WO2022240018A1

WO2022240018A1 - Logistics transportation management method

Info

Publication number: WO2022240018A1
Application number: PCT/KR2022/005964
Authority: WO
Inventors: 박관수; 박병규
Original assignee: 박관수; 박병규
Priority date: 2021-05-12
Filing date: 2022-04-26
Publication date: 2022-11-17
Also published as: KR102364082B1

Abstract

According to an embodiment disclosed herein, a logistics transportation management method performed by at least one processor comprises the steps of: receiving first identification information identified by a first beacon in a loading area, wherein the first identification information relates to a user terminal associated with a transportation vehicle; receiving first load data associated with a load of the transportation vehicle sensed via a first sensor in the loading area; receiving first vehicle number data of the transportation vehicle from a first licensed plate recognition (LPR) device in the loading area; determining whether at least a portion of pre-stored prior information about the loading area matches the first identification information, the first load data, and the first vehicle number data; and approving the loading of the transportation vehicle in the loading area in response to determining that at least a portion of pre-stored prior information about the loading area matches the first identification information, the first load data, and the first vehicle number data.

Description

Logistics transportation management method

The present disclosure relates to a logistics transportation management method, and more specifically, to a logistics transportation management method for calculating a transportation cost of a vehicle based on whether a vehicle transporting loads is loaded/unloaded and information about loads loaded in the vehicle. .

As technology advances in recent years, automation systems have been established in most industrial fields. The establishment of these automation systems has led to the establishment of efficient, convenient and accurate management processes. Nevertheless, it is difficult to find cases where automation systems are generally applied at construction sites.

Specifically, the loading and unloading of soil, aggregate, rock, etc., which are construction materials at a construction site, is still manually managed by construction personnel. Accordingly, the transportation cost of loads calculated based on the records of loading and unloading causes human errors, insufficiency of management record preservation, and insufficiency of supervision of loading and unloading. In addition, these problems cause a large cost loss to the ordering party, construction company, subcontractor, equipment engineer, and the like in addition to the above-mentioned problems.

The present disclosure provides a logistics transportation management method for solving the above problems. Specifically, a logistics transportation method is provided for calculating a transportation cost of a vehicle based on whether a vehicle transporting a load is loaded/unloaded and information about the load loaded in the vehicle.

According to an embodiment of the present disclosure, a logistics transportation management method performed by at least one processor includes first identification information identified by a first beacon of a loading area - the first identification information associated with a transportation vehicle. It is related to a user terminal - Receiving step, receiving first load data related to the load of the transportation vehicle detected through the first sensor of the loading site, transportation from the first LPR (Licensed Plate Recognition) device of the loading site Receiving first license plate data of a vehicle, determining whether at least a part of previously stored preliminary information about a loading site matches first identification information, first load data, and first license plate data, and and in response to determining that at least a part matches the first identification information, the first load data, and the first license plate data, permitting the transportation vehicle to be loaded at the loading site.

According to one embodiment, the first load data includes information related to the weight of the load loaded on the transport vehicle at the loading site.

According to one embodiment, the first load data includes an image of a load loaded on a transportation vehicle at a loading site.

According to an embodiment, in response to determining that at least a portion of the preliminary information matches the first identification information, the first load data, and the first license plate data, authorizing the transportation vehicle to be loaded at the loading site. includes, in response to determining that the weight of the load does not exceed a predetermined threshold weight, permitting the transportation vehicle to be loaded at the loading site.

According to an embodiment, the method further includes transmitting a push alarm notifying whether or not loading is approved to a user terminal in response to approval of loading of the transportation vehicle at the loading site.

According to an embodiment, the second identification information of the transport vehicle identified by the second beacon of the drop-off location - the second identification information is associated with the user terminal - is received, and the second sensor of the drop-off location is Receiving second load data associated with the load of the transport vehicle detected through the step of receiving the second vehicle number data of the transport vehicle from the second LPR device at the drop-off location, and prior information and second identification information, the second In response to determining whether the load data and the second license plate data match, and determining that at least a portion of the prior information matches the second identification information, the second load data, and the second license plate data, the transport vehicle A step of approving alighting at the drop-off location is further included.

According to an embodiment, the method further includes calculating a transport cost of the transport vehicle based on location information of the loading and unloading locations.

According to an embodiment, the method further includes calculating a transport cost per unit distance based on the location information of the loading area.

According to an embodiment, the method further includes receiving location information of a transport vehicle based on a Global Positioning System (GPS) at predetermined intervals, and calculating a transport cost of the transport vehicle based on the location information.

According to one embodiment of the present disclosure, a computer program stored in a computer readable recording medium is provided to execute a logistics transportation management method on a computer.

According to an embodiment of the present disclosure, a more accurate transportation cost in which information associated with a transportation route is reflected can be calculated even if the same loading and unloading location and the same disembarkation location are used.

According to an embodiment of the present disclosure, a more accurate transportation cost in which the difficulty of transporting loads is reflected may be calculated by calculating the transportation cost based on the geographical characteristics of loading and unloading locations and road information.

According to an embodiment of the present disclosure, by calculating transportation costs only for transport vehicles approved for loading/unloading based on prior information, transportation costs for inappropriate loading/unloading that do not match prior information are prevented from being imposed. It can be prevented.

