11 results about "Demand point" patented technology

The present application relates to the field of transportation engineering, and discloses a truck-unmanned aerial vehicle joint distribution path planning method and system for emergency supplies in an urban waterlogging scene, which divides demand points into type I demand points, type II demand points and type III demand points based on road interruption data, the quantity of supplies required by each demand point, the maximum load capacity of unmanned aerial vehicles and the maximum range of unmanned aerial vehicles; minimizes the latest time at which all demand points are serviced as an objective function, and constructs a truck-unmanned aerial vehicle joint distribution path planning model with demand point access, flow balance, path coordination, time, truck load capacity, unmanned aerial vehicle range, path access order and variable relationship as constraint conditions; the truck-unmanned aerial vehicle joint distribution path planning model is optimized and solved to obtain a joint distribution path scheme. The present application can provide an efficient and reliable joint distribution scheme for emergency decision-making in a waterlogging scene, thereby improving the overall efficiency and response speed of emergency supply distribution.

The application discloses a kind of electric heavy truck battery swap station planning method, device and readable storage medium, comprising: first, based on preset heavy truck trip data simulation freight process, generate battery swap demand data set by battery swap demand trigger mechanism;Demand point clustering is combined with service area information to form initial alternative site;The demand coverage of each site is calculated by nearest neighbor search, and sites with insufficient coverage are screened out;According to the sliding window algorithm, the number of batteries is determined according to the distribution of battery swap request time;After eliminating single-battery sites, the remaining sites are selected to cover the remaining demand using the greedy algorithm with coverage frequency priority to form the final planning set;Finally, the evaluation indexes including replacement rate, planning cost and battery swap loss time are output through the simulation model to realize the scientific evaluation of planning scheme.

The present invention relates to an efficient optimal facility location determination method for convex demand position demand points implemented to arrange a plurality of facilities as close as possible to given demand locations when positions of the demand locations satisfy a convex position condition. According to the efficient optimal facility location determination method for convex position demand points of the present invention, when demand locations satisfy a convex position condition, it is possible to quickly calculate a method of efficiently arranging a plurality of facilities in a very short time. In addition, according to the present invention, even if positions of demand locations do not satisfy a convex position condition, it is possible to approximate arrangement of facilities, and to be applied to various fields such as a location selection of a warehouse or data clustering.

The application relates to the technical field of supply chain regulation, and particularly discloses a supply chain space intelligent regulation method and system of a digital cloud warehouse, which comprises the following steps: acquiring a supply point of a product and passenger flow information of the product, and creating a supply layer according to the supply point and the passenger flow information; acquiring order information of the product, inserting a demand point into the supply layer according to the order information, and obtaining a supply-demand feature map; generating a buffer node based on the supply-demand feature map of different time periods, and generating a supply chain regulation instruction pointing to the buffer node; the supply information and the demand information are subjected to two-dimensional processing, the supply-demand feature map is constructed, supply-demand analysis is carried out in the supply-demand feature map, the buffer point is determined, the buffer point is taken as a new temporary supply point, a supply chain update request is synchronously generated, a new supply chain control instruction is determined, and thus the regulation process is realized, so that the overall supply efficiency is greatly improved.

The application discloses a session group establishment method, system, device and storage medium. The application stores the association information of a plurality of demand points of an application in a development version in an online document. A target object can initiate a group creation operation for a target demand point in the online document, and a group configuration page is displayed to the target object. In the group configuration page, an initial creation option is provided. The target object can perform a first interaction operation on the initial creation option to determine to establish a session group. In the group configuration page, a group name determined according to the target demand point, a pending group member matched according to the association information of the target demand point and a creation component are displayed to the target object. The target object can determine that the group name and the pending group member are correct, and perform a second interaction operation on the creation component, thereby completing the task of creating a session group in an instant messaging application. The application can improve the efficiency of establishing a session group and can be widely applied to the technical field of software application.

