Method and system for monitoring supply chain of stockpiled materials based on satellite information

Abstract

The present invention relates to a method and system for monitoring a supply chain of stockpiled materials based on satellite information, wherein the method comprises the steps of: identifying at least one stockpiled material included in a received satellite image by using an artificial intelligence model; on the basis of a weight estimation algorithm, estimating the weight of stockpiled materials identified in the satellite image; mapping at least some of the satellite image, satellite image capturing time point information, and type information and weight information of the stockpiled materials identified in the satellite image, and storing same as stockpiled material volume information in a DB; and providing a user terminal with a monitoring screen using stockpiled material volume information corresponding to different time points.

Description

Satellite-based supply chain monitoring method and system for offshore storage facilities

[0001] The present invention relates to a satellite information-based offshore storage supply chain monitoring solution.

[0002] Recent advancements in satellite technology have made their use increasingly important in areas such as Earth observation, weather forecasting, and communications. Satellites, through their sensors and cameras, can collect diverse types of data, including high-definition images and videos, geographic information, and environmental data.

[0003] With the advancement of satellite technology, it has become possible to monitor the movement of various types of cargo, such as forest changes in an area of ​​interest, the degree of urbanization, and the presence of building construction and expansion, using satellite imagery.

[0004] Meanwhile, since Japan became the first country in the world to launch futures trading for yard metals in 2005, yard metals have become a key investment product for the global financial investment industry. Driven by China's economic growth, demand for base metals has increased, leading to a corresponding surge in the value of yard metals. However, securing a continuous supply of yard metals is more difficult than securing a steady supply of yard metals, resulting in significant price volatility. Consequently, yard metals represent a high-risk investment for investors.

[0005] Accordingly, there is a need for a solution that reduces the uncertainty of futures trading.

[0006] The present invention relates to a method and system for monitoring a supply chain of arable goods based on satellite information.

[0007] More specifically, the present invention relates to a method and system for periodically monitoring the amount of cargo in a major storage yard.

[0008] In particular, the present invention relates to a method and system for identifying the type of cargo loaded in a storage yard and estimating its weight.

[0009] Furthermore, the present invention relates to a method and system for providing a user-friendly interface for displaying the amount of cargo moving per storage site of interest.

[0010] Furthermore, the present invention relates to a method and system for supporting optimal transaction decision-making in a stock futures market.

[0011] In order to solve the problem discussed above, a method for monitoring a supply chain of stalemate materials based on satellite information according to the present invention may include the steps of receiving a satellite image through a communication unit, identifying at least one stalemate material included in the satellite image using an artificial intelligence model that has learned characteristic information for each type of stalemate material, estimating the weight of the stalemate material identified in the satellite image based on a weight estimation algorithm, mapping at least some of the satellite image, information on the time point at which the satellite image was taken, information on the type and weight of the stalemate material identified in the satellite image, and storing the mapping in a DB as stalemate material movement volume information, and providing a monitoring screen to a user terminal using stalemate material movement volume information corresponding to different time points.

[0012] In this case, the characteristic information for each type of the above-mentioned waste material may include at least one of spectral information, color information, loading characteristic information, and external shape information for each type of the above-mentioned waste material.

[0013] Furthermore, in the step of identifying the above-mentioned waste, a specific type of waste pile is identified from the satellite image, and in the step of estimating the weight of the waste pile, the weight of the waste pile can be estimated using the weight estimation algorithm.

[0014] Furthermore, the weight estimation algorithm can be derived using at least one of the loading characteristic information of the specific type of cargo and the density information of the specific type of cargo.

[0015] Furthermore, the satellite image includes piles of different types of debris, and in the step of estimating the weight of the debris, the weight of each pile of different types of debris can be estimated using the coordinates of the vertices of the areas where the piles of different types of debris are identified.

[0016] Furthermore, in the step of providing the monitoring screen, the monitoring screen is provided using the information on the volume of cargo in the area of ​​interest to monitor the supply chain of cargo in the area of ​​interest, and the monitoring screen may include an image area in which a satellite image taken of the area of ​​interest is displayed and a data area in which the information on the volume of cargo in the area of ​​interest is displayed.

[0017] Furthermore, the information on the volume of waste material in the area of ​​interest includes first waste material volume information including type information and weight information of a pile of waste material piled in the area of ​​interest at a first time point, and second waste material volume information including type information and weight information of a pile of waste material piled in the area of ​​interest at a second time point different from the first time point, and in the data area, a first graphic object corresponding to the first waste material volume information and a second graphic object corresponding to the second waste material volume information may be displayed.

[0018] Furthermore, in the image area, one of a satellite image of the area of ​​interest captured at the first point in time and one of a satellite image of the area of ​​interest captured at the second point in time may be displayed, and in the data area, a graphic object corresponding to one of the first graphic object and the second graphic object displayed in the image area may be highlighted.

[0019] Furthermore, the method further includes a step of receiving a user input for an icon linked to a function of displaying an area where the pile of debris is identified while a satellite image of a specific point in time among the first point in time and the second point in time is displayed in the image area, and based on the user input, a label graphic object corresponding to the pile of debris is mapped and displayed in an area where the pile of debris is identified in the satellite image of the specific point in time, and the color of the label graphic object may vary depending on the type of the pile of debris.

[0020] Meanwhile, a satellite information-based supply chain monitoring system for stalemate materials according to the present invention may include a communication unit that receives a satellite image, an artificial intelligence model that learns characteristic information for each type of stalemate material to identify at least one stalemate material included in the satellite image, an estimation algorithm for estimating the weight of the stalemate material identified in the satellite image, and at least some of the satellite image, the time point information for taking the satellite image, the type information of the stalemate material identified in the satellite image, and the weight information, and a control unit that stores the stalemate material movement volume information in a DB by mapping the stalemate material movement volume information corresponding to different time points to a user terminal.