1 shows an example of a loading and unloading location and a drop-off location to which a logistics transportation management system according to an embodiment of the present disclosure is applied.

2 is a schematic diagram illustrating a configuration in which an information processing system is communicatively connected to a plurality of user terminals in order to provide a logistics transportation management service according to an embodiment of the present disclosure.

3 is a block diagram showing a functional configuration of an information processing system for calculating transportation costs according to an embodiment of the present disclosure.

4 illustrates an example of selecting a construction site in a user terminal associated with a transport vehicle according to an embodiment of the present disclosure.

5 illustrates an example of checking first preliminary information and second preliminary information of a construction site in a user terminal associated with a transportation vehicle according to an embodiment of the present disclosure.

6 illustrates an example of modifying prior information before driving in a user terminal associated with a transport vehicle according to an embodiment of the present disclosure.

7 is a diagram illustrating a transportation cost prediction machine learning model configured to infer or output transportation costs based on identification information, load data, license plate data, or GPS-based location information according to an embodiment of the present disclosure.

8 is a structural diagram illustrating an artificial neural network according to an embodiment of the present disclosure.

9 shows an example of a logistics transportation management method according to an embodiment of the present disclosure.

Hereinafter, specific details for the implementation of the present disclosure will be described in detail with reference to the accompanying drawings. However, in the following description, if there is a risk of unnecessarily obscuring the gist of the present disclosure, detailed descriptions of well-known functions or configurations will be omitted.

In the accompanying drawings, identical or corresponding elements are given the same reference numerals. In addition, in the description of the following embodiments, overlapping descriptions of the same or corresponding components may be omitted. However, omission of a description of a component does not intend that such a component is not included in an embodiment.

Advantages and features of the disclosed embodiments, and methods of achieving them, will become apparent with reference to the following embodiments in conjunction with the accompanying drawings. However, the present disclosure is not limited to the embodiments disclosed below and may be implemented in various different forms, only the present embodiments make the present disclosure complete, and the present disclosure fully covers the scope of the invention to those skilled in the art. It is provided only to inform you.

Terms used in this specification will be briefly described, and the disclosed embodiments will be described in detail. The terms used in this specification have been selected from general terms that are currently widely used as much as possible while considering the functions in the present disclosure, but they may vary according to the intention of a person skilled in the related field, a precedent, or the emergence of new technology. In addition, in a specific case, there is also a term arbitrarily selected by the applicant, and in this case, the meaning will be described in detail in the description of the invention. Therefore, terms used in the present disclosure should be defined based on the meaning of the term and the general content of the present disclosure, not simply the name of the term.

Expressions in the singular number in this specification include plural expressions unless the context clearly dictates that they are singular. Also, plural expressions include singular expressions unless the context clearly specifies that they are plural. When it is said that a certain part "includes" a certain component throughout the specification, it means that it may further include other components without excluding other components unless otherwise stated.

Also, the term 'module' or 'unit' used in the specification means a software or hardware component, and the 'module' or 'unit' performs certain roles. However, 'module' or 'unit' is not meant to be limited to software or hardware. A 'module' or 'unit' may be configured to reside in an addressable storage medium and may be configured to reproduce one or more processors. Thus, as an example, 'module' or 'unit' refers to components such as software components, object-oriented software components, class components, and task components, processes, functions, properties, It may include at least one of procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, or variables. Functions provided within components and 'modules' or 'units' may be combined into a smaller number of components and 'modules' or 'units', or further components and 'modules' or 'units'. can be further separated.

According to an embodiment of the present disclosure, a 'module' or 'unit' may be implemented as a processor and a memory. 'Processor' should be interpreted broadly to include general-purpose processors, central processing units (CPUs), microprocessors, digital signal processors (DSPs), controllers, microcontrollers, state machines, and the like. In some circumstances, 'processor' may refer to an application specific integrated circuit (ASIC), programmable logic device (PLD), field programmable gate array (FPGA), or the like. 'Processor' refers to a combination of processing devices, such as, for example, a combination of a DSP and a microprocessor, a combination of a plurality of microprocessors, a combination of one or more microprocessors in conjunction with a DSP core, or a combination of any other such configurations. You may. Also, 'memory' should be interpreted broadly to include any electronic component capable of storing electronic information. 'Memory' includes random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), programmable read-only memory (PROM), erasable-programmable read-only memory (EPROM), It may also refer to various types of processor-readable media, such as electrically erasable PROM (EEPROM), flash memory, magnetic or optical data storage, registers, and the like. A memory is said to be in electronic communication with the processor if the processor can read information from and/or write information to the memory. Memory integrated with the processor is in electronic communication with the processor.

In the present disclosure, a 'system' may include at least one of a server device and a cloud device, but is not limited thereto. For example, a system may consist of one or more server devices. As another example, a system may consist of one or more cloud devices. As another example, the system may be operated by configuring a server device and a cloud device together.

In the present disclosure, a 'machine learning model' may include any model used to infer an answer to a given input. According to one embodiment, the machine learning model may include an artificial neural network model including an input layer (layer), a plurality of hidden layers, and an output layer. Here, each layer may include one or more nodes. For example, it may be learned to infer transportation costs (or transportation costs per unit distance) for loading and/or dropping off locations. For another example, it may be taught to infer transportation costs for distance traveled (or haulage distance) and/or transportation routes. In the present disclosure, a machine learning model may refer to an artificial neural network model, and an artificial neural network model may refer to a machine learning model.

