Method, computing device and computer program for analyzing carbon emissions and presenting carbon emission reduction plan based on same

Abstract

A method for analyzing carbon emissions and presenting a carbon emission reduction plan based on same, according to various embodiments of the present invention, is disclosed. The method performed by means of one or more processors of a computing device comprises the steps of: obtaining process material information, carbon emission information, distribution management information, and carbon emission offset information; obtaining carbon emission information for each of one or more predetermined scopes on the basis of the process material information, the carbon emission information, the distribution management information, and the carbon emission offset information; and generating suggestion information on the basis of comparison between the carbon emission information for each of the one or more scopes and target carbon emission information, wherein the one or more predetermined scopes can include a first scope related to direct emissions, a second scope related to indirect emissions, and a third scope related to additional emissions.

Description

Methods, computing devices, and computer programs for analyzing carbon emissions and suggesting carbon emission reduction measures based on the analysis.

[0001] Various embodiments of the present invention relate to a method for analyzing carbon emissions, and more specifically, to a method, a computing device, and a computer program for analyzing future carbon emissions using big data built based on carbon emissions detected in real time during a process and distribution process, and suggesting a plan for reducing carbon emissions based on the analysis.

[0002]

[0003] Rapid urban growth and industrialization worldwide have led to a rise in environmental concerns related to global warming. To address this issue, the Kyoto Protocol came into effect in 1997. The Kyoto Protocol, an international agreement to regulate and prevent global warming, aims to reduce emissions of six greenhouse gases, including carbon dioxide. Many countries, including the European Union and Japan, have committed to achieving these reduction targets.

[0004] Among the greenhouse gases that cause global warming, carbon is mainly generated from energy use and industrial processes, and this carbon accounts for more than 80% of total greenhouse gas emissions, so reducing carbon emissions is important.

[0005] Carbon emissions refer to the total amount of carbon dioxide generated directly or indirectly through human activities or the production / consumption of goods. Efforts to reduce carbon emissions are on the rise in countries around the world, including Europe, where environmental concerns are high.

[0006] Furthermore, countries are trading carbon credits to meet their reduction targets, thereby generating and recognizing their economic value. Since the implementation of a nationwide carbon emissions trading system, global interest in carbon emissions has grown. Korea also implemented a carbon emissions trading system in 2015, and in line with this, carbon credits have been allocated to various industrial sectors.

[0007] Meanwhile, since carbon emissions are a key factor in setting product prices in various industrial process fields, it is crucial to develop an appropriate carbon emissions strategy through analysis and prediction of carbon emissions.

[0008] However, accurate detection and prediction of carbon emissions can be difficult due to the complex intertwining of various sources of carbon emissions from the manufacturing and production of products to their delivery processes. For example, when manufacturing a specific product, information on the carbon emissions of each material used in the product, the carbon emissions generated during the transportation of each material, the carbon emissions generated during the manufacturing process, the carbon emissions generated during the transportation of the manufactured product, or the carbon emissions reduced according to carbon emission reduction measures is required. Since many carbon emission situations are intertwined from the production of a single product to the delivery of the manufactured product, in order to analyze this, a database must be built by integrating data on each carbon emission factor. However, currently, a database for accurately identifying carbon dioxide emissions is not established for industrial processes.

[0009] Additionally, since it is impossible to predict future carbon emissions using a database built with data from various carbon sources, it is difficult to establish an accurate carbon emissions strategy.

[0010] Additionally, current carbon emissions detection systems can only identify whether carbon emissions exceed a certain standard (e.g., a target setting value) and provide warnings or notifications, but cannot provide appropriate suggestions for reducing carbon emissions at that point.

[0011] (Prior art literature)

[0012] (Patent Document)

[0013] (Patent Document 1) Republic of Korea Patent Publication No. 10-2014-0022552

[0014]

[0015] The present invention has been devised in response to the aforementioned background technology, and is intended to provide a method for analyzing future carbon emissions using big data built on the basis of carbon emissions detected in real time during the manufacturing and distribution process, and suggesting a plan for reducing carbon emissions based on the analysis.

[0016] The technical problems of the present invention are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the description below.

[0017]

[0018] According to one embodiment of the present invention for solving the above-described problem, a method for analyzing carbon emissions and suggesting a carbon emissions reduction plan based thereon is disclosed. The method includes the steps of: obtaining process material information, carbon emission information, distribution management information, and carbon emission offset information; obtaining carbon emission information for one or more predetermined scopes based on the process material information, the carbon emission information, the distribution management information, and the carbon emission offset information; and generating proposal information based on a comparison between the carbon emission information for one or more scopes and target carbon emission information. The one or more predetermined scopes may include a first scope related to direct emissions, a second scope related to indirect emissions, and a third scope related to additional emissions.

[0019] In an alternative embodiment, the process material information may include material type information of each of one or more materials utilized in the process, usage information for each material, and carbon emission information for each material, and may be generated based on a user's input or based on a sensing value sensed through an input amount detection sensor.

[0020] In an alternative embodiment, the carbon emission information may include information about the amount of carbon emitted during the process, and may be generated based on the amount of carbon detected through a gas detection sensor, generated based on the amount of power of a device utilized in the process detected through a power detection sensor, or generated through OCR analysis of a document related to power usage.

[0021] In an alternative embodiment, the distribution management information may include first delivery information related to delivery of materials used in product production, second delivery information related to delivery of finished products, and information on carbon emissions calculated in response to each delivery information, and the carbon emission offset information may include information on an application method for reducing carbon emissions and information on the amount of carbon reduced through the application method.

[0022] In an alternative embodiment, the step of generating proposal information based on a comparison between the one or more scope-specific carbon emission information and the target carbon emission information may include the steps of: obtaining comprehensive carbon emission information based on the one or more scope-specific carbon emission information; comparing the comprehensive carbon emission information with the target carbon emission information; not generating the proposal information when the carbon emission corresponding to the comprehensive carbon emission information does not exceed the carbon emission corresponding to the target carbon emission information; and generating the proposal information when the carbon emission corresponding to the comprehensive carbon emission information exceeds the carbon emission corresponding to the target carbon emission information.

[0023] In an alternative embodiment, the step of generating the suggestion information may include the step of selecting at least one of the first scope, the second scope, and the third scope as an inappropriate scope based on the carbon emission information for each of the one or more scopes, and the step of generating suggestion information corresponding to the inappropriate scope.

[0024] In an alternative embodiment, the step of generating the proposal information may include the step of generating first proposal information related to carbon reduction corresponding to transportation information, the step of generating second proposal information related to reduction of power usage of process equipment based on whether the first proposal information has been applied, the step of generating third proposal information related to carbon reduction corresponding to material production based on whether the second proposal information has been applied, and the step of generating fourth proposal information related to carbon reduction using a carbon offset method when the third proposal information has been applied.

[0025] In an alternative embodiment, the second proposal information may include second proposal first sub-information related to a proposal to minimize carbon emissions in equipment power usage, second proposal second sub-information related to a proposal to minimize peak power usage of components, and second proposal third sub-information related to a proposal to minimize standby power of the equipment.

[0026] In an alternative embodiment, the method may include the steps of generating predicted carbon emission information including information about carbon emissions at a future point in time by utilizing a carbon emission prediction model, generating suggestion information based on a comparison between the predicted carbon emission information and target carbon emission information, and generating carbon reduction prediction information according to application of the suggestion information.

[0027] In another embodiment of the present invention, a computing device is disclosed for providing a method for analyzing carbon emissions and suggesting a plan for reducing carbon emissions based thereon. The computing device includes a memory storing one or more instructions and a processor executing the one or more instructions stored in the memory, wherein the processor executes the one or more instructions, thereby performing the aforementioned carbon emissions analysis and suggesting a plan for reducing carbon emissions based thereon.

[0028] In another embodiment of the present invention, a computer program stored on a computer-readable recording medium is disclosed. The computer program, when combined with a computer as hardware, can perform a method for analyzing carbon emissions and suggesting a plan for reducing carbon emissions based on the analysis.

[0029] Other specific details of the present invention are included in the detailed description and drawings.

[0030]

[0031] The present invention can provide a method for analyzing future carbon emissions using big data built on the basis of carbon emissions detected in real time during the manufacturing and distribution process, and suggesting a plan for reducing carbon emissions based on the analysis.

[0032] The effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description below.