[0021] Furthermore, the program according to the present invention is a program executed by one or more processes in an electronic device and stored in a computer-readable recording medium, and may include commands for performing the steps of: receiving a satellite image through a communication unit; identifying at least one yard load included in the satellite image using an artificial intelligence model that has learned characteristic information by type of yard load; estimating the weight of the yard load identified in the satellite image based on a weight estimation algorithm; mapping at least some of the satellite image, information on the time point at which the satellite image was taken, information on the type of yard load identified in the satellite image, and weight information, and storing the mapping in a DB as yard load volume information; and providing a monitoring screen using yard load volume information corresponding to different time points to a user terminal.

[0022] The satellite information-based offshore storage supply chain monitoring method and system according to the present invention periodically acquires high-resolution satellite images of major offshore storage sites around the world, and enables monitoring of offshore storage volume through time-series satellite image analysis.

[0023] The satellite-based method and system for monitoring the supply chain of stray cargo according to the present invention can identify at least one stray cargo contained in satellite images using an artificial intelligence model that has learned the characteristics of each stray cargo type. This allows the present invention to quickly and accurately identify stray cargo accumulated in stray cargo yards and generate key data necessary for stray cargo market transactions.

[0024] Furthermore, the satellite-based offshore storage supply chain monitoring method and system according to the present invention can estimate the weight of offshore storage identified in satellite images based on a weight estimation algorithm. This allows the present invention to quantitatively assess the volume of offshore storage accumulated in offshore storage yards and precisely analyze offshore storage supply, thereby supporting price prediction and transaction decision-making.

[0025] Furthermore, the satellite information-based supply chain monitoring method and system according to the present invention maps at least a portion of the satellite image, the satellite image shooting time information, and the type information and weight information of the arable material identified in the satellite image, stores the arable material movement volume information in a DB, and provides a monitoring screen that can be utilized for arable material futures investment through time series analysis.

[0026] Figure 1 is a screen for monitoring the amount of cargo movement provided by a satellite information-based cargo supply chain monitoring system according to the present invention.

[0027] Figures 2 and 3 are conceptual diagrams for explaining a satellite information-based offshore storage supply chain monitoring system according to the present invention.

[0028] Figure 4 is a flowchart for explaining a satellite information-based offshore material supply chain monitoring method according to the present invention.

[0029] Figures 5, 6 and 7 are conceptual diagrams for explaining a method of identifying the type of cargo from satellite images and estimating the weight of each cargo in the present invention.

[0030] Figure 8 is a conceptual diagram for explaining a method for monitoring the amount of cargo movement through time series satellite image analysis in the present invention.

[0031] FIG. 9a, FIG. 9b, FIG. 9c, FIG. 9d and FIG. 10 are conceptual diagrams for explaining a user interface that provides monitoring information on the amount of cargo movement in the present invention.

[0032] Hereinafter, embodiments disclosed in this specification will be described in detail with reference to the attached drawings. Regardless of the drawing numbers, identical or similar components will be given the same reference numbers, and redundant descriptions thereof will be omitted. The suffixes "module" and "part" used for components in the following description are assigned or used interchangeably only for the convenience of writing the specification, and do not in themselves have distinct meanings or roles. In addition, when describing the embodiments disclosed in this specification, if it is determined that a specific description of a related known technology may obscure the gist of the embodiments disclosed in this specification, a detailed description thereof will be omitted. In addition, the attached drawings are only intended to facilitate easy understanding of the embodiments disclosed in this specification, and the technical ideas disclosed in this specification are not limited by the attached drawings, and should be understood to include all modifications, equivalents, and substitutes included in the spirit and technical scope of the present invention.

[0033] Terms that include ordinal numbers, such as first, second, etc., may be used to describe various components, but the components are not limited by these terms. These terms are used solely to distinguish one component from another.

[0034] When a component is referred to as being "connected" or "connected" to another component, it should be understood that it may be directly connected or connected to that other component, but that there may be other components intervening. Conversely, when a component is referred to as being "directly connected" or "connected" to another component, it should be understood that there are no other components intervening.

[0035] Singular expressions include plural expressions unless the context clearly indicates otherwise.

[0036] In this application, terms such as “include” or “have” are intended to specify the presence of a feature, number, step, operation, component, part or combination thereof described in the specification, but should be understood not to exclude in advance the possibility of the presence or addition of one or more other features, numbers, steps, operations, components, parts or combinations thereof.

[0037] The present invention provides information that can be utilized in investing in offshore oil futures, and relates to a method and system for monitoring offshore oil supply chains through time-series satellite image analysis.

[0038] In the present invention, “stockpiled materials” may refer to resources accumulated in a certain location (usually an outdoor or open storage space). For example, in the present invention, stockpiled materials may include at least one of energy stockpiled materials (e.g., oil, etc.), agricultural stockpiled materials (e.g., wheat, corn, soybeans, etc.), mineral stockpiled materials (e.g., silica sand, rare earth elements, lime, etc.), metallic stockpiled materials (e.g., scrap iron, recycled metal scrap, etc.), and raw materials (basic materials for producing goods or products, such as resources mined or extracted from nature).

[0039] The present invention can monitor the volume of cargo in open seas by periodically analyzing satellite images. As illustrated in Figure 1, the present invention can provide a monitoring screen (1000) that can be utilized for open seas futures investment using open seas cargo volume information. The monitoring screen (1000) can include information such as the type of open seas cargo (2), the weight of the open seas cargo (3), and a graph (3) showing the accumulated weight of the open seas cargo.

[0040] Hereinafter, a method and system for monitoring a supply chain of open cargo volume provided by the present invention will be described in detail with reference to the attached drawings. FIGS. 2 and 3 are conceptual diagrams for explaining a satellite information-based open cargo supply chain monitoring system according to the present invention. FIG. 4 is a flowchart for explaining a satellite information-based open cargo supply chain monitoring method according to the present invention, FIGS. 5, 6 and 7 are conceptual diagrams for explaining a method for identifying the type of open cargo from satellite images and estimating the weight of each in the present invention, FIG. 8 is a conceptual diagram for explaining a method for monitoring open cargo volume through time-series satellite image analysis in the present invention, and FIGS. 9a, 9b, 9c, 9d and 10 are conceptual diagrams for explaining a user interface for providing open cargo volume monitoring information in the present invention.