1 shows an example of a loading and unloading location and a drop-off location to which a logistics transportation management system according to an embodiment of the present disclosure is applied. As shown, (a) of FIG. 1 shows a transportation vehicle 100 loading and unloading at a loading site, and (b) of FIG. 1 shows a transportation vehicle 100 getting off at a loading site. Devices 120 to 170 for recognizing/identifying the transportation vehicle 100 or receiving information related to the transportation vehicle 100 may be installed at the loading and unloading areas to which the logistics transportation management system is applied.

The server of the logistics transportation management system (hereinafter, referred to as 'server') may identify a user terminal associated with the transportation vehicle 100 (eg, a driver's user terminal, a terminal mounted on the transportation vehicle 100, etc.) . According to one embodiment, the logistics transportation management system may include a beacon (Beacon) 120, 150 for identifying a user terminal associated with the transport vehicle 100. Here, the

beacons

120 and 150 may receive identification information of user terminals approaching the

beacons

120 and 150 by periodically transmitting (or receiving) signals. In this case, the identification information includes identification information unique to the user terminal (eg, phone number of the user terminal), identification time information, location information (eg, location of the user terminal, location information of a loading/unloading location where a beacon is installed, etc.) can include

Beacons

120 and 150 may receive identification information and transmit it to a server of a logistics transportation management system.

The server may recognize the license plate number of the transportation vehicle 100 . According to one embodiment, the logistics transportation management system may include

image sensors

130 and 160 for recognizing vehicle numbers. Also, the

image sensors

130 and 160 may include a Licensed Plate Recognition (LPR) device. The server may recognize the license plate number of the transportation vehicle 100 by analyzing the license plate image of the transportation vehicle 100 received from the

image sensors

130 and 160 . In (a) and (b) of FIG. 1, although it is shown to recognize the license plate attached to the front of the transport vehicle 100, it is not limited thereto, and the vehicle number is obtained from the license plate attached to the rear of the transport vehicle 100. may be recognized.

The server may recognize the load 110 loaded on the transport vehicle 100. According to an embodiment, the

image sensors

130 and 160 may include a camera (eg, an Internet Protocol (IP) camera) for recognizing the load 110 of the transport vehicle 100 . In this case, the load 110 may include blast rock, sand, masato, aggregate, gravel, leafing rock, soft rock, mud, waste, and the like. The server may analyze the image of the load 110 received from the first image sensor 130 to determine the type of load 110 loaded on the transportation vehicle 100 . Additionally, the logistics transportation management system may determine whether or not the transportation vehicle 100 is loaded. As shown in (b) of FIG. 1 , when no load is loaded on the transportation vehicle 100, the logistics transportation management system analyzes the image received from the second image sensor 160 and moves the transportation vehicle 100 It can be determined that the load is not loaded in

The server may receive weight data of the load 110 loaded on the transport vehicle 100 . According to one embodiment, the logistics transportation management system may include weight sensors 140 and 170 . For example, the transportation vehicle 100 may stop at a predetermined location to determine loading/unloading. In this case, weight sensors 140 and 170 may be installed on the floor where the transportation vehicle 100 is stopped, and the weight of the transportation vehicle 100 and/or the load 110 may be measured and transmitted to the server. have.

The server determines the transport vehicle 100 based on at least one of the identification information of the user terminal associated with the transport vehicle 100, the vehicle number of the transport vehicle 100, or the type (or whether or not) of the loaded cargo 110 is loaded. ) may approve loading and/or unloading at the loading and/or unloading locations, respectively. Here, the approval result of loading and/or unloading may be the basis for calculating transportation costs described later. According to an embodiment, the server may include prior information on the loading and unloading location (or drop-off location) of the transport vehicle 100 stored in the database, identification information of the transport vehicle 100, vehicle number and/or information associated with the load 110. By comparing/analyzing, the transportation vehicle 100 may approve loading or unloading.

When loading and/or unloading of the transportation vehicle 100 is approved, the server may calculate a transportation cost of the transportation vehicle 100 . According to an embodiment, the server may calculate a transportation cost based on location information of a loading and unloading location and/or a drop-off location. According to another embodiment, the server may calculate a transportation cost based on information about a transportation route of the transportation vehicle 100 . According to another embodiment, the logistics transportation management system may calculate transportation costs based on information about the type and/or weight of the load 110 .

2 is a schematic diagram illustrating a configuration in which an information processing system 230 is communicatively connected to a plurality of user terminals 210_1, 210_2, and 210_3 in order to provide a logistics transportation management service according to an embodiment of the present disclosure. The information processing system 230 may include a logistics transportation management system(s) capable of providing a logistics transportation management service through the network 220 . In one embodiment, the information processing system 230 includes one or more server devices and/or databases capable of storing, providing, and executing computer-executable programs (eg, downloadable applications) and data related to logistics and transportation management services; or one or more distributed computing devices and/or distributed databases based on cloud computing services. For example, the information processing system 230 may include separate systems (eg, logistics management servers) for providing logistics management services.

