Method, server, program, and system for generating delivery schedule based on multimodal delivery means

Abstract

Disclosed is a method for generating a delivery schedule based on a multimodal delivery means, according to various embodiments of the present invention. The method may comprise the steps of: when generation of a delivery schedule of an item is requested, acquiring information about a departure point, a destination, and requirements of the item; recognizing at least one delivery location node on the basis of the departure point and the destination; acquiring a unit delivery schedule from a delivery location node server corresponding to the at least one delivery location node; and generating a multimodal delivery schedule on the basis of the unit delivery schedule and the requirements.

Description

Method, server, program and system for generating a delivery schedule based on multimodal delivery means

[0001] The present invention relates to a method, server, program and system for generating a delivery schedule based on a multimodal delivery means, and more particularly, to a method, server, program and system for optimizing the delivery route and schedule of goods by comprehensively utilizing various delivery means (e.g., drones, trucks, autonomous vehicles, etc.).

[0002]

[0003] The logistics and delivery industries are rapidly evolving due to the expansion of the global market and the rapid growth of e-commerce. Users are demanding faster and more affordable delivery services. Consequently, logistics companies are adopting multimodal transportation methods to maximize delivery efficiency by utilizing various modes of transportation to meet this demand.

[0004] Multimodal transportation refers to the use of two or more different modes of transportation in a single delivery process. For example, a truck might transport goods from a logistics center to a port, then a ship from the port to the destination country, and then a drone or autonomous vehicle to the final destination. This method maximizes the benefits of each mode of transportation while reducing overall delivery costs and time by selecting the optimal mode for each route.

[0005] However, existing multimodal transportation systems have several limitations in efficiently scheduling and linking transportation modes. For example, existing systems struggle to simultaneously consider each transportation mode's unique characteristics (e.g., speed, cost, loading capacity, etc.) and constraints (e.g., weather, legal regulations, etc.). Furthermore, multimodal transportation systems must consider multiple variables to satisfy diverse user requirements (e.g., fast delivery, cost reduction, preferences for specific transportation modes, etc.), creating a complex optimization problem.

[0006] Meanwhile, with the globalization of the global economy and growing interest in sustainable transportation, many countries are actively supporting multimodal transportation. For example, the European Union is building a multimodal transportation network integrating rail, road, and maritime transport through the "European Logistics Optimization" project, part of its efforts to reduce carbon emissions and enhance logistics efficiency. Consequently, logistics operators are in need of new technologies that effectively combine multiple transportation modes to meet user demands, reduce operating costs, and flexibly respond to real-time fluctuations.

[0007] Therefore, there is a demand in the industry for a system that generates an optimal delivery schedule based on multimodal delivery methods. In this regard, Republic of Korea Patent Publication No. 10-2012-0100601 discloses an optimization system for a smart logistics network.

[0008]

[0009] The present invention has been conceived in response to the aforementioned background technology and aims to provide a method, server, program and system for generating a delivery schedule based on a multimodal delivery means.

[0010] The technical problems of the present invention are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the description below.

[0011]

[0012] According to one embodiment of the present invention for solving the aforementioned problem, a method for generating a delivery schedule based on a multimodal delivery method is disclosed. The method may include: when a request is made to generate a delivery schedule for an item, obtaining information on the origin, destination, and requirements of the item; recognizing at least one delivery destination node based on the origin and destination; obtaining a unit delivery schedule from a delivery destination node server corresponding to the at least one delivery destination node; and generating a multimodal delivery schedule based on the unit delivery schedule and the requirements.

[0013] In an alternative embodiment, the step of obtaining at least one delivery destination node based on the origin and the destination may include: obtaining location information of the origin and the destination; generating a virtual shape including the locations of the origin and the destination; and recognizing delivery destination nodes corresponding to the virtual shape as the at least one delivery destination node.

[0014] In an alternative embodiment, the step of obtaining at least one delivery destination node based on the origin and the destination may include: recognizing a past delivery schedule corresponding to the origin and the destination; recognizing a list of delivery destination nodes for delivery destination nodes used in the past delivery schedule; and recognizing first delivery destination nodes included in the list of delivery destination nodes and second delivery destination nodes existing within a preset radius from the delivery destination nodes as the at least one delivery destination node.

[0015] In an alternative embodiment, the step of obtaining at least one delivery destination node based on the departure point and the destination includes the step of obtaining the at least one delivery destination node by inputting information about the departure point and the destination into a pre-trained neural network model; and the neural network model may be a neural network model that learns based on delivery record big data to generate an optimal delivery route between the departure point and the destination and predicts a delivery destination node included in the route.

[0016] In an alternative embodiment, the unit delivery schedule includes at least one of a unit travel time between each delivery destination node, a unit travel distance, a type of delivery means to be used in a unit travel section, a cost of the unit travel section, and information on a logistics operator operating the unit travel section, and the requirement includes at least one of a maximum delivery time, a maximum cost limit, a preference for a delivery means, a preference for a logistics operator, and stability of delivery specified by a user sending or receiving the goods, and the step of generating a multimodal delivery schedule based on the unit delivery schedule and the requirement may include a step of comparing the unit delivery schedule with the requirement to determine a specific delivery route and a specific delivery means corresponding to the requirement.

[0017] In an alternative embodiment, the step of comparing the unit delivery schedule with the requirements to determine a specific delivery route and a specific delivery means corresponding to the requirements may include: extracting, for each of a plurality of unit delivery schedules, information on a travel time, a travel distance, a type of delivery means to be used, a cost for the corresponding section, and information on a logistics operator; comparing the extracted information with the maximum delivery time, the maximum cost limit, a preference for the delivery means, and a preference for a logistics operator, to evaluate the suitability of each of the plurality of unit delivery schedules; determining, based on the result of the suitability evaluation, at least one unit delivery schedule to be used for delivery of the goods; and recognizing the specific delivery route and the specific delivery means corresponding to the at least one unit delivery schedule.

[0018] In an alternative embodiment, the method further comprises the step of optimizing a multimodal delivery schedule based on the specific delivery route and the specific delivery means; wherein the step of optimizing the multimodal delivery schedule may include the step of recognizing a delivery cost and a delivery time according to a connection between two or more operators when two or more operators must be used to deliver the goods; the step of recognizing a reduced cost and a reduced time that are less than the delivery cost and the delivery time when the goods are delivered using a delivery means of the same operator; and the step of generating an optimal multimodal delivery schedule based on the reduced cost and the reduced time.

[0019] According to one embodiment of the present invention for solving the above-described problem, a server is disclosed. The server includes: a memory storing one or more instructions; and a processor executing the one or more instructions stored in the memory, wherein the processor can perform the above-described methods by executing the one or more instructions.

[0020] According to one embodiment of the present invention for solving the above-described problem, a computer program stored in a computer-readable recording medium is disclosed, which is combined with a computer as hardware and can perform the above-described methods.

[0021] According to one embodiment of the present invention for solving the above-described problem, a system is disclosed. The system includes: a delivery schedule generation server; a delivery destination node server; a delivery means server; and a common information database; wherein, when a request is made to generate a delivery schedule for an item, the delivery schedule generation server obtains information on a departure point, a destination point, and requirements of the item, recognizes at least one delivery destination node based on the departure point and the destination point, obtains a unit delivery schedule from a delivery destination node server corresponding to the at least one delivery destination node, and generates a multimodal delivery schedule based on the unit delivery schedule and the requirements.

