Method and apparatus for providing cloud-based cargo dispatching service reflecting driver's driving style

Abstract

Embodiments relate to a method for providing a cloud-based cargo dispatching service reflecting a driver's driving style by means of a platform executed by a computing device, the method comprising the steps of: constructing a data set on the basis of driver information, shipper information, and environment information; receiving a first cargo transportation request from a computing device of a first shipper, wherein, when the first cargo transportation request is received from the computing device of the first shipper, the platform further obtains first cargo type and characteristic information through the cloud, selects a first driver in a driver listing including at least one driver on the basis of the first cargo transportation request, and transmits the first cargo transportation request to the computing device of the first driver; and receiving a first cargo transportation response from the computing device of the first driver.

Description

Method and device for providing a cloud-based cargo dispatch service that reflects the driver's driving type

[0001] The embodiments relate to a method and device for providing a cloud-based freight dispatch service that reflects a driver's driving style. More specifically, the embodiments relate to a method and device for providing a freight dispatch service by obtaining a driver's driving information and driving habits through a cloud-based method.

[0002] In the past, the primary form of purchasing was door-to-door shopping, where customers would visit the retailer directly to purchase items. However, recent advancements in transportation and storage technology have led to an increase in online shopping, with customers viewing and ordering items online rather than purchasing them in person. Consequently, the volume of packages delivered to homes via courier service has increased exponentially, and the number of trucks is also increasing to accommodate this trend.

[0003] However, the destinations each truck must deliver to may vary, and considering the delivery times, a method may be needed to determine the optimal route for each truck to efficiently deliver the cargo. For example, the current delivery method involves establishing a warehouse for each region to gather cargo, transporting the cargo to each region, and then delivering it to individual homes within the region.

[0004] At this time, multiple cargoes with different routes can be delivered simultaneously on a vehicle. Therefore, it may be efficient for a vehicle to carry cargo that can be unloaded at the same drop-off point or at a drop-off point along the route. However, methods for efficiently setting up transport routes for cargo vehicles are currently lacking. Considering the above, there is a need to implement reinforcement learning-based transport route setting (or routing) for cargo vehicles. A method for this purpose is described below.

[0005] This specification relates to a method and device for providing a cloud-based cargo dispatch service that reflects the driving type of a driver.

[0006] This specification relates to a method and device for obtaining driving information and driving habit information of a driver based on a cloud.

[0007] This specification relates to a method and device for providing a cargo dispatch service by applying a driver's driving information and driving habit information to artificial intelligence (AI).

[0008] This specification relates to a method and device for acquiring a driver's driving information and driving habit information based on a sensor mounted on a vehicle and transmitting the information to the cloud.

[0009] According to one embodiment of the present specification, there is provided a method for providing a cloud-based cargo dispatch service reflecting a driver's driving type through a platform executed by a computing device, the method comprising the steps of: constructing a data set based on driver information, shipper information, and environment information; receiving a first cargo transportation request from a computing device of a first shipper, wherein the platform further acquires first cargo type and characteristic information through a cloud when receiving the first cargo transportation request from the computing device of the first shipper; selecting a first driver from a driver listing including at least one driver based on the first cargo transportation request, transmitting the first cargo transportation request to the computing device of the first driver; and receiving a first cargo transportation response from the computing device of the first driver, wherein the driver listing is determined through suitability information derived based on the driver's driving information and driving habit information, and the first driver may be determined as a driver having the highest suitability information among at least one driver in the driver listing.

[0010] In addition, according to one embodiment of the present specification, the driving information includes information obtained through at least one sensor attached to the vehicle of the vehicle owner and information obtained based on the vehicle driving of the vehicle of the vehicle owner, wherein the information obtained through the at least one sensor includes at least one of surrounding object information obtained through sensors attached to the front, rear, and side of the vehicle of the vehicle owner, vehicle interior information obtained through sensors attached to the interior of the vehicle of the vehicle owner, and vehicle component information obtained through sensors attached to components within the vehicle of the vehicle owner, and the information obtained based on the vehicle driving of the vehicle of the vehicle owner may include a movement path, speed information for each movement path, and other driving-related information of the vehicle owner obtained based on the vehicle driving of the vehicle of the vehicle owner.

[0011] In addition, according to one embodiment of the present specification, the method may include a step of registering at least one vehicle owner on a platform, a step of transmitting a request for consent to obtain driving information to the at least one registered vehicle owner, a step of receiving a response for consent to obtain driving information from the at least one vehicle owner, and a step of directly obtaining driving information from each vehicle of the at least one vehicle owner through a cloud based on the response for consent to obtain driving information.

[0012] In addition, according to one embodiment of the present specification, the platform derives driving habit information for each of at least one vehicle owner based on driving information obtained from each of at least one vehicle owner, wherein the driving habit information is based on a driving habit information learning model.

[0013] It can be derived as follows.

[0014] Additionally, according to one embodiment of the present specification, the driving habit information learning model may be a learning model that takes driving information as input and derives driving habit information as output.

[0015] Additionally, according to one embodiment of the present specification, the driving habit information may further include the driver's preferred route information, driving style information, brake strength information, driving habit information, lane change habit information, curve driving information, and other driving habit-related information.

[0016] Additionally, according to one embodiment of the present specification, when a route based on a first cargo is set by a first vehicle owner based on a first cargo transport response and the first vehicle owner transports the first cargo through the route, the platform may provide route-based navigation information and guide information.

[0017] Additionally, according to one embodiment of the present specification, the guide information may be determined based on surrounding information obtained from a plurality of sensors based on first cargo type and characteristic information and driving habit information.

[0018] In addition, according to one embodiment of the present specification, the method may further include a step of constructing a reinforcement learning-based route setting model using the constructed data set, a step of obtaining actual generated cargo information, actual environmental information, and actual vehicle owner information and providing them as inputs to the reinforcement learning-based route setting model, a step of deriving an initial cargo transport route for each vehicle owner through the route setting model based on the inputs, a step of removing at least one cargo information within the derived initial cargo transport route for each vehicle owner and obtaining a reward and a penalty, and a step of updating the route setting model by repeating the step of obtaining the reward and penalty, and a step of deriving an optimal cargo transport route for each vehicle owner.

[0019] In addition, according to one embodiment of the present specification, when a first cargo transportation initial route for a first vehicle owner is derived through a route setting model, a step of performing an operation of removing first order information within the first cargo transportation initial route and obtaining compensation information based on the removed first order information, a step of assigning the removed first order information to a second vehicle owner's second cargo transportation initial route, a step of obtaining compensation information based on the assignment, a step of removing order information different from the first order information within the first cargo transportation initial route and repeating the operation of obtaining compensation information by assigning the removed order information to another vehicle owner's cargo transportation initial route, and a step of updating the first cargo transportation initial route to a first cargo transportation optimal route based on the repeating operation, wherein the vehicle owner information includes the vehicle owner's cargo vehicle information and vehicle owner driver information, the cargo information includes order information and cargo route information, and the environment information includes at least one of traffic information, map information, and hub location information, and the cargo vehicle information includes information on the type, year, size of the cargo compartment, and type of cargo contained in the cargo vehicle of the cargo vehicle, and the vehicle owner driver information includes information on the vehicle owner's age, gender, career, and health status, and the order information includes information on the departure point, destination, and The cargo type information may include cargo route information including information on the movement route, total carbon emissions, and delivery completion time according to order information for each vehicle owner, traffic information including information on road conditions and signal systems by time, map information including a map of the movement route for each vehicle owner, hub location information including information on possible locations for loading and unloading cargo, and reward information may include at least one of reward information and penalty information determined based on the delivery completion time and carbon emissions information.

