KR-20260066991-A - Temperature Prediction AI-Based Cargo Monitoring System

KR20260066991AKR 20260066991 AKR20260066991 AKR 20260066991AKR-20260066991-A

Abstract

A cargo monitoring system based on AI temperature prediction is disclosed. A cargo monitoring system according to one embodiment of the present invention may include: a temperature sensor that detects the temperature of a cargo during a cargo delivery process; a database in which first temperature data during the cargo delivery process is recorded; and a control unit that preprocesses the first temperature data of the cargo to generate second temperature data, learns the generated second temperature data based on AI, and detects whether there is an abnormality in the cargo condition based on third temperature data of the cargo confirmed at predetermined intervals through the temperature sensor.

Inventors

오수영

Assignees

주식회사 옵티로

Dates

Publication Date: 20260512
Application Date: 20241105

Claims (10)

A temperature sensor that detects the temperature of the cargo during the cargo delivery process; A database in which first temperature data from the cargo delivery process is recorded; and A cargo monitoring system comprising a control unit that preprocesses first temperature data of the cargo to generate second temperature data, learns the generated second temperature data based on AI, and checks for abnormalities in the cargo condition based on third temperature data of the cargo confirmed at predetermined intervals through the temperature sensor.
In paragraph 1, The above control unit is, A cargo monitoring system that generates the second temperature data by inserting virtual temperature data when the temperature interval of the first temperature data is less than or equal to a predetermined value.
In paragraph 2, The above control unit is, A cargo monitoring system that inserts the above-mentioned virtual temperature data through exponential average shifting.
In paragraph 1, The above control unit is, A cargo monitoring system that performs data parallel processing to simultaneously process multiple first temperature data of the above cargo, and generates second temperature data by supplementing each first temperature data with virtual temperature data.
In paragraph 4, The above control unit is, When preprocessing the first temperature data of the above cargo, previously preprocessed data is stored in a cache memory to prevent duplicate calculations, and A cargo monitoring system that skips preprocessing of data when new temperature data matches previous data stored in the cache memory.
As a cargo monitoring method, A step of collecting first temperature data of the cargo during the cargo delivery process; A step of preprocessing the first temperature data of the above cargo to generate second temperature data; A step of learning the generated second temperature data based on AI; and A cargo monitoring method comprising the step of checking for abnormalities in the cargo condition based on third temperature data of the cargo confirmed at predetermined intervals through the temperature sensor.
In paragraph 6, The step of generating second temperature data by preprocessing the first temperature data of the above cargo is: A cargo monitoring method comprising the step of generating the second temperature data by inserting virtual temperature data when the temperature interval of the first temperature data is less than or equal to a predetermined value.
In Paragraph 7, The step of generating the second temperature data by inserting virtual temperature data is: A cargo monitoring method comprising the step of inserting the above-mentioned virtual temperature data through exponential average shifting.
In paragraph 6, The step of generating second temperature data by preprocessing the first temperature data of the above cargo is: A step of performing data parallel processing to simultaneously process a plurality of first temperature data of the above cargo; and A cargo monitoring method comprising the step of generating second temperature data by supplementing each first temperature data with virtual temperature data.
In Paragraph 9, The step of generating second temperature data by preprocessing the first temperature data of the above cargo is: When preprocessing the first temperature data of the above cargo, the step of storing previously preprocessed data in a cache memory; and A cargo monitoring method comprising the step of omitting preprocessing of data when new temperature data matches previous data stored in the cache memory.

Description

Temperature Prediction AI-Based Cargo Monitoring System Various embodiments of the present invention relate to a system for monitoring the temperature of cargo more efficiently. Recently, there has been an increasing need for systems that continuously monitor temperature changes during the logistics process when transporting temperature-sensitive pharmaceuticals or high-value cargo. Since specialized cargo such as pharmaceuticals is sensitive to temperature fluctuations, maintaining an appropriate temperature during transport is essential. Since the efficacy of pharmaceuticals may decrease if the temperature deviates from the permissible range, technology is required to detect temperature in real time during transportation and quickly identify any abnormal conditions. Traditional cargo monitoring systems operate by measuring cargo temperature data at regular intervals and transmitting it to a server. If temperature data is recorded at relatively long intervals, the low data density limits the ability to quickly detect sudden temperature changes. This intermittent data collection method makes it difficult to track detailed patterns of temperature changes, and the lack of data for training AI models limits the ability to accurately detect abnormal conditions. FIG. 1 is a configuration diagram showing a cargo monitoring system according to one embodiment. FIG. 2 is a flowchart illustrating an operation for monitoring cargo status according to one embodiment. FIG. 3 is a flowchart of the operation of generating virtual temperature data during a temperature data preprocessing process according to one embodiment. FIG. 4 is a flowchart of an operation for performing parallel processing during a temperature data preprocessing process according to one embodiment. Figure 5 is an example of a screen displaying first temperature data. Figure 6 is an example of a screen showing second temperature data with virtual temperature data inserted. Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the attached drawings. However, the technical concept of the present invention is not limited to some of the described embodiments but can be implemented in various different forms, and within the scope of the technical concept of the present invention, one or more of the components among the embodiments may be selectively combined or substituted. In addition, terms used in the embodiments of the present invention (including technical and scientific terms) may be interpreted in a sense that is generally understood by those skilled in the art to which the present invention belongs, unless explicitly and specifically defined otherwise. Terms that are commonly used, such as terms defined in advance, may be interpreted in consideration of their meaning in the context of the relevant technology. Furthermore, the terms used in the embodiments of the present invention are for the purpose of describing the embodiments and are not intended to limit the present invention. In this specification, the singular form may include the plural form unless specifically stated otherwise in the text, and when described as “at least one of A and B, C (or more than one of them),” it may include one or more of all combinations that can be formed from A, B, and C. In addition, terms such as first, second, A, B, (a), (b), etc. may be used when describing the components of the embodiments of the present invention. These terms are intended merely to distinguish a component from other components and are not limited by the nature, order, sequence, etc., of the said component. And, where it is stated that a component is ‘connected,’ ‘combined,’ or ‘joined’ to another component, this may include not only cases where the component is directly connected, combined, or joined to the other component, but also cases where it is ‘connected,’ ‘combined,’ or ‘joined’ due to another component located between the component and the other component. Furthermore, when described as being formed or placed on the “top or bottom” of each component, “top or bottom” includes not only cases where two components are in direct contact with each other, but also cases where one or more other components are formed or placed between the two components. Additionally, when expressed as “top or bottom,” it may include the meaning of a downward direction as well as an upward direction relative to a single component. In the various flowcharts of this document, at least some of the steps may be omitted or their order may be changed, and at least some of the various embodiments of this document may be performed at specific points in time of each step of the flowchart. Each action according to the various flowcharts of this document may be performed by at least one of a cargo monitoring system (1), a server (200), a control unit (230), a processor, or a computer program. Hereinafter, embodiments will be described in detail with reference to the a