73 results about "Delivery cost" patented technology

A system uses both a present cost model and a future cost model trained on logged order data to compute a prediction of costs for delivering orders either without further delay, or with delay to allow time to potentially batch orders for delivery with other orders (and thereby reduce delivery cost). A comparison of the outputs of the present and future cost models is used to determine whether to delay assigning the order in expectation of batching order with other orders. Calculations may additionally be performed for the constituent orders of an order batch to apportion the delivery cost saving resulting from batching among the different orders. The system can analyze previously-logged data associated with prior orders to obtain features that characterize the prior orders. Using these features, and the known actual delivery costs from the prior completed deliveries, the system can train the present and future cost models.

The invention discloses a port bulk cargo storage location intelligent distribution method and system, and belongs to the technical field of port storage location management and intelligent distribution. The method comprises the following steps: collecting historical data related to port cargo scheduling, and defining a storage location distribution demand; designing constraint conditions for a storage location distribution process; constructing a multi-objective optimization model based on a genetic algorithm according to the constraint condition; generating an initial feasible solution meeting all hard constraints by adopting a greedy pre-screening algorithm; taking the initial feasible solution as the input of a multi-objective optimization model, and solving the optimal solution of storage location allocation by using an improved NSGA-II algorithm; and according to an optimization result, distributing the goods to the optimal storage location. By adopting the technical scheme of the invention, the problems of low storage location utilization rate, load imbalance and high delivery cost in port multi-type cargo scheduling can be effectively solved, so that the port resource utilization rate and the operation efficiency are improved.

Disclosed in the present application are a method for fault localization and an apparatus. The method may comprise: acquiring service data; on the basis of the service data, generating a first service topology model, the first service topology model comprising M nodes, N edges, first attribute information of the M nodes and second attribute information of the N edges, one node amongst the M nodes corresponding to one network entity amongst M network entities, and one edge amongst the N edges corresponding to one service path amongst N service paths; determining J abnormal sub-graphs in the first service topology model; and, on the basis of the first attribute information corresponding to each node and the second attribute information corresponding to each edge in the first service topology model, as well as the J abnormal sub-graphs, determining J first root cause nodes, one abnormal sub-graph corresponding to one first root cause node. Using the embodiments of the present application can solve the problems of high delivery costs and low efficiency caused by excessive dependence on human experience in existing fault localization technology.

The invention relates to a multi-store distribution vehicle management method and system, a terminal and a medium, and relates to the field of logistics distribution, and the method comprises the steps: obtaining real-time data, the real-time data comprises store data, vehicle data and traffic data, the store data comprises order demands, inventory states and distribution customer priority information of all stores, and the vehicle data comprises the order demands, the inventory states and the distribution customer priority information of all the stores; the vehicle data comprises vehicle load and volume parameters, and the traffic data comprises road traffic conditions; constructing a data model based on the real-time data; based on the data model, a dispatching algorithm is adopted to generate a distribution plan, and the distribution plan comprises vehicle distribution and distribution paths; and pushing the distribution path to the distribution vehicle. The method has the advantages that the fresh goods can be delivered on time in a fresh-keeping manner, the use efficiency of the vehicle is improved, and the delivery cost is reduced.

Methods and systems for enabling a host provider to provide a consumer homeowner with improved maintenance and repair services for items in the home are included under a subscription model that provides the consumer with predictable cost while assuring reliable services. The predictable cost and that presented for acceptance may be modified by interaction with remote data input from potential subscribers in real-time to maximize the amount of actual subscribers.

An improved computerized e-commerce system, optimized for smaller merchants such as florists who often provide seasonal perishable gifts that require local delivery, and who often desire to form cooperative networks with local merchants offering related gift services, as well as other related merchants such as other florists in more distant locations. The web server based system allows merchants to easily set up non-static (time variable) websites that automatically provide schedule driven promotions. New products can be quickly uploaded from smartphones, and sophisticated time and location aware algorithms can compute accurate delivery costs and make such costs transparent to customers. The system may also include backend software configured to assist in product production, inventory control, product cost optimization, and product delivery optimization, management, and tracking.