[0041] As illustrated in FIG. 2, the satellite information-based off-site material supply chain monitoring system (100) according to the present invention may include at least one of an input unit (110), an output unit (120), a communication unit (130), a storage unit (140), and a control unit (150). At this time, the off-site material supply chain monitoring system (100) according to the present invention is not limited to the above-described components, and may further include components that perform the same or similar roles as the functions described in this specification.

[0042] The satellite-based offshore oil supply chain monitoring system (hereinafter, "offshore oil supply chain monitoring system" 100) according to the present invention periodically monitors offshore oil storage yard and can support users' offshore oil futures investment decisions. In the present invention, the "offshore oil supply chain monitoring system" may also be referred to as a "offshore oil volume monitoring system," a "offshore oil supply chain monitoring platform," a "offshore oil futures investment support service system," etc.

[0043] The input unit (110) can be configured to receive various information required for the present invention. The input unit (110) can receive at least one image. In addition, the input unit (110) can receive user input.

[0044] The output unit (120) can output information that can be utilized for futures investment in offshore assets. The output unit (120) can output an image that matches the original satellite image, the offshore assets identified in the satellite image, and the classification results of the identified offshore assets. The output unit (120) can also output weight estimates by offshore asset type, a daily cumulative graph, and the like.

[0045] In the present invention, the output unit (120) may include the output unit (120) of the user terminal (10). For example, the output unit (120) may include a display, touch screen, etc. of the user terminal.

[0046] Here, the user terminal (10) may include at least one of a mobile phone, a smart phone, a notebook computer, a laptop computer, a slate PC, a tablet PC, an ultrabook, a desktop computer, a digital broadcasting terminal, a personal digital assistant (PDA), a portable multimedia player (PMP), a navigation device, and a wearable device (e.g., a smartwatch, a smart glass, a head mounted display (HMD)).

[0047] The communication unit (130) can communicate with at least one of a user terminal (10), a satellite server (20), a cargo ship / port (30), and an artificial intelligence server (40).

[0048] The communication unit (130) can receive satellite images from the satellite server (20). The communication unit (130) can periodically receive satellite images of an area of ​​interest. In addition, the communication unit (130) can be connected to an artificial intelligence server (40) where an artificial intelligence model (or deep learning model) is stored.

[0049] The storage unit (140) may be configured to store various information related to the present invention. In the present invention, the storage unit (140) may be provided in the outdoor material supply chain monitoring system (100) itself, or alternatively, at least a portion of the storage unit (140) may refer to a database (DB). That is, the storage unit (140) may be sufficient as long as it stores information necessary for monitoring the outdoor material movement volume according to the present invention, and it can be understood that there are no restrictions on physical space. Accordingly, hereinafter, the storage unit (140) and the database will not be separately distinguished, and will be referred to as the storage unit (140) altogether.

[0050] In the storage unit (140), information on the amount of cargo movement obtained from satellite images can be accumulated and stored in a time series manner.

[0051] In the present invention, “load volume information of open cargo” may include at least one of satellite image, information on the type of open cargo identified in the satellite image, information on the weight of open cargo estimated from the satellite image, information on the time point at which the satellite image was taken, and information on the coordinates of the polygon vertices of the area in which the open cargo is identified in the satellite image.

[0052] Furthermore, the storage unit (140) may store and exist characteristic information for each type of wild material.

[0053] In the present invention, “characteristic information by type of off-site material” may include at least one of i) spectral information, ii) color information, iii) loading characteristic information, iv) external shape information, and v) density information for each type of off-site material.

[0054] Here, the spectral information of a sedimentary rock may refer to the unique spectral characteristics of the sedimentary rock, such as its ability to reflect or absorb light of a specific wavelength range (e.g., visible light, near-infrared light, etc.). For example, the spectral information for each type of sedimentary rock may include sedimentary rock-specific reflectance information and sedimentary rock-specific spectral information.

[0055] Furthermore, the color information of the scrap material is based on the RGB values ​​visually recognized from satellite images of the scrap material's surface. Depending on the type of scrap material or its degree of oxidation, it may have unique color characteristics. For example, oxidized scrap iron may exhibit brown or reddish hues, aluminum scrap may exhibit light gray, and coal may exhibit dark black hues.

[0056] Furthermore, information on the loading characteristics of the yard stockpile may include visual characteristics such as the way the yard stockpile is repeatedly stacked in space, layer structure, density, and alignment direction. The present invention may include information on the loading characteristics of the yard stockpile and information on the pattern in which the yard stockpile is stacked in space. For example, iron ore may be stacked in a circular mound shape, while scrap iron may be stacked in an irregularly scattered pattern.

[0057] Furthermore, the external shape information of the pile may include morphological characteristics of the pile itself, such as particle size, degree of fragmentation, and clump state. In the present invention, the external shape information of the pile may refer to information visually observable in satellite images. For example, crushed scrap metal may have a dense, small fragment form, while industrial scrap may have a large, irregular, clump form.

[0058] Furthermore, the density information of the raw material may refer to a characteristic indicating the physical quantity or degree of compression of raw materials distributed within a certain area or volume.

[0059] The control unit (150) may be configured to control the overall operation of the wild cargo supply chain monitoring system (100). The control unit (150) may process signals, data, information, etc. input or output through the components discussed above, or provide or process appropriate information and functions to the user.

[0060] The control unit (150) can identify the type of debris included in a satellite image and classify different types of debris using an artificial intelligence model (151) for identifying debris.

[0061] In the present invention, the artificial intelligence model (151) for identifying wild animals can identify the type of wild animals in satellite images based on learning characteristic information for each type of wild animals.

