Greenhouse gas emissions management method

Abstract

To provide a method of enabling efficient greenhouse gas emissions management, including computation of greenhouse gas emissions by a business operator.SOLUTION: According to an embodiment of the present invention, a method of managing greenhouse gas emissions is disclosed, the method comprising receiving image data of manifest information from a business operator terminal, analyzing the image data through machine learning to estimate product disposal activity, and deriving greenhouse gas emissions based on the information on the disposal activity.SELECTED DRAWING: Figure 11

Description

[Technical field]

[0001] The present invention relates to a method for managing greenhouse gas emissions. [Background technology]

[0002] Regarding greenhouse gas emissions by businesses associated with the use of fuel, electricity, etc., reporting systems covering SCOPE 1 emissions (a company's direct emissions) and SCOPE 2 emissions (a company's indirect emissions) have become widespread, and calculation of emissions in SCOPE 1 and SCOPE 2 and efforts to reduce them have progressed.

[0003] Non-Patent Document 1 proposes calculating SCOPE 3 emissions, that is, emissions from the supply chain of other related businesses (the entire series of processes including raw material procurement, manufacturing, logistics, sales, disposal, etc.), as emissions other than SCOPE 1 and SCOPE 2, with the aim of further reducing greenhouse gas emissions by businesses. [Prior art documents] [Non-patent literature]

[0004] [Non-Patent Document 1] "Concepts for Calculating Supply Chain Emissions," Ministry of the Environment, November 2017 Summary of the Invention [Problem to be solved by the invention]

[0005] However, although the technology disclosed in Non-Patent Document 1 discloses a method for calculating greenhouse gas emissions related to SCOPE 3, each business entity, particularly a company or local government, spends a great deal of time and effort collecting and inputting a huge amount of data to calculate emissions, calculate emissions, and manage the calculation results. In particular, in the area of ​​GHG emissions management, the scope of emissions to be calculated is expanding, the data on which emissions calculations are based is diverse for each scope, and the data management methods differ for each business entity, so the introduction of advanced technology to improve business efficiency is lagging behind.

[0006] Therefore, the present invention aims to provide a method for efficiently managing GHG emissions by reducing the amount of work required through the use of advanced technology in the area of ​​GHG emissions management, such as the calculation of greenhouse gas emissions by businesses. [Means for solving the problem]

[0007] A method for managing greenhouse gas emissions according to one embodiment of the present invention, comprising: receiving image data of manifest information from a business operator terminal; estimating product disposal activities by analyzing the image data using machine learning; and calculating greenhouse gas emissions based on information regarding the disposal activities. Effect of the Invention

[0008] According to the present invention, it is possible to provide a method for efficiently realizing management such as calculation of greenhouse gas emissions by business operators. [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram illustrating a greenhouse gas emission management system according to a first embodiment of the present invention. [Diagram 2] 2 is a functional block diagram of a management terminal constituting the greenhouse gas emission management system. FIG. [Diagram 3] FIG. 2 is a functional block diagram of a business operator terminal constituting the greenhouse gas emission management system. [Figure 4] FIG. 4 is a diagram illustrating details of business entity data according to the first embodiment of the present invention. [Diagram 5] FIG. 4 is a diagram illustrating details of invoice information according to the first embodiment of the present invention. [Figure 6] FIG. 2 is a diagram illustrating an example of transaction information according to the first embodiment of the present invention. [Figure 7] FIG. 11 is a diagram illustrating another example of transaction information according to the first embodiment of the present invention. [Figure 8] FIG. 2 is a flowchart showing an example of a calculation process of greenhouse gas emissions according to the first embodiment of the present invention. [Figure 9] FIG. 2 is a flow chart illustrating an example of a process for predicting the cause of a change in greenhouse gas emission amount according to the first embodiment of the present invention. [Figure 10] FIG. 4 is a flow chart illustrating an example of a transaction process of greenhouse gas emissions according to the first embodiment of the present invention. [Figure 11] FIG. 4 is a flowchart showing an example of a waste process estimation process in the greenhouse gas emission calculation process according to the first embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] The contents of an embodiment of the present invention will be listed and described below. A management system for greenhouse gas emissions (hereinafter simply referred to as the "system") according to an embodiment of the present invention has the following configuration. [Item 1] 1. A method for managing greenhouse gas emissions, comprising: Accepts image data of invoice information from the business operator's terminal, extracting information relating to the amount of energy usage by analyzing the image data using machine learning; Calculating greenhouse gas emissions based on the usage information; When analyzing the image data, the method further comprises referring to billing information having the same or similar format as the billing information stored in a storage unit. [Item 2] 2. The method of claim 1, wherein the bill information includes information regarding energy usage. [Item 3] 2. The method according to item 1, further comprising supplementing information contained in the invoice information based on invoice information having the same or a similar format as the invoice information. [Item 4] 2. The method according to item 1, further comprising predicting an emission intensity unit for calculating greenhouse gas emissions, which is included in the invoice information, based on invoice information having the same or a similar format as the invoice information.