[0020]

[0021] This specification relates to a method and device for providing a cloud-based cargo dispatch service that reflects the driving type of a driver.

[0022] This specification relates to a method and device for obtaining driving information and driving habit information of a driver based on a cloud.

[0023] This specification relates to a method and device for providing a cargo dispatch service by applying a driver's driving information and driving habit information to artificial intelligence (AI).

[0024] This specification relates to a method and device for acquiring a driver's driving information and driving habit information based on a sensor mounted on a vehicle and transmitting the information to the cloud.

[0025] It should be understood that the effects of this specification are not limited to the matters described above, but can be expanded to various contents that can be derived from the detailed description of the embodiments of the invention below.

[0026] FIG. 1 is a diagram illustrating an example of an operating environment of a system according to one embodiment of the present specification.

[0027] FIG. 2 is a block diagram for explaining the internal configuration of a computing device (200) in one embodiment of the present specification.

[0028] FIG. 3 is a diagram illustrating a platform connecting a shipper and a driver and a cloud for obtaining driver information in one embodiment of the present specification.

[0029] FIG. 4 is a diagram illustrating a method for performing cargo transport routing based on artificial intelligence in one embodiment of the present specification.

[0030] FIG. 5 is a diagram illustrating a method for obtaining driving information and driving habit information based on a vehicle equipped with a sensor according to one embodiment of the present specification.

[0031] FIG. 6 is a diagram illustrating a method for providing guidance to a driver based on information acquired through a cloud in one embodiment of the present specification.

[0032] FIG. 7 is a diagram illustrating a method for providing guidance to a driver based on information acquired through a cloud in one embodiment of the present specification.

[0033] FIG. 8 is a flowchart illustrating a method for providing a cloud-based cargo dispatching service reflecting a driver driving type in one embodiment of the present specification.

[0034] In describing the embodiments of this specification, if a detailed description of a known configuration or function is judged to obscure the gist of the embodiments of this specification, a detailed description thereof will be omitted. In addition, parts of the drawings that are not related to the description of the embodiments of this specification have been omitted, and similar parts have been designated with similar drawing reference numerals.

[0035] In the embodiments of this specification, when a component is said to be "connected," "coupled," or "connected" to another component, this may include not only a direct connection, but also an indirect connection in which another component exists in between. Furthermore, when a component is said to "include" or "have" another component, unless otherwise specifically stated, this does not exclude the other component, but rather implies that the other component may be included.

[0036] In the embodiments of this specification, the terms first, second, etc. are used only for the purpose of distinguishing one component from another component, and do not limit the order or importance between components unless specifically stated otherwise. Therefore, within the scope of the embodiments of this specification, a first component in an embodiment may be referred to as a second component in another embodiment, and similarly, a second component in an embodiment may be referred to as a first component in another embodiment.

[0037] In the embodiments of this specification, distinct components are used to clearly illustrate their respective characteristics and do not necessarily imply separation. That is, multiple components may be integrated into a single hardware or software unit, or a single component may be distributed into multiple hardware or software units. Therefore, even if not specifically mentioned, such integrated or distributed embodiments are also included within the scope of the embodiments of this specification.

[0038] In this specification, the term "network" may encompass both wired and wireless networks. In this case, the term "network" may refer to a communications network that enables data exchange between devices, systems, and devices, and is not limited to a specific network.

[0039] Embodiments described herein may be entirely hardware, partially hardware and partially software, or entirely software. As used herein, "unit," "device," or "system" refers to a computer-related entity such as hardware, a combination of hardware and software, or software. For example, a unit, module, device, or system as used herein may be, but is not limited to, a running process, a processor, an object, an executable, a thread of execution, a program, and / or a computer. For example, both an application running on a computer and the computer itself may correspond to a unit, module, device, or system as used herein.

[0040] Additionally, in this specification, the device may be a mobile device such as a smartphone, tablet PC, wearable device, or head-mounted display (HMD), as well as a fixed device such as a PC or home appliance with display functions. Furthermore, as an example, the device may be an in-vehicle cluster or an IoT (Internet of Things) device.

[0041] That is, in this specification, the term "device" may refer to any device capable of operating an application, and is not limited to a specific type. For convenience of explanation, the term "device" refers to a device on which an application operates.

[0042] In this specification, the network communication method is not limited, and connections between each component may not be made using the same network method. The network may include not only communication methods utilizing communication networks (e.g., mobile communication networks, wired Internet, wireless Internet, broadcasting networks, satellite networks, etc.), but also short-range wireless communication between devices. For example, the network may include all communication methods that enable objects to network with each other, and is not limited to wired communication, wireless communication, 3G, 4G, 5G, or other methods. For example, wired and / or networks include Local Area Network (LAN), Metropolitan Area Network (MAN), Global System for Mobile Network (GSM), Enhanced Data GSM Environment (EDGE), High Speed ​​Downlink Packet Access (HSDPA), Wideband Code Division Multiple Access (W-CDMA), Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Bluetooth, Zigbee, Wi-Fi, VoIP (Voice over Internet Protocol), LTE Advanced, IEEE802.16m, WirelessMAN-Advanced, HSPA+, 3GPP Long Term Evolution (LTE), Mobile WiMAX (IEEE 802.16e), UMB (formerly EV-DO Rev. C), Flash-OFDM, iBurst and MBWA (IEEE 802.20) It may refer to a communication network using one or more communication methods selected from the group consisting of systems, HIPERMAN, Beam-Division Multiple Access (BDMA), Wi-MAX (World Interoperability for Microwave Access), and ultrasonic communication, but is not limited thereto.

[0043] The components described in various embodiments are not necessarily essential components, and some may be optional. Therefore, embodiments comprising a subset of the components described in the embodiments are also included within the scope of the embodiments of the present disclosure. Furthermore, embodiments including other components in addition to the components described in various embodiments are also included within the scope of the embodiments of the present disclosure.

[0044] Hereinafter, embodiments of the present specification will be described in detail with reference to the drawings.

[0045] FIG. 1 is a diagram illustrating an example of the operating environment of a system according to one embodiment of the present specification. Referring to FIG. 1, one or more user devices (110-1, 110-2) and one or more servers (120, 130, 140) are connected via a network (1). FIG. 1 is merely an example for explaining the invention, and the number of user devices or servers is not limited to that shown in FIG. 1.

