Method and apparatus for providing artificial intelligence model-based LCL optimization service for fresh food cold chains

Abstract

The present invention provides a method for providing an artificial intelligence model-based LCL optimization service for fresh food cold chains, wherein the method is performed by at least one server. The method comprises a step of training an artificial intelligence model to learn an optimal loading location for fresh food on the basis of information regarding a transport route and a transport period of a first container, information regarding a type and a quantity of first fresh food, information regarding temperature and humidity of the first container, information regarding a loading location of the first fresh food, and a difference between a departure state and an arrival state of the first fresh food.

Description

Method and apparatus for providing fresh food cold chain LCL optimization service based on artificial intelligence model

[0001] This specification relates to a method and apparatus for providing a fresh food cold chain LCL optimization service based on an artificial intelligence model, and more specifically, to a technology for optimizing the loading of fresh food into containers using an artificial intelligence model.

[0002] In the distribution of fresh food, the cold chain must strictly manage environmental conditions such as temperature, humidity, and weight to maintain product quality and ensure freshness. Failure to maintain the cold chain can lead to food spoilage or damage, resulting in a higher waste rate. In particular, the risk of product damage varies depending on the container loading method. The FCL (Full Container Load) method is relatively easy to manage because a single shipper utilizes the entire container space to control the environment. In contrast, the LCL (Less Than Container Load) method, where goods from multiple shippers are mixed in a single container, requires loading optimization based on order and location because various products with different environmental requirements are transported together.

[0003] However, the existing LCL method simply arranges cargo based on volume and size, often failing to adequately consider actual product characteristics and environmental conditions. To address this, there is a need for technology that utilizes artificial intelligence (AI) models to monitor and analyze environmental requirements in real time and propose optimal loading strategies. The AI-based optimization system aims to enhance freshness and reduce waste rates by optimizing product placement to match environmental conditions within the container.

[0004] The present invention aims to minimize food damage and spoilage that may occur during transportation through artificial intelligence model-based LCL optimization of fresh food cold chains, improve loading methods through continuous learning using the AI ​​model, and implement a layout that minimizes loss by calculating the optimal loading position for each product even when multiple fresh foods are mixed in a single container.

[0005] A method for providing an artificial intelligence model-based fresh food cold chain LCL optimization service according to an embodiment of the present specification for achieving the above objective (problem) may include: a step of obtaining information regarding the type, quantity, and departure state of a first fresh food to be shipped in a first container; a step of obtaining temperature and humidity information of the first container loaded with the first fresh food; a step of obtaining location information where the first fresh food is loaded within the first container; a step of obtaining information regarding the arrival state of the first fresh food after the first container arrives at a destination; a step of obtaining information regarding the transportation route and transportation period of the first container; and a step of learning the optimal fresh food loading location by utilizing an artificial intelligence model based on information regarding the transportation route and transportation period of the first container, information regarding the type and quantity of the first fresh food, temperature and humidity information of the first container, location information where the first fresh food is loaded, and the difference between the departure state and arrival state of the first fresh food.

[0006] Here, information regarding the departure state of the first fresh food includes weight information at the time the first fresh food is loaded into the first container, and information regarding the arrival state of the first fresh food may include weight information of the first fresh food after the first container arrives at the destination.

[0007] Here, information regarding the departure state of the first fresh food includes photographic information at the time the first fresh food is loaded into the first container, and information regarding the arrival state of the first fresh food may include photographic information of the first fresh food and information on the loss rate of the first fresh food obtained from the consignee terminal of the first fresh food after the first container arrives at the destination.

[0008] Herein, the method may include: a step of obtaining information regarding the type and quantity of a second fresh food loaded in the first shipping case from an information terminal of the first shipping case; a step of obtaining information regarding the type and quantity of a third fresh food loaded in the second shipping case from an information terminal of the second shipping case; a step of obtaining temperature and humidity information of the first shipping case and a second container to which the second shipping case is to be loaded; a step of obtaining information regarding the transport route and transport period of the second container; a step of calculating the loading position of the second fresh food within the second container using the artificial intelligence model based on the information regarding the transport route and transport period of the second container, the information regarding the type and quantity of the second fresh food, the information regarding the type and quantity of the third fresh food, and the temperature and humidity information of the second container; and a step of transmitting information regarding the loading position of the first shipping case to a display terminal of the first shipping case.

[0009] Herein, the method may include: a step of obtaining information about the consignor of the third fresh food from an information terminal of the second shipping case; a step of obtaining priority information about the consignor of the third fresh food; a step of calculating an optimal loading location within the second container where the lowest loss rate of the third fresh food is expected by utilizing the artificial intelligence model based on information about the transportation route and transportation period of the second container, information about the type and quantity of the second fresh food, and temperature and humidity information of the second container; and a step of transmitting information about the loading location of the second shipping case to a display terminal of the second shipping case.

[0010] Here, based on the fact that the loading position of the first shipping case is located at the innermost part of the second container, the method may include the step of transmitting an alarm trigger signal for the first shipping case to the alarm terminal of the first shipping case.

[0011] An apparatus for providing an artificial intelligence model-based fresh food cold chain LCL optimization service according to another embodiment of the present specification for achieving the above objective comprises a processor and a memory storing at least one command executed by the processor, wherein the at least one command is configured to learn the optimal fresh food loading location by utilizing an artificial intelligence model based on the information regarding the type, quantity, and departure status of a first fresh food to be shipped in a first container; the temperature and humidity information of the first container loaded with the first fresh food; the location information of the first fresh food loaded within the first container; the arrival status of the first fresh food after the first container arrives at a destination; the transportation route and transportation period of the first container; and the information regarding the transportation route and transportation period of the first container, the information regarding the type and quantity of the first fresh food, the temperature and humidity information of the first container, the location information of the first fresh food loaded, and the difference between the departure status and arrival status of the first fresh food.