[0062] As illustrated in FIG. 3, the control unit (150) can collect satellite images containing various types of burial mounds for training and building an artificial intelligence model (151) for identifying burial mounds (S310). The control unit (150) can collect satellite images of an area of ​​interest for a certain period of time. The area of ​​interest refers to an area for monitoring the amount of burial mounds, and the control unit (150) can specify the area of ​​interest using various methods. For example, the control unit (150) can specify the area of ​​interest at the request of a user. The control unit (150) can perform data preprocessing on the collected satellite images (S320). The control unit (150) can normalize the satellite images by band and correct the brightness. In addition, the control unit (150) can collect labeling results for each image for map and semi-map learning. The control unit (150) can build a data set for identifying burial mounds based on the collected satellite images (S330). In this case, the control unit (150) can build a learning data set and a verification data set separately. In addition, the control unit (150) can augment the data set by rotating, adjusting the size, changing the brightness, and inserting noise of the satellite image. The control unit (150) can train an artificial intelligence model (151) for identifying open-air objects based on the data set built with the satellite image (S340). The control unit (150) can train characteristic information for each type of open-air objects (spectral information for each type of open-air objects, color information, loading characteristic information, external shape information, density information, etc.)) using the satellite image. In addition, the control unit (150) can repeat the training of the artificial intelligence model (151) so that the loss function is minimized. The control unit (150) can utilize at least one of accuracy, precision, and recall of the artificial intelligence model (151) for identifying open-air objects as a performance evaluation index, and can optimize the model based on the results of the performance evaluation index.

[0063] Furthermore, the control unit (150) can estimate the weight of the identified stray cargo using a stray cargo weight estimation algorithm. The control unit (150) can derive the stray cargo weight estimation algorithm in various ways. For example, the control unit (150) can calculate the stray cargo area based on the inference result (vertex coordinates of the segmented polygon for each stray cargo) of the stray cargo identification artificial intelligence model (151). In addition, the control unit (150) can derive an area-weight relationship according to the stray cargo loading characteristics (e.g., loading shape, height, alignment structure, area, etc.) and density, and estimate the weight of the stray cargo.

[0064] Below, we will describe in more detail a method for monitoring the movement of cargo using satellite images with different resolutions.

[0065] In the present invention, a process of receiving satellite images through a communication unit can be performed (S410, see FIG. 4).

[0066] As illustrated in (a) of FIG. 5, the present invention can receive (or input) a new satellite image (500). The control unit (150) can receive a new satellite image (500) from the satellite server (20). In addition, the control unit (150) can receive the satellite image (500) from another external server (20). In addition, the control unit (150) can also input the satellite image (500) through the input unit (110). Hereinafter, the satellite image is described as “receiving” or “collecting” without distinguishing whether the satellite image is received (or input) through the satellite server (20), another external server, or the input unit (110). The control unit (150) can collect, together with the satellite image, information on the time point (or date information) at which the satellite image was captured, information on the target area where the satellite image was captured, location information, spatial resolution information, etc., as satellite information.

[0067] The control unit (150) can receive a satellite image (500) of an area of ​​interest. The area of ​​interest may refer to an area to be monitored in a supply chain for yard materials. The control unit (150) can specify an area that is determined to require monitoring as an area of ​​interest based on satellite image analysis. In addition, the control unit (150) can specify an area of ​​interest at the request of the user terminal (10). For example, the control unit (150) can receive information specifying an area (e.g., an address or latitude and longitude coordinates) from the user terminal (10) and set an area corresponding to the received information as an area of ​​interest.

[0068] The control unit (150) can periodically collect satellite images of an area of ​​interest for a set period of time. The control unit (150) can describe the satellite image collection period and cycle in various ways. As described above, the control unit (150) can describe the collection period and cycle based on satellite image analysis or set them based on a user request.

[0069] In the present invention, a process of identifying at least one wild boar included in a satellite image can be performed using an artificial intelligence model that has learned characteristic information for each type of wild boar (S420, see FIG. 4).

[0070] The control unit (150) can train an artificial intelligence model (151) to identify characteristic information of each type of wild material from satellite images.

[0071] As described above, “characteristic information by type of off-site material” may include at least one of i) spectral information, ii) color information, iii) loading characteristic information, iv) appearance shape information, and v) density information for each type of off-site material.

[0072] And in the present invention, the artificial intelligence model (151) that has learned characteristic information for each type of wild material can be named a wild material identification artificial intelligence model (151).

[0073] As illustrated in (b) of FIG. 5, the control unit (150) processes the satellite image (500) as input to the artificial intelligence model (151) for identifying wild animals, thereby identifying and classifying at least one wild animal (510 to 530).

[0074] As illustrated in (a) of FIG. 6, the control unit (150) can perform multi-spectral analysis on satellite images using an artificial intelligence model (151) for identifying arthropods. The control unit (150) can identify arthropods in units of arthropod piles (610, 620, 630), which are aggregates formed by piling up arthropods of the same type. For example, the control unit (150) can identify arthropod piles based on the similarity of spectral information of pixels forming the satellite image. In addition, as illustrated in (b) of FIG. 6, the control unit (150) can classify the identified arthropod piles and determine the type of arthropod (e.g., “PIPE”, “HMS”, 610a, 620a, 630a) of each identified arthropod pile.

[0075] Furthermore, the control unit (150) can use the artificial intelligence model (151) for identifying the debris in the satellite image to specify the coordinates of the vertices of the area where the debris piles (610, 620, 630) are identified. The control unit (150) can specify that the area where the debris piles (610, 620, 630) are identified has a polygon. The control unit (150) can extract the coordinates of each vertex of the polygonal area. In this case, the coordinates of the vertices may refer to the world coordinate system, or the satellite image may refer to the pixel coordinate system.

[0076] In the present invention, a process of estimating the weight of a cargo identified in a satellite image based on a weight estimation algorithm can be performed (S430, see FIG. 4).

[0077] The control unit (150) can estimate the weight of the pile of waste material by using a weight estimation algorithm and the coordinates of the vertices of the area where the pile of waste material is identified.

[0078] The control unit (150) can derive an area-weight relationship (weight estimation algorithm) of an area where a wild material is identified according to the loading characteristics and density of each type of wild material.