[0046] One or more user devices (110-1, 110-2) may be fixed or mobile terminals implemented as a computer system. The one or more user devices (110-1, 110-2) may be, for example, a smart phone, a mobile phone, a navigation device, a computer, a laptop, a digital broadcasting terminal, a PDA (Personal Digital Assistants), a PMP (Portable Multimedia Player), a tablet PC, a game console, a wearable device, an IoT (Internet of Things) device, a VR (Virtual Reality) device, an AR (Augmented Reality) device, etc. For example, in the embodiments, the user device (110) may mean one of various physical computer systems that can communicate with other servers (120 to 140) through a network (1) using a wireless or wired communication method.

[0047] Each server may be implemented as a computer device or multiple computer devices that communicate with one or more user devices (110-1, 110-2) via a network (1) to provide commands, codes, files, contents, services, etc. For example, the server may be a system that provides each service to one or more user devices (110-1, 110-2) connected via the network (1). As a more specific example, the server may provide a service (e.g., provision of information, etc.) intended by an application to one or more user devices (110-1, 110-2) through an application as a computer program that is installed and run on one or more user devices (110-1, 110-2). As another example, the server may distribute a file for installing and running the above-described application to one or more user devices (110-1, 110-2) and receive user input information to provide a corresponding service.

[0048] FIG. 2 is a block diagram illustrating the internal configuration of a computing device (200) according to one embodiment of the present specification. This computing device (200) may be applied to one or more user devices (110-1, 110-2) or servers (120-140) described above with reference to FIG. 1, and each device and server may have the same or similar internal configuration by adding or excluding some components.

[0049] Referring to FIG. 2, a computing device (200) may include a memory (210), a processor (220), a communication module (230), and a transceiver (240). The memory (210) is a non-transitory computer-readable recording medium and may include a non-permanent mass storage device such as a random access memory (RAM), a read only memory (ROM), a disk drive, a solid state drive (SSD), a flash memory, etc. Here, the non-permanent mass storage device such as a ROM, an SSD, a flash memory, a disk drive, etc. may be included in the above-described device or server as a separate permanent storage device distinct from the memory (210). In addition, the memory (210) may store an operating system and at least one program code (for example, a browser installed and operated on a user device (110), or a code for an application installed on a user device (110) to provide a specific service). These software components may be loaded from a computer-readable recording medium separate from the memory (210). This separate computer-readable recording medium may include a computer-readable recording medium such as a floppy drive, a disk, a tape, a DVD / CD-ROM drive, a memory card, etc.

[0050] In another embodiment, the software components may be loaded into the memory (210) via a communication module (230) rather than a computer-readable recording medium. For example, at least one program may be loaded into the memory (210) based on a computer program (e.g., the application described above) that is installed by files provided by developers or a file distribution system (e.g., the server described above) that distributes the installation files of the application via a network (1).

[0051] The processor (220) may be configured to process instructions of a computer program by performing basic arithmetic, logic, and input / output operations. Instructions may be provided to the processor (220) by the memory (210) or the communication module (230). For example, the processor (220) may be configured to execute instructions received according to program code stored in a storage device such as the memory (210).

[0052] The communication module (230) can provide a function for the user device (110) and the server (120 - 140) to communicate with each other via the network (1), and can provide a function for each of the device (110) and / or the server (120 - 140) to communicate with other electronic devices.

[0053] The transceiver (240) may be a means for interfacing with an external input / output device (not shown). For example, external input devices may include devices such as a keyboard, mouse, microphone, camera, etc., and external output devices may include devices such as a display, speaker, haptic feedback device, etc.

[0054] As another example, the transmitter / receiver (240) may be a means for interfacing with a device that integrates input and output functions, such as a touch screen.

[0055] In addition, in other embodiments, the computing device (200) may include more components than the components of FIG. 2 depending on the nature of the device to which it is applied. For example, when the computing device (200) is applied to a user device (110), it may be implemented to include at least some of the above-described input / output devices, or may further include other components such as a transceiver, a Global Positioning System (GPS) module, a camera, various sensors, a database, etc. As a more specific example, when the user device is a smartphone, it may be implemented to further include various components that are generally included in a smartphone, such as an acceleration sensor or a gyro sensor, a camera module, various physical buttons, buttons using a touch panel, input / output ports, and a vibrator for vibration.

[0056] The operations described herein below are described based on a computing device. For example, the computing device may be at least one of the servers and devices described above. In other words, the operations described below may be performed on a server or device and are not limited to a specific device. However, for convenience of explanation, the operations described below are based on a computing device.

[0057] Figure 3 is a drawing showing a platform connecting a shipper and a vehicle owner in one embodiment of the present specification.

[0058] Referring to FIG. 3, a platform (310) connecting a cargo owner (hereinafter, “shipper”) and a vehicle owner (hereinafter, “vehicle owner”) can be provided. For example, the platform (310) can be provided via a network (1) based on the aforementioned FIG. 1. Here, the platform (310) can be provided based on a computing device. The computing device providing the platform (310) can be a computing device implemented as a server (120-140) of the aforementioned FIG. 1. In addition, as an example, the shipper and the vehicle owner can also access the platform (310) based on a computing device. As a specific example, the computing devices of the shipper and the vehicle owner can access the platform (310) based on the network (1) and receive transportation services through the accessed platform (310). That is, the computing devices for the shipper and the vehicle owner can be terminals, and can be any of the devices disclosed in FIG. 1.

[0059] Referring to Figure 3, a shipper's computing device can register its cargo through the platform and request a transportation price. At this time, the shipper's computing device can provide the platform (310) with information regarding at least one of the following: cargo type information, destination (route) information, transportation time information, and other cargo-related information. For example, based on the cargo type information, the transportation price may be determined higher for items that are fragile or easily damaged and require careful handling. As another example, the farther the destination is from the shipper's location, the higher the transportation price. In yet another example, the shorter the transportation time, such as for urgent delivery, the higher the transportation price may be determined. In other words, the transportation price of a cargo can be determined based on various information. In this case, each piece of cargo-related information can be quantified.

[0060] Here, as an example, the information described above may be information provided by the shipper via a cloud (320) controlled by the platform (310). That is, the shipper's computing device may upload cargo and destination information to the cloud (320) provided based on the platform (310). For example, the information described above may be uploaded in real time or within a preset time frame considering the matching of the shipper and the driver, and is not limited to a specific embodiment.

[0061] For example, when providing a freight dispatch service through a platform (310), it is difficult to quantitatively assess all information to determine the transport price. Therefore, the platform (310) can quantify each piece of information based on freight information. Furthermore, each piece of quantified information can be weighted. For example, information that significantly impacts the transport price can be given a higher weight. Conversely, information that has a smaller impact on the transport price can be given a lower weight. Based on the aforementioned information, the platform can quantify and calculate the transport price.