[0033]

[0034] FIG. 1 is a schematic diagram of a system for providing a method for analyzing carbon emissions and suggesting a plan for reducing carbon emissions based on the analysis, according to one embodiment of the present invention.

[0035] FIG. 2 illustrates a block diagram of a computing device that performs carbon emission analysis and a method for suggesting a carbon emission reduction plan based thereon, according to one embodiment of the present invention.

[0036] FIG. 3 is an exemplary diagram illustrating a process for obtaining carbon emission information for one or more scopes in accordance with one embodiment of the present invention.

[0037] FIG. 4 is an exemplary diagram illustrating a process of displaying information on a display unit based on comprehensive carbon emission information acquired in real time in accordance with one embodiment of the present invention.

[0038] FIG. 5 is an exemplary diagram illustrating a process of deriving predicted carbon emission information related to one embodiment of the present invention and displaying information on a display unit based on the derived predicted carbon emission information.

[0039] FIG. 6 is an exemplary diagram illustrating a process for generating one or more pieces of suggestion information related to one embodiment of the present invention.

[0040] FIG. 7 is an exemplary diagram illustrating a process for generating second proposal information related to one embodiment of the present invention.

[0041] FIG. 8 illustrates an exemplary flowchart for providing a method for analyzing carbon emissions and suggesting a plan for reducing carbon emissions based thereon in accordance with one embodiment of the present invention.

[0042] Figure 9 is a schematic diagram showing a network function related to one embodiment of the present invention.

[0043]

[0044] Various embodiments are now described with reference to the drawings. In this specification, various descriptions are provided to facilitate an understanding of the present invention. However, it will be apparent that these embodiments may be practiced without these specific details.

[0045] As used herein, the terms "component," "module," "system," and the like refer to computer-related entities, hardware, firmware, software, a combination of software and hardware, or an execution of software. For example, a component may be, but is not limited to, a procedure running on a processor, a processor, an object, a thread of execution, a program, and / or a computer. For example, both an application running on a computing device and the computing device may be a component. One or more components may reside within a processor and / or a thread of execution. A component may be localized within a single computer. A component may be distributed between two or more computers. Furthermore, these components may execute from various computer-readable media having various data structures stored therein. Components may communicate via local and / or remote processes, for example, by signals comprising one or more data packets (e.g., data from one component interacting with another component in a local system, a distributed system, and / or data transmitted to another system via a network such as the Internet via signals).

[0046] Furthermore, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or." That is, unless otherwise specified or clear from context, "X employs A or B" is intended to mean either of the natural inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, "X employs A or B" can apply to any of these cases. Furthermore, the term "and / or" as used herein should be understood to refer to and include all possible combinations of one or more of the associated items listed.

[0047] Additionally, the terms "comprises" and / or "comprising" should be understood to imply the presence of the features and / or components in question. However, it should be understood that the terms "comprises" and / or "comprising" do not exclude the presence or addition of one or more other features, components, and / or groups thereof. Furthermore, unless otherwise specified or clear from the context to refer to the singular form, the singular in the specification and claims should generally be construed to mean "one or more."

[0048] Those skilled in the art should further recognize that the various illustrative logical blocks, components, modules, circuits, means, logics, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate the interchangeability of hardware and software, various illustrative components, blocks, components, means, logics, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application. However, such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

[0049] The description of the disclosed embodiments is provided to enable those skilled in the art to make or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art. The general principles defined herein may be applied to other embodiments without departing from the scope of the present invention. Therefore, the present invention is not limited to the disclosed embodiments. The present invention is to be construed in the widest scope consistent with the principles and novel features disclosed herein.

[0050] In this specification, the term "computer" refers to any type of hardware device including at least one processor, and may also be understood to encompass software components operating on the hardware device, depending on the embodiment. For example, the term "computer" may be understood to encompass, but is not limited to, smartphones, tablet PCs, desktops, laptops, and all user clients and applications running on each device.

[0051] Hereinafter, embodiments of the present invention will be described in detail with reference to the attached drawings.

[0052] Although each step described in this specification is described as being performed by a computer, the subject of each step is not limited thereto, and at least some of each step may be performed by different devices depending on the embodiment.

[0053]

[0054] FIG. 1 is a schematic diagram of a system for providing a method for analyzing carbon emissions and suggesting a plan for reducing carbon emissions based on the analysis, according to one embodiment of the present invention.

[0055] Referring to FIG. 1, a system according to an embodiment of the present invention may include a computing device (100), a manufacturing execution server (200), a user terminal (300), and an external server (400). The components illustrated in FIG. 1 are exemplary, and additional components may exist or some of the components illustrated in FIG. 1 may be omitted. The computing device (100), the manufacturing execution server (200), the user terminal (300), and the external server (400) according to embodiments of the present invention may mutually transmit and receive data for the system according to embodiments of the present invention through a network.

[0056] The network according to embodiments of the present invention can use various wired communication systems such as a public switched telephone network (PSTN), xDSL (x Digital Subscriber Line), RADSL (Rate Adaptive DSL), MDSL (Multi Rate DSL), VDSL (Very High Speed ​​DSL), UADSL (Universal Asymmetric DSL), HDSL (High Bit Rate DSL), and a local area network (LAN).

[0057] Additionally, the network presented herein can use various wireless communication systems such as Code Division Multi Access (CDMA), Time Division Multi Access (TDMA), Frequency Division Multi Access (FDMA), Orthogonal Frequency Division Multi Access (OFDMA), Single Carrier-FDMA (SC-FDMA), and other systems.

[0058] Networks according to embodiments of the present invention can be configured regardless of the communication mode, such as wired or wireless, and can be configured as various communication networks, such as a Personal Area Network (PAN) and a Wide Area Network (WAN). In addition, the network may be the well-known World Wide Web (WWW), and may utilize wireless transmission technologies used for short-distance communication, such as Infrared Data Association (IrDA) or Bluetooth. The technologies described herein can be used not only in the networks mentioned above, but also in other networks.

[0059] According to one embodiment of the present invention, a computing device (100) (hereinafter referred to as 'computing device (100)') for providing a method for analyzing carbon emissions and suggesting a plan for reducing carbon emissions based thereon can obtain information on real-time carbon emissions and construct a database for predicting carbon emissions, and can utilize the constructed database to provide analysis on carbon emissions and prediction information related to predicted carbon emissions at a future point in time.

[0060] Specifically, the computing device (100) can obtain various information related to carbon emissions and / or reduction from the manufacturing execution server (200). For example, the manufacturing execution server (200) may be a server related to a manufacturing execution system. For example, the manufacturing execution system may be a system related to the production of a manufacturing company's products, and may be a system that performs the manufacturing process until the ordered product becomes a final product. Such a manufacturing execution system may obtain on-site information in real time, direct process activities through the data, and perform various controls in response to on-site situations. In an embodiment, the manufacturing execution system may further include, in addition to various process equipment for the process, a barcode and scanner for tracking materials and products, and a sensor device and a machine vision device for detecting defects or defects in products produced on each production line.

[0061] The manufacturing execution server (200) of the present invention may store various carbon emission-related information acquired in real time during the production process using the manufacturing execution system. The various carbon emission-related information stored by the manufacturing execution server (200) may include, but is not limited to, information on the types of one or more materials used in the manufacturing process of a product, information on the amount of carbon emissions generated during the production process of each material, information on the amount of carbon emissions used during the delivery process of each material, information on the amount of carbon emissions generated during the process using the materials, or information on a device that reduces carbon emissions and the amount of carbon emissions reduced through the device.

[0062] Additionally, the computing device (100) can obtain information in real time from the manufacturing execution server (200) and build a database for carbon emission analysis based on the obtained information. In a specific embodiment, the computing device (100) can build an integrated database for carbon emission analysis by classifying the information obtained from the manufacturing execution server (200) by one or more scopes.

[0063] Here, one or more scopes refer to a classification by cause of carbon emissions, and may include a first scope related to direct emissions, a second scope related to indirect emissions, and a third scope related to additional emissions. For example, the first scope may relate to carbon emissions directly generated through combustion or chemical reactions during the process, the second scope may relate to the amount of carbon emitted for generating electricity to drive the process equipment, and the third scope may relate to the amount of carbon emissions generated during the delivery of materials / products. In an embodiment, the third scope may relate to carbon emissions other than those of the first and second scopes.

[0064] That is, the computing device (100) obtains various information related to carbon emissions from the manufacturing execution server (200) and can build a database by classifying the information by cause of carbon emissions.