[0022] Other specific details of the present invention are included in the detailed description and drawings.

[0023]

[0024] The present invention efficiently combines multimodal delivery methods to create an optimal delivery schedule that meets user requirements, thereby constructing a logistics network that can reduce overall delivery costs and time while taking into account the characteristics and limitations of each transportation method.

[0025] The effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description below.

[0026]

[0027] FIG. 1 is a diagram illustrating a system according to one embodiment of the present invention.

[0028] Figure 2 is a hardware configuration diagram of a server according to one embodiment of the present invention.

[0029] FIG. 3 is a diagram illustrating a delivery schedule generation server according to one embodiment of the present invention.

[0030] FIG. 4 is a diagram for explaining a delivery node server according to one embodiment of the present invention.

[0031] FIG. 5 is a drawing for explaining a delivery means server according to one embodiment of the present invention.

[0032] FIGS. 6 to 11 are drawings for explaining an example of a method for generating a delivery schedule based on a multimodal delivery means according to one embodiment of the present invention.

[0033]

[0034] Various embodiments are now described with reference to the drawings. In this specification, various descriptions are provided to facilitate an understanding of the present invention. However, it will be apparent that these embodiments may be practiced without these specific details.

[0035] As used herein, the terms "component," "module," "system," and the like refer to computer-related entities, hardware, firmware, software, a combination of software and hardware, or an execution of software. For example, a component may be, but is not limited to, a procedure running on a processor, a processor, an object, a thread of execution, a program, and / or a computer. For example, both an application running on a computing device and the computing device may be a component. One or more components may reside within a processor and / or a thread of execution. A component may be localized within a single computer. A component may be distributed between two or more computers. Furthermore, these components may execute from various computer-readable media having various data structures stored therein. Components may communicate via local and / or remote processes, for example, by signals comprising one or more data packets (e.g., data from one component interacting with another component in a local system, a distributed system, and / or data transmitted to another system via a network such as the Internet via signals).

[0036] Furthermore, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or." That is, unless otherwise specified or clear from context, "X employs A or B" is intended to mean either of the natural inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, "X employs A or B" can apply to any of these cases. Furthermore, the term "and / or" as used herein should be understood to refer to and include all possible combinations of one or more of the associated items listed.

[0037] Additionally, the terms "comprises" and / or "comprising" should be understood to imply the presence of the features and / or components in question. However, it should be understood that the terms "comprises" and / or "comprising" do not exclude the presence or addition of one or more other features, components, and / or groups thereof. Furthermore, unless otherwise specified or clear from the context to refer to the singular form, the singular in the specification and claims should generally be construed to mean "one or more."

[0038] Those skilled in the art should further appreciate that the various illustrative logical blocks, configurations, modules, circuits, means, logics, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate the interchangeability of hardware and software, various illustrative components, blocks, configurations, means, logics, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application. However, such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

[0039] The description of the disclosed embodiments is provided to enable those skilled in the art to make or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art. The general principles defined herein may be applied to other embodiments without departing from the scope of the present invention. Therefore, the present invention is not limited to the disclosed embodiments. The present invention is to be construed in the widest scope consistent with the principles and novel features disclosed herein.

[0040] In this specification, the term "computer" refers to any type of hardware device including at least one processor, and may also be understood to encompass software components operating on the hardware device, depending on the embodiment. For example, the term "computer" may be understood to encompass, but is not limited to, smartphones, tablet PCs, desktops, laptops, and all user clients and applications running on each device.

[0041] Hereinafter, embodiments of the present invention will be described in detail with reference to the attached drawings.

[0042] Although each step described in this specification is described as being performed by a computer, the subject of each step is not limited thereto, and at least some of each step may be performed by different devices depending on the embodiment.

[0043]

[0044] FIG. 1 is a diagram illustrating a system according to one embodiment of the present invention.

[0045] Referring to FIG. 1, a system according to one embodiment of the present invention may include a delivery schedule generation server (100), a delivery destination node server (200), a delivery means server (300), and a common information database (400). The system illustrated in FIG. 1 is according to one embodiment, and its components are not limited to the embodiment illustrated in FIG. 1, and may be added, changed, or deleted as needed.

[0046] In one embodiment, the delivery schedule generation server (100) can generate a delivery schedule based on a multimodal delivery means.

[0047] Specifically, when a request is made to create a delivery schedule for an item, the delivery schedule generation server (100) can obtain information regarding the item's origin, destination, and requirements. Here, the requirements may include at least one of the following: a maximum delivery time, a maximum cost limit, a preference for a delivery method, a preference for a logistics operator, and delivery stability, as specified by the user sending or receiving the item.

[0048] In addition, the delivery schedule generation server (100) can recognize at least one delivery destination node based on the departure point and the destination point. In addition, the delivery schedule generation server (100) can obtain a unit delivery schedule from the delivery destination node server (200) corresponding to at least one delivery destination node. Here, the unit delivery schedule can include at least one of the following: unit movement time between each delivery destination node, unit movement distance, type of delivery means to be used in the unit movement section, cost of the unit movement section, and information on the logistics operator operating the unit movement section.

[0049] In addition, the delivery schedule generation server (100) can generate a multimodal delivery schedule based on unit delivery schedules and requirements.

[0050] Accordingly, the delivery schedule generation server (100) of the present invention efficiently combines multimodal delivery means to generate an optimal delivery schedule that satisfies user requirements, and can provide a delivery schedule that can reduce overall delivery costs and time while taking into account the characteristics and limitations of each transportation means.

[0051] Hereinafter, an example of a method for generating a delivery schedule based on a multimodal delivery method through a system including a delivery schedule generation server (100) will be described with reference to FIGS. 3 to 11.

[0052] In various embodiments, the delivery schedule generation server (100) may provide a web- or application-based service. However, the present invention is not limited thereto.

[0053] The delivery schedule generation server (100) may include any type of computer system or computer device, such as, but not limited to, a microprocessor, a mainframe computer, a digital processor, a portable device, and a device controller.

[0054] Below, a description of the hardware configuration of the delivery schedule creation server (100) is provided with reference to FIG. 2.

[0055] In one embodiment, the delivery destination node server (200) manages data for a specific delivery destination node and performs operations that provide information on various delivery methods accessing the node. The delivery destination node server (200) may include information such as the location of each delivery destination node, the status of available delivery methods, and information on currently waiting items, and based on this, assists the delivery schedule generation server (100) in selecting an optimal route. For example, it may provide information on the departure times of trucks waiting at a specific logistics warehouse and the landing availability of drones.

[0056] In one embodiment, the delivery vehicle server (300) manages data on various types of delivery vehicles (e.g., drones, trucks, autonomous vehicles, etc.) and monitors the availability, current location, status, and expected arrival time of each delivery vehicle. The delivery vehicle server (300) provides the delivery schedule generation server (100) with information on the types of delivery vehicles available for a specific route, along with their associated costs, times, and other information, thereby providing information necessary for schedule optimization. For example, the server may provide information on whether autonomous vehicles are available for a specific route, their battery status, and their expected arrival times.