[0012] Here, information regarding the departure state of the first fresh food includes weight information at the time the first fresh food is loaded into the first container, and information regarding the arrival state of the first fresh food may include weight information of the first fresh food after the first container arrives at the destination.

[0013] Here, information regarding the departure state of the first fresh food includes photographic information at the time the first fresh food is loaded into the first container, and information regarding the arrival state of the first fresh food may include photographic information of the first fresh food and information on the loss rate of the first fresh food obtained from the consignee terminal of the first fresh food after the first container arrives at the destination.

[0014] Here, the at least one command may be configured to obtain information regarding the type and quantity of a second fresh food loaded in the first shipping case from an information terminal of the first shipping case; obtain information regarding the type and quantity of a third fresh food loaded in the second shipping case from an information terminal of the second shipping case; obtain temperature and humidity information of the first shipping case and a second container to which the second shipping case is to be loaded; obtain information regarding the transport route and transport period of the second container; calculate the loading location of the second fresh food within the second container using the artificial intelligence model based on the information regarding the transport route and transport period of the second container, the information regarding the type and quantity of the second fresh food, the information regarding the type and quantity of the third fresh food, and the temperature and humidity information of the second container; and transmit information regarding the loading location of the first shipping case to the display terminal of the first shipping case.

[0015] Here, the at least one command may be configured to obtain information regarding the consignor of the third fresh food from the information terminal of the second shipping case; obtain priority information regarding the consignor of the third fresh food; calculate the optimal loading location within the second container where the lowest loss rate of the third fresh food is expected by utilizing the artificial intelligence model based on information regarding the transportation route and transportation period of the second container, information regarding the type and quantity of the second fresh food, and temperature and humidity information of the second container; and transmit information regarding the loading location of the second shipping case to the display terminal of the second shipping case.

[0016] Here, the at least one command may be configured to transmit an alarm trigger signal for the first shipping case to the alarm terminal of the first shipping case, based on the fact that the loading position of the first shipping case is located at the innermost part of the second container.

[0017] According to one embodiment of the present specification, by utilizing an artificial intelligence model to support cold chain optimization using the LCL method, the quality and safety of fresh food can be maximized. In particular, the AI ​​learns various data such as temperature, humidity, loading location, and cargo condition to propose an optimal loading method, thereby minimizing food damage and spoilage that may occur during transportation. Through this, the consignee can receive products in a fresher state, and the shipper can reduce loss costs.

[0018] According to one embodiment of the present specification, a function is provided to quantitatively evaluate changes in the quality of fresh food by comparing and analyzing the departure and arrival status of cargo. To this end, the condition of fresh food is monitored based on information such as weight and photos, and an artificial intelligence model is configured to continuously improve the loading method through learning by reflecting the difference between the departure and arrival statuses. This process goes beyond simple data collection and serves as an AI-based quality management system, enabling continuous optimization suitable for the cold chain environment through real-time feedback.

[0019] According to one embodiment of the present specification, it provides the advantage of calculating the optimal loading position for each product even when multiple fresh food products are mixed in a single container. This allows for the priority consideration of food products sensitive to quality during transport and enables the implementation of a layout that minimizes losses by reflecting priority information for each shipper. Accordingly, quality maintenance effects similar to those of FCL can be expected even in LCL methods, providing significant benefits to both shippers and consignees.

[0020] FIG. 1 is a system diagram including a server providing a fresh food cold chain LCL optimization service based on an artificial intelligence model according to one embodiment of the present specification.

[0021] FIG. 2 is a block diagram showing the configuration of a server providing an artificial intelligence model-based fresh food cold chain LCL optimization service according to one embodiment of the present specification.

[0022] FIG. 3 is a diagram illustrating a method for providing a fresh food cold chain LCL optimization service based on an artificial intelligence model according to one embodiment of the present specification.

[0023] FIG. 4 is a diagram illustrating a method for providing a fresh food cold chain LCL optimization service based on an artificial intelligence model according to one embodiment of the present specification.

[0024] FIG. 5 is a diagram illustrating the fresh food cold chain LCL loss rate according to one embodiment of the present specification.

[0025] FIG. 6 is a diagram illustrating the change in temperature and humidity inside a container during transport according to one embodiment of the present specification.

[0026] FIG. 7 is a drawing illustrating a method of loading a shipping case into a container according to one embodiment of the present specification.

[0027] FIG. 8 is a drawing illustrating a shipping case according to one embodiment of the present specification.

[0028] A method for providing an artificial intelligence model-based fresh food cold chain LCL optimization service according to one embodiment of the present specification may include: a step of obtaining information regarding the type, quantity, and departure status of a first fresh food to be shipped in a first container; a step of obtaining temperature and humidity information of the first container loaded with the first fresh food; a step of obtaining location information where the first fresh food is loaded within the first container; a step of obtaining information regarding the arrival status of the first fresh food after the first container arrives at a destination; a step of obtaining information regarding the transportation route and transportation period of the first container; and a step of learning the optimal fresh food loading location by utilizing an artificial intelligence model based on information regarding the transportation route and transportation period of the first container, information regarding the type and quantity of the first fresh food, temperature and humidity information of the first container, location information where the first fresh food is loaded, and the difference between the departure status and arrival status of the first fresh food.

[0029] Here, information regarding the departure state of the first fresh food includes weight information at the time the first fresh food is loaded into the first container, and information regarding the arrival state of the first fresh food may include weight information of the first fresh food after the first container arrives at the destination.

[0030] Here, information regarding the departure state of the first fresh food includes photographic information at the time the first fresh food is loaded into the first container, and information regarding the arrival state of the first fresh food may include photographic information of the first fresh food and information on the loss rate of the first fresh food obtained from the consignee terminal of the first fresh food after the first container arrives at the destination.