The application provides a multi-objective robust optimization method, system, storage medium and electronic equipment for production task planning of a multi-agent collaborative platform, and relates to the technical field of production task planning of a multi-agent collaborative platform. First, according to the production task planning resources of the multi-agent collaborative platform and a single-batch overseas order, a production task planning model with a manufacturing enterprise agent as the core is constructed under the premise of determining the sea transportation time. Then, an infinity norm ball uncertain set is designed for the sea transportation time of a large ship, a robust quadratic multi-objective model for cross-regional multi-agent production task planning is constructed, and the total delivery time deviation and total delivery cost multi-objective of the single-batch overseas order are optimized. The model considers the sea transportation plan prepared in advance by a shipping company agent and the long and uncertain sea transportation time. In addition, the model is converted into an equivalent model, a two-stage hybrid heuristic algorithm is designed for solving, and a reasonable and feasible production task planning suggestion is given for the single-batch overseas order.

The invention discloses a cold-chain logistics electric vehicle path optimization method based on model decomposition, and relates to the technical field of electric vehicle path planning, and the method comprises the steps: obtaining path planning reference data, constructing a cost analysis model, building a partial nonlinear charging model, and selecting a model decomposition strategy. A solution mode is dynamically adjusted according to the number of customers, a random allocation algorithm is adopted in a small scale to be combined with the first model, a clustering-time window sorting algorithm is adopted in a large scale to be combined with the second model, and a CPLEX solver is called to generate a high-quality distribution scheme. According to the method, the problem complexity can be remarkably reduced, the solving time is shortened, the flexibility of small-scale and large-scale problems is considered, meanwhile, the fresh product distribution timeliness is enhanced, and the distribution cost is optimized.

The present invention discloses a method and system for delivering goods by drone for multi-story buildings based on maximizing the ratio of active to passive cost. Specifically: Step 1, initialize the user set. If [conditions not specified], then [actions not specified], and the algorithm ends; otherwise, execute Step 2; Step 2, select the user on the lowest floor in the participant set as the target floor user for the drone to deliver goods, so that the payment function reaches the maximum value; Step 3, update the maximum load capacity of the drone and the participant set. If [conditions not specified] or [conditions not specified], the algorithm ends; otherwise, execute Step 4; Step 4, if the weight of the goods of the users on the [n]th floor is relatively large or the goods transportation of the users lower than the [n]th floor is less, the payment function of the users on the [n]th floor is relatively large; otherwise, vice versa; Step 5, select the user with the maximum minimum payment function in the set as the target floor user for the drone to deliver goods, and return to Step 3. The present invention delivers goods to users on different floors reasonably, considering not only its own cost but also the delivery costs of other users.

The invention discloses a logistics distribution task allocation method and device. Comprising the steps of obtaining task information of respective distribution tasks of a plurality of logistics distribution demand points and respective distribution service information of a plurality of unmanned aerial vehicles; clustering the plurality of logistics distribution demand points based on the distribution demand information to obtain a plurality of target clusters, and determining total task information of each target cluster; the distribution service information of the multiple unmanned aerial vehicles and the total task information of the multiple target clustering clusters are analyzed through a target grey wolf optimization algorithm, and a target unmanned aerial vehicle task allocation execution strategy with the lowest logistics distribution cost is obtained; and distributing the distribution tasks corresponding to the target clusters to the corresponding target unmanned aerial vehicles according to the target unmanned aerial vehicle task distribution execution strategy, and controlling the target unmanned aerial vehicles to execute the distribution tasks of the corresponding target clusters in sequence. According to the method, the technical problems of low calculation efficiency and low solving precision when a large-scale and multi-constraint unmanned aerial vehicle distribution execution problem is solved in related technologies are solved.