The logistics transport management service provided by the information processing system 230 may be provided to the user through an application for transport management or a web browser application installed in each of the plurality of user terminals 210_1, 210_2, and 210_3. The logistics transport management service provided by the information processing system 230 may be provided to the user through an application for transport management or a web browser application installed in each of the plurality of user terminals 210_1, 210_2, and 210_3.

A plurality of user terminals 210_1 , 210_2 , and 210_3 may communicate with the information processing system 230 through the network 220 . The network 220 may be configured to enable communication between the plurality of user terminals 210_1, 210_2, and 210_3 and the information processing system 230. Depending on the installation environment, the network 220 may be, for example, a wired network such as Ethernet, a wired home network (Power Line Communication), a telephone line communication device and RS-serial communication, a mobile communication network, a wireless LAN (WLAN), It may consist of a wireless network such as Wi-Fi, Bluetooth, and ZigBee, or a combination thereof. The communication method is not limited, and the user terminals (210_1, 210_2, 210_3) as well as a communication method utilizing a communication network (eg, mobile communication network, wired Internet, wireless Internet, broadcasting network, satellite network, etc.) that the network 220 may include ), short-range wireless communication between them may also be included.

In FIG. 2, a mobile phone terminal 210_1, a tablet terminal 210_2, and a PC terminal 210_3 are illustrated as examples of user terminals, but are not limited thereto, and the user terminals 210_1, 210_2, and 210_3 may perform wired and/or wireless communication. This is possible and may be any computing device equipped with an image sensor. For example, the user terminal includes a smartphone, a mobile phone, a navigation device, a computer, a laptop computer, a digital broadcasting terminal, a personal digital assistant (PDA), a portable multimedia player (PMP), a tablet PC, a game console, and a wearable device ( wearable device), internet of things (IoT) device, virtual reality (VR) device, augmented reality (AR) device, and the like. In addition, although three user terminals 210_1, 210_2, and 210_3 are shown in FIG. 2 to communicate with the information processing system 230 through the network 220, it is not limited thereto, and a different number of user terminals may be connected to the network ( 220) to communicate with the information processing system 230. The plurality of user terminals 210_1 , 210_2 , and 210_3 may be user terminals of users using the logistics transportation management service.

In one embodiment, the information processing system 230 may receive data (eg, photos, texts, etc.) input from a user terminal through an application for logistics and transport management operating in the user terminal. After that, the information processing system 230 may store/save the received data and transmit it to other user terminals connected to the network 220 . Additionally, the information processing system 230 may control a logistics transportation management service based on a user input input from a user terminal.

3 is a block diagram showing a functional configuration of an information processing system 230 for calculating transportation costs according to an embodiment of the present disclosure. As shown, the information processing system 230 includes a vehicle number recognition module 310, a beacon recognition module 320, a load detection module 330, an loading/unloading approval module 340, and a transport cost calculation module. (350). Also, although not shown in FIG. 2 , the information processing system 230 may receive various types of information and/or data such as GPS (Global Positioning System)-based location information from an external device.

The license plate recognition module 310 may receive an image to be analyzed from an external device (eg, an image sensor). According to one embodiment, the license plate recognition module 310 may receive a license plate image, which is a target of license plate recognition, from an external device. Then, the license plate recognition module 310 may extract information and/or data analyzed or included in the license plate image. Additionally, the license plate recognition module 310 may infer/output license plate data from the license plate image by a machine learning model. In addition, the license plate recognition module 310 extracts information on the time the license plate image was taken (eg, date taken, time taken, etc.) and location information of the loading and/or dropping off location where the license plate image was taken from the license plate image. You may. In addition, the vehicle number recognition module 310 calculates transportation from an external server (eg, a server associated with a manufacturer of a transport vehicle, a server associated with the Korea Road Traffic Authority, etc.) in which transport vehicle-related information is registered, based on the extracted vehicle number data. It is possible to call or read vehicle specification information (eg, transport vehicle weight information). The vehicle number recognition module 310 transmits at least one of the output vehicle number data, time information, or location information of the loading/unloading and/or unloading locations to the loading/unloading approval module 340 and/or the transport cost calculation module 350. or transmit the extracted specification information to the load detection module 330.

The beacon recognition module 320 may extract information and/or data analyzed or included in a signal received from a Bluetooth-based short-range wireless communication device (eg, a driver's user terminal, a terminal mounted in a transportation vehicle, etc.). According to an embodiment, the beacon recognition module 320 may read or call any one of information about a plurality of user terminals previously stored in a database based on identification information about the user terminal included in the received signal. Then, the beacon recognition module 320 may transmit the extracted identification information to the loading/unloading approval module 340 .

The load detection module 330 may receive weight data of a transportation vehicle including a load from an external device (eg, a weight sensor). In addition, the load detection module 330 may receive specification information (eg, weight information of a transportation vehicle) received from an external server by the license plate recognition module 310 . According to an embodiment, the load detection module 330 may calculate the weight of the load based on the weight information of the transport vehicle including the load and the weight information of the transport vehicle. Additionally, the load detecting module 330 may compare the calculated weight of the load with a critical weight (eg, the maximum load capacity of a transportation vehicle according to laws and regulations) and transmit a comparison result to the loading/unloading approval module 340 .