[0079] The control unit (150) can calculate the area of ​​the pile of debris using the coordinates of the vertices of the area where the pile of debris is identified. In this case, as illustrated in FIG. 7, the control unit (150) can divide the satellite image (500) into a plurality of sub-regions (610b, 620b, 630b) based on the pile of debris. The control unit (150) can estimate the weight of each pile of debris (610c, 620c, 630c) by applying an area-weight relationship (weight estimation algorithm) to the area of ​​the pile of debris.

[0080] When different types of waste piles are identified in the satellite image (500), the control unit (150) can derive a customized weight estimation algorithm for each different type of waste pile. The control unit (150) can estimate the weight by applying the customized weight estimation algorithm to each waste pile.

[0081] More specifically, when the control unit (150) identifies a first type of waste pile (e.g., “PIPE”) and a second type of waste pile (e.g., “HMS”) different from the first type in a satellite image (500), the control unit (150) can derive a weight estimation algorithm corresponding to the first type of waste pile and a weight estimation algorithm corresponding to the second type of waste pile, respectively.

[0082] The control unit (150) can derive an algorithm for estimating the weight of a first type of pile of waste material by reflecting the loading characteristics and density of the first type of waste material, and can derive an algorithm for estimating the weight of a second type of pile of waste material by reflecting the loading characteristics and density of the second type of waste material.

[0083] That is, the control unit (150) can derive weight estimation algorithms for the number of types of debris identified in the satellite image. Then, by applying each weight estimation algorithm, the weight of each different type of debris pile can be estimated.

[0084] In the present invention, a process may be performed to map at least some of the satellite image, the satellite image shooting time information, the type information of the cargo identified in the satellite image, and the weight information, and store them in a DB as cargo movement information (S440, see FIG. 4).

[0085] The control unit (150) can store information extracted from satellite images in a DB as information on the amount of cargo moving in the area of ​​interest.

[0086] As illustrated in Fig. 8, the control unit (150) can periodically monitor the area of ​​interest. The control unit (150) can store the type information of the pile of debris and the weight information of the pile of debris identified in the satellite image capturing the area of ​​interest at preset intervals as the pile volume information in the DB.

[0087] The DB may accumulate and exist the cargo volume information generated at preset intervals. As illustrated in Fig. 9a, the DB may store at least one of the following: satellite image capture time point information (910), supplier information (920), region of interest information (continent, country, city, 930), regional total cargo weight information (940), cargo type-specific weight information (950), and original satellite image information, which are matched with each other as cargo volume information. In addition, such cargo volume information may be generated at preset intervals and accumulated time-seriesly to be stored in the DB.

[0088] In the present invention, a process of providing a monitoring screen to a user terminal using information on the amount of cargo moving at different times can be performed (S450, see FIG. 4).

[0089] The control unit (150) can implement and provide a monitoring screen so that the user can utilize the information on the volume of cargo in the area of ​​interest stored in the DB for cargo futures investment.

[0090] The control unit (150) can implement a monitoring screen using at least a portion of the accumulated time-series cargo movement information.

[0091] As illustrated in FIG. 9a, the control unit (150) may provide a table (900a) in which information on the volume of cargo in a time series is arranged to the user terminal (10). The table (900a) may include a plurality of items (960), and each of the plurality of items may correspond to a plurality of points in time (910) at which satellite images were captured. The control unit (150) may receive a request for a visualization monitoring screen for at least one item among the plurality of items (960). The user may select a desired date (point in time) within an area of ​​interest in the table (900a) and select an icon (ex: “View Selected”, 970) linked to the visualization monitoring screen to analyze the weight of cargo identified on a specific date.

[0092] The control unit (150) may receive a request for a screen for monitoring the amount of waste material accumulated in an area of ​​interest from the user terminal (10). The control unit (150) may receive a screen for monitoring the amount of waste material accumulated in an area of ​​interest at least at some of the multiple points in time at which the area of ​​interest was monitored. As described above, the control unit (150) may provide a table (900a) to the user terminal (10). This table (900a) may include a plurality of items (960) corresponding to a plurality of points in time (910) at which the area of ​​interest was monitored. When at least one of the multiple items (960) is selected and an icon (ex: “View Selected”, 970) linked to the visualization monitoring screen is selected, the control unit (150) may receive a screen for monitoring the amount of waste material accumulated in an area of ​​interest.

[0093] As illustrated in FIG. 9b, the control unit (150) may, in response to a request for a screen for monitoring the amount of accumulated waste material in an area of ​​interest, provide a monitoring screen (900) for the area of ​​interest to the user terminal (10). In this case, the control unit (150) may provide, through the monitoring screen, information on the amount of accumulated waste material at some point in time selected by the user among multiple points in time at which the area of ​​interest was monitored.

[0094] The monitoring screen (900) may include an image area (980) where satellite images are displayed and a data area (990) where cargo volume information is displayed.

[0095] A satellite image (500) capturing a region of interest may be displayed in the image area (980). The control unit (150) may display any one of a plurality of satellite images capturing a region of interest at different time points in the image area (980). Among the satellite images capturing a region of interest at a first time point, a second time point, and a third time point, the control unit (150) may display a satellite image captured at a second time point in the image area (980).

[0096] The control unit (150) can, in response to a user request, switch the satellite image displayed in the image area (980) to a satellite image captured at a different point in time. In response to the selection of an icon (980a) corresponding to the previous point in time, the control unit (150) can display, in the image area (980), a satellite image captured at a point in time previous to the currently displayed satellite image. When the icon (980a) corresponding to the previous point in time is selected while the satellite image captured at a second point in time is displayed in the image area (980), the control unit (150) can switch the satellite image displayed in the image area (980) to a satellite image captured at a first point in time, which is a point in time previous to the second point in time. In addition, the control unit (150) can, in response to the selection of an icon (980b) corresponding to the next point in time, display, in the image area (980), a satellite image captured at a point in time subsequent to the currently displayed satellite image (e.g., a third point in time).