[0062] At this time, for example, the shipper's computing device can register the cargo on the platform (310) based on the above-described information. The platform (310) can check records of similar cargo based on the above-described information. At this time, the platform (310) can determine the similarity between the similar cargo and the registered cargo. If the shipper and the cargo are identical but have different destinations, the similarity may be determined to be high. For example, as described above, cargo-related information may be digitized, and the similarity with existing cargo may be determined based on the digitized cargo-related information. For example, cargo with similar values ​​considering the weight and digitized information may be priced similarly. At this time, for example, the platform (310) operating based on the computing device may determine the similarity of the cargo based on a similarity function. When the cargo is registered on the platform (310) based on the above-described information, an initial route may be set for each vehicle owner based on the cargo information. For example, the initial route setting for each vehicle owner may be an initial routing setting. Here, the platform (310) can perform initial route setting based on the response information provided by the driver and the location and type of the driver, and a number of delivery routes can be generated based on this.

[0063] As a specific example, the platform (310) may configure a route setting model for setting the initial route of cargo. The route setting model may be a model built based on artificial intelligence or machine learning, but may not be limited thereto. For example, the route setting model may be a model built by reflecting the aforementioned information about the cargo and characteristic information about each vehicle owner. For example, the platform (310) may build a data set based on vehicle owner information, shipper information, and environmental information, and acquire a route setting model based on the built data. For example, the route setting model may be a reinforcement learning-based route setting model, which will be described later. Thereafter, actual cargo information, actual environmental information, and actual vehicle owner information may be provided as inputs to the acquired route setting model, and an initial cargo transport route for each vehicle owner may be derived as output. Here, as an example, the vehicle owner information may include the vehicle owner's cargo vehicle information and the vehicle owner's driver information, the cargo information may include order information and cargo route information, and the environmental information may include at least one of traffic information, map information, and hub location information.

[0064] Here, as an example, the platform (310) can obtain additional owner-related information through the cloud (320). The owner-related information may include vehicle information, cost information, time information, and other information related to vehicle operation. As a specific example, the vehicle-related information may include information on the type and year of the cargo vehicle, the size of the cargo compartment, and the type of cargo contained in the cargo vehicle; the owner driver information may include information on the age, gender, career, and health of the driver; the order information may include information on the departure point, destination, and type of cargo; the cargo route information may include information on the movement route, total carbon emissions, and delivery completion time according to the order information of each owner; the traffic information may include information on road conditions and signaling systems by time; the map information may include a map of the movement route of each owner; and the hub location information may include information on possible locations for loading and unloading cargo.

[0065] As another example, the platform (310) may further acquire driving information and driving habit information based on the driver's driving type based on the cloud (320). Here, the driving information may be driving record information about the driver. For example, the driving information may include information acquired through at least one sensor attached to the driver's vehicle and information acquired based on the driver's driving. Here, the information acquired through the at least one sensor may include at least one of surrounding object information acquired through sensors attached to the front, rear, and side of the driver's vehicle, vehicle interior information acquired through sensors attached to the inside of the driver's vehicle, and vehicle component information acquired through sensors attached to components within the driver's vehicle, which will be described later. In addition, the information acquired based on the driver's driving may include travel paths, cargo types, speed information for each travel path, and other driving-related information acquired based on the driver's driving. The driving information may be provided by the driver through the cloud (320) of the platform (310). That is, the driver can upload driving information to the cloud (320) of the platform (310) through the driver's computing device or vehicle.

[0066] As another example, driving information may be derived from a vehicle sensor, and the derived information may be provided as driving information to the cloud (320) of the platform (310). In other words, the driving information may be information derived from a vehicle sensor and transmitted to the cloud (320) of the platform (310) without being changed or controlled by the vehicle owner. As a specific example, the vehicle owner may register with the platform (310) through the vehicle owner's computing device or vehicle in order to be assigned a cargo delivery through the platform (310). When the vehicle owner registers with the platform (310), the platform (310) may transmit a sensor information consent request to the vehicle owner's computing device or vehicle so that the platform (310) may obtain driving information obtained through the vehicle's sensors. The vehicle owner may transmit a response to the sensor information consent request to the platform (310) through the vehicle owner's computing device or vehicle. The platform (310) may obtain driving information obtained from the vehicle's sensors based on the received sensor information consent response. Here, the sensors may include lidar sensors, radar sensors, vibration sensors, cameras, and other sensors. For example, the sensors may sense information about the vehicle owner based on information surrounding the vehicle and information inside the vehicle. As another example, the sensors may be installed on each vehicle component and may obtain driving information based on the measured degree of wear and tear and use of the components, but this is not limited to a specific embodiment. If the vehicle owner consents to providing driving information to the platform (310), the platform (310) may obtain driving information directly from the vehicle. As described above, the vehicle owner may be prevented from changing or manipulating the driving information, and the cloud (320) of the platform (310) may directly obtain driving information about the vehicle owner.

[0067] As another example, driving habit information may be information related to the driver's driving. For example, the driving habit information may include information about the driver's preferred route (e.g., national highway, expressway). Furthermore, the driving habit information may include information about the driver's driving style. This driving style information may include information about whether the driver avoids narrow roads or maintains driving even on narrow roads. Furthermore, the driving habit information may include information about brake intensity. This may include information about how hard and when the driver presses the brake pedal when slowing down. Furthermore, the driving habit information may include information about accelerator intensity. This may include information about how hard and when the driver presses the accelerator when speeding up. Furthermore, the driving habit information may include information about lane change habits. This may include information about whether the driver changes lanes frequently or only when absolutely necessary. Furthermore, the driving habit information may include information about curve driving. This information may include information about the forward distance, turning angle, turning speed, and other curve driving methods when the driver makes a left or right turn. Additionally, driving habit information may include other information related to other driving habits and is not limited to a specific form.

[0068] Here, as an example, driving habit information may be information derived from the owner's vehicle. The vehicle may directly derive driving habit information based on driving information acquired through the aforementioned sensors and provide the information to the cloud (320) of the platform (310). As a specific example, the platform (310) may transmit request information for acquiring driving habit information to the vehicle. The vehicle may directly derive the driving habit information requested by the platform (310) based on the driving information acquired through the aforementioned sensors and provide the information to the cloud (320) of the platform (310).

[0069] As another example, a vehicle may transmit only driving information to the cloud (320) of the platform (310). The platform (310) may derive driving habit information based on the driving information acquired from the vehicle. In other words, the platform (310) may directly acquire driving habit information from the driving information. As a specific example, the platform (310) may be equipped with an artificial intelligence learning model that derives driving habit information, and may perform learning on the learning model based on previously acquired driving information and driving habit information. The platform (310) may provide the driving information acquired from the vehicle as input to the learning model based on the learned driving habit information, and may derive driving habit information as output based on the input. As described above, the platform (310) may acquire driving information from multiple vehicle owners, directly derive driving habit information for each vehicle owner, and utilize the information to provide a cargo dispatch service.

