Method and apparatus for generating delivery route based on freshness index of product and moving route of transportation

Abstract

One embodiment of the present invention relates to a method and apparatus for generating a delivery route based on a product freshness index and a transport route. It is characterized by calculating a delivery urgency for a purchased product based on its expiration date and storage conditions, and performing a process of determining a final delivery order for multiple delivery destinations based on the calculated delivery urgency and route information of the transport route obtained from an external server. The present invention simultaneously achieves logistics efficiency and product quality maintenance through an optimization algorithm that combines a freshness index and the physical route of the transport route, and can provide a user-customized freshness guarantee service through nanofiber film and calendar integration. Furthermore, it supports autonomous logistics control that preserves freshness even in extreme conditions through active cooling, spoilage gas monitoring, and logic for responding to deadline reversals.

Description

Technology Field

[0001] The present invention relates to a method and apparatus for generating a delivery route based on a product freshness index and a transportation route. More specifically, it relates to a technical field that determines an optimal delivery order by combining a freshness index, which quantifies the unique characteristics of a purchased product such as expiration date, quantity, and storage temperature, with the operation schedule of a transportation means such as a high-speed train, and provides visual freshness information and schedule-customized delivery to the user. Background Technology

[0002] Along with the recent rapid growth of the e-commerce market, the demand for fresh food delivery has increased significantly; however, existing logistics systems primarily rely on the shortest route or simple zone-based allocation methods. Consequently, biological characteristics such as expiration dates and optimal storage temperatures, which vary by product even within the same delivery route, are not accurately reflected. This frequently leads to issues where specific products rapidly deteriorate in freshness or spoil during the delivery process, resulting in their disposal.

[0003] In particular, in wide-area delivery systems linked with public transportation such as high-speed trains, there is currently a lack of technology to organically combine the fixed routes and timetables of transport vehicles with the real-time freshness status of individual products. If delivery urgency is prioritized solely, it may conflict with the physical movement of transport vehicles, thereby lowering overall logistics efficiency; conversely, if only the transport route is followed, there is a risk of missing the appropriate quality maintenance period because the extent of freshness degradation based on the delivery location cannot be predicted.

[0004] Furthermore, from the perspective of the end recipient, there is a lack of objective methods to verify whether products were maintained at the appropriate temperature during delivery, and blind spots in management persist, such as fresh food being left at the door for extended periods when the recipient is absent. Therefore, there is an urgent need to introduce an intelligent delivery control system capable of quantitatively calculating product freshness characteristics and integrating them with the real-time routes of transportation vehicles to perform optimal routing, while simultaneously providing recipients with visual freshness assurance and schedule-linked services. The problem to be solved

[0005] The objective of the present invention to solve the above-mentioned problems is to provide a method for generating a delivery route based on the freshness index of a product and the movement path of a transportation means.

[0006] Another objective of the present invention to solve the above-mentioned problems is to provide a delivery route generation device based on the freshness index of a product and the movement path of a transportation means.

[0007] The problems that this application aims to solve are not limited to those described above, and problems not mentioned will be clearly understood by those skilled in the art from this specification and the attached drawings. means of solving the problem

[0008] A method for generating a delivery route based on a product freshness index and a transport means movement path according to an embodiment of the present invention for achieving the above objective may include the step of calculating a delivery urgency for a purchased product based on the expiration date and storage conditions of the purchased product, and the step of determining a final delivery order for a plurality of delivery destinations based on the calculated delivery urgency and route information of the transport means obtained from an external server.

[0009] The means for solving the problem are not limited to the means described above, and unmentioned means of solving will be clearly understood by those skilled in the art to which this application pertains from this specification and the attached drawings. Effects of the invention

[0010] The present invention has the technical advantage of overcoming the limitations of simple distance-based delivery and precisely determining the delivery order optimized for maintaining the freshness of individual products by mathematically modeling various parameters such as the expiration date, quantity, and storage temperature of purchased products to calculate a freshness index and a delivery urgency index.