[0065] In an embodiment, when the computing device (100) identifies that the carbon emissions detected in real time exceed a target standard set by a user (e.g., an administrator), it may generate notification information and transmit it to the user terminal (300) or display it through the display unit (130). That is, when carbon emissions exceeding the target standard set during the product production process are detected, notification information may be generated to warn the user of this and notify the user so that the user can take appropriate action at that time.

[0066] In addition, in an embodiment, an artificial intelligence model (e.g., a carbon emission prediction model) may be utilized to generate prediction information regarding predicted carbon emissions at a future point in time. In an embodiment, the computing device (100) may process information acquired in the past and in real time as input to the artificial intelligence model to output prediction information regarding future carbon emissions. In an embodiment, the artificial intelligence model of the present invention may be a neural network model trained to generate prediction information regarding how much carbon will be emitted during a process. According to an embodiment, the computing device (100) may perform training on the artificial intelligence model to perform prediction of carbon emissions based on time-series information regarding carbon emissions that change over time. The computing device (100) may utilize the trained artificial intelligence model to predict carbon emissions for each process or each scope. For example, the artificial intelligence model may predict that the carbon emissions of the first scope will be 14g after 24 hours. The specific numerical description of the carbon emissions of a specific scope described above is merely an example, and the present invention is not limited thereto.

[0067] The advantage of this system is that it allows for the development of a carbon emissions strategy with enhanced reliability across the entire process, based on carbon emissions forecasts for future time periods. Specifically, since carbon emissions forecasts are generated for each process based on future time periods, precise carbon emissions strategies can be developed in a granular manner for each process.

[0068] Additionally, in an embodiment, the computing device (100) may present an appropriate proposal related to carbon reduction based on predicted information related to carbon emissions predicted at a future point in time.

[0069] If the computing device (100) identifies that the carbon emissions predicted at a future point in time exceed a preset target standard, it can generate information regarding a plan to reduce carbon emissions, i.e., suggestion information. That is, if the computing device (100) identifies that the carbon emissions in the future exceed the target standard, it can generate and provide suggestion information for reducing them. In an embodiment, the computing device (100) can analyze each carbon emission by cause and generate suggestion information based on the analysis. For example, the suggestion information may include, but is not limited to, information suggesting the introduction of eco-friendly vehicles to reduce the amount of carbon emitted during the transportation of materials / products, information suggesting the minimization of electricity usage, information suggesting the use of materials or consumables of the same type with lower carbon emissions, information suggesting the introduction of a carbon offset plan, etc. In addition, the computing device (100) can predict and provide the amount of carbon emissions to be reduced when a plan corresponding to the suggestion information is applied. Accordingly, users can clearly understand how much carbon emissions will be reduced in which process when various proposed measures are actually applied.

[0070] As described above, the computing device (100) can perform an analysis of carbon emissions by constructing a database with data on various carbon sources during the manufacturing process, and can help establish an accurate carbon emissions strategy by predicting carbon emissions at a future point in time. In addition, the computing device (100) can generate and provide suggestion information related to an appropriate method for reducing carbon emissions when carbon emissions are predicted to exceed a set target at a future point in time, and can predict and provide an expected reduction in carbon emissions when the suggestion information is applied.

[0071] This allows users to easily identify when carbon emissions are expected to exceed targets. If emissions are predicted to exceed targets, they can receive various suggestions for reducing emissions, thereby enhancing the convenience of developing carbon emissions strategies. A more detailed description of the carbon emissions analysis and the method for suggesting reduction measures based on this analysis is provided below with reference to Figure 2.

[0072] Although only one computing device (100) is illustrated in FIG. 1, it will be apparent to those skilled in the art that more servers may also fall within the scope of the present invention and that the computing device (100) may include additional components. That is, the computing device (100) may be comprised of multiple computing devices. In other words, a set of multiple nodes may constitute the computing device (100).

[0073] According to one embodiment of the present invention, the computing device (100) may be a server providing a cloud computing service. More specifically, the computing device (100) may be a server providing a cloud computing service, a type of Internet-based computing that processes information using another computer connected to the Internet rather than the user's computer. The cloud computing service may be a service that stores data on the Internet and allows users to access it anytime and anywhere via an Internet connection without having to install necessary data or programs on their own computers. Furthermore, the data stored on the Internet can be easily shared and transmitted with simple operations and clicks. Furthermore, the cloud computing service may be a service that not only stores data on an Internet server, but also allows users to perform desired tasks using the functions of web-based application programs without having to install separate programs. Furthermore, the cloud computing service may be a service that allows multiple people to simultaneously share and work on documents. Furthermore, the cloud computing service may be implemented in at least one of the following forms: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), a virtual machine-based cloud server, and a container-based cloud server. In other words, the computing device (100) of the present invention may be implemented in at least one of the aforementioned cloud computing services. The specific description of the cloud computing service described above is merely an example, and may include any platform that constructs the cloud computing environment of the present invention.

[0074] According to one embodiment of the present invention, the manufacturing execution server (200) may be a server related to a manufacturing execution system (MES). The manufacturing execution system may refer to a system that controls all processes occurring in the manufacturing process, such as tracking production logistics material work history and defects, within a single system. The manufacturing execution system may establish a management system for the manufacturing site based on collection of production performance operating rates, real-time management of incoming and outgoing information, and management of defective products. For example, the manufacturing execution system may be a system related to the production of a manufacturing company's products, and may be a system that performs the manufacturing process until an ordered product becomes a final product. Such a manufacturing execution system may acquire on-site information in real time, direct process activities through the data, and perform various controls in response to on-site situations. In an embodiment, the manufacturing execution system may further include, in addition to various process equipment for the process, a barcode and scanner for tracking materials and products, and a sensor device and machine vision device for detecting defects or defects in products produced on each production line.

[0075] In various embodiments, the manufacturing execution system may further include an input detection sensor that detects the input amount of materials during the process. The input detection sensor may be installed at the input end of the process equipment and may detect the input amount of materials flowing into the process equipment. For example, the input detection sensor may be a first device and detect that 4 kg of material A has been input. The sensing information acquired through the input detection sensor is transmitted to the manufacturing execution server (200) and then transferred to the computing device (100). In an embodiment, information on the carbon emissions of each material utilized during the product manufacturing process may be calculated based on the information detected by the input detection sensor.

[0076] In various embodiments, the manufacturing execution system may further include a gas detection sensor for detecting the amount of carbon generated during the process. In embodiments, carbon may be emitted during the process through combustion or chemical reactions, and the gas detection sensor is configured to detect such carbon. The sensing information obtained through the gas detection sensor is transmitted to the manufacturing execution server (200) and then forwarded to the computing device (100).

[0077] In various embodiments, the manufacturing execution server (200) can acquire and manage information related to the transportation of materials used in product manufacturing and the transportation of finished products in real time. For example, the manufacturing execution server (200) can acquire information related to the transportation of materials or products through linkage with a transportation server that manages transportation or by receiving input from a user.

[0078] For example, in the process of producing product A, a first material, a second material, and a third material may be utilized, and the manufacturing execution server (200) may identify the production site and destination of the first to third materials, calculate the distance, and include information on a mobile device utilized in the transportation process corresponding to the distance. Here, the information on the mobile device serves as the basis for calculating the amount of carbon emissions emitted during the transportation process, and may include information on the amount of carbon emissions emitted by a specific mobile device when moving a certain distance. For example, information indicating that 5 g of carbon is emitted per km corresponding to a first mobile device that utilizes fossil fuels may be stored, and information indicating that no carbon emissions are emitted corresponding to a second mobile device that utilizes electricity may be stored. The specific description of the first and second mobile devices is merely an example, and the present invention is not limited thereto. Information related to the transportation of materials and products may be acquired for the purpose of identifying and analyzing carbon emissions corresponding to the third scope.

[0079] Although the computing device (100) and the manufacturing execution server (200) are expressed as separate entities in FIG. 1, according to an embodiment of the present invention, the computing device (100) may be included in the manufacturing execution server (200), and the functions of providing prediction and suggestion information through manufacturing process management and carbon emission analysis may be performed in a single integrated server.

[0080] According to one embodiment of the present invention, the user terminal (300) may refer to any type of node(s) in a system having a mechanism for communication with the computing device (100). The user terminal (300) may refer to a terminal that can receive notification information or suggestion information according to carbon emissions through information exchange with the computing device (100), and may refer to a terminal carried by the user. In an embodiment, information regarding carbon emissions acquired through the manufacturing execution server (200) may be transmitted to the computing device (100), and the computing device (100) may provide prediction information or suggestion information generated through analysis of the carbon emissions acquired in real time to the user terminal (300).