[0057] In one embodiment, the common information database (400) can perform operations for storing and managing common data used in a multimodal delivery system. The common information database (400) stores basic information related to delivery destination nodes, delivery methods, logistics operators, etc., and this information is referenced during the data linkage process between the delivery destination node server (200) and the delivery method server (300) and the optimization process of the delivery schedule generation server (100). For example, the common information database (400) can store location information for each delivery destination node, basic specifications of various delivery methods, and basic fee structures of each logistics operator.

[0058] Each of the delivery node server (200), the delivery means server (300), and the common information database (400) may include any type of computer system or computer device, such as, for example, a microprocessor, a mainframe computer, a digital processor, a portable device, and a device controller, but is not limited thereto.

[0059] A system according to one embodiment of the present invention can provide a method for generating an optimal delivery schedule based on multimodal delivery means (e.g., drones, AGVs, elevators, etc.), delivery destination nodes connecting them (e.g., drone stations, logistics warehouses, AGV stations, etc.), and a related database (DB). Within the system, the path between each delivery destination node is evaluated using a cost function, with the goal of generating a minimum-cost path from the origin to the destination. For example, the cost function can utilize optimization indices utilized in control techniques such as Dynamic Programming, Linear Quadratic Regulation, and Model Predictive Control. In particular, the system of the present invention can maximize cost savings when using linked delivery means from the same operator.

[0060] In various embodiments, the system of the present invention may further include a user terminal (not shown).

[0061] The user terminal may be connected to the delivery schedule generation server (100) via a network (500) and may be a user terminal that receives the delivery schedule provided by the delivery schedule generation server (100). For example, the user terminal may include a terminal of a product delivery person or a product recipient.

[0062] Here, the user terminal may include, for example, various types of computer devices. For example, the user terminal may refer to various terminal devices such as a smartphone, tablet PC, desktop, or laptop.

[0063] The user terminal includes a display on at least a portion of the terminal, and may include an operating system for driving an application or extension program-based service provided from a delivery schedule generation server (100). For example, the user terminal may be a smart phone, but is not limited thereto, and the user terminal may include all types of handheld-based wireless communication devices such as navigation, PCS (Personal Communication System), GSM (Global System for Mobile communications), PDC (Personal Digital Cellular), PHS (Personal Handyphone System), PDA (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), Wibro (Wireless Broadband Internet) terminals, smart pads, tablet PCs, etc., as wireless communication devices that ensure portability and mobility.

[0064] In various embodiments, the system of the present invention may further include an external server (not shown).

[0065] The external server can be connected to the delivery schedule generation server (100) via a network (500), and the delivery schedule generation server (100) can transmit and receive various information / data necessary for generating a delivery schedule based on a multimodal delivery means, and the delivery schedule generation server (100) can store and manage various information / data generated as it generates a delivery schedule based on a multimodal delivery means.

[0066] For example, the external server may be a database server that stores information used to create a multimodal delivery schedule. In another example, the external server may be a server that provides information used to create a multimodal delivery schedule.

[0067] A network (500) may refer to a connection structure that enables information exchange between each node, such as a computing device, multiple terminals, and servers. For example, the network (500) includes a local area network (LAN), a wide area network (WAN), the Internet (WWW), a wired and wireless data communication network, a telephone network, a wired and wireless television communication network, etc.

[0068] Wireless data communication networks include, but are not limited to, 3G, 4G, 5G, 3GPP (3rd Generation Partnership Project), 5GPP (5th Generation Partnership Project), LTE (Long Term Evolution), WIMAX (World Interoperability for Microwave Access), Wi-Fi, the Internet, LAN (Local Area Network), Wireless LAN (Wireless Local Area Network), WAN (Wide Area Network), PAN (Personal Area Network), RF (Radio Frequency), Bluetooth network, NFC (Near-Field Communication) network, satellite broadcasting network, analog broadcasting network, and DMB (Digital Multimedia Broadcasting) network.

[0069]

[0070] Figure 2 is a hardware configuration diagram of a server according to one embodiment of the present invention.

[0071] Referring to FIG. 2, a delivery schedule generation server (100) according to one embodiment of the present invention may include one or more processors (110), a memory (120) for loading a computer program (151) executed by the processor (110), a bus (130), a communication interface (140), and a storage (150) for storing the computer program (151). Here, only components related to the embodiment of the present invention are illustrated in FIG. 2. Therefore, a person skilled in the art to which the present invention pertains will understand that other general components may be included in addition to the components illustrated in FIG. 2.

[0072] The processor (110) controls the overall operation of each component of the delivery schedule generation server (100). The processor (110) may be configured with one or more cores and may include a processor for data analysis and deep learning, such as a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), or a tensor processing unit (TPU) of a computing device. Alternatively, the processor may be configured to include any type of processor well known in the technical field of the present invention.

[0073] Additionally, the processor (110) can perform operations for at least one application or program for executing a method according to embodiments of the present invention, and the delivery schedule generation server (100) can have one or more processors.

[0074] In various embodiments, the processor (110) may further include a Random Access Memory (RAM, not shown) and a Read-Only Memory (ROM, not shown) that temporarily and / or permanently store signals (or data) processed within the processor (110). In addition, the processor (110) may be implemented in the form of a system on chip (SoC) that includes at least one of a graphics processing unit, RAM, and ROM.

[0075] The memory (120) stores various data, commands, and / or information. The memory (120) can load a computer program (151) from the storage (150) to execute methods / operations according to various embodiments of the present invention. When the computer program (151) is loaded into the memory (120), the processor (110) can perform the method / operation by executing one or more instructions constituting the computer program (151). The memory (120) may be implemented as a volatile memory such as RAM, but the technical scope of the present invention is not limited thereto.

[0076] The bus (130) provides communication functions between components of the delivery schedule generation server (100). The bus (130) can be implemented as various types of buses, such as an address bus, a data bus, and a control bus.

[0077] The communication interface (140) supports wired and wireless Internet communication of the delivery schedule generation server (100). Furthermore, the communication interface (140) may support various communication methods other than Internet communication. To this end, the communication interface (140) may be configured to include a communication module well known in the technical field of the present invention. In some embodiments, the communication interface (140) may be omitted.

[0078] Storage (150) can non-temporarily store a computer program (151). When performing a process according to an embodiment of the present invention through the delivery schedule generation server (100), storage (150) can store various information necessary to perform a method or provide a service according to the disclosed embodiment.

[0079] Storage (150) may be configured to include non-volatile memory such as ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), flash memory, a hard disk, a removable disk, or any type of computer-readable recording medium well known in the art to which the present invention pertains.

[0080] The computer program (151) may include one or more instructions that cause the processor (110) to perform a method / operation according to various embodiments of the present invention when loaded into the memory (120). That is, the processor (110) may perform the method / operation according to various embodiments of the present invention by executing the one or more instructions.

[0081] In one embodiment, the computer program (151) may include one or more instructions for performing various methods associated with various tasks related to learning a neural network model.