[0097] In the data area (990), water volume information of an area of ​​interest corresponding to multiple points in time can be displayed.

[0098] The control unit (150) can identify at least one pile of debris accumulated in an area of ​​interest from satellite images captured at multiple points in time. Furthermore, the control unit can specify the type of the identified pile of debris, estimate its weight, and store it in a database as debris movement volume information. The database can accumulate and store movement volume information for an area of ​​interest corresponding to multiple points in time in a time-series fashion.

[0099] The control unit (150) can provide a weight chart for each type of cargo to the data area (990) using the water volume information of the area of ​​interest corresponding to multiple points in time.

[0100] The control unit (150) can provide information on the volume of water in the area of ​​interest in the form of a chart in the data area (990).

[0101] More specifically, the control unit (150) may provide, in the data area (990), a plurality of bar graphic objects (991, 992, 993) representing the cargo volume information of each of a plurality of points in time. The control unit (150) may provide, in the data area (990), a bar graphic object (991) corresponding to a first point in time based on the cargo volume information of a first point in time, a bar graphic object (992) corresponding to a second point in time based on the cargo volume information of a second point in time, a bar graphic object (993) corresponding to a third point in time based on the cargo volume information of a third point in time, a bar graphic object (993) corresponding to a third point in time based on the cargo volume information of a third point in time, and the control unit (150) may arrange the plurality of bar graphic objects (991, 992, 993) in the data area (990) in chronological order.

[0102] The control unit (150) can highlight a bar graphic object (992) at a specific point in time in the data area (990) in conjunction with the display of a satellite image at a specific point in time (second point in time) in the image area (980) so that the bar graphic object (992) at a specific point in time is distinguished from other graphic objects (991, 993).

[0103] The control unit (150) can change the highlighted bar graphic object in the data area (990) in conjunction with the satellite image displayed in the image area (980) switching to a satellite image of a different time point. For example, when the satellite image displayed in the image area (990) switches from a satellite image of a second time point to a satellite image of a first time point, the control unit (150) can highlight the first bar graphic object (991) in the data area (990).

[0104] Meanwhile, the visual appearance of multiple stick graphic objects (991, 992, 993) may differ depending on the weight of each type of wild material identified at each of the multiple points in time.

[0105] A bar graphic object corresponding to a specific point in time may be composed of multiple sub-graphic objects. The multiple sub-graphic objects may correspond to different types of yard objects identified at the specific point in time. Furthermore, the multiple sub-graphic objects may have different colors.

[0106] The control unit (150) can determine the size (area or width) of the sub-graphic object by reflecting the weight of each type of cargo. The control unit (150) can determine the size (area or width) of the sub-graphic object so as to be proportional to the weight of each type of cargo.

[0107] As illustrated in FIG. 9c, the bar graphic object (992) may be composed of only sub-graphic objects (992a, 992b) corresponding to some of the identified types of waste materials. The control unit (150) may control, based on a user selection, a plurality of graphic objects in the data area (990) to be displayed including only sub-graphic objects corresponding to specific types. The control unit (150) may provide, in one area (990a) of the user terminal (10), waste material items of the types identified in the area of ​​interest (e.g., “BONUS”, “PIPE”, “PLATIRON”, “HMS”, “NP”, “SHREDDED”). When at least one of the above waste material items is selected, the control unit (150) may configure the bar graphic object so that the sub-graphic object corresponding to the selected waste material item is included.

[0108] As illustrated in FIG. 9d, the control unit (150) can map and provide label graphic objects (981, 982) corresponding to the type of arable material to an area where a arable material (or a pile of arable material) is identified in a satellite image (500). For example, in response to a label icon (“Label”, 990b) being selected in a user terminal (10), the control unit (150) can map and display label graphic objects (981, 982) on the satellite image (500). This label icon (990b) can be understood as an icon linked to a function of displaying an area where a pile of arable material is identified.

[0109] The control unit (150) can receive a user selection for a label icon (990b) linked to a function for displaying an area where a pile of debris is identified on the monitoring screen (900). In response to the selection of the label icon, the control unit (150) can display an area where a pile of debris is identified on a satellite image. That is, the control unit (150) can display a label graphic object corresponding to a pile of debris by overlapping (mapping) the area where a pile of debris is identified on a satellite image at a specific point in time. The color of this label graphic object can vary depending on the type of the pile of debris.

[0110] The control unit (150) can correspond the colors of the label graphic objects (981, 982) and the sub-graphic objects (992a, 992b) in the bar graphic object based on the type of the waste material. The control unit (150) can overlap a label graphic object (981) of the same color (e.g., “pink)) as the sub-graphic object of the first type in an area where a pile of the first type of waste material is identified in the satellite image (500). In addition, the control unit (150) can overlap a label graphic object (982) of the same color (e.g., “purple)) as the sub-graphic object of the second type in an area where a pile of the second type of waste material is identified in the satellite image (500).

[0111] As illustrated in FIG. 10, the control unit (150) can display a label (e.g., “420.75”) containing information on the weight of the debris in an area where debris is identified in the satellite image (500). The user can intuitively recognize the weight of the debris accumulated in the area through the label displayed overlapping the satellite image (500).

[0112] The control unit (150) can provide a burial object (1010, 1020) to the monitoring screen (1000). When an area of ​​a satellite image (500) is selected, the control unit (150) can output the type of burial object identified in the area to the monitoring screen (1000). A user can select the area by looking at the label displayed on the satellite image (500) and check the type of burial object. The control unit (150) can output a 3D burial object (1010, 1020) identified in the selected area to the monitoring screen (1000) in conjunction with the selection of an area in the satellite image. And the control unit (150) can display the weight information of the three-dimensional junk object (1010, 1020) and the type information of the junk object (1010, 1020) around the junk object (1010, 1020).

[0113] Furthermore, the control unit (150) can provide weight information by type of wild cargo in the data area (1030) of the monitoring screen (1000) as a cumulative graph by date (or time point). In response to a user selection of a graph corresponding to a specific date, the control unit (150) can output a wild cargo weight information label (1031) for a specific date to the data area (1030).