[0081] A user terminal (300) may refer to any type of entity(ies) in a system having a mechanism for communicating with a computing device (100). For example, such a user terminal (300) may include a personal computer (PC), a notebook, a mobile terminal, a smart phone, a tablet PC, a wearable device, etc., and may include all types of terminals capable of connecting to a wired / wireless network. In addition, the user terminal (300) may include any server implemented by at least one of an agent, an Application Programming Interface (API), and a plug-in. In addition, the user terminal (300) may include an application source and / or a client application.

[0082] According to one embodiment of the present invention, an external server (400) may be connected to a computing device (100) via a network, and may provide various information / data necessary for the computing device (100) to perform a carbon emission analysis and a method for suggesting a carbon emission reduction plan based thereon, or may receive, store, and manage result data derived by performing a carbon emission analysis and a method for suggesting a carbon emission reduction plan based thereon. For example, the external server (400) may be a storage server separately provided outside the computing device (100), but is not limited thereto.

[0083] Additionally, in the embodiment, information stored in the external server (400) may be utilized as learning data, verification data, and test data for training the artificial intelligence model of the present invention. That is, the external server (400) may store data for training the artificial intelligence model of the present invention. The computing device (100) of the present invention may construct multiple learning data sets based on the information received from the external server (400). The computing device (100) may generate multiple artificial intelligence models by performing learning on one or more network functions through each of the multiple learning data sets.

[0084] The external server (400) may be a digital device equipped with a processor, memory, and computing power, such as a laptop computer, notebook computer, desktop computer, web pad, or mobile phone. The external server (400) may be a web server that processes services. The types of servers described above are merely examples, and the present disclosure is not limited thereto.

[0085]

[0086] FIG. 2 illustrates a block diagram of a computing device that performs carbon emission analysis and a method for suggesting a carbon emission reduction plan based thereon, according to one embodiment of the present invention.

[0087] As illustrated in FIG. 2, the computing device (100) may include a network unit (110), a memory unit (120), a display unit (130), and a processor (140). The components described above are exemplary, and the scope of the present invention is not limited to the components described above. That is, additional components may be included or some of the components described above may be omitted depending on the implementation aspect of the embodiments of the present invention.

[0088] According to one embodiment of the present invention, the computing device (100) may include a network unit (110) that transmits and receives data with a user terminal (300) and an external server (400). The network unit (110) may receive input from the user terminal (300). For example, the network unit (110) may receive information about the types of materials required for a process, information related to the input amount of each material, information about the amount of carbon emissions emitted during the transportation of materials, etc. from the user terminal. Additionally, the network unit (110) may allow information transmission between the computing device (100) and the user terminal (300) by calling a procedure to the computing device (100).

[0089] The network unit (110) according to one embodiment of the present invention can use various wired communication systems such as a public switched telephone network (PSTN), xDSL (x Digital Subscriber Line), RADSL (Rate Adaptive DSL), MDSL (Multi Rate DSL), VDSL (Very High Speed ​​DSL), UADSL (Universal Asymmetric DSL), HDSL (High Bit Rate DSL), and a local area network (LAN).

[0090] In addition, the network unit (110) presented in this specification can use various wireless communication systems such as CDMA (Code Division Multi Access), TDMA (Time Division Multi Access), FDMA (Frequency Division Multi Access), OFDMA (Orthogonal Frequency Division Multi Access), SC-FDMA (Single Carrier-FDMA) and other systems.

[0091] In the present invention, the network unit (110) may be configured regardless of the communication mode, such as wired or wireless, and may be configured as various communication networks, such as a personal area network (PAN) and a wide area network (WAN). In addition, the network may be the well-known World Wide Web (WWW), and may also utilize a wireless transmission technology used for short-distance communication, such as infrared (IrDA: Infrared Data Association) or Bluetooth.

[0092] The techniques described in this specification can be used in other networks as well as the networks mentioned above.

[0093] According to one embodiment of the present invention, the memory (120) can store any type of information generated or determined by the processor (140) and any type of information received by the network unit (110). The memory (120) stores various types of data, commands, and / or information. The memory (120) can load a computer program from storage to execute a method / operation according to various embodiments of the present invention. When the computer program is loaded into the memory, the processor (140) can execute one or more instructions constituting the computer program, thereby performing a method for analyzing carbon emissions and suggesting a plan for reducing carbon emissions based thereon. The memory (120) may be implemented as a volatile memory such as RAM, but the technical scope of the present invention is not limited thereto.

[0094] In one embodiment, the memory (120) may include at least one type of storage medium among a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (e.g., SD or XD memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, and an optical disk. The computing device (100) may also operate in relation to web storage that performs the storage function of the memory (120) on the internet. The description of the above-described memory is merely an example, and the present invention is not limited thereto.

[0095] According to one embodiment of the present invention, the computing device (100) has a display unit (130) and can receive user input related to information that serves as a basis for calculating carbon emissions and provide output of any form to the user.

[0096] For example, the computing device (100) may receive delivery information for each material used in the product process from the user through the display unit (130). Here, the delivery information may include information about the origin and destination of the material and the distance between the origin and destination. As another example, the computing device (100) may receive information about the amount of carbon emissions generated during the manufacturing process of the material used in the process from the user through the display unit (130). As yet another example, the computing device (100) may receive information about the power usage of the device used in the process from the user through the display unit (130). The specific description of the information received through the above-described display unit is merely an example, and the present invention is not limited thereto.

[0097] In an embodiment, the display unit (130) displays (outputs) information processed in the computing device (100). For example, the display unit (130) may display execution screen information of an application program running in the computing device (100), or UI (User Interface) or GUI (Graphical User Interface) information according to such execution screen information.

[0098] The display unit (130) may include at least one of a liquid crystal display (LCD), a thin film transistor-liquid crystal display (TFT LCD), an organic light-emitting diode (OLED), a flexible display, a 3D display, and an e-ink display.

[0099] In various embodiments, two or more display units (130) may be present depending on the implementation form of the computing device (100). In this case, the computing device (100) may have multiple display units spaced apart from each other or arranged integrally on one surface, or may be arranged on different surfaces.

[0100] The display unit (130) may include a touch sensor that detects a touch on the display unit (130) so that a control command can be input by a touch method. Using this, when a touch is made on the display unit (130), the touch sensor detects the touch, and the processor (140) may generate a control command corresponding to the touch based on this. The content input by the touch method may be characters or numbers, or instructions or designable menu items in various modes.

[0101] The display unit (130) can be formed as a touch screen by forming a mutual layer structure with the touch sensor or by forming an integral structure. This touch screen can function as a user input unit that provides an input interface between the computing device and the user, and at the same time, can provide an output interface between the computing device and the user. According to an embodiment, the user can generate various information related to carbon emission calculation or analysis through input via the touch screen of the display unit (130).

[0102] According to one embodiment of the present invention, the processor (140) can typically process the overall operation of the computing device (100). The processor (140) can process signals, data, information, etc. input or output through the components described above, or can operate an application program stored in the memory (120) to provide or process appropriate information or functions to a user or a user terminal.

[0103] Additionally, the processor (140) may perform operations for at least one application or program for executing a method according to embodiments of the present invention, and the computing device (100) may have one or more processors.

[0104] According to one embodiment of the present invention, the processor (140) may be configured with one or more cores and may include a processor for data analysis and deep learning, such as a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), and a tensor processing unit (TPU) of a computing device.

[0105] The processor (140) can read a computer program stored in the memory (120) to perform a method for analyzing carbon emissions and suggesting a plan for reducing carbon emissions based on the analysis according to one embodiment of the present invention.

[0106] In various embodiments, the processor (140) may further include a Random Access Memory (RAM, not shown) and a Read-Only Memory (ROM, not shown) that temporarily and / or permanently store signals (or data) processed within the processor (140). In addition, the processor (140) may be implemented in the form of a system on chip (SoC) that includes at least one of a graphics processing unit, RAM, and ROM.

[0107] According to one embodiment of the present invention, the processor (140) can obtain process material information, carbon emission information, distribution management information, and carbon emission offset information.