[0070] As another example, the platform (310) may include a route setting model. The route setting model may derive an initial route for each vehicle by considering a combination of multiple cargoes and multiple vehicle owners. The route setting model may operate based on artificial intelligence, and each of the multiple cargo-related information and the multiple vehicle owner-related information may be provided as inputs to the route setting model. Here, the multiple vehicle owner-related information may further include the driving information and driving habit information described above, and is not limited to a specific embodiment. The route setting model may derive an initial route for each of the multiple vehicle owners as an output based on the above-described inputs. For example, the initial route for each of the multiple vehicle owners may include all of the multiple cargoes (or order information) provided as inputs, and the initial route setting (or initial routing) may be performed as described above.

[0071] FIG. 4 is a diagram illustrating a method for performing cargo transport routing based on artificial intelligence according to one embodiment of the present disclosure. Referring to FIG. 4, the platform (310) can determine the cargo to be loaded onto each vehicle owner based on artificial intelligence and set an initial route for cargo transport based on this. For example, the AI ​​model may receive input from at least one shipper, including at least one of the following: the type of cargo assigned to the vehicle owner, the destination, the required arrival time of the cargo (delivery time limit), the cargo transport distance, the traffic weight of the new route, additional time, priority, and vehicle owner information. In addition, the AI ​​model may receive input from at least one vehicle owner-related information, including the type and year of the cargo vehicle, the size of the cargo compartment, the type of cargo contained in the vehicle, the vehicle owner's driver information, order information, cargo route information, total carbon emissions and delivery completion time information, traffic information, map information, hub location information, and other information. Here, the AI ​​model may further receive the aforementioned driving information and driving habit information from the vehicle owner. For example, the platform (310) may further include the aforementioned driving habit information learning model, which may be a learning model that takes driving information as input and derives driving habit information as output. For example, driving habit information derived as output through the driving habit information learning model may be provided as input to an artificial intelligence model.

[0072] As described above, it is possible to determine which cargoes among multiple cargoes to be mixed. The platform (310) provides the above-described information as input to an artificial intelligence model, and based on this, it can determine which cargoes to be mixed for each driver, and based on this, it can set the initial route for cargo transportation. For example, the artificial intelligence model of FIG. 4 may be a route setting model. As another example, the artificial intelligence model and the route setting model of FIG. 4 may each exist and be interrelated. That is, the platform (310) can perform shipper and driver allocation based on the artificial intelligence model of FIG. 4, and the platform (310) can determine which cargoes to be mixed by reflecting the registered cargo (or order information) and the driver's location information. In addition, the above-described information may be reflected in the route setting model to perform the initial route setting.

[0073] For example, in FIG. 4, a specific driver can determine whether to mix cargo based on the cargo type. That is, for a specific driver, a specific cargo may be mixable, while another specific cargo may not be mixable. As a specific example, the platform (310) may assign fragile cargo to a specific driver. Here, the platform (310) may not assign cargo that weighs more than a preset value, taking into account the risk of cargo damage for the specific driver. That is, the platform (310) should assign the cargo to a specific driver when considering the minimum time and optimal route, but may not assign the cargo based on the cargo type.

[0074] As another example, the platform (310) may assign a cargo type requiring expedited delivery, such as food delivery, to a single driver. For example, even if another cargo offers the optimal route for that driver, the platform (310) may not assign that cargo, considering that food is assigned and requires expedited delivery. In other words, the platform (310) may assign cargo to a driver based on the cargo type or delivery method.

[0075] As another example, the platform (310) may consider the possibility of mixed loading for each cargo, and may perform transport route selection by giving priority to mixed loading zones where the transport efficiency of the corresponding vehicle is high, or may provide information on mixed loading zones. For example, if the vehicle currently has little cargo and the lowest mixed loading rate, and there is a lot of mixed loading possible cargo in a specific mixed loading zone, the platform (310) may provide a transport route for the vehicle by giving priority to the mixed loading zone. As another example, the platform (310) may give priority to the mixed loading zone based on the above-described method and allocate cargo to the vehicle, thereby increasing transport efficiency.

[0076] As another example, the platform (310) can allocate cargo to drivers and set initial routes based on incentives. Specifically, the platform (310) can provide incentive information to drivers who select routes via areas where mixed loads are likely to occur. The incentive information can include monetary information, insurance information, priority allocation information when mixed load requests occur, and other information. Specifically, the platform (310) can enhance utilization efficiency by providing compensation information when drivers consider additional cargo among various transport routes, consider detours, or increase transport time to transport additional cargo, thereby increasing transport efficiency. For example, the incentive information can be determined by considering at least one of the following: additional travel distance, additional time, and additional energy (electricity / fuel) consumption, which are generated based on areas where mixed loads are likely to occur. For example, the platform (310) can provide incentive information to drivers based on point information and other information, including a portion of the actual additional cost, and may not be limited to a specific form. Here, the platform (310) can perform route setting for each of multiple borrowers by reflecting the above-described incentive information.

[0077] Based on the above, the platform (310) can perform initial route setting for each of the multiple drivers based on the multiple assigned cargoes, thereby establishing a cargo transport route for each of the multiple drivers. Here, the cargo transport route for each of the multiple drivers can include all assigned cargoes (or order information).

[0078] As another example, the platform (310) may further reflect the driver's driving information and driving habit information for the aforementioned cargo transport route setting. Here, to obtain the aforementioned driving information and driving habit information, the vehicle may include multiple sensors. Referring to FIG. 5 , the driver's vehicle (510) may include multiple sensors (511, 512, 513, 514). The multiple sensors (511, 512, 513, 514) may further include a lidar sensor, a radar sensor, a vibration sensor, a camera, and other sensors, and may not be limited to a specific form. For example, FIG. 5 may include sensors (511, 512, 513) for obtaining front, rear, and side information of a vehicle (510) and a camera (514) for performing sensing of an owner inside the vehicle (510), but this is only one example for convenience of explanation and may not be limited thereto. For example, an image of the surroundings of a vehicle may be obtained based on a plurality of sensors (511, 512, 513, 514) included in the vehicle (510). Here, the image of the surroundings of the vehicle may include information on surrounding vehicles (520, 530, 540) of the vehicle (510) and surrounding objects. In addition, for example, information inside the vehicle may be obtained based on the camera (514). Here, the information inside the vehicle may include information on the driving attitude of the owner based on a photograph of the owner's face and other information inside the vehicle, and may not be limited to a specific embodiment. Additionally, for example, based on the driving of the owner's vehicle (510), further information on the travel path and speed for each travel path can be acquired. Furthermore, the owner's vehicle (510) can further acquire driving-related information by considering the type of cargo. As a more specific example, the owner's vehicle (510) can acquire speed information and driving pattern information of surrounding vehicles based on surrounding vehicle information acquired based on a plurality of sensors (511, 512, 513, 514).In addition, the driver's vehicle (510) can perform a comparison with the above-described information by considering the current vehicle's moving speed and moving path, and obtain driving information based on this.