The invention relates to the technical field of unmanned logistics, and discloses an unmanned aerial vehicle and vehicle collaborative distribution method, system and device and a medium. The method comprises the following steps: obtaining to-be-delivered order data, unmanned aerial vehicle transport capacity data, vehicle transport capacity data and geographic space data of a delivery area, and constructing a unified collaborative management system comprising an order pool, a transport capacity pool and a connection network; based on a unified collaborative management system, a multi-vehicle and multi-unmanned aerial vehicle collaborative distribution problem considering distribution task completion mode selection and collaborative distribution feasibility constraints is converted into a global collaborative optimization model with the lowest total distribution cost; and solving the global collaborative optimization model to obtain an initial collaborative distribution strategy, and performing real-time adjustment on the initial collaborative distribution strategy based on a real-time operation state and dynamic event feedback in a collaborative distribution process to obtain a target collaborative distribution strategy. According to the invention, through unified collaborative management, global optimization and dynamic adjustment, efficient and low-cost collaborative distribution of the unmanned aerial vehicle and the vehicle is realized.

The application discloses a device information delivery method and device, equipment and storage medium. Based on the current area, the target device type is acquired, the alternative delivery space and the delivery form list are determined according to the target device type; the target delivery space and the target delivery form are determined based on the alternative delivery space and the delivery form list, wherein the target delivery form includes the coordinate delivery form and the non-coordinate delivery form; when the target delivery form is the non-coordinate delivery form, the device information delivery is carried out according to the target delivery form. The target delivery space and form are determined through the alternative delivery space and the delivery form list, different methods are adopted for different user requirements, the user requirements without the requirements for the device style and the accurate position are solved, the delivery is more in line with the actual situation and the requirements, and the efficiency and the quality of the delivery are improved. Through the digital delivery of the device information, the artificial workload is reduced, the on-site delivery efficiency is improved, and the delivery cost is reduced.

The application provides a bill data universal conversion method and device, the method comprises the following steps: through a big model business flow processor and an intelligent agent, input text of a user is analyzed in a business scene and rule field extraction is performed, a recommended rule template is generated, and configuration fields of the recommended rule template are updated according to adjustment requirement text input by the user to generate a target bill conversion rule template; according to the target bill conversion rule template, source single data is universally mapped and converted to generate a bill data model of the target single, dynamic no-code automatic configuration is realized through the configuration bill conversion rule template, reusability is improved, automatic conversion of isomorphic data and heterogeneous data to a universal bill model in a complex scene can be realized, the delivery cycle is shortened, and the delivery cost is reduced; multiple cancellation relationships can be recorded according to a cancellation relationship table, an arbitrary cancellation mechanism is supported, bill redundancy is reduced, and the flexibility of cancellation relationship processing in bill conversion is improved.

The invention discloses a logistics management system based on the Internet of Things, and the system comprises a delivery material classification module, an optimal delivery calculation planning module, and a delivery cost calculation control module. The optimal delivery calculation planning module is used for calculating and planning the optimal delivery path and the optimal delivery time of the unmanned aerial vehicle; the delivery cost calculation control module is used for calculating and controlling the total delivery cost of the unmanned aerial vehicle and the cooperative equipment according to the occurrence period and the delivery path, and the system classifies the materials needing to be delivered according to the occurrence period of the emergency through material delivery based on the unmanned aerial vehicle under the emergency; and optimal calculation planning is carried out based on the delivery path and the delivery time of the unmanned aerial vehicle, and the total delivery cost in the whole delivery process is synchronously controlled, so that the problems that the delivery of emergency materials is prolonged and the cost of each link of equipment collaboration is greatly increased under the condition of inconsistent collaboration are solved.