[0011] <First embodiment> Hereinafter, a system according to an embodiment of the present invention will be described with reference to the drawings.

[0012] FIG. 1 is a diagram illustrating a greenhouse gas emission management system according to a first embodiment of the present invention.

[0013] As shown in FIG. 1, in an emission management system 1 in this embodiment, an administrator terminal 100 and a plurality of business operator terminals 200A, 200B are connected to each other via a communication network NW.

[0014] For example, the management terminal 100 receives basic information on the business operator and input information (eg, image data of invoice information) for calculating greenhouse gas (eg, CO2) emission amounts from the business operator terminals 200A and 200B.

[0015] The management terminal 100 also analyzes the received image data of the invoice information using machine learning, extracts necessary items of the invoice information contained in the image data, and calculates the greenhouse gas emissions. The management terminal 100 also analyzes the calculated change in greenhouse gas emissions over time (e.g., increase or decrease) using machine learning, and predicts the cause of the change.

[0016] Furthermore, the management terminal 100 has a wallet and is connected to the public blockchain network NW. The management terminal 100 generates a single hash value using SHA256 or other hash function based on the information on greenhouse gas emissions for each predetermined period, and records the value as transaction information in the blockchain network. On the blockchain network, this block is generated based on the transaction information, the hash value recorded in the previous block, and the nonce value mined by the node, and is recorded following the previous block, forming a blockchain. Here, the hash generation and / or recording of the transaction information in the blockchain can be performed via another terminal, not via the management terminal 100. In this case, the management terminal 100 transmits the greenhouse gas emissions calculated by the matching process to the other terminal. Furthermore, the management terminal 100 can record the information on greenhouse gas emissions in the blockchain network as a smart contract. By using the smart contract, a contract regarding emissions trading with other businesses can be automatically generated, approved, and executed without the intervention of a third party, based on the information on emissions. In addition, smart contracts will enable each business operator to access transaction information without going through a management terminal, increasing the convenience of the service and reducing operational costs.

[0017] As described above, a public blockchain is a system in which transactions are approved by an unspecified number of nodes and miners, rather than by a specific administrator, and therefore can ensure higher data tamper resistance and fault tolerance than a private blockchain, and therefore the security of transactions is guaranteed. Therefore, a public blockchain is preferable as a destination for recording energy transactions in this embodiment. Representative public blockchains include Bitcoin and Ethereum, and for example, Ethereum has higher tamper resistance and reliability than other public blockchains.

[0018] Furthermore, the management terminal 100 can associate information regarding greenhouse gas emissions with an identifier or the like and record it on the blockchain network as a non-fungible token (hereinafter, "NFT"). NFTs are tokens issued, for example, in a standard called "ERC721" by Etherium, a blockchain network platform, and are a unit of data recorded on the blockchain network and are non-fungible by nature. NFTs are recorded on the blockchain together with smart contracts and are traceable, making it possible to certify transaction information including details and history of business operators managing greenhouse gas emissions.