[0108] In an embodiment, obtaining process material information, carbon emission information, distribution management information, and carbon emission offset information may be by receiving or loading data stored in the memory (120). Obtaining process material information, carbon emission information, distribution management information, and carbon emission offset information may be by receiving or loading data from another storage medium, another computing device, or a separate processing module within the same computing device based on a wired / wireless communication means. For example, the computing device (100) may obtain information related to carbon emissions, such as process material information, carbon emission information, distribution management information, and carbon emission offset information, from at least one of the manufacturing execution server (200), the user terminal (300), or the external server (400).

[0109] In an embodiment, the process material information may include material type information, usage information for each material, and carbon emission information for each material used in the process. For example, the process material information may include information that 2,000 g of material A is used in the process, and that material A emits 1.45 g of carbon per 100 g. The specific numerical description of the process material information described above is merely an example, and the present invention is not limited thereto.

[0110] Process material information may be generated based on user input or based on sensing values ​​sensed by an input quantity detection sensor. For example, a user may input information on the type of material used in a process or the amount of material used through a display unit (or a separate input unit), and process material information may be generated based on this. Furthermore, for example, the input quantity of a material may be identified through an input quantity detection sensor installed in a process device of a manufacturing execution system, and process material information may be generated based on this.

[0111] That is, the process material information includes information on the carbon emissions of each material used in the process, and may be characterized as being generated based on sensing values ​​and user input during the process.

[0112] In an embodiment, the carbon emission information may include information regarding the amount of carbon emitted during the process. The carbon emission information may include information regarding carbon emissions directly generated through combustion or chemical reactions during the process. For example, the carbon emission information may include information indicating that 200 grams of carbon were emitted through combustion.

[0113] Carbon emission information may be generated based on the amount of carbon detected by a gas detection sensor, based on the amount of power of a device used in a process detected by a power detection sensor, or generated through OCR (Optical Character Recognition) analysis of documents related to power usage.

[0114] In a specific embodiment, the manufacturing execution system may be equipped with a gas detection sensor, and the gas detection sensor may detect the amount of carbon generated during the process and transmit it to the computing device (100). That is, carbon emission information is generated through the gas detection sensor equipped in the manufacturing execution system, and the computing device (100) is configured to receive it.

[0115] Additionally, in the embodiment, the manufacturing execution system is equipped with a sensor (i.e., a power detection sensor) that measures the amount of power used by the process equipment utilized in each process, and information on the total amount of power used is obtained through the power detection sensor. If a sensor measuring power is equipped in connection with each process equipment, the amount of power is automatically identified, and carbon emissions can be calculated based on the identified amount of power.

[0116] In addition, in the embodiment, when an image of a report (e.g., a bill) related to power consumption is uploaded to the computing device (100), the computing device (100) can calculate the amount of power consumed through OCR (optical character recognition) and thereby calculate the amount of carbon emitted in generating the corresponding amount of power. The computing device (100) can extract text information from the bill image through OCR and extract information on the amount of power consumed through natural language analysis of the extracted text information.

[0117] In an embodiment, the distribution management information may include first delivery information related to the delivery of materials used in the production of a product, second delivery information related to the delivery of a finished product, and information regarding carbon emissions calculated in response to each delivery information. The first delivery information may include information regarding one or more materials used in the production of a product, including information regarding the delivery location and destination of each material, and information regarding the transport device used in the delivery process. For example, the first delivery information may include information that Material A is delivered from Icheon-si, Gyeonggi-do to Gangnam-gu, Seoul via a fossil fuel vehicle. In an embodiment, the delivery address may be expressed or identified based on a delivery ID pre-stored in a computing device. The specific description of the first delivery information described above is merely an example, and the present invention is not limited thereto.

[0118] Additionally, secondary delivery information refers to information about a completed product, and may include information regarding the product's shipping and destination locations, as well as information regarding the transport device used during the delivery process. For example, secondary delivery information may include information that Product A is being delivered from Gangnam-gu, Seoul to Gangseo-gu, Seoul via an electric vehicle. The specific description of the secondary delivery information described above is merely an example, and the present invention is not limited thereto.

[0119] Additionally, distribution management information may include information on carbon emissions calculated for each delivery item. For example, distribution management information may include information indicating that the carbon emissions for Material A are 300g due to the use of fossil fuel vehicles during the delivery process, and information indicating that the carbon emissions for Product B are 0g due to the use of electric fuel vehicles during the delivery process. The specific numerical descriptions of carbon emissions for each delivery process described above are merely examples and the present invention is not limited thereto.

[0120] In an embodiment, the carbon emission offset information may include information about an application method for reducing carbon emissions and information about the amount of carbon reduced through the application method.

[0121] For example, carbon offset information may include information that 50g of carbon emissions were offset by chip recycling during the manufacturing process of a specific product. Beyond chip recycling, carbon offset information may also include information on carbon emissions offset by other factors, such as cutting oil recycling, energy harvesting, wind turbines, solar power generation, and tree planting.

[0122] Additionally, the processor (140) can obtain carbon emission information for one or more predetermined scopes based on process material information, carbon emission information, distribution management information, and carbon emission offset information. The processor (140) can obtain information related to carbon emissions and construct a database by classifying the information by scope.

[0123] Here, one or more predetermined scopes refer to categories based on the cause of carbon emissions, and may include a first scope related to direct emissions, a second scope related to indirect emissions, and a third scope related to additional emissions. For example, the first scope may relate to carbon emissions directly generated through combustion or chemical reactions during the process, the second scope may relate to the amount of carbon emitted for generating electricity to drive process equipment, and the third scope may relate to carbon emissions generated during the delivery of materials / products.

[0124] That is, as illustrated in FIG. 3, the processor (140) can obtain various information related to carbon emissions (i.e., process material information, carbon emission information, distribution management information, and carbon emission offset information) from the manufacturing execution server (200), and can build a database by classifying the information by cause of carbon emissions. Accordingly, carbon emissions can be identified by cause of carbon emissions through the built database.

[0125] In addition, the processor (140) may generate proposal information based on a comparison between the carbon emission information for one or more scopes and the target carbon emission information. The target carbon emission information may include information related to a target standard value set as a target by a process manager. The target carbon emission information may be generated based on a user's input through an input unit. The processor (140) may compare the carbon emission amount obtained in real time with the preset target standard value, and may generate proposal information corresponding to a point in time when the real-time carbon emission amount exceeds the target standard value as a result of the comparison. Here, the proposal information relates to a method for reducing carbon emissions, and may include, for example, information on various methods for reducing carbon emissions for each process.

[0126] More specifically, referring to FIG. 4, the processor (140) may obtain comprehensive carbon information based on carbon emission information for one or more scopes. In an embodiment, the processor (140) may accumulate carbon emissions detected for each scope to generate information on the total carbon emissions for each scope. In this case, when the carbon emissions of all scopes are added together, the total carbon emissions are calculated. That is, the processor (140) may add up the accumulated carbon emissions for all scopes to generate comprehensive carbon information, i.e., information on all carbon emissions emitted during the process and delivery.

[0127] The processor (140) can compare the comprehensive carbon emission information with the target carbon emission information. If the carbon emissions corresponding to the comprehensive carbon emission information do not exceed the carbon emissions corresponding to the target carbon emission information, the processor (140) may not generate the proposed information. If the proposed information is not generated, information indicating that the carbon emissions are appropriate or the comprehensive carbon emissions emitted in real time may be displayed on the display unit.

[0128] Additionally, the processor (140) can generate suggestion information when the carbon emission corresponding to the comprehensive carbon emission information exceeds the carbon emission corresponding to the target carbon emission information.

[0129] That is, if the total carbon emissions generated during the process and delivery exceed a preset target standard, the processor (140) determines that reduction of carbon emissions is necessary and generates suggestion information.

[0130] In one embodiment, generating the proposal information may include selecting at least one of the first scope, the second scope, and the third scope as an inappropriate scope based on carbon emission information for one or more scopes, and generating proposal information corresponding to the inappropriate scope.

[0131] In a specific embodiment, the processor (140) may identify a scope with the highest carbon emissions among one or more scopes and select it as an inappropriate scope. For example, if the carbon emissions corresponding to a first scope are 100 g, the carbon emissions corresponding to a second scope are 300 g, and the carbon emissions corresponding to a third scope are 400 g, the processor (140) may select the third scope as an inappropriate scope.

