6 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 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.

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 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.