[0031] Herein, the method may include: a step of obtaining information regarding the type and quantity of a second fresh food loaded in the first shipping case from an information terminal of the first shipping case; a step of obtaining information regarding the type and quantity of a third fresh food loaded in the second shipping case from an information terminal of the second shipping case; a step of obtaining temperature and humidity information of the first shipping case and a second container to which the second shipping case is to be loaded; a step of obtaining information regarding the transport route and transport period of the second container; a step of calculating the loading position of the second fresh food within the second container using the artificial intelligence model based on the information regarding the transport route and transport period of the second container, the information regarding the type and quantity of the second fresh food, the information regarding the type and quantity of the third fresh food, and the temperature and humidity information of the second container; and a step of transmitting information regarding the loading position of the first shipping case to a display terminal of the first shipping case.

[0032] Herein, the method may include: a step of obtaining information about the consignor of the third fresh food from an information terminal of the second shipping case; a step of obtaining priority information about the consignor of the third fresh food; a step of calculating an optimal loading location within the second container where the lowest loss rate of the third fresh food is expected by utilizing the artificial intelligence model based on information about the transportation route and transportation period of the second container, information about the type and quantity of the second fresh food, and temperature and humidity information of the second container; and a step of transmitting information about the loading location of the second shipping case to a display terminal of the second shipping case.

[0033] Here, based on the fact that the loading position of the first shipping case is located at the innermost part of the second container, the method may include the step of transmitting an alarm trigger signal for the first shipping case to the alarm terminal of the first shipping case.

[0034] An apparatus for providing an artificial intelligence model-based fresh food cold chain LCL optimization service according to another embodiment of the present specification for achieving the above objective comprises a processor and a memory storing at least one command executed by the processor, wherein the at least one command is configured to learn the optimal fresh food loading location by utilizing an artificial intelligence model based on the information regarding the type, quantity, and departure status of a first fresh food to be shipped in a first container; the temperature and humidity information of the first container loaded with the first fresh food; the location information of the first fresh food loaded within the first container; the arrival status of the first fresh food after the first container arrives at a destination; the transportation route and transportation period of the first container; and the information regarding the transportation route and transportation period of the first container, the information regarding the type and quantity of the first fresh food, the temperature and humidity information of the first container, the location information of the first fresh food loaded, and the difference between the departure status and arrival status of the first fresh food.

[0035] Here, information regarding the departure state of the first fresh food includes weight information at the time the first fresh food is loaded into the first container, and information regarding the arrival state of the first fresh food may include weight information of the first fresh food after the first container arrives at the destination.

[0036] Here, information regarding the departure state of the first fresh food includes photographic information at the time the first fresh food is loaded into the first container, and information regarding the arrival state of the first fresh food may include photographic information of the first fresh food and information on the loss rate of the first fresh food obtained from the consignee terminal of the first fresh food after the first container arrives at the destination.

[0037] Here, the at least one command may be configured to obtain information regarding the type and quantity of a second fresh food loaded in the first shipping case from an information terminal of the first shipping case; obtain information regarding the type and quantity of a third fresh food loaded in the second shipping case from an information terminal of the second shipping case; obtain temperature and humidity information of the first shipping case and a second container to which the second shipping case is to be loaded; obtain information regarding the transport route and transport period of the second container; calculate the loading location of the second fresh food within the second container using the artificial intelligence model based on the information regarding the transport route and transport period of the second container, the information regarding the type and quantity of the second fresh food, the information regarding the type and quantity of the third fresh food, and the temperature and humidity information of the second container; and transmit information regarding the loading location of the first shipping case to the display terminal of the first shipping case.

[0038] Here, the at least one command may be configured to obtain information regarding the consignor of the third fresh food from the information terminal of the second shipping case; obtain priority information regarding the consignor of the third fresh food; calculate the optimal loading location within the second container where the lowest loss rate of the third fresh food is expected by utilizing the artificial intelligence model based on information regarding the transportation route and transportation period of the second container, information regarding the type and quantity of the second fresh food, and temperature and humidity information of the second container; and transmit information regarding the loading location of the second shipping case to the display terminal of the second shipping case.

[0039] Here, the at least one command may be configured to transmit an alarm trigger signal for the first shipping case to the alarm terminal of the first shipping case, based on the fact that the loading position of the first shipping case is located at the innermost part of the second container.

[0040] As the present specification is subject to various modifications and may have various embodiments, specific embodiments are illustrated in the drawings and described in detail in the detailed description. However, this is not intended to limit the present specification to specific embodiments, and it should be understood that it includes all modifications, equivalents, and substitutions that fall within the spirit and scope of the present specification. Similar reference numerals have been used for similar components in the description of each drawing.

[0041] Terms such as first, second, A, B, etc., may be used to describe various components, but said components should not be limited by said terms. These terms are used solely for the purpose of distinguishing one component from another. For example, without departing from the scope of the rights of this specification, the first component may be named the second component, and similarly, the second component may be named the first component. The term "and / or" includes a combination of a plurality of related described items or any of a plurality of related described items.

[0042] When it is stated that one component is "connected" or "connected" to another component, it should be understood that while it may be directly connected or connected to that other component, there may also be other components in between. On the other hand, when it is stated that one component is "directly connected" or "directly connected" to another component, it should be understood that there are no other components in between.

[0043] The terms used in this application are used merely to describe specific embodiments and are not intended to limit this specification. The singular expression includes the plural expression unless the context clearly indicates otherwise. In this application, terms such as "comprising" or "having" are intended to indicate the presence of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof.

[0044] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which this specification pertains. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in this application.

[0045] Hereinafter, preferred embodiments of the present specification will be described in more detail with reference to the attached drawings. To facilitate overall understanding in describing the present specification, the same reference numerals are used for identical components in the drawings, and redundant descriptions of identical components are omitted.