[0011] In particular, by linking the calculated delivery urgency index with the actual movement route information of high-speed trains and dynamically adjusting delivery priorities within the index error range, it is possible to simultaneously achieve the conflicting goals of physical transportation route efficiency and product quality maintenance.

[0012] In addition, by transmitting an image of a nanofiber film whose color or transparency changes depending on the temperature exposure environment to a user terminal and supporting the setting of a delivery time linked to the user's calendar schedule, it provides reliable freshness information to the recipient and simultaneously prevents spoilage accidents caused by product neglect, thereby providing the effect of dramatically improving customer satisfaction.

[0013] Furthermore, it offers unrivaled effectiveness in preventing product waste and optimizing logistics costs even in the event of unexpected delays through pre-cooling control based on thermal shock prediction at transfer points, refrigerant refreezing utilizing an active cooling grid, nano-sensor-based self-tuning of spoilage acceleration, and a thermodynamic recovery mechanism in the event of deadline reversal. Brief explanation of the drawing

[0014] FIG. 1 is a block diagram of an electronic device that performs a method and device for generating a delivery route based on a product freshness index and a transport means movement path according to one embodiment of the present invention. FIG. 2 is a diagram showing terminals performing a method and apparatus for generating a delivery route based on a product freshness index and a transport means movement path according to an embodiment of the present invention. FIG. 3 is a flowchart of a method and apparatus for generating a delivery route based on a product freshness index and a transport means movement route according to an embodiment of the present invention. Specific details for implementing the invention

[0015] The present invention is susceptible 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 invention to specific embodiments, and it should be understood that the invention includes all modifications, equivalents, and substitutions that fall within the spirit and scope of the invention. Similar reference numerals have been used for similar components in the description of each drawing.

[0016] Terms such as first, second, A, B, etc., may be used to describe various components, but said components shall 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 present invention, 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.

[0017] 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.

[0018] The terms used in this application are used merely to describe specific embodiments and are not intended to limit the invention. 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 specify 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.

[0019] 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 the present invention 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.

[0020] Hereinafter, preferred embodiments of the present invention will be described in more detail with reference to the attached drawings. In order to facilitate an overall understanding of the present invention, the same reference numerals are used for identical components in the drawings, and redundant descriptions of identical components are omitted.

[0021] FIG. 1 is a block diagram of an electronic device that performs a method and device for generating a delivery route based on a product freshness index and a transport means movement path according to one embodiment of the present invention.

[0022] Referring to FIG. 1, an electronic device according to one embodiment of the present invention may be implemented as various types of hardware devices capable of performing computing operations, such as, for example, a cloud server, a rack server in a data center, a desktop PC, a laptop, a smartphone, a tablet PC, and a dedicated logistics terminal. The electronic device may be configured to include a processor, memory, a communication unit, and an input / output interface to implement the core delivery path generation method and intelligent integrated delivery routing system of the present invention.

[0023] The processor is a component that controls the overall operation of the electronic device and performs various data processing and calculations, and may be implemented as a Central Processing Unit (CPU), an Application Processor (AP), a Micro Controller Unit (MCU), or a Graphics Processing Unit (GPU) and a Neural Processing Unit (NPU) for advanced AI model calculations. Specifically, the processor can proactively control a series of processes, such as calculating the delivery urgency of a purchased product based on its expiration date and storage conditions by executing one or more instructions stored in memory, and determining the final delivery order for multiple delivery destinations based on the calculated delivery urgency and route information of the transport means obtained from an external server. Furthermore, complex optimization calculations, such as logic for dynamically recalculating a delivery urgency index based on real-time external environment perception using a machine learning model and a routing-linked smart packaging automatic calculation algorithm, can also be performed through the computing resources allocated to the processor.

[0024] A processor included in a delivery path generation device according to one embodiment of the present invention may be implemented as a plurality of processors that are physically or logically separated. As a specific example, the processor may be configured to include: a first processor that calculates and computes a delivery urgency for a purchased product based on the expiration date and storage conditions of the purchased product; and a second processor that determines a final delivery order for a plurality of delivery destinations based on the delivery urgency computed by the first processor and route information of the transport means obtained from an external server.