[0132] In addition, in an embodiment, the processor (140) may compare the carbon emission information for each scope acquired in real time with a threshold reference value set in advance for one or more scopes to select an inappropriate scope. In an embodiment, a threshold reference value may be pre-matched to each scope according to the type of product or process. Here, the threshold reference value may include information limiting the maximum amount of carbon that can be emitted. For example, a first threshold reference value pre-matched to a first scope may include information limiting the maximum carbon emission to 10,000 g, a second threshold reference value pre-matched to a second scope may include information limiting the maximum carbon emission to 2,000 g, and a third threshold reference value pre-matched to a third scope may include information limiting the maximum carbon emission to 40,000 g. Different threshold reference values ​​may be pre-matched to each scope, i.e., each carbon source.

[0133] In addition, the accumulated carbon emissions detected in response to the first to third real-time scopes may be 8,000 g, 4,000 g, and 30,000 g, respectively. In this case, the processor (140) may identify that the real-time carbon emissions of the second scope exceed a predetermined threshold reference value corresponding to the second scope, and may select the second scope as an inappropriate scope. The specific numerical descriptions of the pre-matched threshold reference value and the accumulated carbon emissions detected in real time for each scope described above are merely examples, and the present invention is not limited thereto.

[0134] When an inappropriate scope is selected, the processor (140) can generate suggestion information based on the selected inappropriate scope.

[0135] More specifically, if the first scope related to direct emissions is selected as an inappropriate scope, the processor (140) determines that carbon emissions directly generated through combustion or chemical reactions during the process have a significant impact on the increase in the total carbon emissions, and thus may generate suggestion information for reducing the amount of carbon generated during the direct process. In this case, the generated suggestion information may be, for example, information suggesting that, when manufacturing or processing a specific material, a specific material be replaced with another material that provides the same performance as the specific material but has lower carbon emissions than the specific material.

[0136] In addition, if the second scope related to indirect emissions is selected as an inappropriate scope, the processor (140) determines that the amount of carbon emitted for generating power to drive the process equipment has a large impact on the increase in the total carbon emissions, and thus may generate suggestion information to minimize the use of the equipment during the process. In this case, the generated suggestion information may include, for example, information on various suggestions for minimizing electricity consumption. For example, the processor (140) may select a sensor with many peak values ​​among various sensing information, determine that a large amount of electricity is consumed during the process of a component with many peaks in the values ​​related thereto, and generate suggestion information to minimize the electricity consumption during the process.

[0137] Additionally, if the selected inappropriate scope is related to a third scope, the processor (140) determines that carbon emissions generated during the delivery process of materials or products have a significant impact on the increase in overall carbon emissions, and thus may generate suggestion information to reduce the amount of carbon generated during the delivery process. In this case, the generated suggestion information may be, for example, information suggesting that fossil fuel vehicles used during the delivery process of materials be replaced with electric fuel vehicles.

[0138] As described above, the processor (140) generates suggestion information based on a scope selected as an inappropriate scope among one or more scopes. That is, information on total carbon emissions is managed by classifying each scope according to the cause of carbon emissions, and if the total carbon emissions accumulated in real time exceed the target carbon emissions, the scope that had the greatest impact on the increase in total carbon emissions is selected as an inappropriate scope, and suggestion information appropriate for the cause can be generated based on this. In other words, carbon emissions for each cause can be analyzed to generate reduction suggestion information according to the cause. Therefore, if carbon emissions exceed the target, the manager can be provided with reduction suggestion information according to the cause.

[0139] Additionally, in an embodiment, the processor (140) may calculate and provide information on the amount of carbon emissions reduced when each suggestion information is applied. For example, the processor (140) may provide suggestion information suggesting that fossil fuel vehicles used in the delivery process be replaced with electric fuel vehicles. In this case, the processor (140) may perform a simulation based on the suggestion information to obtain information on the amount of carbon emissions after the suggestion is applied. For example, by converting the carbon emissions generated in the delivery process due to the use of electric fuel vehicles to zero, information on the amount of carbon emissions expected to be reduced when the suggestion information is applied can be generated. As described above, the processor (140) may calculate and provide information on the extent to which carbon emissions are reduced when the generated suggestion information is applied. In this case, the user can more easily establish a carbon emission strategy by referring to the provided information.

[0140] According to one embodiment of the present invention, the processor (140) utilizes an artificial intelligence model to predict carbon emissions at a future point in time and generate proposal information based on the predicted carbon emissions. A detailed description of the process of predicting carbon emissions at a future point in time and generating proposal information based on the predicted carbon emissions will be described below with reference to FIG. 5.

[0141] More specifically, referring to FIG. 5, the processor (140) can generate predicted carbon emission information including information on carbon emissions at a future point in time by utilizing a carbon emission prediction model.

[0142] Here, the carbon emission prediction model can be a neural network model trained to predict carbon emissions. A carbon emission prediction model can receive various types of data and provide predictions about how much carbon will be generated during each process. A carbon emission prediction model can make predictions based on time-series data related to carbon emissions that change over time.

[0143] For example, predicted carbon emissions information related to the output of a carbon emissions prediction model may include predicted information that total carbon emissions will reach 20,000g after 48 hours.

[0144] In an embodiment, a trained neural network model (carbon emission prediction model) can predict carbon emissions for each process or each scope. For example, the carbon emission prediction model can predict the carbon emissions directly generated in the first process as 2000 based on information stored in a database in a time series manner. In addition, for example, the carbon emission prediction model can predict the carbon emissions of the first scope 24 hours later as 14g based on information stored in a database in a time series manner. The specific numerical description of the carbon emissions of the specific scopes described above is merely an example, and the present invention is not limited thereto.

[0145] As mentioned above, if predictive information on carbon emissions is generated for future time periods, there is an advantage in that a carbon emissions strategy with improved reliability can be developed for the entire process.

[0146] In one embodiment, the processor (140) may generate suggestion information based on a comparison between predicted carbon emission information and target carbon emission information. Referring to FIG. 5 , the processor (140) may not generate suggestion information if the carbon emissions corresponding to the predicted carbon emission information do not exceed the carbon emissions corresponding to the target carbon emission information. If suggestion information is not generated, the processor (140) may display information indicating that the carbon emissions are appropriate or the carbon emissions expected at a future point in time through the display unit.

[0147] Additionally, the processor (140) can generate suggestion information when the carbon emissions corresponding to the predicted carbon emissions information exceed the carbon emissions corresponding to the target carbon emissions information.

[0148] That is, when the predicted carbon emissions in response to a future point in time exceed a preset target standard, the processor (140) predicts that reduction of carbon emissions will be necessary at a future point in time and generates suggestion information.

[0149] Additionally, the processor (140) can generate carbon reduction prediction information based on the application of the proposed information. In this case, the carbon reduction prediction information may include information related to the amount of carbon emissions reduced when the provided proposed information is applied. For example, the processor (140) may provide proposal information suggesting that fossil fuel vehicles used in the delivery process be replaced with electric fuel vehicles. In this case, the processor (140) may perform a simulation based on the proposed information to obtain information on the amount of carbon emissions after the proposal is applied. For example, by converting the carbon emissions generated during the delivery process due to the use of electric fuel vehicles to zero, information on the amount of carbon emissions expected to be reduced when the proposed information is applied can be generated. As described above, the processor (140) may calculate and provide information on the extent to which carbon emissions are reduced when the generated proposed information is applied. In this case, the user can more easily establish a carbon emission strategy by referring to the provided information.

[0150]

[0151]

[0152]

[0153] In various embodiments, the processor (140) may be characterized by generating one or more pieces of suggestion information based on a predetermined suggestion order.

[0154] Specifically, referring to FIG. 6, the processor (140) may first generate first suggestion information related to carbon reduction corresponding to transportation information. For example, the first suggestion information may be information regarding a suggestion to minimize carbon emissions from the transportation of materials or products. For example, the first suggestion information may be suggestion information to change carbon fuel vehicles used in the delivery process to electric fuel vehicles.

[0155] Additionally, the processor (140) may generate second suggestion information related to reducing power consumption of process equipment based on whether the first suggestion information has been applied. If the first suggestion information is not currently applied to the system (e.g., if a carbon emission corresponding to the third scope is identified), the processor (140) may not generate the second suggestion information. Additionally, if the first suggestion information is currently applied to the system (e.g., if a carbon emission corresponding to the third scope is not identified), the processor (140) may generate the second suggestion information. That is, if the first suggestion information is currently applied to the system, the processor (140) may generate the second suggestion information and provide it to the user.