The present invention discloses a battery delivery path optimization method, device, computer equipment and storage medium. The method includes: establishing delivery path related constraints based on delivery path influencing factors; under the delivery path related constraints, establishing a logistics objective function based on the total delivery cost of battery replacement; quantifying the timeliness and satisfaction of battery delivery to establish a soft time window penalty cost function; constructing a path optimization model based on the logistics objective function and the soft time window penalty cost function; using a genetic algorithm to solve the output result of the path optimization model, and using the solution result as the final battery delivery path. The present invention quantifies the soft time window constraint into the objective function in the form of penalty cost, and at the same time establishes a battery delivery path optimization model in combination with the total delivery cost of battery replacement, and solves it through a genetic algorithm to obtain the optimal battery delivery path, thereby improving the delivery efficiency of battery delivery vehicles and reducing delivery costs.

The invention relates to the technical field of cargo distribution, and particularly discloses a freight vehicle on-the-way order delivery method and system, and the method comprises the steps: generating a delivery track according to the warehousing information and navigation service when the loading of a freight vehicle is completed; periodically updating a service area based on the delivery track; the update service area is a set of circular areas based on all point locations in the delivery track; receiving a delivery request sent by a user on the way, and matching a delivery track according to the delivery request; inquiring whether the on-the-way starting point is included in the service area of the matched delivery track, and if so, establishing a connection channel between the driver and the user; according to the invention, for the urban distribution link of cargo delivery, in the urban distribution link, the same-city order request sent by the personnel in the urban area is received, and some users needing short-distance distribution are additionally served on the basis of original distribution, so that on one hand, the utilization rate of transportation resources is improved, and on the other hand, the transportation efficiency is improved. And the short-distance distribution cost of the user is greatly reduced.

The invention discloses a logistics intelligent management method and system, and the method and system achieve the full-process optimization of an express logistics scene through three-dimensional scanning, dynamic path planning and digital twinning technologies. The method comprises the following steps: collecting parcel data in real time and intelligently sorting; the distribution path is updated every 30 seconds to avoid congestion; and resources are automatically scheduled to deal with parcel retention and vehicle empty driving. The system comprises a high-precision scanner, an edge computing node and a cloud decision center. According to the method, the sorting efficiency is effectively improved, the distribution cost is effectively reduced, and the method has remarkable commercial value.

The invention provides a transportation route optimization method and device, electronic equipment and a storage medium, and the method comprises the steps: determining a shipping task corresponding to a cargo shipping order based on the order information of the cargo shipping order, and obtaining the original transportation route information corresponding to the shipping task; constructing a transport network of the delivery task based on pre-configured transport resource information, and determining optimized transport route information corresponding to the delivery task based on evaluation information of all transport routes traversed in the transport network; determining target transport routing information corresponding to the shipping task based on the evaluation information corresponding to the original transport routing information and the evaluation information of the optimized transport routing information; the evaluation information comprises routing cost and routing time efficiency. According to the scheme, the transportation network can be automatically constructed, the final target transportation route is determined according to the route cost and the route time efficiency of the optimized transportation route and the original transportation route, optimization of the transportation route can be achieved, the cargo delivery time efficiency is improved, and the cargo delivery cost is reduced.

This specification discloses an order scheduling method, apparatus, storage medium, and electronic device. A characterization value is introduced to measure the dispatch difficulty of orders to be allocated, and the orders to be allocated are sorted according to the dispatch difficulty. The orders to be allocated are sequentially allocated to the delivery capacity according to the sorting to obtain an allocation result with a smaller global delivery cost. When this method first allocates the orders to be allocated with low dispatch difficulty, for the orders to be allocated with high dispatch difficulty, the known information at the time of allocation is increased by delaying the decision time, so that the delivery capacity can be allocated to the orders to be allocated with high dispatch difficulty under relatively sufficient information; when this method first allocates the orders to be allocated with high dispatch difficulty, the delivery capacity suitable for the orders that have a greater impact on the global delivery cost is preferentially selected. Therefore, the above two order scheduling methods that sequentially allocate orders according to the dispatch difficulty can both obtain an allocation result with a smaller global delivery cost.