[0046]

[0047] FIG. 1 is a system diagram including a battery management service providing server based on battery SoC (state of charge) estimation using an artificial intelligence model according to one embodiment of the present specification.

[0048] Referring to FIG. 1, the method for providing a fresh food cold chain LCL optimization service based on an artificial intelligence model according to an embodiment of the present specification may be performed on a computing device that is equipped with storage space and connected to the Internet, such as a PC (Personal Computer), and is not easily portable, or on a portable terminal such as a smartphone. In this case, the method for providing a fresh food cold chain LCL optimization service based on an artificial intelligence model may be executed after an application implementing the method for providing a fresh food cold chain LCL optimization service based on an artificial intelligence model is downloaded from an App Store, etc., and installed on the portable terminal.

[0049] In addition, the method for providing a fresh food cold chain LCL optimization service based on the above artificial intelligence model may be performed by inserting it into a computing device such as a PC while it is recorded on a recording medium such as a CD (Compact Disc) or USB (Universal Serial Bus) memory and executing it through an access operation of said computing device, or it may be performed by storing it from said recording medium into the storage space of said computing device and then executing it through an access operation of said computing device.

[0050] Meanwhile, if the above computing device or portable terminal can access a server connected to the Internet, the method for providing a fresh food cold chain LCL optimization service based on the artificial intelligence model can also be executed on the server in response to a request from the computing device or portable terminal.

[0051] In the following, a computing device, portable terminal, or server, etc., on which the above-mentioned method for providing a fresh food cold chain LCL optimization service based on an artificial intelligence model is executed may be collectively referred to as an artificial intelligence model-based fresh food cold chain LCL optimization service providing device.

[0052] The artificial intelligence model-based fresh food cold chain LCL optimization service provider described above may have the same configuration as the artificial intelligence model-based fresh food cold chain LCL optimization service provider illustrated in FIG. 2, and the artificial intelligence model-based fresh food cold chain LCL optimization service provider may not be limited to the artificial intelligence model-based fresh food cold chain LCL optimization service provider illustrated in FIG. 1.

[0053] A system according to one embodiment may include a shipping case terminal (110), a shipping case terminal (120), a consignee terminal (130), and a server (140) (hereinafter, server (140)) for providing a fresh food cold chain LCL optimization service based on an artificial intelligence model. The network may include an internet portal site server, a social media server, a server operating a blog, etc.

[0054] The shipping case terminal (110), shipping case terminal (120), and consignee terminal (130) may be, but are not limited to, automobiles, mobility devices, smartphones, tablet PCs, PCs, mobile phones, PDAs (personal digital assistants), laptops, media players, micro servers, GPS (global positioning system) devices, and other mobile or non-mobile computing devices. Additionally, the shipping case terminal (110), shipping case terminal (120), and consignee terminal (130) may be wearable devices equipped with communication functions and data processing functions. However, they are not limited to.

[0055] The server (140) may be implemented as a computer device or a plurality of computer devices that communicate with the shipping case terminal (110), shipping case terminal (120), and consignee terminal (130) through a network to provide commands, codes, files, content, services, etc.

[0056] For example, the server (140) may provide a file for installing an application to a shipping case terminal (110), a shipping case terminal (120), and a consignee terminal (130) connected via a network. In this case, the shipping case terminal (110), the shipping case terminal (120), and the consignee terminal (130) may install the application using the file provided by the server (140).

[0057] Additionally, the shipping case terminal (110), the shipping case terminal (120), and the consignee terminal (130) can connect to the server (140) under the control of an operating system (OS) and at least one program (e.g., a browser or an installed application) to receive services or content provided by the server (140).

[0058] As another example, the server (140) may establish a communication session for data transmission and reception and route data transmission and reception between the shipping case terminal (110), the shipping case terminal (120), and the consignee terminal (130) through the established communication session.

[0059] The shipping case terminal (110), shipping case terminal (120), consignee terminal (130), and the server providing the fresh food cold chain LCL optimization service based on an artificial intelligence model (140) can communicate using a network. For example, the network includes a Local Area Network (LAN), a Wide Area Network (WAN), a Value Added Network (VAN), a mobile radio communication network, a satellite communication network, and combinations thereof, and is a data communication network in a comprehensive sense that enables each network constituent entity shown in FIG. 1 to communicate smoothly with each other, and may include wired internet, wireless internet, and mobile wireless communication networks. In addition, wireless communication may include, for example, Wi-Fi, Bluetooth, Bluetooth Low Energy, LoRaWAN, Zigbee, Wi-Fi Direct (WFD), Ultra Wideband (UWB), Infrared Data Association (IrDA), and Near Field Communication (NFC), but is not limited to these.

[0060]

[0061] FIG. 2 is a block diagram showing the configuration of a server providing an artificial intelligence model-based fresh food cold chain LCL optimization service according to one embodiment of the present specification.

[0062] Referring to FIG. 2, a device (200) (hereinafter referred to as the server (200)) providing an artificial intelligence model-based fresh food cold chain LCL optimization service may include a communication unit (210), a processor (220), and a DB (230). Only the components related to the embodiment are shown in the server (200) of FIG. 2. Therefore, a person skilled in the art will understand that other general-purpose components may be included in addition to the components shown in FIG. 2.

[0063] The communication unit (210) may include one or more components that enable wired / wireless communication with a user terminal and a work provider terminal. For example, the communication unit (210) may include at least one of a short-range communication unit (not shown), a mobile communication unit (not shown), and a broadcast receiving unit (not shown).