[0079] As another example, some of the plurality of sensors (511, 512, 513, 514) may be mounted on components inside the vehicle (510) to obtain component status information. That is, the vehicle (510) may obtain information about the exterior and interior of the vehicle (510) as driving information through the plurality of sensors (511, 512, 513, 514). In addition, driving habit information may be derived based on the driving information, as described above.

[0080] For example, the platform (310) may provide a cargo dispatching service that determines a vehicle to deliver cargo based on an optimal route, which may need to allocate multiple cargoes to multiple drivers, but may also further reflect the aforementioned driver's driving information and driving habits to prevent damage to cargo and ensure reliable cargo delivery. As a specific example, the shipper may upload the type and characteristics of the cargo to the cloud (320) of the platform (310). For example, the shipper may request cargo transportation by providing information on the type and characteristics of the cargo, such as whether it is a living thing, whether it is frozen, whether it is destructible (whether it is a sensitive electronic product or glass product, etc.), and other information to the cloud (320) of the platform (310), as described above.

[0081] The platform (320) calculates the delivery suitability of the vehicle based on the above-described information, lists the vehicle owners based on the suitability, and selects at least one of the listed vehicle owners to perform cargo transport. While the platform (310) may need to derive an optimal route, it may also need to consider safe delivery and reliability of the cargo. For example, if the cargo is sensitive to impact or is prone to significant damage due to damage, the platform (310) may determine the vehicle owner by incorporating the aforementioned driving information and driving habits into the vehicle owner's driving style information. For example, a vehicle that brakes early upon detecting an obstacle or speed bump may be suitable for cargo that is susceptible to damage. As another example, the vehicle owner's driving information and driving habits may be reflected by considering road and traffic information on the expected route for the cargo. For example, if the expected route has a narrow road and many curves, the platform may use curve driving information as driving habit information to determine the vehicle owner who minimizes cargo damage. For example, a driver who maintains a constant speed and narrow left / right turns based on curve driving information may be more suitable.

[0082] In addition, as an example, the platform (310) can provide feedback information (620) to the driver, along with the navigation information (610). The platform (310) can provide the navigation information (610) to the driver's vehicle or the driver's computing device, taking into account the aforementioned cargo delivery allocation. That is, the driver's vehicle or the driver's computing device can be allocated cargo deliveries based on the platform (310) and perform the deliveries, and can also receive navigation information (610) for this purpose. Here, the platform (310) can further provide feedback information (620) to the driver, along with the navigation information (610). For example, the platform (310) can provide the feedback information (620) to the driver when cargo allocation is performed based on the suitability information. That is, the platform (310) calculates suitability information and provides it to the vehicle owner, allowing the vehicle owner to recognize suitability information based on his or her driving information and driving habits. More specifically, the vehicle owner can recognize his or her relative suitability information based on the suitability information for multiple vehicle owners within the vehicle owner listings provided by the platform (310).

[0083] As another example, the platform (310) can provide feedback information (620) in real time along with navigation information (610). That is, the platform (310) can provide navigation information (610) through an in-vehicle display or the driver's computing device, while also providing guidance information based on transport route, cargo information, and driving habit information. As an example, the platform (310) can provide cargo information (622) through an in-vehicle display or the driver's computing device. Here, the cargo information (622) may include fragile items, but this is merely a configuration for convenience of explanation and is not limited to the above-described embodiment.

[0084] For example, if the driver's driving habit information indicates that the driver is slow to detect obstacles or speed bumps and hastily applies the brakes, the platform (310) may provide advance guidance information (621) with guidance information that takes into account damage to cargo. In other words, the platform (310) may provide guidance information (621) that reflects the characteristics of cargo information (622) and the driver's driving habit information, thereby improving the driver's driving habits.

[0085] As another example, referring to FIG. 7, curve driving information may be considered as driving habit information of the vehicle owner. For example, the vehicle owner may have a habit of making large turns when turning left or right. As a specific example, the vehicle (510) may acquire driving information such as surrounding objects, road width, and other information as driving information based on a plurality of sensors (511, 512, 513, 514) when driving around a curve, and may acquire curve driving information as driving habit information based on this. Here, if the vehicle owner has a habit of making large turns when turning left or right, the vehicle owner may have a long forward distance and a small turning angle at an intersection. Here, based on the driving habit information described above, the platform (310) may provide guidance information (620) to a display in the vehicle or the vehicle owner's computing device. For example, if the driver has a habit of making large turns when turning left / right, the platform (310) may provide around view information (710) for the corresponding direction as guide information (620) to the display in the vehicle or the driver's computing device because there is a risk of collision with the outer part at a narrow intersection. Here, the around view information (710) may be information that provides information on surrounding objects located around the vehicle (510) and curve driving in the form of images or videos based on information acquired through a plurality of sensors (511, 512, 513, 514) of the vehicle (510). Conversely, if the driver has a habit of making small turns when turning left / right, the platform (310) may provide around view information (720) for the corresponding direction as guide information (620) to the display in the vehicle or the driver's computing device because there is a risk of collision with the inner part at a narrow intersection. That is, when a vehicle (510) has a short forward distance and a large turning angle at an intersection, around view information (720) for the corresponding direction can be provided as guide information (620) in consideration of the risk of collision with the inner part.That is, the platform (310) can provide guide information (620) that enables driving habit information to be improved or supplemented based on the driver's driving, and is not limited to a specific embodiment.

[0086]

[0087] Based on the above, the platform (310) can perform initial route setting for each of the plurality of drivers based on the assigned multiple cargoes, thereby setting a cargo transport route for each of the plurality of drivers. Here, the cargo transport route for each of the plurality of drivers may include all assigned cargoes (or order information). For example, the platform (310) may set the initial route as a cargo transport route for a specific driver. As a specific example, the platform (310) may determine point A as the starting location and point B as the destination location. The starting location point A may be determined by reflecting the current location information of the specific driver, but may not be limited thereto. Thereafter, the platform (310) may set a cargo unloading area as a cargo delivery route from point A to point B. Here, the platform (310) may provide the set cargo transport initial route information to the specific driver. As described above, the platform (310) may provide information on each point of cargo transport to the specific driver. Additionally, as an example, the platform (310) may provide initial freight transport route information, including freight loading / unloading information, vehicle ID information, transport time information, and other information, to a specific vehicle owner, without being limited to a specific embodiment. Furthermore, as an example, the platform (310) may set an initial freight transport route for each of a plurality of vehicle owners and provide the corresponding information to each vehicle owner. Here, the platform (310) may perform routing configuration by removing some of the points within the set initial freight transport route. Specifically, the platform (310) may perform routing configuration by removing specific points within the initial freight transport route. Here, freight for the removed specific points may be restored from the existing initial freight transport route, allowing new routing to be configured. As an example, the platform (310) may provide freight for the removed specific points to another vehicle owner.Here, the platform (310) may provide a reward or penalty along with the cargo for the specific point removed, but may not be limited thereto.