[0025] Additionally, the "average shelf life value" mentioned in the detailed description of the present invention may refer to the "average shelf life" described in the claims. In the same context, the "time elapsed since the point of purchase" described in the detailed description may refer to the "time elapsed since purchase" described in the claims, and the "pre-set error standard" may encompass the "pre-set standard" described in the claims.

[0026] The memory may be a non-transitory computer-readable storage medium that stores program code for processing and controlling the processor and temporarily or permanently stores a vast amount of input / output data. The memory may include various types of volatile or non-volatile storage media, such as flash memory, hard disk, SSD (Solid State Drive), RAM (Random Access Memory), ROM (Read Only Memory), and EEPROM (Electrically Erasable Programmable Read-Only Memory). In an embodiment of the present invention, the memory may store characteristic data of purchased goods (quantity, storage temperature, expiration date, etc.), parameters and mathematical formulas for deriving a freshness index, information on the movement path and operation schedule of public transportation means such as high-speed trains, weight networks of machine learning models, and recipient's calendar schedule data in the form of a systematized database, and may provide a workspace necessary for the processor to dynamically generate and correct delivery paths.

[0027] The communication unit may be a network interface module that supports the electronic device to smoothly transmit and receive data through a wired or wireless communication network with external objects including a first external server, a second external server, a control system for a means of transport, a logistics worker terminal, and a recipient terminal. The communication unit may be equipped with a transceiver that supports various standard communication specifications including 5G, LTE (Long Term Evolution), Wi-Fi (Wireless Fidelity), Bluetooth, Zigbee, and NFC (Near Field Communication). In the present invention, the communication unit functions as a channel for collecting real-time external environmental information, such as obtaining route information of a means of transport, linking with a recipient's calendar schedule, weather data from the Korea Meteorological Administration, and information on unexpected delays, and may perform the role of an organic data exchange bridge that transmits logistics control information, such as the generated final delivery order, image data of a nanofiber film, refrigerant mass, and critical temperature specifications, to the corresponding terminals.

[0028] The above input / output interface may be a physical or logical means for receiving system control commands or data from an administrator or user, and outputting computation results processed by a processor or system operation status information to the outside. Input devices may include a keyboard, mouse, touch panel, microphone, scanner, etc., and output devices may include a high-resolution display, speaker, printer, haptic module, etc. Through this, the system operator can initialize and configure the operating environment of the delivery route generation device, and can monitor and control dynamically changing logistics control information, such as the generation of the first delivery order, switching correction details based on the transport route sequence, and changes in the predicted remaining storage time, in real time through an intuitive GUI (Graphical User Interface)-based dashboard.

[0029] FIG. 2 is a diagram showing terminals performing a method and apparatus for generating a delivery route based on a product freshness index and a transport means movement path according to an embodiment of the present invention.

[0030] Referring to FIG. 2, an intelligent integrated delivery routing system according to an embodiment of the present invention includes a delivery route generation device, a first external server, a second external server, a logistics worker terminal, and a recipient terminal, and these can form a network architecture that organically transmits and receives data by being interconnected through a wired or wireless communication network. The communication network is a broadband data transmission network encompassing the Internet, an intranet, a mobile communication network, and a satellite communication network, and can provide seamless data connectivity so that large-capacity data and real-time control information between entities can be exchanged without delay. The delivery route generation device functions as the control tower of the entire system and can perform a pivotal role in deriving an optimized dynamic delivery route and integrally controlling the entire logistics process based thereon.

[0031] The delivery route generation device described above may communicate with a first external server and a second external server to obtain basic data for route generation and scheduling. The first external server may be a server managing operation information and control systems for public transportation means, such as high-speed trains, or a public data portal server, such as a meteorological agency. The delivery route generation device may receive dynamic environmental information in real time from the first external server, including departure and arrival times at specific locations, transit information, weather data, and information on unexpected delays of transportation means. Additionally, the delivery route generation device may obtain calendar schedule data of individual recipients by linking with the second external server. The second external server may be a cloud server providing a schedule management application or email service used by the recipient. The delivery route generation device may parse and analyze the received calendar schedule to reflect this in the priority correction process of the final delivery order, thereby avoiding times when the recipient is absent or has scheduled appointments and ensuring that delivery takes place during times when there are no appointments.