Method and system of electric vehicle route planning for multi-service delivery and on-route energy replenishment is disclosed. Last mile delivery is a critical component of supply chains that impacts both customer experience and delivery cost. User request is received by a depot of a delivery environment. The user request comprising a current location of the user, one or more required services, and a time window rendered between each of the required services is processed and a graph is generated for the user request. Further, trained learning agent generates a route map indicating a plurality of waypoint locations for the electric vehicle to visit each node in accordance with minimized trip cost of fleet and time duration where each node has a state action pair for the electric vehicle. The learning agent learns continuously during the interaction with delivery environment and obtains feedback for every associated action.

The embodiment of the invention provides an advertisement putting early warning method and device, equipment and a storage medium, and the method comprises the steps: obtaining putting content information, an expected putting channel, an initial putting budget and an expected conversion amount of a to-be-put advertisement, determining at least one matched target historical delivery plan from a plurality of historical delivery plans according to the delivery content information and the expected delivery channel; according to the expected conversion amount, the historical conversion amount of the at least one target historical delivery plan and the historical delivery statistical amount, determining a predicted delivery statistical amount, and according to the historical delivery cost of the at least one target historical delivery plan, the historical delivery statistical amount and the predicted delivery statistical amount, calculating to obtain a predicted delivery cost; and judging whether the initial delivery budget and the predicted delivery cost meet set early warning conditions or not, and outputting early warning information under the condition that the early warning conditions are met. According to the scheme, early warning can be carried out on the initial delivery budget, and large budget deviation in the actual application process is avoided.

The present invention relates to a conflict-free AGV scheduling method for just-in-time material delivery within a workshop, comprising the following steps: obtaining material delivery tasks and dividing the decision cycle into multiple stages; utilizing an independent scheduling algorithm to schedule AGVs in each stage, optimizing the tasks and departure times of each AGV for each trip, and planning a conflict-free optimal path to achieve an AGV scheduling solution that minimizes time window penalties and delivery costs. The scheduling algorithm includes task allocation and path planning. In task allocation, itineraries are divided based on departure time estimates, and AGVs are assigned to each trip based on heuristic rules. In path planning, an A* algorithm is used to perform initial path planning based on the task allocation results, and conflict detection is performed based on the path time window. Conflict resolution is achieved by combining waiting and local replanning. Compared with the prior art, the present invention achieves the coordination of task allocation and path planning under just-in-time delivery requirements, and can improve computing efficiency and resource utilization.

To provide a novel technique of proposing a delivery method for optimizing delivery costs.SOLUTION: A delivery method optimization system for optimizing delivery costs is provided, the delivery method optimization system comprises a database, an acquisition unit, and a specification unit. In the delivery method optimization system, the database stores delivery cost information relating to costs required for delivering products, the acquisition unit acquires shipment information relating to shipments of shippers, and the specification unit specifies a delivery method for optimizing delivery costs on the basis of the shipment information and the delivery cost information.SELECTED DRAWING: Figure 1

The purpose of this invention is to provide a model scheduling method for resource-constrained parallel machines based on intensive strategy coordination. This method includes: acquiring order data; calculating a model scheduling scheme based on the order data using a domain-based heuristic algorithm; and optimizing the model scheduling scheme using an intelligent algorithm based on model-address intensive optimization. This invention addresses the challenge of efficiently generating scientifically effective model scheduling schemes in resource-constrained parallel machine scenarios, solving the computational complexity issue arising from the complex mapping between orders, addresses, and models; and considering the problem of solution space fragmentation caused by multiple constraints. Simultaneously, it proactively considers delivery, reducing the optimization difficulty of delivery scheduling, thereby improving production efficiency and reducing delivery costs.

The present invention provides a Kubernetes-based software deployment method, comprising the following steps: compiling all Kubernetes resource template files of application microservices and packaging them into an installation and deployment tool; creating an installation and deployment tool workload in a Kubernetes cluster; accessing the installation and deployment tool through a browser; inputting collected installation information data according to a guidance step; generating a final Kubernetes resource object installation file from the Kubernetes resource template file based on the collected installation information data; importing the Kubernetes resource object files into the Kubernetes cluster in sequence to generate resource objects; and completing the installation of all application microservices. The present invention reduces the delivery cost of enterprise-level microservice software and improves delivery efficiency; ensures the consistency of all delivered software and reduces subsequent service costs.