[0088] For example, the platform (310) can repeat the above-described actions, and the actions can be performed based on reinforcement learning. Reinforcement learning is a type of machine learning that operates in a dynamic environment rather than relying on a static data set and can be a method of performing learning through collected information. Reinforcement learning can be a model that initiates learning behavior on its own without intervention, operating based on appropriate incentives (or rewards) without requiring pre-training data collection, preprocessing, and labeling required in supervised or unsupervised learning. For example, reinforcement learning can determine the environment to which it will be applied and establish an interface between the agent and the environment within the environment. Then, based on the actions of the agent, reward information can be set to suit the environment. Afterwards, it can be a learning method that derives an optimal conclusion based on the actions of each agent within the environment.

[0089] For example, the platform (310) can derive an optimal cargo transport route by repeatedly performing tasks such as removing some paths from the initial cargo transport route and then restoring the existing route based on the aforementioned reinforcement learning. Here, the initial cargo transport route may be the environment described above, and the agent's action may be to remove some points from each initial cargo transport route. Here, cost- or time-based incentive information may be the reward information, and by repeating the aforementioned actions, the platform (310) can derive an optimal route.

[0090] As a specific example, the platform (310) can construct a data set consisting of order information, traffic information, map information, hub location information, truck information, truck driver information, truck route information, and other information, based on each cargo-related information and each truck owner-related information. For example, order information may include the origin, destination, cargo type, and other information. Furthermore, traffic information may include hourly road traffic conditions, signal system information, and other information. Furthermore, hub location information may include information on locations or points where cargo can be loaded or unloaded, as well as other information. Furthermore, truck information may include the truck type, model year, cargo space size, cargo type, and other information. Furthermore, truck driver information may include the driver's age, gender, career, health status, and other information. Furthermore, truck route information may include the movement route based on order information for each truck, total carbon emissions, delivery completion time, and other information, and is not limited to a specific form.

[0091] The platform (310) can build the aforementioned route setting model that outputs cargo route information based on the information contained in the aforementioned data set. Here, the platform (310) provides actual order information for each cargo, cargo delivery-related information such as traffic information, map information, hub location information, and truck information and truck driver information as owner-related information as inputs to the route setting model, and can derive an initial cargo transport route for each owner.

[0092] Here, the platform (310) can remove at least one piece of cargo information (or order information). For example, the at least one piece of order information removed by the platform (310) may be information set for each location or destination. Here, the platform (310) can obtain rewards or penalties based on the delivery completion time, carbon emissions, and other information from the cargo route information collected based on the removed cargo information (or order information). For example, the reward or penalty may be the reward information from the aforementioned reinforcement learning. Thereafter, the platform (310) can assign the removed order information (or corresponding location information) to another driver with adjacent order information. The platform (310) can repeat the above-described operations to derive an optimal cargo transport route.

[0093]

[0094] FIG. 8 is a flowchart illustrating a method for providing a cloud-based cargo dispatch service reflecting a driver driving type according to one embodiment of the present disclosure. Referring to FIG. 8, the platform (310) may construct a data set based on vehicle owner information, shipper information, and environmental information. (S810) As an example, the platform (310) may receive a first cargo transport request from a computing device of a first shipper. (S820) Here, when the platform (310) receives the first cargo transport request from the computing device of the first shipper, the platform (310) may further obtain first cargo type and characteristic information through the cloud (320), and select a first vehicle from a vehicle listing including at least one vehicle owner based on the first cargo transport request. Thereafter, the platform (310) can transmit the first cargo transport request to the computing device of the first vehicle owner (S830), and based on this, can receive the first cargo transport response from the computing device of the first vehicle owner (S840). Here, the vehicle owner listing is determined through suitability information derived based on the vehicle owner's driving information and driving habit information, and the first vehicle owner can be determined as the vehicle owner with the highest suitability information among at least one vehicle owner in the vehicle owner listing.

[0095] For example, driving information may include information obtained through at least one sensor attached to the vehicle owner and information obtained based on the vehicle driving of the vehicle owner. Here, the information obtained through the at least one sensor may include at least one of surrounding object information obtained through sensors attached to the front, rear, and side of the vehicle owner, vehicle interior information obtained through sensors attached to the interior of the vehicle owner, and vehicle component information obtained through sensors attached to components within the vehicle owner. In addition, the information obtained based on the vehicle driving of the vehicle owner may include a travel path, speed information for each travel path, and other driving-related information obtained based on the vehicle driving of the vehicle owner.

[0096] In addition, as an example, at least one vehicle owner may be registered on the platform (310). The platform (310) may transmit a request for consent to obtain driving information to at least one registered vehicle owner. Thereafter, the platform (310) may receive a response for consent to obtain driving information from at least one vehicle owner, and based on the response for consent to obtain driving information, may directly obtain driving information from each vehicle of at least one vehicle owner through the cloud (320). Here, the platform (310) may derive driving habit information for each vehicle of at least one vehicle owner based on the driving information obtained from each vehicle of at least one vehicle owner. Here, the driving habit information may be derived based on a driving habit information learning model. For example, the driving habit information learning model may be a learning model that takes driving information as input and derives driving habit information as output. In addition, the driving habit information may further include information on the vehicle owner's preferred route, driving style, brake strength, driving habit information, lane change habit information, curve driving information, and other driving habit-related information. Thereafter, based on the first cargo transport response, a route is established for the first cargo. When the first vehicle transports the first cargo along the route, the platform can provide route-based navigation information and guidance information. Furthermore, guidance information can be determined based on surrounding information acquired from multiple sensors, based on the type and characteristics of the first cargo and driving habits.

[0097] As another example, a reinforcement learning-based routing model can be built using the established dataset. Actual cargo information, actual environmental information, and actual vehicle owner information can be acquired and provided as input to the reinforcement learning-based routing model. Then, based on the input, the routing model can derive an initial cargo transport route for each vehicle owner. Furthermore, at least one cargo information item within the derived initial cargo transport route for each vehicle owner can be removed, and rewards and penalties can be obtained. The process of obtaining rewards and penalties can then be repeated to update the routing model and derive an optimal cargo transport route for each vehicle owner.

[0098] Here, for example, when the initial route for the first cargo transport for the first vehicle is derived through the route setting model, an operation may be performed to remove the first order information within the initial route for the first cargo transport and obtain compensation information based on the removed first order information. In addition, the removed first order information may be assigned to the initial route for the second vehicle's second cargo transport. In addition, compensation information may be obtained based on the assignment. In addition, an operation may be repeated to remove order information different from the first order information within the initial route for the first cargo transport and assign the removed order information to the initial route for the cargo transport of another vehicle to obtain compensation information. In this case, the initial route for the first cargo transport may be updated to an optimal route for the first cargo transport based on the repeated operation.