[0064] For example, a request generated according to program code stored in a recording device such as a DB (230) can be transmitted to a user terminal and a work provider terminal via a network under the control of the communication unit (210). Conversely, control signals, commands, content, files, etc. provided under the control of the processors of the user terminal and the work provider terminal can be received by the server (200) via the communication unit (210) through the network. For example, control signals, commands, content, files, etc. of the server (200) received through the communication unit (210) can be transmitted to the processor (220) or transmitted to the DB (230) for storage.

[0065] DB (230) is hardware that stores various data processed within the server (200) and can store programs for processing and controlling the processor (220).

[0066] DB (230) may include RAM (random access memory), such as DRAM (dynamic random access memory) and SRAM (static random access memory), ROM (read-only memory), EEPROM (electrically erasable programmable read-only memory), CD-ROM, Blu-ray or other optical disc storage, HDD (hard disk drive), SSD (solid state drive), or flash memory. DB (230) may also be referred to as memory.

[0067] The processor (220) controls the overall operation of the server (200). For example, the processor (220) can control the input unit (not shown), display (not shown), communication unit (210), DB (230), etc., by executing programs stored in the DB (230). The processor (220) can control the operation of the external server (200) by executing programs stored in the DB (230).

[0068] The processor (220) may be implemented using at least one of ASICs (application specific integrated circuits), DSPs (digital signal processors), DSPDs (digital signal processing devices), PLDs (programmable logic devices), FPGAs (field programmable gate arrays), controllers, microcontrollers, microprocessors, and other electrical units for performing functions.

[0069] The DB (230) may store at least one command executed through the processor (220). The at least one command may be configured to obtain information regarding the type, quantity, and departure status of a first fresh food to be shipped in a first container; obtain information regarding the temperature and humidity of the first container in which the first fresh food is loaded; obtain location information regarding the location where the first fresh food is loaded within the first container; obtain information regarding the arrival status of the first fresh food after the first container arrives at the destination; obtain information regarding the transportation route and transportation period of the first container; and to learn the optimal fresh food loading location using an artificial intelligence model based on the information regarding the transportation route and transportation period of the first container, information regarding the type and quantity of the first fresh food, the temperature and humidity information of the first container, the location information regarding the location where the first fresh food is loaded, and the difference between the departure status and arrival status of the first fresh food.

[0070] Here, information regarding the departure state of the first fresh food includes weight information at the time the first fresh food is loaded into the first container, and information regarding the arrival state of the first fresh food may include weight information of the first fresh food after the first container arrives at the destination.

[0071] Here, information regarding the departure state of the first fresh food includes photographic information at the time the first fresh food is loaded into the first container, and information regarding the arrival state of the first fresh food may include photographic information of the first fresh food and information on the loss rate of the first fresh food obtained from the consignee terminal of the first fresh food after the first container arrives at the destination.

[0072] Here, the at least one command may be configured to obtain information regarding the type and quantity of a second fresh food loaded in the first shipping case from an information terminal of the first shipping case; obtain information regarding the type and quantity of a third fresh food loaded in the second shipping case from an information terminal of the second shipping case; obtain temperature and humidity information of the first shipping case and a second container to which the second shipping case is to be loaded; obtain information regarding the transport route and transport period of the second container; calculate the loading location of the second fresh food within the second container using the artificial intelligence model based on the information regarding the transport route and transport period of the second container, the information regarding the type and quantity of the second fresh food, the information regarding the type and quantity of the third fresh food, and the temperature and humidity information of the second container; and transmit information regarding the loading location of the first shipping case to the display terminal of the first shipping case.

[0073] Here, the at least one command may be configured to obtain information regarding the consignor of the third fresh food from the information terminal of the second shipping case; obtain priority information regarding the consignor of the third fresh food; calculate the optimal loading location within the second container where the lowest loss rate of the third fresh food is expected by utilizing the artificial intelligence model based on information regarding the transportation route and transportation period of the second container, information regarding the type and quantity of the second fresh food, and temperature and humidity information of the second container; and transmit information regarding the loading location of the second shipping case to the display terminal of the second shipping case.

[0074] Here, the at least one command may be configured to transmit an alarm trigger signal for the first shipping case to the alarm terminal of the first shipping case, based on the fact that the loading position of the first shipping case is located at the innermost part of the second container.

[0075]

[0076] This invention relates to a method for providing a service that learns and optimizes the optimal loading location of fresh food in LCL (Less than Container Load) cold chain transportation based on an artificial intelligence model. Optimal loading conditions can be determined by utilizing various data such as the type, quantity, and departure and arrival status of the fresh food.

[0077]

[0078] FIG. 3 is a diagram illustrating a method for providing a fresh food cold chain LCL optimization service based on an artificial intelligence model according to one embodiment of the present specification.

[0079] Referring to FIG. 3, the server can obtain fresh food information (S300). For example, the server can obtain information about the type, quantity, and departure status of the first fresh food to be shipped in the first container.

[0080] For example, if the fresh food item is fruit, the server can obtain specific information about the type (e.g., apple, grape, etc.), quantity (e.g., 100 kg), and starting condition (e.g., weight, freshness, etc.). This information can serve as basic data necessary for an AI model to learn an optimal loading strategy based on the characteristics of the fresh food and environmental sensitivity.

[0081] For example, information regarding the starting state of the first fresh food may include weight information at the time the first fresh food is loaded into the first container.

[0082] For example, information regarding the starting state of the first fresh food may include photographic information at the time the first fresh food is loaded into the first container.

[0083] The server can obtain container information (S310). For example, the server can obtain temperature and humidity information of the first container loaded with the first fresh food.

[0084] For example, the server can continuously collect temperature and humidity information from the container. This temperature and humidity information can be used to understand how internal environmental conditions affect the quality of fresh food. The data is collected in real time via devices such as thermometers or humidity sensors, and the server can utilize this in an AI model to determine the optimal loading location and / or preservation environment for the fresh food.

[0085] The server can obtain fresh food loading location information (S320). For example, the server can obtain location information where the first fresh food is loaded within the first container.