[0032] The final delivery order and dynamic control information generated through these interactions can be immediately transmitted to the logistics worker terminal and the recipient terminal via a communication network. Specifically, the delivery route generation device can calculate the total time required based on the confirmed delivery route and the estimated arrival time for each stopover point, and by synthesizing acquired predicted temperature data, automatically calculate the precise mass of refrigerant to be added to the packaging of individual purchased products and the critical temperature specifications of the nanofiber film to be attached to the packaging material, and then transmit this information to the logistics worker terminal. Upon receiving this, the logistics worker can perform individually customized packaging operations according to the smart packaging instruction information displayed on the terminal. At the same time, as delivery begins, the delivery route generation device can transmit delivery schedule information, including an image of the initial state of the nanofiber film attachment, to the recipient terminal to visually verify the freshness of the product to the recipient and increase confidence in the delivery.

[0033] Furthermore, the real-time data communication flow between the delivery route generation device and each terminal and server can be continuously maintained even while delivery is in progress. The delivery route generation device equipped with a machine learning model processes data on internal temperature changes, sudden traffic congestion, and weather fluctuations collected during the real-time movement of the transport vehicle using a deep learning-based analysis engine to predict the remaining storage time of packaging materials inside the ice box in real time. If the prediction result detects a risk of reaching the freshness threshold compared to the existing schedule, the freshness index and delivery urgency index are immediately recalculated to dynamically change (reroute) the order of the remaining delivery routes, and the updated route and schedule information is transmitted to the logistics worker terminal in the form of a push notification, thereby completing a real-time closed-loop control system that guarantees the freshness of the product even in the face of unpredictable external environmental changes.

[0034] FIG. 3 is a flowchart of a method and apparatus for generating a delivery route based on a product freshness index and a transport means movement route according to an embodiment of the present invention.

[0035] Referring to FIG. 3, the delivery path generating device according to the present invention can initiate a series of operations by extracting characteristic data of the purchased product from a database and performing an initial analysis simultaneously with the receipt of a purchase order from a recipient.

[0036] In operation S310, the processor of the device can calculate the delivery urgency for each product based on the expiration dates and storage conditions (e.g., whether refrigerated, frozen, or room temperature storage) of the extracted purchased products. As a specific computational process, the processor can derive a freshness index, which is an objective numerical value, through a mathematical formula that comprehensively reflects the average expiration date of the purchased products, the duration for which the recommended storage temperature can be maintained, the number of product types bundled for delivery, and the sensitivity weight for each product, departing from the conventional method of simply considering only the physical distance to the destination. Subsequently, the processor can calculate a delivery urgency index, which is a key parameter for generating the final delivery path, by combining the value calculated by applying a logarithmic function to the elapsed time and the distance to the destination with the previously derived freshness index. This logarithmic function-based numerical logic has technical significance in that it prevents divergence of the index value even if the elapsed time or distance increases extremely, and secures the golden time for delivery by precisely and non-linearly amplifying the urgency weight as the product approaches its spoilage threshold.

[0037] After the delivery urgency index is calculated, the processor communicates with a second external server to obtain calendar schedule data for recipients at each delivery location and can merge this into the route generation process. Since the precise storage temperature maintenance time calculated earlier becomes meaningless if fresh food is left at the door for an extended period while the recipient is absent, the processor can identify time slots when the recipient has no appointments or outings and set scheduling conditions to ensure delivery occurs within the intersection of those times. This proactive integration of personalized schedule data goes beyond simple delivery network optimization to provide an advanced logistics control environment that fundamentally prevents the risk of freshness degradation during the last-mile delivery stage.