[0156] In an embodiment, the second proposal information may include second proposal first sub-information related to a proposal to minimize carbon emissions in equipment power usage, second proposal second sub-information related to a proposal to minimize peak power usage of components, and second proposal third sub-information related to a proposal to minimize standby power of equipment.

[0157] According to an embodiment, the processor (140) may generate second proposal information when the first proposal information is applied to the system. In a specific embodiment, referring to FIG. 7, the processor (140) may generate second proposal first sub-information related to a proposal for minimizing carbon emissions from equipment power usage. The second proposal first sub-information may be information suggesting a method for minimizing carbon emissions from equipment power usage.

[0158] The processor (140) may generate second sub-information of the second proposal if a method corresponding to the first sub-information of the second proposal is applied to the system. The second sub-information of the second proposal may be information suggesting a method for minimizing the occurrence of peak power consumption of a component. For example, the second sub-information of the second proposal may include suggestion information for selecting a sensor with a large number of peak values ​​among various sensing information, determining that a large amount of power is consumed during the process of a component with a large number of peak values ​​related to the sensor, and minimizing the amount of power consumed during the process.

[0159] In addition, the processor (140) may generate second proposal third sub-information if a method corresponding to the second proposal second sub-information is applied to the system. The second proposal third sub-information may be information suggesting a method for minimizing the standby power of the equipment. Since the standby power amount is excessively high, the second proposal third sub-information may include information for minimizing this. In an embodiment, the processor (140) determines the standby power amount of the equipment utilized in the process to distinguish whether it is in a standby power state, and if the standby power state continues for a preset threshold time or longer, it may be determined that the standby power state of the equipment is long.

[0160] Additionally, the processor (140) may generate third suggestion information related to reducing carbon emissions corresponding to material production based on whether the second suggestion information related to reducing power consumption of process equipment has been applied. If the second suggestion information is not currently applied to the system, the processor (140) may not generate the third suggestion information. Furthermore, if the second suggestion information is currently applied to the system, the processor (140) may generate the third suggestion information. That is, if the second suggestion information is currently applied to the system, the processor (140) may generate and provide the third suggestion information to the user.

[0161] In an embodiment, the third suggestion information may be suggestion information that suggests a method for minimizing the amount of carbon emitted in the production of materials or consumables. For example, when manufacturing or processing a specific material, the third suggestion information may be information suggesting replacing the specific material with another material that provides the same performance as the specific material but has lower carbon emissions than the specific material.

[0162] In addition, the processor (140) may generate fourth proposal information related to carbon reduction using a carbon offset method when the third proposal information has already been applied. If the third proposal information is not currently applied to the system, the processor (140) may not generate the fourth proposal information. In addition, if the third proposal information is currently applied to the system, the processor (140) may generate the fourth proposal information. That is, if the third proposal information is currently applied to the system, the processor (140) may generate the fourth proposal information and provide it to the user. In an embodiment, the fourth proposal information is proposal information that suggests a carbon reduction plan using a carbon offset method, and may include, but is not limited to, a proposal for recycling chips, a proposal for recycling cutting oil, a proposal for applying energy harvesting, a proposal for applying wind power generators, a proposal for utilizing solar power generation, a proposal for planting seedlings, etc.

[0163] In an additional embodiment, the processor (140) may provide purchase guidance information for purchasing carbon emission rights when the fourth proposal information has already been applied to the system. If carbon emissions exceed the target reference value even after the proposal information up to the fourth proposal information has been sequentially applied, the processor (140) may additionally generate and provide purchase guidance information guiding the purchase of carbon emission rights. The purchase guidance information may include, for example, information on purchasing a carbon emission source and information on the purchase amount. Here, the information on the purchase amount may be calculated based on a comparison of comprehensive carbon emission information detected in real time with target carbon emission information. For example, the processor (140) may calculate the purchase amount as the amount by which the carbon emissions corresponding to the comprehensive carbon emission information exceed the carbon emissions corresponding to the target carbon emission information.

[0164] In summary, the processor (140) can generate sequential suggestion information based on a predetermined order. That is, suggestions for reducing carbon emissions for various carbon emission sources can be sequentially presented, thereby gradually reducing carbon emissions. Furthermore, if carbon emissions still exceed the target standard even after all carbon emission reduction suggestions for each source have been implemented, purchase guidance information can be generated and provided to guide the purchase of carbon emission credits.

[0165]

[0166] FIG. 8 illustrates an exemplary flowchart for providing a method for analyzing carbon emissions and suggesting a plan for reducing carbon emissions based thereon in accordance with one embodiment of the present invention.

[0167] According to one embodiment of the present invention, the method may include a step (S100) of obtaining process material information, carbon emission information, distribution management information, and carbon emission offset information.

[0168] According to one embodiment of the present invention, the method may include a step (S200) of obtaining carbon emission information for one or more predetermined scopes based on process material information, carbon emission information, distribution management information, and carbon emission offset information.

[0169] According to one embodiment of the present invention, the method may include a step (S300) of generating suggestion information based on a comparison between one or more scope-specific carbon emission information and target carbon emission information.

[0170] The steps illustrated in FIG. 8 described above may be rearranged as needed, and at least one step may be omitted or added. That is, the steps described above are merely an embodiment of the present invention, and the scope of the present invention is not limited thereto.

[0171]

[0172] Figure 9 is a schematic diagram showing a network function related to one embodiment of the present invention.

[0173] A deep neural network (DNN) can refer to a neural network that includes multiple hidden layers in addition to input and output layers. Using a deep neural network, one can identify latent structures in data. That is, one can identify latent structures in photos, text, videos, voices, and music (e.g., what objects are in a photo, what the content and emotion of a text are, what the content and emotion of a voice are, etc.). A deep neural network can include a convolutional neural network (CNN), a recurrent neural network (RNN), an autoencoder, a generative adversarial network (GAN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a Q network, a U network, a Siamese network, and the like. The description of the above-described deep neural network is merely an example, and the present invention is not limited thereto.

[0174] In one embodiment of the present invention, the network function may include an autoencoder. An autoencoder may be a type of artificial neural network that outputs output data similar to input data. The autoencoder may include at least one hidden layer, and an odd number of hidden layers may be arranged between input and output layers. The number of nodes in each layer may be reduced from the number of nodes in the input layer to an intermediate layer called a bottleneck layer (encoding), and then expanded symmetrically from the bottleneck layer to the output layer (symmetrical to the input layer). The nodes of the dimensionality reduction layer and the dimensionality restoration layer may or may not be symmetrical. The autoencoder may perform nonlinear dimensionality reduction. The number of input layers and output layers may correspond to the number of sensors remaining after preprocessing the input data. In the autoencoder structure, the number of nodes in the hidden layer included in the encoder may have a structure that decreases as it moves away from the input layer. The number of nodes in the bottleneck layer (the layer with the fewest nodes between the encoder and decoder) may be kept above a certain number (e.g., more than half of the input layer), as too few nodes may not transmit enough information.

[0175] Neural networks can be trained using at least one of supervised learning, unsupervised learning, and semi-supervised learning. Neural network training aims to minimize output errors. Training involves repeatedly inputting training data into the neural network, calculating the neural network output and target error for the training data, and backpropagating the neural network error from the output layer to the input layer to update the weights of each node in the neural network in a direction that reduces the error. Supervised learning uses training data where the correct answer is labeled for each training data (i.e., labeled training data). Unsupervised learning, on the other hand, may not include the correct answer label for each training data. For example, in the case of supervised learning for data classification, the training data may be data where each training data category is labeled. Labeled training data is input to a neural network, and an error can be calculated by comparing the output (categories) of the neural network with the labels of the training data. For example, in unsupervised learning for data classification, the error can be calculated by comparing the input training data with the neural network output. The calculated error is backpropagated backwards (i.e., from the output layer to the input layer) in the neural network, and the connection weights of each node in each layer of the neural network can be updated through backpropagation. The amount of change in the connection weights of each updated node can be determined by the learning rate. The neural network's calculation of the input data and the backpropagation of the error can constitute a learning cycle (epoch). The learning rate can be applied differently depending on the number of iterations of the neural network's learning cycle.For example, in the early stages of training a neural network, a high learning rate can be used to quickly allow the neural network to achieve a certain level of performance, thereby improving efficiency, while in the later stages of training, a low learning rate can be used to improve accuracy.