Additionally or alternatively, the load detection module 330 may receive an image to be analyzed from an external device (eg, an image sensor). According to an embodiment, the loaded object detection module 330 may receive an image of a loaded object, which is a target for determining whether or not to load, from an external device. Then, the load detection module 330 may extract information and/or data included in or analyzed in the image of the load. For example, the loaded object detection module 330 analyzes color, feature point, object information and/or data included in the image of the loaded object to determine whether or not the transport vehicle is loaded, and analyzes the determination result. / Can be transmitted to the getting off approval module 340.

The loading/unloading approval module 340 is stored in the database of the information processing system 230 based on the vehicle number data extracted through the license plate recognition module 310 and/or the identification information received from the beacon recognition module 320. You can read or recall dictionary information. Here, the prior information is the construction name associated with a specific construction site (or loading site), construction site address, ordering agency, construction company, civil engineering company, transportation company, construction period, transportation date, classification (soil/soil), soil type, Information on settlement criteria, transport distance, settlement date, loading time, and non-working days can be included. In addition, the preliminary information may include information about the driver's affiliation, vehicle number, driver, driver's license, user terminal, and vehicle ownership. The loading/unloading approval module 340 compares the prior information with vehicle number data and/or identification information, determines that they match, and approves loading/unloading of the transportation vehicle. In addition, the loading/unloading approval module 340 may request transportation cost calculation from the transportation cost calculation module 350 based on a result of approval of the loading/unloading approval information.

The transport cost calculation module 350 may calculate the transport cost (or transport cost per unit distance) based on the loading/unloading approval result received from the loading/unloading approval module 340 . The transport cost calculation module 350 may calculate the transport cost based on the location information of the loading and/or unloading location received from the license plate recognition module 310 . In addition, the transport cost calculation module 350 may calculate the transport cost based on location information of a transport vehicle based on a Global Positioning System (GPS). Additionally, the transport cost calculation module 350 may implement a machine learning model that outputs transport cost (or transport cost per unit distance) using at least one of location information of a loading and unloading location and/or load data as an input variable. have. According to another embodiment, the transport cost calculation module 350 implements a machine learning model that outputs a transport cost using at least one of location information of a loading and unloading location, load data, or GPS-based location information as an input variable. may be

4 illustrates an example of selecting a construction site in a user terminal associated with a transport vehicle according to an embodiment of the present disclosure. The first operation step 410 to the third operation step 430 are examples of applications or web browser application screens related to the logistics transportation management service provided to the user terminal by the logistics transportation management system. The user terminal may output a map 412 for selecting a construction site in a first operation step 410 . Then, the user terminal responds to a user input for selecting a specific city 414 (eg, 'Seoul') from the map 412, and in a second operation step 420, the entire phrase included in the corresponding city 414. A region selection list 422 including may be output.

When a specific district 424 (eg, 'Dongjak-gu') is selected from the region selection list 422, the user terminal responds to a user input to the region selection interface 426, and in the third operation step 430, the corresponding district A construction site list 432 associated with 424 may be output. Here, the construction site list 432 may include information about the construction name, ordering organization, classification (filling/sato), soil type (eg, soil, aggregate, poor soil, soft rock, wind rock, mud, etc.), transport date, etc. have. Then, when a specific construction site 434 (eg, 'Shinsang-dong underground road expansion construction') is selected from the construction site list 432, in response to a user input to the site selection interface 436, the logistics transportation management system It is possible to receive the first preliminary information of the construction site 434 from. A detailed description of the first dictionary information will be described later with reference to FIG. 5 .

5 illustrates an example of checking first preliminary information 512 and second preliminary information 522 of a construction site in a user terminal associated with a transportation vehicle according to an embodiment of the present disclosure. The first operation step 510 to the third operation step 530 are examples of applications or web browser application screens related to the logistics transportation management service provided to the user terminal by the logistics transportation management system. The user terminal responds to a user input to a site selection interface (eg, the site selection interface 436 of FIG. 4 ), and transmits first preliminary information 512 associated with a construction site (or loading and unloading site) to the logistics transportation management system. It is received from the database of the first operation step 510, the selected construction site (eg, 434 in FIG. 4) of the first preliminary information 512 and the transportation request interface 514 can be output. Here, the first preliminary information 512 is the name of the construction site, address, ordering organization, construction company, civil engineering company, transportation company, construction period, transportation date, classification (filling/sand soil), soil type, settlement standard, transportation It can include information about distance, settlement date, loading time, holiday, etc.

The user terminal may output the second dictionary information 522 and the request confirmation interface 524 in a second operation step 520 in response to a user input to the transport request interface 514 . Here, the second preliminary information 522 may include information about belonging, vehicle number, driver, driver's license, user terminal (cell phone), vehicle ownership, and the like. Here, the second dictionary information 522 may be information extracted/output from data previously stored in the database of the user terminal. Alternatively, the second dictionary information 522 may be information output based on a user's touch input on the screen area where the second dictionary information 522 is displayed.