[0082] The steps of a method or algorithm described in connection with an embodiment of the present invention may be implemented directly in hardware, implemented as a software module executed by hardware, or implemented by a combination thereof. The software module may reside in a random access memory (RAM), a read only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a flash memory, a hard disk, a removable disk, a CD-ROM, or any other form of computer-readable recording medium well known in the art to which the present invention pertains.

[0083] The components of the present invention may be implemented as programs (or applications) and stored on a medium to be executed in conjunction with a computer, which is hardware. The components of the present invention may be implemented as software programs or software elements. Similarly, the embodiments may be implemented in a programming or scripting language such as C, C++, Java, or an assembler, including various algorithms implemented as a combination of data structures, processes, routines, or other programming components. Functional aspects may be implemented as algorithms that are executed on one or more processors.

[0084]

[0085] FIG. 3 is a diagram illustrating a delivery schedule generation server according to one embodiment of the present invention.

[0086] Referring to FIG. 3, the delivery schedule generation server (100) may include a delivery destination node search module (101) and a delivery schedule generation module (102).

[0087] The delivery destination node search module (101) can perform an operation of searching for and identifying a suitable delivery destination node on a delivery route.

[0088] Specifically, the delivery destination node search module (101) can perform an operation of searching for a node by considering various factors (e.g., congestion of the logistics network, availability of transportation means, characteristics of the goods) to select an optimal node among several delivery destination nodes located in the middle based on the departure and arrival points of the goods.

[0089] For example, the delivery destination node exploration module (101) can explore delivery destination nodes, such as logistics centers, ports, and airports, within a specific geographic range between the origin and destination, and select the node most appropriate for the product's characteristics and user requirements. Furthermore, the module can analyze past delivery data and current real-time data to explore optimal transit routes, taking into account factors such as weather changes and traffic congestion.

[0090] The delivery schedule generation module (102) can perform an operation of generating an optimal delivery schedule based on the searched delivery destination node.

[0091] Specifically, the delivery schedule generation module (102) can perform an operation to derive an optimal delivery schedule for the entire delivery route by comprehensively considering the travel time between each node, travel distance, type of available delivery means, expected cost, etc.

[0092] For example, the delivery schedule generation module (102) may calculate the time it takes for an item to pass through each node and arrive at its final destination based on the connectivity between nodes selected by the delivery destination node search module (101), and perform an operation to set the most efficient departure and arrival times based on this. Furthermore, the module may generate a schedule that minimizes waiting time at a specific node or considers cost reduction methods, depending on user requirements.

[0093] Hereinafter, a more specific description of the operation of the delivery schedule creation server (100) will be described with reference to FIGS. 6 to 11.

[0094]

[0095] FIG. 4 is a diagram for explaining a delivery node server according to one embodiment of the present invention.

[0096] Referring to FIG. 4, the delivery node server (200) can manage multiple delivery node data (A, B, C, etc.) and perform operations of collecting, mediating, and providing information about these nodes.

[0097] Specifically, the delivery node server (200) may include a delivery node data broker module and a delivery node database.

[0098] The delivery node data broker module can connect to an external server to collect various delivery node data (e.g., A, B, C), convert it into a consistent format, and store it in the delivery node database. Furthermore, this data can be shared with other servers, such as the common information database (400), the delivery method server (300), and the multimodal delivery generation server (delivery schedule generation server (100)). The delivery node data broker can collect information about each node, such as its location, status, available delivery methods, and pending items, and integrate them into a central database (i.e., the delivery node database).

[0099] The Delivery Node Database stores and manages information about delivery nodes. It can include information such as each delivery node's location, operational status, and available delivery methods. The Delivery Node Database can update data in real time or provide requested information through integration with other systems as needed.

[0100] The components illustrated in Figure 4 interact with each other to enable the delivery node server (200) to efficiently manage and provide the data necessary to establish an optimal delivery route within the logistics network. This configuration enables data transfer between specific nodes, real-time status monitoring, and delivery schedule optimization, thereby maximizing the efficiency of the entire logistics process.

[0101]

[0102] FIG. 5 is a drawing for explaining a delivery means server according to one embodiment of the present invention.

[0103] Referring to FIG. 5, the delivery means server (300) may include multiple delivery means servers (e.g., delivery means A server, delivery means B server, delivery means C server, etc.). Each delivery means server may manage data related to a specific type of delivery means and perform operations to collect, mediate, and provide information about the delivery means.

[0104] For example, the delivery means A server (301) manages data for a specific type of delivery means (e.g., delivery means A) and can process information such as the status, availability, current location, and expected arrival time of the delivery means.

[0105] Additionally, as illustrated in FIG. 5, the delivery means A server (301) may include a delivery means A-only data broker module and a delivery means A-only database.

[0106] A dedicated data broker module for delivery method A can be connected to an external server to collect data from various sub-delivery methods (e.g., delivery method A-1, delivery method A-2, delivery method A-3, etc.), integrate the data, convert it into a consistent format, and then store it in a dedicated database for delivery method A. In addition, this data can be shared with other servers, such as a common information database (400), a delivery node server (200), and a multimodal delivery generation server (delivery schedule generation server (100)).

[0107] The dedicated database for Delivery Method A stores and manages data related to Delivery Method A. It may include information such as the status, availability, and operating hours of each sub-delivery method. Furthermore, the dedicated database for Delivery Method A is updated in real time and can provide data to other servers as needed.

[0108] The components illustrated in FIG. 5 interact with each other, enabling the delivery vehicle server (300) to efficiently manage the status of each delivery vehicle within the logistics network through multiple delivery vehicle servers (e.g., delivery vehicle servers A, B, C, etc.) and provide the data necessary to generate optimal delivery routes and schedules. This configuration enables data transmission between delivery vehicles, real-time status monitoring, and schedule optimization, thereby maximizing the efficiency of the overall logistics process.

[0109]

[0110] FIGS. 6 to 11 are drawings for explaining an example of a method for generating a delivery schedule based on a multimodal delivery means according to one embodiment of the present invention.

[0111] According to one embodiment of the present invention, a delivery schedule generation server (100) can generate a delivery schedule through data transmitted and received between a delivery destination node server (200), a delivery means server (300), and a common information database (400), and can provide a delivery schedule so that goods are delivered according to the delivery schedule.

[0112] For example, referring to FIG. 6, the delivery schedule generation server (100) may request the delivery destination node server (200) to provide information. The delivery destination node server (200) may transmit data corresponding to the information provision request to the delivery schedule generation server (100). Here, the data corresponding to the information provision request may include, but is not limited to, the location, availability, operational status, and information on available delivery methods of each delivery destination node.

[0113] The delivery schedule generation server (100) can collate data to generate and distribute a delivery schedule. Specifically, the delivery schedule generation server (100) can distribute the delivery schedule to the delivery destination node server (200) and store it in the common information database (400).

[0114] Meanwhile, the delivery destination node server (200) that has acquired the delivery schedule from the delivery schedule generation server (100) can transmit the delivery schedule to the delivery means server (300). In this case, the delivery means server (300) can acquire the delivery schedule and transmit the delivery schedule to the delivery means corresponding to the delivery schedule.

[0115] Hereinafter, with reference to FIG. 7, a method for a delivery schedule generation server (100) to generate a multimodal delivery schedule will be described.