[0086] For example, the server can obtain precise location information regarding the loading of fresh food within a container. For instance, the internal location can be converted into coordinates, allowing the server to acquire information on the specific coordinates where the fresh food is located. This location information can play a crucial role in enabling the AI ​​model to propose optimal loading strategies in the future, thereby ensuring the maximum quality of each product.

[0087] The server can obtain fresh food arrival status information (S330). For example, the server can obtain information about the arrival status of the first fresh food after the first container arrives at the destination.

[0088] For example, information regarding the arrival status of the first fresh food may include weight information of the first fresh food after the first container arrives at the destination.

[0089] For example, information regarding the arrival status of the first fresh food may include photographic information of the first fresh food and information on the loss rate of the first fresh food obtained from the recipient terminal of the first fresh food after the first container arrives at the destination.

[0090] Arrival status information can be compared and analyzed with departure status information to evaluate changes in the quality of fresh food and to quantify the impact of environmental conditions on the product during transportation.

[0091] For example, the server can obtain information regarding the transportation route and duration of the first container. Such information can help in understanding the impact of various external environmental factors on fresh food during transportation. For instance, long transportation times or passage through high-temperature regions can have additional effects on the quality of the fresh food, and the AI ​​model can optimize the loading location by taking these factors into account.

[0092] The server can learn the optimal fresh food loading location (S340). For example, the server can learn the optimal fresh food loading location by utilizing an artificial intelligence model based on information regarding the transportation route and transportation period of the first container, information regarding the type and quantity of the first fresh food, temperature and humidity information of the first container, location information where the first fresh food is loaded, and the difference between the departure state and arrival state of the first fresh food.

[0093] In other words, the server can learn at which location each fresh food item should be loaded to calculate the minimum loss rate by considering the container's transport route and duration, the type and quantity of fresh food, the container's temperature and humidity, the location where the fresh food is loaded, and comparative information on the fresh food's departure and arrival states.

[0094]

[0095] FIG. 4 is a diagram illustrating a method for providing a fresh food cold chain LCL optimization service based on an artificial intelligence model according to one embodiment of the present specification.

[0096] Referring to Fig. 4, this flowchart illustrates the entire process of a fresh food cold chain LCL optimization system utilizing an artificial intelligence model. This system is designed to minimize quality loss during transportation by providing optimized loading methods and preservation environments tailored to the characteristics of fresh food and transportation conditions.

[0097] The first step is the data entry stage. Here, relevant data, such as export product specifications for fresh food and quotation forms, is entered into the system. The entered data is matched with the database (DB1) in the next step. In this data matching stage (DB1), the entered data is compared with the existing database based on various characteristics, and necessary information is linked to construct a data set. Through this process, the basic information required for transportation optimization is systematically organized.

[0098] Subsequently, in the mathematical algorithm application stage, various datasets (primary, secondary, etc.) are organized, and analysis is performed by applying algorithms such as "Mathematical Alpha" and "Mathematical Beta." Through this process, the optimal loading order and location of fresh food are calculated, enabling the prediction of potential losses and temperature fluctuations during transportation. Next, in the AI ​​model training and result generation stage, data is processed using AI models such as Deep Learning (DL), Convolutional Neural Networks (CNN), and Deep Neural Networks (DNN), and the optimized loading results are visualized and provided in 2D or 3D form. These results include the optimal placement of fresh food within the container, expected loss rates, and temperature and humidity distributions.

[0099] Finally, in the feedback and correction phase, quantitative data (e.g., photos, buyer feedback, etc.) of fresh food arriving at the destination is collected and stored in the database (DB2). Based on this information, the system compares the predicted loss rate with the actual loss rate, and the AI ​​model learns from the new data to improve prediction accuracy for the next shipment. Through this iterative feedback process, the artificial intelligence model continuously improves the performance of cold chain LCL optimization for fresh food.

[0100]

[0101] FIG. 5 is a diagram illustrating the fresh food cold chain LCL loss rate according to one embodiment of the present specification.

[0102] Referring to Figure 5, the fresh food transportation loss and waste rates (Loss & Waste) can be compared when an AI algorithm is applied and when relying on human experience.

[0103] In the application of AI algorithms, fresh food can be prioritized based on various sensitive information and designed for optimal placement and loading. When other products are loaded together in the same space, the loss and waste rates are significantly reduced by adjusting the loading layout using AI algorithms, and the loss and waste rates at the destination can be estimated at approximately 15%.

[0104] On the other hand, when relying on human experience, various sensitive information cannot be effectively considered during the process of mixing multiple products during loading, making it difficult to adjust factors such as temperature priority. As a result, the loss and waste rate of products upon arrival can be estimated at approximately 24%.

[0105] Through this comparison, it can be confirmed that optimization using AI algorithms is more effective in preserving the quality of fresh food and reducing losses.

[0106]

[0107] FIG. 6 is a diagram illustrating the change in temperature and humidity inside a container during transport according to one embodiment of the present specification.

[0108] Referring to Fig. 6, the results of monitoring temperature and humidity changes during transport can be seen. Overall, the internal environmental conditions and temperature and humidity of the CA container are well maintained during ship transport, but there are slight differences in temperature and humidity depending on the location inside the container.

[0109] The first graph shows temperature changes, demonstrating that the internal temperature of the container remains constant over time. However, slight temperature differences occur depending on the location, and the temperature near the door appears somewhat higher compared to other areas.

[0110] The second graph shows changes in humidity, demonstrating that consistently high humidity is maintained in most locations. Locations near the door tend to show higher humidity than other locations.