[0038] Subsequently, in operation S320, the processor may enter a routing process that determines the final delivery order for multiple delivery destinations by synthesizing the calculated delivery urgency index, physical route information of public transportation means such as high-speed trains obtained from an external server, and the recipient absence schedule. First, the processor may generate a primary delivery order by prioritizing delivery destinations with a high delivery urgency index (i.e., those with a relatively low calculated value indicating urgency or close to a threshold) within the available time slot. However, at this stage, the processor may not rely solely on the index order but may additionally apply an advanced priority correction algorithm based on the actual operation schedule of the transportation means. The difference in delivery urgency index between adjacent delivery destinations in the primary delivery order may be calculated, and if that difference is within a preset standard (e.g., 5 or less), it may be determined that the freshness risk levels between the two products are equivalent. In this case, rather than strictly following the urgency index, the processor may correct the trajectory by prioritizing the actual order of high-speed train stops or route sequences and switching the delivery order of the relevant destinations. This is a very practical optimization technique that compromises the inefficiency of movement paths caused by mathematical scheduling with the specifications of the actual physical logistics environment, and can have the effect of reducing energy consumption of the entire delivery network and drastically shortening the average delivery completion time.

[0039] Once the final delivery order is confirmed, the processor can directly control the packaging process at the logistics site by running a routing-linked smart packaging automatic calculation algorithm. Based on the corrected final delivery route and estimated arrival times at each stopover point, the processor calculates the total time required for the entire shipment and maps hourly predicted temperature data for destinations and stopover points collected through sources such as the meteorological agency portal. Based on these two spatiotemporal environmental variables, the processor can inversely calculate the precise mass of refrigerant (ice packs, dry ice, etc.) to be placed inside the ice box for each purchased product, as well as the critical temperature specifications of the nanofiber film that detects external temperature exposure of the packaging material. The calculated packaging control data can be immediately transmitted to the logistics worker's terminal, allowing the worker to complete the packaging using the specified amount of refrigerant and customized film. This breaks away from the conventional practice of uniformly wasting packaging materials regardless of season or destination, and enables eco-friendly and economical logistics processing by synchronizing routing schedules with packaging resources.

[0040] At the time packaging is completed and the product is dispatched, the processor can simultaneously perform a customer service process for the visual assurance of freshness. The special nanofiber film attached to the outer surface of the packaging by logistics workers may possess the characteristic of undergoing irreversible changes in color or transparency due to a change in molecular structure when exposed to heat exceeding a set critical temperature. As soon as delivery begins, the processor can transmit delivery schedule information to the recipient's terminal, including the estimated completion time and an image capturing the film's initial attachment state (normal temperature state). Upon receiving this, the recipient can visually compare the original image received on their terminal with the actual condition of the film on the delivered box upon receiving the product; this can serve as a differentiated means to maximize consumer trust in the e-commerce platform by transparently verifying the integrity of temperature management during the delivery process.

[0041] According to one embodiment of the present invention, the processor can perform precise control to prevent freshness degradation that may occur at a transfer point between transport means. Specifically, in the step of determining the final delivery order, the processor can predict the surface temperature and expected heat shock of the purchased goods at the transfer point by combining real-time heat map data for each internal zone of the transport means and external weather data. If it is determined that the predicted heat shock exceeds a preset threshold, the processor can instruct the vehicle control server to pre-cool the cargo compartment temperature of the delivery vehicle to a target temperature synchronized with the predicted surface temperature. Furthermore, by dynamically assigning a transfer heat shock weighting factor when calculating the delivery urgency to correct the unloading and vehicle loading order arrangement at the transfer point, external heat exposure can be minimized and a smooth cold-chain connection can be ensured.

[0042] In addition, the processor can accurately predict the remaining shelf life by monitoring the temperature drop curve inside the packaging of the purchased product using a machine learning model. If, as a result of the prediction, a risk is detected that the remaining shelf life will fall below a preset threshold, the processor may activate an active cooling grid equipped in the transport vehicle to forcibly re-freeze the phase change material (PCM) contained in the packaging. Based on the additional shelf life secured through this forcible re-freezing process, the processor recalculates the delivery urgency downward and can dynamically correct the delivery priority trajectory based on the recalculated delivery urgency. This provides an innovative freshness extension mechanism that goes beyond the limitations of simple passive cooling and actively restores the refrigerant state even during transit.