[0116] Referring to Figure 7, when a request is made to create a delivery schedule for an item, the delivery schedule generation server (100) can obtain information regarding the item's origin, destination, and requirements (S110). Here, the requirements may include at least one of the following: a maximum delivery time, a maximum cost limit, a preference for a delivery method, a preference for a logistics operator, and delivery stability, as specified by the user sending or receiving the item.

[0117] Specifically, the delivery schedule generation server (100) can obtain departure, destination, and requirement information from the terminal of the user sending or receiving the goods, the common information database (400), and other related servers of the logistics system.

[0118] For example, the delivery schedule generation server (100) can receive requirements such as maximum delivery time, cost limit, and preference for delivery method specified by the sender or receiver of the goods from the user terminal, and can search for related logistics information of the departure point and destination point from the common information database (400).

[0119] In one embodiment, when the delivery schedule generation server (100) obtains the departure and destination of the goods, it can recognize at least one delivery destination node based on the departure and destination (S120).

[0120] Specifically, the delivery destination node search module (101) included in the delivery schedule generation server (100) can recognize at least one delivery destination node based on the departure point and the destination point. Here, the delivery destination node search module (101) included in the delivery schedule generation server (100) can recognize at least one delivery destination node through various methods.

[0121] For example, the delivery destination node search module (101) may recognize at least one delivery destination node based on a region, at least one delivery destination node based on past records, or at least one delivery destination node using an artificial neural network. Furthermore, the delivery destination node search module (101) may collect at least one delivery destination node and transmit it to the delivery schedule generation module (102).

[0122] Referring to FIG. 8, the delivery node search module (101) can obtain departure and destination information (S210). Furthermore, the delivery node search module (101) can identify at least one delivery node based on the departure and destination information.

[0123] In one embodiment, when the delivery destination node search module (101) recognizes at least one delivery destination node, it can recognize a list of delivery destination nodes located within a shape area generated based on the origin and destination as at least one node (S221). Specifically, the delivery destination node search module (101) can obtain location information of the origin and destination. In addition, the delivery destination node search module (101) can generate a virtual shape including the locations of the origin and destination. In addition, the delivery destination node search module (101) can recognize the delivery destination nodes corresponding to the virtual shape as at least one delivery destination node, and the delivery schedule generation server (100) can recognize the delivery destination nodes corresponding to the virtual shape as at least one delivery destination node.

[0124] For example, referring to FIG. 9, the delivery destination node search module (101) can search for a delivery destination node based on the departure point (ST-102) and the destination point (ST-105).

[0125] The delivery node search module (101) can acquire location information of the origin (ST-102) and destination (ST-105) and then generate a virtual shape (F1) based on this. This virtual shape (F1) can be represented as a rectangular area encompassing the origin and destination. This rectangular area can be a search shape generated based on a straight path between the origin and destination.

[0126] Additionally, the delivery destination node search module (101) can expand a rectangular area based on specific criteria to create an expanded rectangular area (F2). This expanded rectangular area (F2) can enable the search for delivery destination nodes in a wider range, including the surrounding area between the origin and destination.

[0127] The delivery destination node search module (101) can search for delivery destination nodes (ST-101, ST-103, ST-104) located within the expanded rectangular area (F2) thus created and recognize them as at least one delivery destination node. In contrast, a delivery destination node (ST-106) not included in the expanded rectangular area (F2) may not be recognized as a searched delivery destination node.

[0128] Through this process, the delivery destination node search module (101) can effectively recognize multiple delivery destination nodes located between the departure point and the destination point, and transmit this information to the delivery schedule generation module (102) to set an optimal delivery route.

[0129] In an additional embodiment, the delivery destination node search module (101) can dynamically determine the size of the rectangular area according to real-time circumstances when recognizing at least one delivery destination node.

[0130] Specifically, the delivery destination node search module (101) can determine the size of a rectangular area by considering various real-time data, such as traffic conditions between the origin and destination, weather, road conditions, logistics congestion, regional accident rates, expected travel time, and available resources of the logistics operator. By reflecting such real-time data, the delivery destination node search module (101) can automatically adjust not only the size of the virtual geometric area (F1 or F2) but also its shape and direction according to the situation.

[0131] For example, the delivery node search module (101) can collect and analyze traffic situation data in real time. For example, if traffic congestion is expected or has already occurred on the route from the departure point (ST-102) to the destination point (ST-105), the delivery node search module (101) can dynamically expand a rectangular area based on this information and adjust the search area to include nodes on alternative routes that can bypass the congested area. In this process, the module can expand the area in a direction that avoids sections expected to be congested or adjust the shape of the search area to prioritize the search for alternative routes.

[0132] For example, if a traffic accident causes severe congestion on a specific highway section, the delivery destination node search module (101) can expand the virtual geometry area to include nearby national or local roads that can bypass the section. This allows for securing an optimal alternative route that reduces overall travel time, even if it deviates from the previously established straight route.

[0133] Additionally, the delivery node search module (101) can use weather data to determine the size of the search area (i.e., virtual shape). For example, if inclement weather such as heavy rain or heavy snow is expected in a specific area, the delivery node search module (101) can expand or contract the search area in a specific direction to avoid that area. At this time, to avoid risks such as road flooding due to heavy rain or road closures due to heavy snow, the module can adjust the search area by bypassing the area and including safe nodes.

[0134] For example, if heavy snowfall is expected to be concentrated in a specific mountainous region, the delivery node search module (101) can expand the rectangular area eastward or westward to bypass that area and include nodes located in flat or low-lying areas. This can minimize weather-related delivery delays or safety issues.

[0135] Additionally, the delivery node search module (101) can reflect road conditions and logistics congestion in real time. For example, if a specific road section is under construction or a major logistics hub is overloaded, the delivery node search module (101) can adjust the search area based on this information to avoid that section or include an alternative node with less logistics congestion.

[0136] For example, if a specific logistics hub is expected to experience overload and delay processing times, the delivery node search module (101) can adjust the search area to include nearby smaller logistics nodes instead of that logistics hub. This can prevent delays in logistics processing and shorten overall delivery times.

[0137] Dynamic real-time adjustments based on these real-time conditions can significantly improve logistics efficiency. By quickly creating optimal delivery routes while minimizing unexpected delays, it can enhance the flexibility and responsiveness of the entire logistics process. This allows the delivery schedule generation server (100) to provide more sophisticated and reliable multimodal delivery schedules.

[0138] In an additional embodiment, the delivery node search module (101) may recognize nodes on the alternative route before adjusting the search area to include nodes on the alternative route.

[0139] Specifically, the delivery destination node search module (101) can search for an alternative route that bypasses the problematic section and search for one or more nodes within a preset distance from the alternative route. Furthermore, the delivery destination node search module (101) can adjust the search area size to include at least one node among the one or more nodes that is closest to the current rectangular area.

[0140] For example, if traffic congestion occurs on a major route between a departure point (ST-102) and a destination point (ST-105) and the route becomes unusable, the delivery destination node search module (101) can identify a nearby alternative route. Once the alternative route is determined, the delivery destination node search module (101) searches for delivery destination nodes located within 10 km of the route, and selects the node closest to the current search area among these nodes. Thereafter, the delivery destination node search module (101) can expand or adjust the existing search area to include this node, so that the optimal route and nodes are reflected in the delivery schedule.