[0111]

[0112] Referring again to FIG. 3, the server can calculate the optimal loading location for fresh food (S350). For example, the server can obtain information regarding the type and quantity of the second fresh food loaded in the first shipping case from the information terminal of the first shipping case. For example, the server can obtain information regarding the type and quantity of the third fresh food loaded in the second shipping case from the information terminal of the second shipping case. For example, the server can obtain temperature and humidity information of the first shipping case and the second container to which the second shipping case will be loaded. For example, the server can obtain information regarding the transport route and transport period of the second container. For example, the server can calculate the loading location of the second fresh food within the second container by utilizing the artificial intelligence model based on the information regarding the transport route and transport period of the second container, the information regarding the type and quantity of the second fresh food, the information regarding the type and quantity of the third fresh food, and the temperature and humidity information of the second container. For example, the server can transmit information about the loading location of the first shipping case to the display terminal of the first shipping case.

[0113] That is, the server includes a series of steps for collecting information on fresh food loaded in the first and second shipping cases, and based on this, calculating and transmitting the optimal loading location through an artificial intelligence model.

[0114] First, information regarding the type and quantity of the second fresh food is obtained from the information terminal of the first shipment case. Similarly, information regarding the type and quantity of the third fresh food is obtained from the information terminal of the second shipment case. Based on the information collected in this way, the characteristics and requirements of the fresh food are identified.

[0115] Next, temperature and humidity information of the second container is obtained to verify the environmental conditions inside the container. In addition, information on the transportation route and duration is obtained to allow consideration of expected external conditions and time required during transportation.

[0116] Based on the information obtained in this way, an artificial intelligence model is utilized to calculate the loading location of the second fresh food item within the second container. The AI ​​model calculates the optimal loading location by comprehensively considering the characteristics of the fresh food, environmental conditions, and route and time factors.

[0117] Finally, loading location information is transmitted to the display terminal of the first shipping case to guide the case to be loaded at the designated location. This enables cold chain management that optimizes the preservation of fresh food quality and minimizes losses.

[0118] Since the display on the shipping case shows where the corresponding shipping case can be loaded within the container, the shipping case can be conveniently loaded into the container.

[0119] That is, when fresh food arrives at the port, all of the fresh food can be transferred into shipping cases. Before loading the shipping cases into the container, all of the fresh food is transferred into their respective cases, and if the information of the fresh food contained in each shipping case is entered, the server can obtain information about the fresh food contained in the shipping case from the shipping case terminal and obtain information about all fresh food to be shipped into the container. The server can calculate the position where each shipping case should be located by considering information such as the shipping period and shipping route of the container before the shipping case is loaded into the container, and can transmit the calculated position to the shipping terminal (i.e., the display terminal).

[0120]

[0121] FIG. 7 is a drawing illustrating a method of loading a shipping case into a container according to one embodiment of the present specification.

[0122] Referring to Fig. 7, the positions of the boxes loaded inside the container can be represented in three-dimensional coordinates. Each box is indicated by coordinates in the form (x, y, z), and these coordinates represent the location of the box inside the container.

[0123] · (1, 1, 1): A box located at the bottom left of the container floor.

[0124] · (1, 2, 1): The box located to the right of the first box.

[0125] · (1, 3, 1): The box located to the right of the first box, next to the second box.

[0126] · (1, 1, 2): Box stacked on top of the first box.

[0127] In this way, by specifying the location of the box using coordinates, the optimal loading location determined by the AI ​​model can be efficiently communicated to the people actually shipping the fresh food.

[0128]

[0129] FIG. 8 is a drawing illustrating a shipping case according to one embodiment of the present specification.

[0130] Referring to Fig. 8, a box loaded at a specific location within a container can be indicated by the coordinates (1, 1, 1).

[0131] (1, 1, 1): This coordinate represents the leftmost front bottom position of the container bottom in 3D space. In other words, this indicates that the box is loaded in the bottom-left corner of the container.

[0132] In this way, by defining the loading position within the container using coordinates, the arrangement and location of the boxes can be clearly managed. A person loading the shipping case into the container can conveniently load the shipping case to the corresponding location in the container by looking at the display.

[0133] For example, based on the fact that the loading position of the first shipping case is located at the innermost part of the second container, the server may transmit an alarm trigger signal for the first shipping case to the alarm terminal of the first shipping case.

[0134] For example, an alarm trigger signal for a shipping case can be transmitted to an alarm terminal of a shipping case located at the innermost part of the container in FIG. 7 (e.g., when the x value is 1 at the (x, y, z) coordinates) (i.e., a shipping case with the coordinates (1, y, z)). When the alarm terminal attached to each shipping case receives the alarm trigger signal, it can trigger an alarm. For example, the alarm may include a light-emitting form or a form that plays an alarm sound.

[0135] For example, the innermost loading situation of the container can be considered. That is, when all shipping cases are loaded in the innermost (e.g., when the x value is 1 at the (x, y, z) coordinate), an alarm trigger signal for the corresponding shipping case can be transmitted to the alarm terminal of the shipping cases located in the next innermost (e.g., when the x value is 2 at the (x, y, z) coordinate) (i.e., the shipping case with the coordinate (2, y, z)).

[0136]

[0137] For example, the server can obtain information about the consignor of the third fresh food from the information terminal of the second shipment case. Through this, the server can identify who the owner of the third fresh food is and can reflect the consignor's requirements.

[0138] For example, the server may obtain priority information regarding the consignor of the third fresh food. For example, the server obtains priority information regarding the consignor of the third fresh food to determine whether there is a need for the food to be protected preferentially compared to other cargo. This serves as an important criterion in cases where the food is sensitive or requires special protection under specific conditions. Alternatively, the consignor of the third fresh food may be a consignor who has paid additional fees to obtain priority.

[0139] For example, the server can calculate the optimal loading location within the second container where the lowest loss rate of the third fresh food is expected by utilizing the artificial intelligence model based on information regarding the transportation route and transportation period of the second container, information regarding the type and quantity of the second fresh food, and temperature and humidity information of the second container.

