics technology, specifically disclosing a method for the delivery of emergency relief supplies after an earthquake. The method includes data collection and preprocessing, post-earthquake road network capacity assessment, emergency facility site selection and material pre-positioning, assessment of the urgency of needs at disaster sites, multi-objective material delivery route planning, dynamic adjustment and replanning, and result visualization and decision support. This scheme extracts influencing factors from multiple levels, updates the posterior probability of each road segment using Bayesian network theory combined with real-time observation data, and achieves dynamic perception of road network capacity. Based on p-robust optimization theory, it constructs a collaborative site selection and allocation model for emergency facilities, providing feasible and efficient site selection and allocation schemes under various uncertain scenarios, significantly improving the resilience of the emergency collaborative response network. An optimization model is established, and a hybrid large neighborhood search-simulated annealing algorithm is designed. Through improved destruction operators and greedy insertion heuristics, the global search performance and solution efficiency of the algorithm are significantly improved.

This invention belongs to the field of emergency material multimodal transport scheduling technology, specifically involving a risk-sensitive emergency material multimodal transport scheduling optimization method, including: A. Constructing an emergency material multimodal transport network that coordinates trunk and terminal transport, mapping trunk and terminal transport to a multimodal transport network diagram, and clarifying nodes, feasible route sets, and their parameters; B. Establishing a risk-sensitive emergency material multimodal transport scheduling optimization model, based on the emergency material multimodal transport network and the acquired parameters; C. Generating model solution results and scheduling schemes, transforming the scheduling optimization model into a solvable form, generating executable scheduling schemes, and verifying the results to form a complete technical closed loop. The advantages of this invention are: significantly improving the adaptability and robustness of the emergency logistics system under extreme scenarios such as route disruptions, providing important decision support for scientific disaster relief.

This invention relates to the fields of emergency logistics optimization and transportation engineering, specifically to a method and device for allocating emergency supplies in high-altitude areas based on a physiological and psychological efficacy decay model. It includes: constructing a multi-objective optimization model for high-altitude emergency supply allocation; building a comprehensive impedance model of ground road sections based on road conditions and geological data and determining traffic status; introducing altitude parameters to correct for physiological and psychological efficacy decay in road section travel time based on driver physiological efficacy coefficients; determining rescue transportation modes and selecting safe transfer nodes based on comprehensive impedance and traffic status to form an air-ground coordinated transportation logic; optimizing the individual individuals in the algorithm population to obtain a legitimate population; using an improved multi-objective optimization algorithm to obtain a Pareto optimal solution set; and selecting and outputting a scheduling scheme based on real-time meteorological conditions. This invention is adapted to the high-altitude environment, improves the fairness of supply allocation, and enhances the overall efficiency of high-altitude emergency rescue.

The present application relates to the field of emergency logistics engineering and intelligent traffic control technology, and relates to a kind of multi-modal transport intelligent scheduling method for dynamic disturbance of emergency supplies, comprising: A. constructing multi-modal dynamic space-time network based on physical and logical mapping, processor based on time expansion graph technology, data space is opened in memory;B. under the rolling time domain framework, the construction of multi-modal dynamic scheduling optimization model of emergency supplies, construct a continuous dynamic scheduling model based on rolling time domain framework, the global optimization of long cycle is decomposed into a series of interrelated short cycle sub-problems;C. under the rolling time domain framework, the adaptive large field search algorithm of fusion reinforcement learning, design double-layer decision architecture: outer layer uses rolling time domain framework to process dynamic disturbance, inner layer designs adaptive large neighborhood search algorithm of fusion Q learning mechanism;The present application has the advantages that: with the evolution of disaster decision-making speed is faster and faster, quality is higher and higher adaptive evolution ability.