[0141] In this way, the delivery node search module (101) can quickly recognize alternative routes and nodes even in unexpected problem situations, and adjust the search area if necessary to ensure smooth progress of logistics.

[0142] Referring back to FIG. 8, when the delivery destination node search module (101) recognizes at least one delivery destination node, it can obtain a list of delivery destination nodes based on past records (S222a) and recognize a list of delivery destination nodes near the corresponding delivery destination node as at least one delivery destination node (S222b). Specifically, the delivery destination node search module (101) can recognize past delivery schedules corresponding to departure and arrival locations. In addition, the delivery destination node search module (101) can recognize a list of delivery destination nodes for delivery destination nodes used in past delivery schedules. In addition, the delivery destination node search module (101) can recognize the first delivery destination nodes included in the delivery destination node list and the second delivery destination nodes existing within a preset radius from the delivery destination nodes as at least one delivery destination node.

[0143] For example, the delivery destination node search module (101) can search for frequently used delivery destination nodes in a specific section based on past delivery records. Specifically, the delivery destination node search module (101) can obtain a list of delivery destination nodes used in the past for transporting goods between a departure point and a destination point, and based on the node list, can generate a new node list that includes other nearby delivery destination nodes. In this process, the delivery destination node search module (101) can set an optimal route by considering not only frequently used nodes in the existing delivery route, but also nodes located within a specific radius.

[0144] For example, if the nodes included in the past records between the origin and destination in Figure 9 are nodes such as ST-101, ST-103, and ST-104, additional nodes located around these nodes can also be explored. This enables more sophisticated node recognition that reflects past experience and records.

[0145] In one embodiment, the delivery destination node search module (101) may obtain a list of delivery destination nodes through an artificial neural network when recognizing at least one delivery destination node (S223). Specifically, the delivery destination node search module (101) may input information about the origin and destination into a pre-trained neural network model to obtain at least one delivery destination node. Here, the neural network model may be a neural network model that learns based on delivery record big data to generate an optimal delivery route between the origin and destination and predicts delivery destination nodes included in the route. For example, the neural network model may generate an optimal delivery route between the origin and destination from data learned based on past delivery records and predict delivery destination nodes that may be included in the route.

[0146] For example, the delivery node search module (101) can identify and select efficient and cost-effective nodes along the route between the origin and destination using a neural network model. The neural network model can learn from various past delivery routes and successful delivery experiences, thereby suggesting optimal nodes even under complex conditions.

[0147] The delivery schedule generation server (100) can obtain a unit delivery schedule from a delivery destination node server corresponding to at least one delivery destination node (S130). Here, the unit delivery schedule may include at least one of the following: unit travel time between each delivery destination node, unit travel distance, type of delivery vehicle to be used in the unit travel section, cost of the unit travel section, and information on the logistics operator operating the unit travel section.

[0148] Specifically, the delivery schedule generation server (100) can comprehensively analyze data for each travel section for each delivery destination node to derive an optimal delivery route and schedule. In this process, each unit delivery schedule can be optimized based on the characteristics of various transportation methods (e.g., trucks, drones, autonomous vehicles, etc.) available for that section, thereby reducing overall delivery costs or shortening delivery times.

[0149] More specifically, the delivery schedule generation server (100) can optimize the entire delivery route from the origin to the destination of the goods based on the information provided in each unit delivery schedule. For example, if truck transportation is the most economical method for a specific section, the delivery schedule generation server (100) can set the route by assigning trucks to that section, while using drones or autonomous vehicles for other sections. Furthermore, by considering the logistics operator information for each section, the server can prioritize selecting operators with high reliability or operators with significant cost savings.

[0150] Through this, the delivery schedule generation server (100) can compare and analyze not only a single route but also multiple alternatives to derive a multimodal delivery schedule that best suits user requirements. Furthermore, by synthesizing data from each segment, it can generate a schedule that maximizes the efficiency of the entire logistics network and manage the delivery of goods based on this schedule.

[0151] The delivery schedule generation server (100) can generate a multimodal delivery schedule based on unit delivery schedules and requirements (S140).

[0152] Specifically, the delivery schedule generation module (102) included in the delivery schedule generation server (100) can compare the unit delivery schedule with requirements and determine a specific delivery route and specific delivery means corresponding to the requirements.

[0153] For example, referring to FIG. 10, the delivery schedule generation module (102) can acquire user requirements (S311a) and set weights (i.e., weights) for the requirements (S133b). Furthermore, the delivery schedule generation module (102) can acquire delivery method data for each delivery destination node (S312). Furthermore, the delivery schedule generation module (102) can acquire departure and destination information (S313). Furthermore, the delivery schedule generation module (102) can generate a delivery schedule based on the acquired information (S320).

[0154] More specifically, the delivery schedule generation server (100) can extract information on travel time, travel distance, type of delivery method to be used, cost for the corresponding section, and logistics operator for each of the plurality of unit delivery schedules. Furthermore, the delivery schedule generation server (100) can compare the extracted information with maximum delivery time, maximum cost limit, preferences for delivery methods, and preferences for logistics operators to evaluate the suitability of each of the plurality of unit delivery schedules. Furthermore, the delivery schedule generation server (100) can determine at least one unit delivery schedule to be used for delivery of goods based on the results of the suitability evaluation. Furthermore, the delivery schedule generation server (100) can recognize a specific delivery route and a specific delivery method corresponding to at least one unit delivery schedule.

[0155] For example, the delivery schedule generation server (100) can use data extracted from each unit delivery schedule to determine the optimal delivery route and delivery method that best suits the user's requirements.

[0156] Specifically, the delivery schedule generation server (100) can evaluate the travel time and cost of each unit delivery schedule based on user-specified maximum delivery time and cost limits. For example, while truck transportation may be cost-effective for certain sections, drone transportation may be more suitable for other sections to save time. Based on these evaluation results, the delivery schedule generation server (100) generates an overall delivery schedule by combining the optimal route and transportation method.

[0157] Additionally, the delivery schedule generation server (100) can select the delivery method and logistics operator for each section based on the user's preferences (e.g., preference for eco-friendly transportation, preference for a specific logistics operator, etc.). For example, if the user prefers eco-friendly delivery methods, the delivery schedule generation server (100) can optimize the route by preferentially selecting electric trucks or autonomous vehicles.

[0158] Finally, the delivery schedule generation server (100) can complete a multimodal delivery schedule that ensures efficient movement of goods throughout the entire logistics network based on these optimized delivery routes and means. This delivery schedule is stored in a common information database (400), making it accessible to all relevant systems, thereby enhancing the consistency and efficiency of the logistics process.

[0159] According to various embodiments, the delivery schedule generation server (100) can optimize a multimodal delivery schedule based on a specific delivery route and a specific delivery means.

[0160] For example, referring to Figure 11, when delivering goods between the departure point ST-102 and the destination ST-105, various routes can be selected by combining multiple operators and delivery methods. Each route entails different costs and times, and the delivery schedule generation server (100) can compare these routes to generate an optimal delivery schedule.