The user terminal may output a confirmation request interface 532 and a transport request waiting interface 534 in a third operation step 530 in response to a user input to the application confirmation interface 524 . The confirmation request interface 532 refers to a notification (eg text message, instant messaging, application push) transmitted from a server to a user terminal when another user (eg carrier owner) confirms the user's (eg driver) reservation request. ) may refer to an interface for selecting the type of For example, when receiving a user input for a check box of 'text confirmation', the server may transmit a text message notifying that the reservation request has been confirmed to the user terminal. For another example, when receiving a user input for a checkbox of 'app push confirmation', the server may transmit an application push notification notifying that the reservation request has been confirmed to the user terminal. Additionally, after the user terminal transmits the first preliminary information 512 and/or the second preliminary information 522 to the server based on the user's input to the transport request standby interface 534, the server sends the first preliminary information 512 and the second dictionary information 522 may be matched and stored in a database.

6 illustrates an example of modifying prior information before driving in a user terminal associated with a transport vehicle according to an embodiment of the present disclosure. The first operation step 610 to the fourth operation step 640 are examples of applications or web browser application screens related to the logistics transportation management service provided to the user terminal by the logistics transportation management system. The user terminal may output a guidance message (eg, 'Please drive safely today') and a driver information confirmation interface 612 in the first operation step 610 . Then, in response to a user input to the driver information checking interface 612, in a second operation step 620, the dictionary information stored according to the operation step of FIG. 5 (eg, the second dictionary information 532 of FIG. 5) At least part of can be output. For example, vehicle number, belonging company, driver's name, mobile phone number, etc. can be output.

The user terminal may output at least a part of dictionary information (eg, second dictionary information 532 of FIG. 5 ) stored according to the operation step of FIG. 5 in response to a user input to the confirmation interface 624 . For example, information on the construction name, loading and unloading address, drop-off address, type of earth and stone, transport date, etc. may be output. The driver can check the preliminary information output on the user terminal before driving (or before loading the vehicle) and modify the corresponding preliminary information. For example, when the user determines that any one piece of information (eg, 'soil type') included in the dictionary information does not match actual information, the user may select the soil type correction interface 632 . The user terminal may output the soil type selection interface 642 in a fourth operation step 640 in response to a user input to the soil type modification interface 632 . The user may modify the stored soil type ('general soil') to another soil type through the soil type selection interface 642. 6 shows an example in which 'soil type' is modified among the preliminary information, but is not limited thereto, and the vehicle number, belonging company, driver's name, user terminal (mobile phone) information shown in the second operation step 620, Information on the construction name, loading address, drop-off address, type of gravel and transport date shown in the third operation step 630 may also be modified as described above.

7 is to infer or output a transportation cost 730 based on identification information 712, load data 714, license plate data 716 or GPS-based location information 718 according to an embodiment of the present disclosure. It is a diagram showing the configured transportation cost prediction machine learning model 720. According to an embodiment, the transportation cost (or transportation cost per unit distance) 730 calculated by another user (eg, the owner of a carrier) and stored in the database of the logistics transportation management system is learned by the machine learning model 720. can be used as data. Then, the generated machine learning model 720 may be stored in a processor-accessible storage medium (eg, memory).

According to one embodiment, the transportation cost prediction machine learning model uses the identification information 712 received from the beacon recognition module (eg, the beacon recognition module 320) as input data to infer the transportation cost 730 per unit distance. and/or output. For example, the identification information 712 may include location information of a loading location where a beacon is installed, and the machine learning model 720 may use the location information of the loading location to generate a geographical characteristic (eg, city, county, Transportation cost 730 per unit distance can be inferred by extracting information about district, city center, coast, mountain, etc.). Similarly, the transportation cost prediction machine learning model uses the license plate number data 716 received from the license plate recognition module (eg, license plate recognition module 310) as input data per unit distance. The transportation cost 730 may be inferred and/or outputted. In this case, the vehicle number data 716 may also include location information of a loading area where Licensed Plate Recognition (LPR) is installed, and transportation cost prediction machine learning The model 720 may infer the transportation cost 730 per unit distance from the location information of the corresponding loading site.In addition or alternatively, the machine learning model 720 may use the identification information 712 or vehicle number data 716 The transport cost 730 per unit distance may be inferred by extracting location information of the drop-off point extracted from ).

According to one embodiment, the transportation cost prediction machine learning model uses the identification information 712 received from the beacon recognition module (eg, the beacon recognition module 320) as input data to determine the transportation cost 730 of the entire transportation route. can infer and/or output. For example, the identification information 712 may include location information of a loading and/or dropping off location where a beacon is installed, and the machine learning model 720 may use the location information of the loading and/or dropping off location to determine the loading and unloading location. And/or the transportation cost 730 of the entire transportation route can be inferred by extracting information about the distance between the drop-off points. Similarly, the transport cost prediction machine learning model uses the vehicle number data 716 received from the vehicle number recognition module (eg, the vehicle number recognition module (eg, the vehicle number recognition module 310) as input data to transport cost ( 730) may be inferred and/or outputted In this case, the vehicle number data 716 may also include location information of a loading and/or unloading location where Licensed Plate Recognition (LPR) is installed, and transport cost prediction The machine learning model 720 may infer the transportation cost 730 by extracting information about the distance between the loading and/or dropping-off points from location information of the corresponding loading-dropping and/or dropping-off points.