[0099] Here, the owner information includes the owner's cargo vehicle information and the owner's driver information, the cargo information includes order information and cargo route information, and the environmental information may include at least one of traffic information, map information, and hub location information. The cargo vehicle information may include information on the type and year of the cargo vehicle, the size of the cargo compartment, and the type of cargo contained in the cargo vehicle, and the owner's driver information may include information on the owner's age, gender, career, and health status. The order information may include information on the origin, destination, and cargo type, and the cargo route information may include information on the movement route, total carbon emissions, and delivery completion time according to the order information for each owner. The traffic information may include information on road conditions and signaling systems by time, the map information may include a map of the owner's movement route, and the hub location information may include information on possible locations for loading and unloading cargo. The compensation information may include at least one of reward information and penalty information determined based on the delivery completion time and carbon emissions information.

[0100] The embodiments described above may be implemented at least in part as computer programs and recorded on a computer-readable recording medium. A computer-readable recording medium on which a program for implementing the embodiments is recorded includes any type of recording device that stores data that can be read by a computer. Examples of computer-readable recording media include ROMs, RAMs, CD-ROMs, magnetic tapes, and optical data storage devices. Furthermore, the computer-readable recording medium may be distributed across network-connected computer systems, such that computer-readable codes are stored and executed in a distributed manner. Furthermore, functional programs, codes, and code segments for implementing the embodiments will be readily understood by those skilled in the art to which the embodiments pertain.

[0101] Although the present specification has been described with reference to the embodiments illustrated in the drawings, these are merely exemplary, and those skilled in the art will understand that various modifications and variations of the embodiments are possible. However, such modifications should be considered within the technical protection scope of the present specification. Therefore, the true technical protection scope of the present specification should be determined to include other implementations, other embodiments, and equivalents to the claims, based on the technical spirit of the appended claims.

Claims

1. There is a method for providing a cloud-based cargo dispatch service that reflects the driver's driving type through a platform run by a computing device. A step of constructing a data set based on vehicle owner information, shipper information, and environmental information; As a step of receiving a first cargo transportation request from a computing device of a first shipper, the platform further obtains first cargo type and characteristic information through the cloud when receiving the first cargo transportation request from the computing device of the first shipper, A step of selecting a first borrower in a borrower listing including at least one borrower based on the first cargo transportation request, and transmitting the first cargo transportation request to the computing device of the first borrower; and Including the step of receiving a first cargo transport response from the computing device of the first vehicle, A method for providing a cargo dispatch service, wherein the above vehicle listing is determined through suitability information derived based on the vehicle's driving information and driving habit information, and the first vehicle is determined as the vehicle with the highest suitability information among the at least one vehicle in the vehicle listing.

2. In paragraph 1, The driving information includes information obtained through at least one sensor attached to the driver's vehicle and information obtained based on the driver's driving of the vehicle. The information acquired through the at least one sensor includes at least one of surrounding object information acquired through sensors attached to the front, rear, and side of the vehicle of the vehicle owner, vehicle interior information acquired through sensors attached to the interior of the vehicle of the vehicle owner, and vehicle component information acquired through sensors attached to components inside the vehicle of the vehicle owner. A method for providing a cargo dispatch service, wherein the information obtained based on the driving of the vehicle of the above driver includes a movement path obtained based on the driving of the vehicle of the above driver, speed information for each movement path, and other information related to the driving of the vehicle of the above driver.

3. In paragraph 2, A step in which at least one borrower is registered on the platform; A step of transmitting a request for consent to obtain driving information to at least one registered vehicle owner of the platform; A step of receiving a response of consent to obtain driving information from at least one driver; A method for providing a cargo dispatch service, comprising a step of directly obtaining driving information from each vehicle of at least one driver through the cloud based on the consent response to obtain the driving information.

4. In paragraph 3, A method for providing a cargo dispatch service, wherein the platform derives driving habit information for at least one vehicle owner based on driving information obtained from each vehicle of at least one vehicle owner, wherein the driving habit information is derived based on a driving habit information learning model.

5. In paragraph 4, A method for providing a cargo dispatch service, wherein the above driving habit information learning model is a learning model that takes the above driving information as input and derives the above driving habit information as output.

6. In paragraph 5, A method for providing a cargo dispatch service, wherein the above driving habit information further includes information on the driver's preferred route, driving style information, brake strength information, driving habit information, lane change habit information, curve driving information, and other driving habit-related information.

7. In paragraph 6, A method for providing a cargo dispatch service, wherein the platform provides route-based navigation information and guide information when the first owner sets a route based on the first cargo based on the first cargo transport response and the first owner transports the first cargo through the route.

8. In paragraph 7, A method for providing a cargo dispatch service, wherein the guide information is determined based on the first cargo type and characteristic information and the driving habit information, and based on surrounding information obtained from the plurality of sensors.

9. In paragraph 1, A step of constructing a reinforcement learning-based path setting model using the above-mentioned constructed data set; A step of obtaining actual generated cargo information, actual environmental information, and actual vehicle owner information and providing them as inputs to the reinforcement learning-based route setting model; A step of deriving an initial cargo transport route for each vehicle owner through the route setting model based on the above input; A step of removing at least one cargo information within the initial cargo transport route for each of the above-described owners and obtaining rewards and penalties; and A method for providing a cargo dispatch service, further comprising a step of updating the route setting model by repeating the steps of obtaining the reward and penalty, and deriving an optimal cargo transport route for each vehicle owner.

10. In paragraph 9, A step of performing an operation of removing first order information within the first cargo transport initial route and obtaining compensation information based on the removed first order information when the first cargo transport initial route for the first vehicle is derived through the above route setting model; A step of allocating the removed first order information to the second cargo transport initial route of the second vehicle; A step of obtaining compensation information based on the above allocation; A step of repeating an operation of removing order information different from the first order information within the first cargo transport initial route and assigning the removed order information to another vehicle's cargo transport initial route to obtain compensation information; and Including a step of updating the first cargo transport initial route to the first cargo transport optimal route based on the above repeated operation, The above vehicle owner information includes the vehicle owner's cargo vehicle information and vehicle owner driver information. The above cargo information includes order information and cargo route information, The above environmental information includes at least one of traffic information, map information, and hub location information, The above freight vehicle information includes the type of freight vehicle, year of manufacture, size of cargo compartment, and type of cargo contained in the freight vehicle. The above driver information includes the driver's age, gender, career, and health status information. The above order information includes information on origin, destination and cargo type, The above cargo route information includes the movement route, total carbon emissions, and delivery completion time information based on order information for each vehicle owner. The above traffic information includes hourly road conditions and signal system information, The above map information includes a map of the travel route for each vehicle, The above hub location information includes information on locations where cargo can be loaded and unloaded, A method for providing a cargo dispatch service, wherein the above compensation information includes at least one of reward information and penalty information determined based on delivery completion time and carbon emission information.

11. A computer program stored on a computer-readable medium that executes a method for providing a cloud-based cargo dispatch service that reflects the driver driving type according to any one of claims 1 to 10 in combination with hardware.

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