[0161] For example, in Fig. 11, there is a direct transport route from ST-102 to ST-105, which is linked to Company C using the DR-1100 delivery vehicle, and the cost is 50 units. Alternatively, a route from ST-102 to ST-104 and then to ST-105 can be selected, in which case Company D's DR-1201 can be used to transport to ST-104, and then to ST-105 using Company A's AGV-1000 or Company B's AGV-1001. If this route is selected, the cost will vary depending on each section, and additional costs and time may be incurred due to the linkage with the operator.

[0162] The delivery schedule generation server (100) considers these various route and transportation method combinations and calculates the cost and time for each case. For example, a route that transports to ST-104 using Company D's DR-1201 and then to ST-105 using Company A's AGV-1000 has a total cost of 40 units (30 + 10), and the cost savings and time savings that can be achieved by using the same operator can be considered. On the other hand, selecting Company B's AGV-1001 would result in a slightly higher cost of 42 units, but may better suit other priority requirements (e.g., reliability or speed).

[0163] Through these calculations, the delivery schedule generation server (100) selects the optimal route to minimize the additional costs and time incurred due to linking two or more operators. When using delivery methods from the same operator, cost and time savings can be further maximized in the linking section, ultimately providing a multimodal delivery schedule for efficient delivery of goods.

[0164] In one embodiment, the delivery schedule generation server (100) can recognize delivery costs and delivery times based on the connection between two or more operators when using two or more operators to deliver goods. Furthermore, the delivery schedule generation server (100) can recognize reduced costs and delivery times when delivering goods using the same operator's delivery methods. Furthermore, the delivery schedule generation server (100) can generate an optimal multimodal delivery schedule based on the reduced costs and delivery times.

[0165] Specifically, the delivery schedule generation server (100) can compare various routes and operator combinations by calculating in advance the additional costs and time that may be incurred when linking with each operator. This allows the route requiring the least additional costs and time to be selected in sections where two or more operators are linked.

[0166] For example, when transporting goods from a departure point to an intermediate delivery point using truck transportation of operator A and then delivering the goods to the final destination using air transportation of operator B, the delivery schedule generation server (100) can calculate the connection cost and time between A and B to set an optimal route. At this time, the route can be optimized to minimize additional costs (e.g., unloading costs) and time (e.g., waiting time) that may occur during the transition to operator B.

[0167] For example, the delivery schedule generation server (100) estimates that when a truck of operator A is used to travel a distance of 200 km, the estimated cost is $400 and the estimated travel time may be 4 hours. After arriving at an intermediate delivery location, if the goods are transferred to an aircraft of operator B and transported 1,000 km to the final destination, the estimated cost of air transport is $1,500 and the estimated travel time may be an additional 2 hours. However, the potential additional unloading cost for the transfer between the two operators is $100, and the waiting time during this process may be 1 hour.

[0168] In this case, the delivery schedule generation server (100) can optimize the route by evaluating ways to minimize switching costs (USD 100) and waiting time (1 hour) between the two operators. For example, unloading operations at Operator A's warehouse can be efficiently performed, or the schedule of Operator B's aircraft can be adjusted so that Operator B's aircraft can depart immediately without waiting for the truck's arrival time. This can reduce the waiting time to 30 minutes and the additional cost to USD 50.

[0169] As a result, the optimized route reduces the total travel time to 7 hours and 30 minutes, and the total cost from $2,000 to $1,950. This optimization significantly improves the efficiency of logistics operations, and the delivery schedule generation server (100) can provide an optimal multimodal delivery schedule that takes into account the linkage between the two operators.

[0170] In another embodiment, the delivery schedule generation server (100) can evaluate the cost and time savings associated with the linked route when delivering goods within the same operator. For example, when delivering goods by linking a truck and an autonomous vehicle from Operator C, the route can be optimized by considering the cost savings (e.g., discounts applied due to the use of an integrated logistics system) and time savings (e.g., quick turnarounds in the logistics warehouse) resulting from the link between the same operators.

[0171] For example, if the delivery schedule generation server (100) uses Operator C's logistics system to link goods from the origin to the destination, it can utilize seamless data sharing between the operator's internal systems and an integrated logistics management system. This enables efficient goods transfer between the two delivery methods, such as when switching from trucks to autonomous vehicles, and significantly reduces unloading time and costs at the logistics warehouse.

[0172] For example, a 100-kilometer journey using a truck on a specific route might cost approximately $200 and take two hours. Subsequently, switching to an autonomous vehicle for a 50-kilometer journey would cost $100 and take an additional hour. However, by optimizing the handover between the truck and autonomous vehicle using Operator C's internal logistics management system, the unloading time, which typically takes 30 minutes, could be reduced to 15 minutes. This would reduce the total journey time from 3 hours and 15 minutes to 3 hours.

[0173] Furthermore, by utilizing C Operator's integrated logistics management system, the cost of connecting trucks and autonomous vehicles can be reduced from the typical additional cost of $50 to $30 through internal discounts. Consequently, the total cost for the entire route is reduced from $350 to $330.

[0174] The delivery schedule generation server (100) can evaluate these cost and time savings, establish optimal routes, and optimize the entire logistics process. Consequently, this optimization can generate a multimodal delivery schedule that reduces logistics costs by $20 and shortens total delivery time by 15 minutes.

[0175]

[0176] According to a further embodiment of the present invention, the delivery schedule generation server (100) can perform dynamic optimization by utilizing real-time data analysis and predictive models. Unlike conventional static data-based optimization methods, this embodiment may include a method for dynamically adjusting the schedule by reflecting various environmental data collected in real time.

[0177] Specifically, the delivery schedule generation server (100) can continuously monitor real-time data, such as traffic conditions, weather changes, logistics network congestion, and the availability and status of transportation vehicles. Based on this data, the delivery schedule generation server (100) can immediately generate response strategies for unexpected situations (e.g., road traffic accidents, inclement weather, etc.), thereby reorganizing the entire delivery route.

[0178] For example, if unexpected traffic congestion occurs on a specific route, the delivery schedule generation server (100) can analyze traffic conditions in real time and suggest an alternative route. If a truck from Operator A is currently using the route, the server can immediately reroute the truck to avoid congestion or, if possible, add another means of transportation (e.g., rail or air) to minimize delays. During this process, the server maintains an optimal balance between cost and time, ensuring that the goods reach their destination as quickly and economically as possible.

[0179] Additionally, route optimization based on weather changes is also possible. For example, if inclement weather is expected on a scheduled air transport route, the delivery schedule generation server (100) can immediately cancel the air transport and select alternative methods such as rail or land transport. The additional costs and time incurred can be calculated in real time, providing users with information about any unexpected delays and suggesting possible alternative routes.

[0180] This dynamic optimization can be further refined using AI-based predictive models. The delivery schedule generation server (100) utilizes predictive models trained on past data to predict the likelihood of accidents on specific routes, logistics operators' processing speeds, and performance degradation of transportation vehicles, and can proactively set optimal routes to address these issues.

[0181] In conclusion, an additional embodiment of the present invention utilizes a dynamic optimization method that combines real-time data and AI-based predictions to manage delivery schedules more flexibly and effectively. This allows for rapid response to unexpected variables and enhances the stability and efficiency of the entire logistics process.