According to another embodiment, the transportation cost prediction machine learning model 720 may use the GPS-based location information 718 as input data to infer and/or output the transportation cost 730 of the entire transportation route. As described above, GPS (Global Positioning System)-based location information 718 is obtained at a predetermined period (eg, 10 minutes) from a user terminal associated with a transportation vehicle (eg, a driver's user terminal, a terminal mounted on a transportation vehicle, etc.) It may refer to location information received every time, and thus the GPS-based location information 718 may include information about a transportation route (or stopover) of a transportation vehicle. Here, information on transport routes includes road information (e.g., highways, general national roads, special cities, metropolitan cities, provincial roads, cities and provinces, archipelagos, districts, etc.), geographical characteristics (e.g., cities, counties, districts, downtown areas, coastal areas, acid, etc.) Accordingly, the transportation cost prediction machine learning model 720 may infer different transportation costs 730 when transportation routes are different even though the loading and unloading locations are the same.

Additionally, the transportation cost prediction machine learning model 720 uses the load data 714 received from the load detection module (eg, the load detection module 330) as input data to determine the transportation cost 730 or unit of the entire transportation route. A transportation cost per distance 730 may be inferred and/or output. Here, the load data 714 may include information on the type of load (for example, soil, aggregate, poor soil, soft rock, wind rock, mud, etc.) and/or weight information of the load. Accordingly, the transportation cost prediction machine learning model 720 may infer different transportation costs 730 when loads (or weights of loads) are different even though the loading and unloading locations are the same.

8 is a structural diagram illustrating an artificial neural network 800 according to an embodiment of the present disclosure. The artificial neural network 800 is a statistical learning algorithm implemented based on the structure of a biological neural network or a structure that executes the algorithm in machine learning technology and cognitive science. That is, in the artificial neural network 800, as in a biological neural network, nodes, which are artificial neurons that form a network by combining synapses, repeatedly adjust synaptic weights to obtain correct outputs and inferred outputs corresponding to specific inputs. It shows a machine learning model with problem-solving ability by learning to reduce the error between them.

In general, an artificial neural network is implemented as a multilayer perceptron (MLP) consisting of multiple nodes and their different connections. The artificial neural network 800 according to this embodiment may be implemented using one of various artificial neural network structures including MLP. As shown in FIG. 8, the artificial neural network 800 includes an input layer 822 that receives input signals or data 810 to 814 from the outside, and an output layer that outputs output signals or data 830 corresponding to the input data. 842, is located between the input layer 822 and the output layer 842, and is composed of n hidden layers 832_1 to 832_n receiving signals from the input layer 822, extracting characteristics, and transferring the extracted characteristics to the output layer 842. Here, the output layer 842 receives signals from the hidden layers 832_1 to 832_n and outputs them to the outside. In general, the learning method of the artificial neural network 800 includes a supervised learning method that learns to be optimized for problem solving by inputting a teacher signal (correct answer), and unsupervised learning that does not require a teacher signal ( There is an Unsupervised Learning method. Logistics transport management method and system according to the present disclosure, using supervised learning, unsupervised learning, and semi-supervised learning, location information of loading and unloading and / or unloading locations The artificial neural network 800 may be trained by using transportation costs for the corresponding loading and unloading and/or dropping off locations as learning data.

According to an embodiment, an input variable of the artificial neural network 800 capable of extracting transportation costs (or transportation costs per unit distance) for loading and unloading and/or unloading locations is one vector representing beacon identification information. A vector 810 composed of data elements, a vector 812 composed of one vector data element representing load data, and a vector 814 composed of one vector data element representing license plate data may be provided. In this case, the output variable may include a result vector 850 representing a result of calculating transportation cost (or transportation cost per unit distance) for the loading and unloading location and/or the drop-off location.

Additionally or alternatively, the input variable of the artificial neural network 800 capable of extracting transportation costs (or transportation costs per unit distance) for loading and unloading and/or unloading places is a vector data element representing beacon identification information. It can be composed vector 810, vector 812 composed of one vector data element representing load data, and vector 814 composed of one vector data element representing GPS-based location information. Even in this case, the output variable may be composed of a result vector 850 representing a result of calculating transportation cost (or transportation cost per unit distance) for the loading and unloading location and/or the drop-off location.

In this way, by matching a plurality of output variables corresponding to a plurality of input variables in the input layer 822 and the output layer 842 of the artificial neural network 800, respectively, the input layer 822 and the hidden layers 832_1 ... 832_n And by adjusting the synaptic value between the nodes included in the output layer 842, it is possible to learn to infer the correct output corresponding to a specific input. Through this learning process, it is possible to grasp the characteristics hidden in the input variables of the artificial neural network 800, and to reduce the error between the target output and the output variable calculated based on the input variable. Synapse values (or weights) can be adjusted.

Using the artificial neural network 800 learned in this way, a transportation cost per unit distance for a loading area may be extracted. In addition, using the artificial neural network 800 learned in this way, transport costs of the entire transport path for the loading and unloading locations may be extracted.

9 shows an example of a logistics transportation management method 900 according to an embodiment of the present disclosure. Method 900 may be performed by a server (or processor) of a logistics transportation management system. As shown, the method 900 may begin with receiving first identification information identified by a first beacon of a loading and unloading site (S910).

According to an embodiment, the server may receive first load data related to the load of the transportation vehicle detected through the first sensor of the loading area (S920). In this case, the first load data may include data associated with at least one of the type and weight of loads loaded on the transportation vehicle at the loading site. Also, the first load data may include an image of a load loaded on a transportation vehicle at a loading site.