The invention relates to the technical field of logistics management, in particular to an intelligent site selection method for urban and rural parcel loading and unloading sites based on a radiation range, and the method comprises the steps: obtaining geographical coordinates, business attributes and passing time sequence data of routes of demand points and candidate sites; calculating a reference transit time, a peak transit time, and a route instability index based on the time series data; calculating a demand priority index based on the business attribute and matching a dynamic service timeliness threshold; constructing a traffic cost function after asymmetric risk adjustment, and applying a penalty related to priority and instability to a service failure condition in which peak time exceeds an aging threshold; urban and rural demand points are separated based on priorities, and a station with the lowest total risk cost is searched by applying a K-center point algorithm and using a traffic cost function. Through the technical scheme of the invention, the service toughness of the selected site can be high, and the practical stability of the radiation range under various time-varying conditions is ensured.

This invention relates to a method, apparatus, and medium for parallel scheduling optimization of drones and vehicles. The method includes: Step S1: Creating a set of demand points, etc.; Step S2: Sort all demand points in the demand point set and add a first symbol to the end of the initial sequence to obtain an initial node sequence; Step S3: Based on drone range constraints, etc., traverse and filter to obtain ultra-far points, etc., and move them after the first symbol to obtain an encoded sequence corresponding to the initial node sequence; Step S4: Decode the encoded sequence to obtain a decoded sequence; Step S5: Calculate the drone completion time and vehicle completion time of the decoded sequence, and further obtain the corresponding total completion time; Step S6: Apply random perturbation to the encoded sequence, and repeat steps S4 and S5, and take the encoded sequence with the minimum total completion time as the optimization result. Compared with the prior art, this invention can optimize short-term, high-burst, and short-distance scattered delivery scenarios.

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.

The application provides a community and enterprise cooperative meal assistance facility site selection optimization method and system, the method comprises the following steps: obtaining geographic information data, population statistics data and meal assistance demand data of demand points and candidate facility points from a multi-source heterogeneous data set; performing data preprocessing and demand uncertainty modeling processing on the geographic information data, population statistics data and meal assistance demand data to obtain a safety demand value of each demand point; according to the safety demand value of each demand point, a mixed integer linear programming model is established with the target of maximizing the effective coverage of the number of elderly demand persons, the model contains budget allocation constraints, facility capacity constraints, service range constraints and service uniqueness constraints; a reinforcement learning site selection environment is constructed according to the model objective function and constraint conditions, and based on a hybrid solving algorithm that fuses deep reinforcement learning and multi-level heuristic search, an elderly meal assistance facility site selection optimization scheme is generated. The application realizes efficient and reliable coverage of meal assistance service demand.

The application relates to a shared bicycle parking point location method and system based on multi-point joint distribution, which comprises the following steps: acquiring information of multiple demand points and multiple alternative parking points, and determining optimization targets and / or constraint conditions of a non-dominated sorting genetic model of multi-point joint distribution according to the information; generating an initial population of the non-dominated sorting genetic model based on a binary and integer combined chromosome coding mode; calculating non-dominated levels and crowded distances of each chromosome; screening the initial population by using a tournament selection method, and performing crossover and variation on the screened population based on a position vector crossover operator and a mutation operator to obtain a child population; and iteratively processing the parent population based on an elite strategy and the non-dominated levels and the crowded distances until an optimal solution meeting one or more constraint conditions is found. The application establishes an NSGA-II model based on multi-point joint distribution, and optimizes genetic operators in the model, so that the practicability of the model is improved.

The application provides an improved two-step moving search method considering selection probability and variable radius, which combines the advantages of Huff model and variable radius 2SFCA. The distance attenuation between supply and demand, the service capacity of supply points, the selection probability of the population to the facilities and the variable search radius of supply and demand points are comprehensively considered. The travel time cost between supply and demand is obtained through the path planning interface of Gaode API, the selection probability of demand points to all supply points within the search radius range is calculated according to the Huff model, and the search radius of each demand point and supply point is dynamically determined, and the spatial accessibility of the public service facilities is calculated at the population grid scale and the street scale. Experiments show that the application can minimize the overestimation and underestimation of the accessibility, the spatial distribution result is more continuous and real, and the spatial accessibility of the urban public service facilities can be effectively measured.