[0043] To further enhance the logic for calculating delivery urgency based on the log function described above, the processor may receive biological spoilage gas concentration data in real time from a multi-nano sensor array installed inside the packaging of the purchased product after calculating the delivery urgency. The processor analyzes the rate of increase of the received gas concentration data to derive the actual spoilage acceleration of the purchased product, and if an error is detected between the trajectory of the delivery urgency calculated based on the log function and the spoilage stage according to the actual spoilage acceleration, the processor may recalculate the delivery urgency by self-tuning the parameters of the log function or the product-specific sensitivity weights. Based on this, by completely readjusting the final delivery order, the gap between the mathematical prediction and the actual biological spoilage state can be filled in real time, thereby preventing product disposal at the source.

[0044] In addition, to address critical delays that may occur during the full-scale delivery process, the processor can detect in real-time a Deadline Inversion phenomenon, in which the remaining storage time of the purchased product becomes shorter than the estimated arrival time at the destination while delivery is proceeding according to the final delivery order. When the Deadline Inversion phenomenon is detected, the processor scans the locations of logistics hubs and refrigerant inventory data existing within the radius of the delivery vehicle's current location. Subsequently, the processor calculates and compares the estimated waste loss cost of proceeding directly without passing through the logistics hub with the detour cost of delivering after performing Thermodynamic Recovery by passing through the scanned logistics hub and reintroducing new refrigerant. If the calculation result determines that the latter is economically advantageous, the processor dynamically inserts a node that forces passage through the logistics hub to update the delivery route, thereby making an optimal autonomous recovery decision that minimizes economic losses even in the event of force majeure delivery delays.

[0045] Meanwhile, in preparation for cases where the recipient's unexpected absence is detected retrospectively via a second external server, the processor can execute active last-mile detour logic at the final consumer delivery stage. When the recipient's unexpected absence is detected, the processor establishes a direct communication session with a smart storage device installed at the recipient's residence and can control a specific storage compartment of the smart storage device to automatically pre-chill by transmitting the delivery person's estimated arrival time and the recommended storage temperature data for the product. If it is reported that there is no available space in the smart storage device or that the cooling function is poor, the processor can pinpoint detour the destination of the purchased product to another nearby available public smart storage location and transmit the changed pickup location and authentication key to the recipient's terminal to confirm and correct the delivery route in real time. Through this, it is possible to completely prevent temperature deviations near the destination and implement a highly reliable end-to-end cold chain even in the event of an unexpected customer absence.

[0046] Finally, the present invention further enhances its inventiveness by performing a real-time closed-loop control step based on a machine learning model even in a dynamic mobile environment where delivery is in progress. While the transport vehicle moves along the track, the processor continuously streams real-time external environmental information via the communication unit, such as real-time temperatures inside and outside the train, data on sudden weather deterioration, and information on train delays or congestion received from the control server. This data is input into a pre-trained artificial intelligence prediction model, which predicts and returns the rate of refrigerant consumption inside the packaging and the remaining shelf life in real-time, down to the millisecond. If a risk is detected that the remaining shelf life of a specific product will be shorter than the estimated arrival time at the destination due to a sudden delay (risk of reaching the freshness threshold), the processor immediately recalculates the previously calculated freshness index and delivery urgency index. Based on the newly updated indices, the processor performs a calculation to dynamically change (reroute) the order of the remaining delivery routes and sends the updated route details as a push notification to the terminal of the logistics worker in transit, thereby inducing them to immediately modify the delivery order on-site. This real-time dynamic path correction technology overcomes the limitations of static scheduling before departure in responding to environmental changes and completes the unique autonomous logistics control mechanism of the present invention, which can preserve the quality of the product 100% even amidst any unexpected variables.

[0047] 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.

[0048] 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.

[0049] 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.

[0050] Some aspects of the invention 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.

[0051] 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.