The invention discloses an elevator media delivery management system based on the Internet of Things, relates to the technical field of elevator media management, and forms a core architecture of the elevator media delivery management system based on the Internet of Things through mutual cooperation of three groups of algorithm units. The advertisement effect index AEi is calculated by comprehensively considering multiple influence factors such as advertisement putting cost, return income and different durations of different advertisements, so that a manager can intuitively understand the actual performance of each advertisement in the elevator media putting system, and the advertisement effect index AEi is obtained by comparing the AEi values of different advertisements. By means of the method, which advertisements are better in performance on elevator media can be visually known, scientific and reliable data support is provided for resource distribution in a media putting management system, the advertisement putting strategy can be adjusted based on different advertisement effect indexes AEi of different advertisements, dynamic monitoring and adjustment on different advertisements put in an elevator are completed, and the advertisement putting efficiency is improved. More efficient advertisement resource allocation is carried out, and the advertisement putting effect is improved.

To provide a product sales method capable of reducing delivery costs and product prices.SOLUTION: A sales method includes: (1) reading package information regarding individual delivery items addressed to users permitted to enter facilities, the individual delivery items to be delivered to spaces set up within a plurality of facilities where security against intrusion by unspecified persons is ensured, with a reading device disposed in each of the spaces; (2) causing the reading devices to transmit the package information to a server communicably connected to the reading devices; (3) causing the server to calculate an available capacity of each of the spaces in the plurality of facilities on the basis of the package information; (4) causing the server to determine products and quantities to be delivered to the spaces of each of the plurality of facilities by using facility information regarding the facilities and user information regarding the users, and select one or more facilities to which the products are to be delivered from the plurality of facilities, as delivery destination facilities; and (5) placing the products in the spaces of the delivery destination facilities.SELECTED DRAWING: Figure 2

The invention discloses a package scheduling control method based on an optimal scheduling algorithm and a related device. The method comprises the steps that a far-end server obtains logistics package information corresponding to a to-be-distributed logistics package arriving at a managed logistics distribution point in a current distribution period; analyzing the logistics package information corresponding to the to-be-distributed logistics packages, and extracting distribution time efficiency, distribution destination and package parameters corresponding to each to-be-distributed logistics package based on an analysis result; carrying out preliminary sorting processing on the to-be-distributed logistics parcels based on distribution destinations in the logistics parcel information to form sorted to-be-distributed logistics parcels; and carrying out distribution scheduling control processing on the sorted to-be-distributed logistics packages by using the distribution time efficiency and package parameters corresponding to each to-be-distributed package. According to the embodiment of the invention, the method achieves the timely distribution scheduling control of the to-be-distributed logistics package in each distribution period, achieves the better distribution scheduling control, and reduces the distribution cost.

The present invention discloses an intelligent monitoring system and method for advertising delivery based on multi-source data fusion, which relates to the technical field of intelligent monitoring of advertising delivery. The present invention comprises: S10: generating real-time delivery characteristics of a target advertisement; S20: finding a target associated node and analyzing the optimization index of the target advertisement delivery strategy; S30: determining the strategy optimization object of the target advertisement; and S40: generating the delivery optimization strategy of the target advertisement. The present invention analyzes the real-time optimization factor of the target advertisement delivery strategy through the target associated node, thereby analyzing the user's acceptance of the target advertisement, which is conducive to quickly capturing the real-time delivery effect of the target advertisement. By generating a time management matrix for the target advertisement through key associated nodes, the priority of each effective delivery time period of the target advertisement can be intuitively obtained, which facilitates the rapid adjustment of the delivery time of the target advertisement, thereby reducing the delivery cost of the target advertisement.