According to an embodiment, the server may receive first license plate number data of a transportation vehicle from a first LPR (Licensed Plate Recognition) device of a loading site (S930). Then, it may be determined whether at least a part of the pre-stored information about the loading site matches the first identification information, the first load data, and the first license plate data (S940). In response to determining that at least a part of the preliminary information matches the first identification information, the first load data, and the first license plate data, the server may approve loading of the transportation vehicle at the loading site (S950). .

According to an embodiment, the server may approve loading at the loading site for the transportation vehicle in response to determining that the weight of the load does not exceed a predetermined threshold weight. Additionally, the server may transmit a push alarm notifying the user terminal of whether or not loading is approved in response to approval of loading of the transport vehicle at the loading site.

According to an embodiment, the server may receive second identification information of a transport vehicle identified by a second beacon of a drop-off location. In addition, second load data related to the load of the transportation vehicle detected through the second sensor of the drop-off location may be received. In addition, it is possible to receive the second car number data of the transportation vehicle from the second LPR device at the drop-off location. Then, the server may determine whether the preliminary information and the second identification information, the second load data and the second license plate data match. Then, in response to determining that at least part of the prior information matches the second identification information, the second load data, and the second license plate data, the server may authorize the transport vehicle to get off at the drop-off point. .

According to an embodiment, the server may calculate the transport cost of the transport vehicle based on location information of the loading and unloading location and the drop-off location. Additionally or alternatively, the server may calculate a transportation cost per unit distance based on the location information of the loading area. In addition, the server may receive location information of a transportation vehicle based on a Global Positioning System (GPS) at predetermined intervals. In addition, the server may calculate the transportation cost of the transportation vehicle based on the location information.

The preceding description of the present disclosure is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications of this disclosure will be readily apparent to those skilled in the art, and the general principles defined herein may be applied in various modifications without departing from the spirit or scope of this disclosure. Thus, the present disclosure is not intended to be limited to the examples set forth herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Although example implementations may refer to utilizing aspects of the presently disclosed subject matter in the context of one or more standalone computer systems, the subject matter is not so limited, but rather in conjunction with any computing environment, such as a network or distributed computing environment. may be implemented. Further, aspects of the presently-disclosed subject matter may be implemented in or across a plurality of processing chips or devices, and storage may be similarly affected across a plurality of devices. Such devices may include PCs, network servers, and handheld devices.

Although the present disclosure has been described in relation to some embodiments in this specification, it should be noted that various modifications and changes can be made without departing from the scope of the present disclosure that can be understood by those skilled in the art. something to do. Moreover, such modifications and variations are intended to fall within the scope of the claims appended hereto.

Claims

In the logistics transportation management method performed by at least one processor,

Receiving first identification information identified by a first beacon of a loading area, wherein the first identification information relates to a user terminal associated with a transportation vehicle;

receiving first load data related to the load of the transportation vehicle detected through a first sensor of the loading area;

receiving first license plate number data of the transportation vehicle from a first LPR (Licensed Plate Recognition) device of the loading area;

determining whether at least a portion of pre-stored information about the loading area matches the first identification information, the first load data, and the first license plate data; and

In response to determining that at least a part of the preliminary information matches the first identification information, the first load data, and the first license plate data, authorizing the transportation vehicle to be loaded at the loading site.

Including, logistics transport management method.
According to claim 1,

Wherein the first load data includes information related to the weight of the load loaded on the transportation vehicle at the loading area.
According to claim 1,

Wherein the first load data includes an image of the load loaded on the transportation vehicle at the loading area.
According to claim 2,

In response to determining that at least a part of the preliminary information matches the first identification information, the first load data, and the first license plate data, authorizing the transportation vehicle to be loaded at the loading site. Is,

In response to determining that the weight of the load does not exceed a predetermined threshold weight, authorizing the transportation vehicle to be loaded at the loading site.

Including, logistics transport management method.
According to claim 1,

Transmitting a push alarm informing whether or not loading is approved to the user terminal in response to the approval of loading of the transport vehicle at the loading site.

Further comprising, logistics transport management method.
According to claim 1,

Receiving second identification information of the transportation vehicle identified by a second beacon of a drop-off location, wherein the second identification information is associated with the user terminal;

receiving second load data associated with the load of the transport vehicle detected through a second sensor of the drop-off location;

receiving second license plate data of the transport vehicle from a second LPR device at the drop-off location;

determining whether the prior information, the second identification information, the second load data, and the second vehicle number data match; and

In response to determining that at least a part of the preliminary information matches the second identification information, the second load data, and the second license plate data, authorizing the transport vehicle to get off at the drop-off location.

Further comprising, logistics transport management method.
According to claim 6,

Calculating a transportation cost of the transport vehicle based on location information of the loading and unloading location and the drop-off location

Further comprising, logistics transport management method.
According to claim 1,

Calculating a transportation cost per unit distance based on the location information of the loading area

Further comprising, logistics transport management method.
According to claim 1,

Receiving location information of the transportation vehicle based on a Global Positioning System (GPS) at predetermined intervals; and

Calculating a transportation cost of the transportation vehicle based on the location information

Further comprising, logistics transport management method.
A computer program stored in a computer-readable recording medium to execute the logistics transportation management method according to claim 1 on a computer.