[0140] In other words, the server can calculate the optimal loading location within the second container where the lowest loss rate of the third fresh food is expected by utilizing an artificial intelligence model based on the second container's transportation route and duration, the type and quantity of the second fresh food, and the second container's temperature and humidity information. The artificial intelligence model can derive the location where the fresh food can be stored most stably during transportation by comprehensively analyzing various environmental factors and cargo characteristics.

[0141] For example, the server may transmit information regarding the loading location of the second shipping case to the display terminal of the second shipping case. That is, the server may transmit information regarding the calculated loading location to the display terminal of the second shipping case to guide the case to be loaded at the proposed location. Through this, the quality of the fresh food can be preserved to the maximum extent while minimizing losses during the transportation process.

[0142]

[0143] The operation according to the embodiments of this specification may be implemented as a computer-readable program or code on a computer-readable recording medium. A computer-readable recording medium includes all types of recording devices in which data that can be read by a computer system is stored. Additionally, a computer-readable recording medium may be distributed across networked computer systems, allowing computer-readable programs or code to be stored and executed in a distributed manner.

[0144] When the embodiment is implemented in software, the above-described technique may be implemented as a module (process, function, etc.) that performs the above-described function. The module may be stored in memory and executed by a processor. The memory may be located inside or outside the processor and may be connected to the processor by various well-known means.

[0145] In addition, computer-readable recording media may include hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, and flash memory. Program instructions may include machine code, such as that generated by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc.

[0146] Some aspects of this specification have been described in the context of a device, but may also be described according to a corresponding method, wherein a block or device corresponds to a method step or a feature of a method step. Similarly, aspects described in the context of a method may also be described according to a corresponding block or item or a feature of a corresponding device. Some or all of the method steps may be performed by (or using) a hardware device, such as, for example, a microprocessor, a programmable computer, or an electronic circuit. In some embodiments, one or more of the most important method steps may be performed by such a device.

[0147] In the embodiments, a programmable logic device (e.g., a field-programmable gate array) may be used to perform some or all of the functions of the methods described herein. In the embodiments, the field-programmable gate array may operate with a microprocessor to perform one of the methods described herein. Generally, it is preferable that the methods be performed by some hardware device.

[0148] Although the foregoing has been described with reference to preferred embodiments of this specification, those skilled in the art will understand that various modifications and changes can be made to this specification without departing from the spirit and scope of the specification as set forth in the following claims.

Claims

1. A method for providing an artificial intelligence model-based fresh food cold chain LCL optimization service performed by at least one server, A step of obtaining information on the type, quantity, and departure status of the first fresh food to be shipped in the first container; A step of obtaining temperature and humidity information of the first container loaded with the first fresh food; A step of obtaining location information where the first fresh food is loaded within the first container; A step of obtaining information on the arrival status of the first fresh food after the first container arrives at the destination; A step of obtaining information on the transportation route and transportation period of the first container; and A step comprising learning the optimal fresh food loading location using an artificial intelligence model based on information regarding the transportation route and transportation period of the first container, information regarding the type and quantity of the first fresh food, temperature and humidity information of the first container, location information where the first fresh food is loaded, and the difference between the departure state and arrival state of the first fresh food. Method for providing fresh food cold chain LCL optimization services based on artificial intelligence models.

2. In Paragraph 1, Information regarding the starting state of the first fresh food includes weight information at the time the first fresh food is loaded into the first container, and Information regarding the arrival status of the first fresh food includes weight information of the first fresh food after the first container arrives at the destination. Method for providing fresh food cold chain LCL optimization services based on artificial intelligence models.

3. In Paragraph 1, Information regarding the starting state of the first fresh food includes photographic information at the time the first fresh food is loaded into the first container, and Information regarding the arrival status of the first fresh food includes photographic information of the first fresh food and information on the loss rate of the first fresh food obtained from the consignee terminal of the first fresh food after the first container arrives at the destination. Method for providing fresh food cold chain LCL optimization services based on artificial intelligence models.

4. In Paragraph 1, A step of obtaining information regarding the type and quantity of a second fresh food loaded in the first shipping case from an information terminal of the first shipping case; A step of obtaining information regarding the type and quantity of a third fresh food loaded in the second shipping case from an information terminal of the second shipping case; A step of obtaining temperature and humidity information of a second container on which the first shipping case and the second shipping case are to be loaded; A step of obtaining information regarding the transportation route and transportation period of the second container; A step of calculating the loading position of the second fresh food within the second container using the artificial intelligence model based on information regarding the transportation route and transportation period of the second container, information regarding the type and quantity of the second fresh food, information regarding the type and quantity of the third fresh food, and temperature and humidity information of the second container; and A method comprising the step of transmitting information regarding the loading position of the first shipping case to the display terminal of the first shipping case. Method for providing fresh food cold chain LCL optimization services based on artificial intelligence models.

5. In Paragraph 4, A step of obtaining information about the consignor of the third fresh food from the information terminal of the second shipment case; A step of obtaining priority information regarding the consignor of the third fresh food above; A step of calculating an optimal loading location within the second container where the lowest loss rate of the third fresh food is expected by utilizing the artificial intelligence model based on information regarding the transportation route and transportation period of the second container, information regarding the type and quantity of the second fresh food, and temperature and humidity information of the second container; and A method comprising the step of transmitting information regarding the loading position of the second shipping case to the display terminal of the second shipping case. Method for providing fresh food cold chain LCL optimization services based on artificial intelligence models.

6. In Paragraph 5, Based on the fact that the loading position of the first shipping case is located at the innermost part of the second container, the method includes the step of transmitting an alarm trigger signal for the first shipping case to an alarm terminal of the first shipping case. Method for providing fresh food cold chain LCL optimization services based on artificial intelligence models.

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