[0052] Although the present invention has been described above with reference to preferred embodiments, those skilled in the art will understand that various modifications and changes can be made to the invention without departing from the spirit and scope of the invention as described in the following claims.

Claims

Claim 1 A method for generating a delivery route based on a freshness index of a product and a movement path of a means of transport, performed by an electronic device including a processor and memory, comprising: a step of calculating a delivery urgency for a purchased product based on the expiration date and storage conditions of the purchased product; and a step of determining a final delivery order for a plurality of delivery destinations based on the calculated delivery urgency and route information of the means of transport obtained from an external server; wherein the step of calculating the delivery urgency includes: a step of deriving a freshness index by reflecting the average expiration date of the purchased products, the duration for which the recommended storage temperature can be maintained, the number of product types for bundled delivery, and a sensitivity weight for each product; and a step of calculating a delivery urgency index as the delivery urgency by applying the elapsed time of purchase and the distance to the destination to a logarithmic function and combining it with the derived freshness index; and wherein the step of determining the final delivery order includes: a step of generating a primary delivery order by determining the destinations for the plurality of delivery destinations in order of the highest delivery urgency index. and, if the difference in the delivery urgency index between adjacent delivery destinations in the first delivery order is below a preset standard, the method includes the step of correcting the final delivery order by switching the delivery order of the relevant delivery destinations with priority given to the route order of the transportation means; further includes the step of obtaining the recipient's calendar schedule through a second external server and, by referring to the calendar schedule, correcting the final delivery order so that the delivery is set for a time when the recipient has no appointment; further includes the step of attaching a nanofiber film, whose color or transparency changes according to temperature exposure, to the packaging material of the purchased product, and, at the start of delivery, transmitting delivery schedule information including an image of the initial attachment state of the nanofiber film to the recipient's terminal to visually provide the freshness of the purchased product; and further includes the step of calculating the total time required based on the delivery route according to the determined final delivery order and the estimated arrival time for each transit point.The method further includes the step of automatically calculating the mass of refrigerant to be injected into the packaging of individual purchased goods and the critical temperature specification of the nanofiber film to be attached to the packaging material based on predicted temperature data and the total time required, and transmitting this to a logistics worker terminal; wherein the step of determining the final delivery order includes: predicting the surface temperature and expected heat shock of the purchased goods at the transfer point by combining real-time heat map data for each internal zone of the transport means and external weather data; instructing the vehicle control server to pre-cool the cargo compartment temperature of the delivery vehicle to a target temperature synchronized with the predicted surface temperature if the predicted heat shock exceeds a preset threshold; and correcting the unloading and vehicle loading order arrangement at the transfer point by dynamically assigning a transfer heat shock weighting in the step of calculating the delivery urgency; and, after the step of calculating the delivery urgency, receiving biological spoilage gas concentration data in real time from a multi-nano sensor array provided inside the packaging material of the purchased goods; and deriving the actual spoilage acceleration of the purchased goods by analyzing the rate of increase of the received gas concentration data. and, if an error is detected between the trajectory of the delivery urgency calculated based on the logarithmic function and the spoilage stage according to the actual spoilage acceleration, recalculate the delivery urgency by self-tuning the parameters of the logarithmic function or the sensitivity weights per product, and completely readjust the final delivery order based thereon; further comprising the step of obtaining real-time external environment information including at least one of real-time temperature, weather data from the meteorological agency, and sudden delay information of the transport means by utilizing a machine learning model while the transport means is moving;A delivery route generation method further comprising: a step of predicting the remaining storage time of the packaging material in real time based on the real-time external environment information, and if the predicted remaining storage time is less than a preset threshold, activating an active cooling grid equipped in the transport means to forcibly re-freeze the phase change material (PCM) contained in the packaging material, and recalculating the delivery urgency downward based on the additional storage time secured through the forcible re-freezing; and a step of dynamically changing the remaining delivery route order by recalculating the freshness index and the delivery urgency index if a risk is detected that the remaining storage time predicted based on the real-time external environment information will be shorter than the estimated time of arrival at the destination. Claim 2 delete Claim 3 delete Claim 4 delete Claim 5 delete

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