[0176] In neural network training, training data can typically be a subset of real-world data (i.e., the data to be processed using the trained neural network). Therefore, there can be a learning cycle where errors on the training data decrease but errors on the real-world data increase. Overfitting is a phenomenon where excessive training on the training data leads to increased errors on the real-world data. For example, a neural network trained on yellow cats may fail to recognize cats when shown non-yellow colors, a type of overfitting. Overfitting can increase errors in machine learning algorithms. Various optimization methods can be used to prevent overfitting. Methods such as increasing the training data, regularization, and dropout, which omits some nodes from the network during the training process, can be applied.

[0177] Throughout this specification, the terms computational model, neural network, network function, and neural network may be used interchangeably. (Hereinafter, they are collectively referred to as neural networks.) A data structure may include a neural network. And, a data structure including a neural network may be stored on a computer-readable medium. A data structure including a neural network may also include data input to the neural network, weights of the neural network, hyperparameters of the neural network, data obtained from the neural network, activation functions associated with each node or layer of the neural network, and loss functions for training the neural network. A data structure including a neural network may include any of the components disclosed above. That is, a data structure including a neural network may be configured to include all or any combination of data input to the neural network, weights of the neural network, hyperparameters of the neural network, data obtained from the neural network, activation functions associated with each node or layer of the neural network, and loss functions for training the neural network. In addition to the aforementioned components, a data structure including a neural network may include any other information that determines the characteristics of the neural network. Additionally, the data structure may include any form of data used or generated in the computational process of the neural network, and is not limited to the aforementioned. The computer-readable medium may include a computer-readable recording medium and / or a computer-readable transmission medium. The neural network may be composed of a set of interconnected computational units, which may generally be referred to as nodes. These nodes may also be referred to as neurons. The neural network is composed of at least one node.

[0178]

[0179] The steps of a method or algorithm described in connection with an embodiment of the present invention may be implemented directly in hardware, implemented as a software module executed by hardware, or implemented by a combination thereof. The software module may reside in a random access memory (RAM), a read only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a flash memory, a hard disk, a removable disk, a CD-ROM, or any other form of computer-readable recording medium well known in the art to which the present invention pertains.

[0180] The components of the present invention may be implemented as programs (or applications) and stored on a medium to be executed in conjunction with a computer, which is hardware. The components of the present invention may be implemented as software programs or software elements. Similarly, the embodiments may be implemented in a programming or scripting language such as C, C++, Java, or an assembler, including various algorithms implemented as a combination of data structures, processes, routines, or other programming components. Functional aspects may be implemented as algorithms that are executed on one or more processors.

[0181] Those skilled in the art will appreciate that the various illustrative logical blocks, modules, processors, means, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, various forms of programs or design code (referred to herein, for convenience, as “software”), or a combination of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Those skilled in the art may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

[0182] The various embodiments presented herein can be implemented as a method, apparatus, or article of manufacture using standard programming and / or engineering techniques. The term "article of manufacture" includes a computer program, carrier, or media accessible from any computer-readable device. For example, computer-readable media include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic strips, etc.), optical disks (e.g., CDs, DVDs, etc.), smart cards, and flash memory devices (e.g., EEPROMs, cards, sticks, key drives, etc.). Furthermore, the various storage media presented herein include one or more devices and / or other machine-readable media for storing information. The term "machine-readable medium" includes, but is not limited to, wireless channels and various other media capable of storing, retaining, and / or carrying instructions and / or data.

[0183] It should be understood that the specific order or hierarchy of steps in the presented processes is merely an example of exemplary approaches. It should be understood that the specific order or hierarchy of steps in the processes may be rearranged within the scope of the present invention based on design priorities. The appended method claims provide elements of various steps in a sample order, but are not intended to be limited to the specific order or hierarchy presented.

[0184] The description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments without departing from the scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments disclosed herein, but is to be construed in the broadest scope consistent with the principles and novel features disclosed herein.

[0185]

[0186] The best mode for carrying out the invention as described above has been described.

[0187]

[0188] The present invention can be utilized in various industrial fields that manage carbon emissions and purchase carbon emission rights.

Claims

1. A method performed on one or more processors of a computing device, A step of obtaining process material information, carbon emission information, distribution management information, and carbon emission offset information; A step of obtaining carbon emission information for one or more predetermined scopes based on the process material information, the carbon emission information, the distribution management information, and the carbon emission offset information; A step of generating suggestion information based on a comparison between the carbon emission information for each of the above one or more scopes and the target carbon emission information; One or more of the above predefined scopes, Including the first scope related to direct emissions, the second scope related to indirect emissions and the third scope related to additional emissions. A method for analyzing carbon emissions and suggesting carbon emission reduction measures based on the analysis.

2. In paragraph 1, The above process material information is: Includes information on the type of material for each material used in the process, information on the amount of use for each material, and information on carbon emissions for each material. Characterized in that it is generated based on the user's input or based on the sensing value sensed through the input detection sensor. A method for analyzing carbon emissions and suggesting carbon emission reduction measures based on the analysis.

3. In paragraph 1, The above carbon emissions information is: Includes information on the amount of carbon emitted during the manufacturing process. It is characterized in that it is generated based on the amount of carbon detected through a gas detection sensor, or generated based on the amount of power of the device used in the process detected through a power detection sensor, or generated through OCR analysis of documents related to power usage. A method for analyzing carbon emissions and suggesting carbon emission reduction measures based on the analysis.

4. In paragraph 1, The above distribution management information is: Includes first delivery information related to the delivery of materials used in product production, second delivery information related to the delivery of finished products, and information on carbon emissions calculated in response to each delivery information. The above carbon emission offset information includes information on application methods to reduce carbon emissions and information on the amount of carbon reduced through the application methods. A method for analyzing carbon emissions and suggesting carbon emission reduction measures based on the analysis.

5. In paragraph 1, The step of generating suggestion information based on a comparison between the carbon emission information by one or more scopes and the target carbon emission information is as follows: A step of obtaining comprehensive carbon emission information based on the carbon emission information of one or more scopes; A step of comparing the above comprehensive carbon emission information with the above target carbon emission information; A step of not generating the proposal information when the carbon emission corresponding to the comprehensive carbon emission information does not exceed the carbon emission corresponding to the target carbon emission information; and Including a step of generating the proposal information when the carbon emission corresponding to the comprehensive carbon emission information exceeds the carbon emission corresponding to the target carbon emission information; A method for analyzing carbon emissions and suggesting carbon emission reduction measures based on the analysis.

6. In paragraph 1, The steps for generating the above suggestion information are: A step of selecting at least one of the first scope, the second scope, and the third scope as an inappropriate scope based on the carbon emission information for each of the one or more scopes; and A step of generating suggestion information corresponding to the above inappropriate scope; comprising; A method for analyzing carbon emissions and suggesting carbon emission reduction measures based on the analysis.

7. In paragraph 1, The steps for generating the above suggestion information are: A step of generating first proposal information related to carbon reduction corresponding to transportation information; A step of generating second proposal information related to reducing power consumption of process equipment based on whether the first proposal information has been applied; A step of generating third proposal information related to carbon reduction corresponding to material production based on whether the second proposal information has been applied; and In case the third proposal information is applied, a step of generating fourth proposal information related to carbon reduction using a carbon offset method is included; A method for analyzing carbon emissions and suggesting carbon emission reduction measures based on the analysis.

8. In paragraph 7, The above second proposal information is, Including the second proposal 1st sub-information related to the proposal to minimize carbon emissions from equipment power usage, the second proposal 2nd sub-information related to the proposal to minimize peak power usage of components, and the second proposal 3rd sub-information related to the proposal to minimize standby power of equipment. A method for analyzing carbon emissions and suggesting carbon emission reduction measures based on the analysis.

9. In paragraph 1, The above method, A step of generating predicted carbon emission information including information on carbon emissions at a future point in time by utilizing a carbon emission prediction model; A step of generating suggestion information based on a comparison between the predicted carbon emission information and the target carbon emission information; and A step of generating carbon reduction prediction information according to application of the above proposed information; including; A method for analyzing carbon emissions and suggesting carbon emission reduction measures based on the analysis.

10. Memory that stores one or more instructions; and A processor comprising: a processor that executes one or more instructions stored in the memory; The processor executes one or more of the instructions, A server that performs the method of paragraph 1.

11. A computer program stored on a computer-readable recording medium that is combined with a computer as hardware and enables the method of claim 1 to be performed.

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