[0182]

[0183] According to another embodiment of the present invention, a transparent and reliable delivery schedule management system utilizing blockchain technology can be implemented. This embodiment proposes a new method that enhances transparency and ensures reliability throughout the entire delivery process by recording all data generated at each stage of the logistics process on blockchain.

[0184] Specifically, the delivery schedule generation server (100) records the status and schedule information for each logistics operator, delivery destination node, and delivery method on the blockchain network. These records are stored in a decentralized manner, without relying on a central server, making data tampering or deletion impossible. This allows all stakeholders (transporters, users, logistics managers, etc.) to check the movement path and status of goods in real time, minimizing potential disputes or errors.

[0185] For example, when goods are transported from their origin to an intermediate destination via a truck operated by Operator A, data such as the truck's location, condition, route, and expected arrival time are automatically recorded on the blockchain. Later, when goods are transferred from the intermediate destination to an aircraft operated by Operator B, all data from this transition process is transparently recorded, and all transportation information up to the final destination is managed in the same manner.

[0186] Furthermore, by leveraging blockchain technology, each participant in the logistics network can update data generated in the sections they manage in real time, thereby maintaining consistency throughout the entire logistics process. For example, if an unexpected delay occurs in a specific section, this information is recorded on the blockchain, immediately shared with all stakeholders, and the delivery schedule generation server (100) can reflect this information and dynamically adjust the schedule for the remaining sections.

[0187] Furthermore, all data recorded on the blockchain ensures transparency throughout the logistics process and can serve as solid evidence in any disputes that may arise. For example, if a specific item is damaged or lost, the data recorded on the blockchain can accurately track when, where, and why the problem occurred, thereby clarifying responsibility.

[0188] This blockchain-based logistics scheduling system can be particularly useful when transporting expensive or sensitive items. Users can reliably track the route and status of their ordered items, significantly increasing user satisfaction.

[0189] In conclusion, additional embodiments of the present invention utilize blockchain technology to significantly enhance the transparency and reliability of a logistics scheduling management system. This enhances the stability and efficiency of the entire logistics network, providing users with more reliable services.

[0190]

[0191] While the embodiments of the present invention have been described above with reference to the attached drawings, those skilled in the art will appreciate that the present invention can be implemented in other specific forms without altering the technical concept or essential features thereof. Therefore, the embodiments described above should be understood to be illustrative in all respects and not restrictive.

[0192]

[0193] The best mode for carrying out the invention as described above has been described.

[0194] The present invention can provide a method, server, program and system for generating a delivery schedule based on a multimodal delivery means.

Claims

1. A method performed by a computing device including at least one processor, When a request is made to create a delivery schedule for goods, a step of obtaining information on the origin, destination and requirements of the goods; A step of recognizing at least one delivery destination node based on the above departure point and the above destination point; A step of obtaining a unit delivery schedule from a delivery node server corresponding to at least one delivery node; and A step of generating a multimodal delivery schedule based on the above unit delivery schedule and the above requirements; including, A method for generating a delivery schedule based on multimodal delivery methods.

2. In paragraph 1, The step of obtaining at least one delivery node based on the above departure point and the above destination point is: A step of obtaining location information of the above departure point and the above destination point; A step of generating a virtual shape including the locations of the starting point and the destination; and A step of recognizing the delivery destination nodes corresponding to the above virtual shape as at least one delivery destination node; including, A method for generating a delivery schedule based on multimodal delivery methods.

3. In paragraph 1, The step of obtaining at least one delivery node based on the above departure point and the above destination point is: A step of recognizing past delivery schedules corresponding to the above departure point and the above destination point; A step of recognizing a list of delivery destination nodes for delivery destination nodes used in the above past delivery schedule; and A step of recognizing first delivery destination nodes included in the delivery destination node list and second delivery destination nodes existing within a preset radius from the delivery destination nodes as at least one delivery destination node; including, A method for generating a delivery schedule based on multimodal delivery methods.

4. In paragraph 1, The step of obtaining at least one delivery node based on the above departure point and the above destination point is: A step of obtaining at least one delivery destination node by inputting information about the departure point and the destination point into a pre-learned neural network model; Including, The above neural network model is, A neural network model that learns from big data on delivery records to create an optimal delivery route between the departure and arrival points and predicts the delivery destination nodes included in the route. A method for generating a delivery schedule based on multimodal delivery methods.

5. In paragraph 1, The above unit delivery schedule is: Contains at least one of the following: unit travel time between each delivery destination node, unit travel distance, type of delivery method to be used in the unit travel section, cost of the unit travel section, and information on the logistics operator operating the unit travel section; The above requirements are: Contains at least one of the maximum delivery time, maximum cost limit, preference for delivery method, preference for logistics operator and reliability of delivery specified by the user sending or receiving the above goods; The step of creating a multimodal delivery schedule based on the above unit delivery schedule and the above requirements is: A step of comparing the above unit delivery schedule with the above requirements and determining a specific delivery route and specific delivery means corresponding to the requirements; including, A method for generating a delivery schedule based on multimodal delivery methods.

6. In paragraph 5, The step of comparing the above unit delivery schedule with the above requirements and determining a specific delivery route and specific delivery means corresponding to the above requirements is as follows: A step of extracting information on travel time, travel distance, type of delivery method to be used, cost of the corresponding section, and logistics operator for each of multiple unit delivery schedules; A step of evaluating the suitability of each of the plurality of unit delivery schedules by comparing the extracted information with the maximum delivery time, the maximum cost limit, the preference for the delivery method, and the preference for the logistics operator; A step of determining at least one unit delivery schedule to be used for delivery of the goods based on the results of the above suitability assessment; and A step of recognizing the specific delivery route and the specific delivery means corresponding to the at least one unit delivery schedule; including, A method for generating a delivery schedule based on multimodal delivery methods.

7. In paragraph 5, The above method, A step of optimizing a multimodal delivery schedule based on the specific delivery route and the specific delivery means; Including more, The steps for optimizing the above multimodal delivery schedule are: In cases where two or more operators must be used to deliver the above goods, a step of recognizing the delivery cost and delivery time according to the connection between the two or more operators; When delivering the above goods using the same operator's delivery method, a step of recognizing the reduced cost and reduced time compared to the above delivery cost and delivery time; and A step of generating an optimal multimodal delivery schedule based on the above-mentioned reduced cost and reduced time; including, A method for generating a delivery schedule based on multimodal delivery methods.

8. Memory that stores one or more instructions; and A processor that executes one or more instructions stored in the memory. Including, The processor executes one or more of the instructions, A server performing the method of clause 1.

9. A computer program stored on a computer-readable recording medium that is combined with a computer as hardware and can perform the method of claim 1.

10. In a system for generating a delivery schedule based on multimodal delivery means, Delivery schedule generation server; Delivery destination node server; Delivery method server; and Common Information Database; Including, The above delivery schedule generation server, When a request is made to create a delivery schedule for goods, information on the origin, destination and requirements of the goods is obtained, Recognize at least one delivery node based on the above departure point and the above destination point, Obtain a unit delivery schedule from a delivery node server corresponding to at least one delivery node, Generate a multimodal delivery schedule based on the above unit delivery schedule and the above requirements. A system for generating delivery schedules based on multimodal delivery methods.

Images