[0114] Meanwhile, the control unit (150) can analyze the type and degree of contamination of the sediment from satellite images using the sediment identification artificial intelligence model (151).

[0115] Analysis of yard waste contamination can play a crucial role in the yard waste trade. It's a crucial factor in assessing the contamination level of yard waste. Highly contaminated yard waste represents lower quality, which can impact market prices and demand. Therefore, accurately analyzing and predicting yard waste contamination levels can provide valuable information for selecting high-quality yard waste or assessing the risks associated with highly contaminated yard waste when trading yard waste futures.

[0116] In the present invention, the artificial intelligence model (151) for identifying open waste may be trained to analyze the contamination level of each type of open waste based on spectral information for each type of open waste. The contamination level of open waste varies depending on the surface characteristics and materials, and the artificial intelligence model (151) for identifying open waste may learn the influence of the contamination level on the spectral information for each type of open waste.

[0117] The control unit (150) can analyze the contamination level of the sediment identified by the satellite using the sediment identification artificial intelligence model (151). The control unit (150) can store the contamination level information as sediment movement volume information in the database.

[0118] The control unit (150) can analyze the contamination level of the open seabed through time-series data analysis. As described above, the control unit (150) collects satellite images of the area of ​​interest at multiple time points, extracts information on the volume of open seabed cargo in the area of ​​interest from the multiple satellite images, and stores the information as time-series data in the database. The control unit (150) can analyze the contamination level of the open seabed by tracking changes in the contamination level of the open seabed over time.

[0119] Meanwhile, the artificial intelligence model (151) for identifying waste materials can be trained to identify the type of waste material and the level of contamination of the waste material from satellite images by further considering the environmental characteristics of the area of ​​interest.

[0120] Depending on the environmental characteristics of the area where the waste is accumulated, the characteristics of the waste may vary. These environmental factors can lead to contamination of the waste, including dust, oil, water, powder, and chemicals. This, in turn, can lead to variations in the characteristics of the waste (e.g., spectral data by type of waste, color, loading characteristics, external shape, and density). For example, if the waste corrodes in a high-humidity environment, its reflectance and spectrum may change. Furthermore, dust accumulation in the same waste can reduce its reflectance in a dusty environment.

[0121] The control unit (150) can identify the type of waste material and analyze the level of contamination of the waste material by reflecting environmental characteristic information of the area of ​​interest using an artificial intelligence model (151) for identifying waste material.

[0122] The control unit (150) can extract environmental characteristic information of an area of ​​interest from satellite images. In addition, the control unit (150) can specify characteristic information of an area of ​​interest using location information of the area of ​​interest.

[0123] Environmental information of an area of ​​interest includes factors that affect the characteristics of the cargo loaded in the area of ​​interest, such as the climate of the area of ​​interest, whether it is around a mountain or the sea, around a factory, an area with high humidity, an area with high precipitation, an area with high temperature, an area with high precipitation, and an area with significant temperature changes (seasonal changes).

[0124] The control unit (150) can analyze the impact of climate change and natural disasters (e.g., floods, tsunamis, typhoons, earthquakes, etc.) in the area of ​​interest on the amount of cargo transported, and provide a real-time prediction and warning system (100) to the user based on the analysis results.

[0125] Climate change and natural disasters affect the movement of cargo. For example, floods can cause cargo to become submerged and polluted (deteriorating quality) due to erosion. Typhoons and earthquakes can cause cargo to move to different locations or change its properties. Climate change and natural disasters are crucial for monitoring cargo movement. For convenience, the following explanation focuses on natural disasters.

[0126] The control unit (150) can predict changes in the amount of cargo transported by the occurrence of a natural disaster in the area of ​​interest by using at least one of a plurality of satellite images capturing the area of ​​interest, information on the amount of cargo transported by the satellite images analyzed, and information on the environmental characteristics of the area of ​​interest.

[0127] The control unit (150) can predict in advance a natural disaster (e.g., flood) that may occur in an area of ​​interest, and can predict at least one of the following: the possibility of movement, path, loading status, contamination level, and weight of the accumulated waste in the area of ​​interest in the event of a natural disaster. Furthermore, the control unit (150) can generate evaluation information on the impact of the prediction results on the market volatility of the waste. In this case, the control unit (150) can generate evaluation information centered on the market volatility of the types of waste identified in the area of ​​interest.

[0128] The control unit (150) may transmit at least one of information on the possibility of a natural disaster occurring (e.g., natural disaster type (flood, typhoon, earthquake, etc.), expected time of occurrence (or expected date of occurrence), natural disaster intensity (e.g., earthquake intensity 9), probability of occurrence), predicted information on changes in cargo volume due to the occurrence of a natural disaster, and market volatility evaluation information due to the occurrence of a natural disaster to the user terminal (10). In this case, the control unit (150) may provide an unconstitutional warning to the user terminal (10) based on the market volatility evaluation information.

[0129] The satellite information-based offshore storage supply chain monitoring method and system according to the present invention periodically acquires high-resolution satellite images of major offshore storage sites around the world, and enables monitoring of offshore storage volume through time-series satellite image analysis.

[0130] The satellite-based method and system for monitoring the supply chain of stray cargo according to the present invention can identify at least one stray cargo contained in satellite images using an artificial intelligence model that has learned the characteristics of each stray cargo type. This allows the present invention to quickly and accurately identify stray cargo accumulated in stray cargo yards and generate key data necessary for stray cargo market transactions.

[0131] Furthermore, the satellite-based offshore storage supply chain monitoring method and system according to the present invention can estimate the weight of offshore storage identified in satellite images based on a weight estimation algorithm. This allows the present invention to quantitatively assess the volume of offshore storage accumulated in offshore storage yards and precisely analyze offshore storage supply, thereby supporting price prediction and transaction decision-making.

[0132] Furthermore, the satellite information-based supply chain monitoring method and system according to the present invention maps at least a portion of the satellite image, the satellite image shooting time information, and the type information and weight information of the arable material identified in the satellite image, stores the arable material movement volume information in a DB, and provides a monitoring screen that can be utilized for arable material futures investment through time series analysis.

[0133] Meanwhile, computer-readable media include all types of recording devices that store data that can be read by a computer system. Examples of computer-readable media include hard disk drives (HDDs), solid-state disk drives (SSDs), silicon disk drives (SDDs), ROMs, RAMs, CD-ROMs, magnetic tapes, floppy disks, and optical data storage devices.

[0134] Furthermore, the computer-readable medium may include a storage device and may be a server or cloud storage device accessible via communication. In this case, the computer may download the program according to the present invention from the server or cloud storage device via wired or wireless communication.

[0135] Furthermore, in the present invention, the computer described above is an electronic device equipped with a processor, i.e., a CPU (Central Processing Unit), and there is no particular limitation on its type.

[0136] Meanwhile, the above detailed description should not be construed as limiting in any respect and should be considered illustrative. The scope of the present invention should be determined by a reasonable interpretation of the appended claims, and all modifications within the equivalent scope of the present invention are intended to be included within the scope of the present invention.

Claims

1. Step of receiving satellite images through the communications department; A step of identifying at least one wild boar included in the satellite image using an artificial intelligence model that has learned characteristic information for each type of wild boar; A step of estimating the weight of the cargo identified in the satellite image based on a weight estimation algorithm; A step of mapping at least a portion of the satellite image, the satellite image shooting time information, the type information and weight information of the cargo identified in the satellite image, and storing them in a DB as cargo volume information; and A satellite information-based wild cargo supply chain monitoring method, characterized in that it includes a step of providing a monitoring screen using the wild cargo volume information corresponding to different points in time to a user terminal.

2. In paragraph 1, The characteristics of each type of the above-mentioned waste are as follows: A satellite information-based off-site material supply chain monitoring method characterized in that it includes at least one of spectral information, color information, loading characteristic information, and external shape information for each type of the above-mentioned off-site material.

3. In paragraph 2, In the step of identifying the above-mentioned waste, Identifying a specific type of waste pile from the above satellite imagery, In the step of estimating the weight of the above-mentioned cargo, A satellite information-based method for monitoring a supply chain of waste materials, characterized in that the weight of the waste material pile is estimated using the above weight estimation algorithm.

4. In paragraph 3, The above weight estimation algorithm is, A satellite information-based offshore storage supply chain monitoring method characterized in that it is derived using at least one of the loading characteristic information of the specific type of offshore storage and the density information of the specific type of offshore storage.

5. In paragraph 4, The above satellite image contains different types of debris piles, In the step of estimating the weight of the above-mentioned cargo, A satellite information-based method for monitoring a supply chain of waste materials, characterized in that the weight of each of the different types of waste materials is estimated using the coordinates of the vertices of the areas where the different types of waste materials are identified.

6. In paragraph 5, In the step of providing the above monitoring screen, In order to monitor the supply chain of the yard goods in the area of ​​interest, the monitoring screen is provided using the yard goods movement information in the area of ​​interest, The above monitoring screen is, Image area where satellite images capturing the above area of ​​interest are displayed and A satellite information-based offshore cargo supply chain monitoring method characterized in that it includes a data area in which offshore cargo volume information of the above-mentioned area of ​​interest is displayed.

7. In paragraph 6, Information on the cargo volume in the above area of ​​interest is as follows: First pile of waste material volume information including type information and weight information of pile of waste material accumulated in the area of ​​interest at the first point in time, Includes second pile of waste material movement information including type information and weight information of piles of waste material accumulated in the area of ​​interest at a second time point different from the first time point, In the above data area, A first graphic object corresponding to the first cargo volume information and A satellite information-based offshore cargo supply chain monitoring method characterized in that a second graphic object corresponding to the above-mentioned second offshore cargo volume information is displayed.

8. In paragraph 7, In the above image area, One of the satellite image of the area of ​​interest captured at the first point in time and the satellite image of the area of ​​interest captured at the second point in time is displayed, In the above data area, A satellite information-based offshore storage supply chain monitoring method, characterized in that a graphic object corresponding to one of the first graphic object and the second graphic object displayed in the image area is highlighted.

9. In paragraph 8, In a state where a satellite image of a specific point in time among the first point in time and the second point in time is displayed in the image area, the step of receiving a user input for an icon linked to a function of displaying an area where the pile of debris is identified is further included. Based on the user input, a label graphic object corresponding to the pile of debris is mapped and displayed in an area where the pile of debris is identified in the satellite image at the specific point in time. A method for monitoring a supply chain of waste materials based on satellite information, wherein the label graphic object has a different color depending on the type of the waste material pile.

10. A communication unit that receives satellite images; and Using an artificial intelligence model that has learned characteristic information for each type of sediment, at least one sediment included in the satellite image is identified, Based on the weight estimation algorithm, the weight of the cargo identified in the satellite image is estimated, At least a portion of the above satellite image, the satellite image shooting time information, the type information and weight information of the cargo identified in the satellite image are mapped and stored in the DB as cargo volume information. A satellite information-based offshore storage supply chain monitoring system characterized by including a control unit that provides a monitoring screen using offshore storage volume information corresponding to different points in time to a user terminal.

11. A program that is executed by one or more processes in an electronic device and stored in a computer-readable recording medium, The above program is, Step of receiving satellite images through the communications department; A step of identifying at least one wild boar included in the satellite image using an artificial intelligence model that has learned immediate information on each type of wild boar; A step of estimating the weight of the cargo identified in the satellite image based on a weight estimation algorithm; A step of mapping at least a portion of the satellite image, the satellite image shooting time information, the type information and weight information of the cargo identified in the satellite image, and storing them in a DB as cargo volume information; and A program characterized by including commands for performing a step of providing a monitoring screen using cargo volume information corresponding to different points in time on a user terminal.

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