[0019] FIG. 2 is a functional block diagram of a management terminal constituting the emission management system.

[0020] The communication unit 110 is a communication interface for communicating with an external terminal via the network NW, and communication is performed according to a communication protocol such as TCP / IP (Transmission Control Protocol / Internet Protocol).

[0021] The storage unit 120 stores programs for executing various control processes and various functions in the control unit 130, input data, etc., and is composed of a RAM (Random Access Memory), a ROM (Read Only Memory), etc. The storage unit 120 also has an operator data storage unit 121 that stores various data related to operators, and an AI model storage unit 122 that stores learning data and a learning model in which an AI (artificial intelligence) has learned the learning data. A database (not shown) that stores various data may be constructed outside the storage unit 120 or the management terminal 100.

[0022] The control unit 130 controls the overall operation of the management terminal 100 by executing the programs stored in the storage unit 120, and is composed of a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), etc. The functions of the control unit 130 include an information receiving unit 131 that receives information from an external terminal such as the business operator terminal 200, an image analysis unit 132 that analyzes image data such as invoice information received from the business operator terminal and calculates greenhouse gas emissions, a cause analysis unit 133 that analyzes the image data and analyzes the cause of the time-series change in the greenhouse gas emissions calculated based on the information included in the extracted invoice information, a transaction processing unit 134 that performs processing to collect information on greenhouse gas emissions for a predetermined period, generate a hash value, and record it as transaction information in the blockchain network, and a report generation unit 135 that generates and transmits report data for outputting the results of the analysis of the greenhouse gas emissions and the cause of the change in the emission amount to the business operator every predetermined period.

[0023] Although not shown, the control unit 130 has an image generation unit, which generates screen information to be displayed via a user interface of an external terminal such as the business operator terminal 200. For example, using image and text data stored in the storage unit 120 as material, various images and text are arranged in a predetermined area of ​​the user interface based on a predetermined layout rule to generate information to be displayed on the user interface. Processing related to the image generation unit can also be executed by a GPU (Graphics Processing Unit).

[0024] The management terminal 100 further includes a wallet (not shown) required for recording transaction information in the blockchain network. This wallet may be located outside the management terminal 100.

[0025] FIG. 3 is a functional block diagram of the business operator terminal constituting the emission management system.

[0026] The operator terminal 200 includes a communication unit 210 , a display operation unit 220 , a storage unit 230 , and a control unit 240 .

[0027] The communication unit 210 is a communication interface for communicating with the management terminal 100 via the network NW, and communication is performed according to a communication protocol such as TCP / IP.

[0028] The display operation unit 220 is a user interface used for the operator to input instructions and display text, images, etc. according to input data from the control unit 240, and is configured with a display, keyboard, and mouse when the operator terminal 200 is configured as a personal computer, and is configured with a touch panel, etc. when the operator terminal 200 is configured as a smartphone or tablet terminal. This display operation unit 220 is started up by a control program stored in the storage unit 230 and executed by the operator terminal 200, which is a computer (electronic calculator).

[0029] The storage unit 230 stores programs for executing various control processes and functions in the control unit 240, input data, etc., and is composed of RAM, ROM, etc. The storage unit 230 also temporarily stores the contents of communication with the management terminal 100.

[0030] The control unit 240 controls the overall operation of the operator terminal 200 by executing the programs stored in the storage unit 230, and is composed of a CPU, a GPU, and the like.

[0031] FIG. 4 is a diagram for explaining details of business data according to the first embodiment of the present invention.

[0032] The business operator data 1000 shown in FIG. 4 stores various data related to the business operator acquired from the business operator via the business operator terminal 200. In FIG. 4, for convenience of explanation, an example of one business operator (a business operator identified by a business operator ID "10001") is shown, but information on a plurality of business operators can be stored. The various data related to the business operator can include, for example, basic information of the business operator (for example, the business operator's corporate name, user name, business establishment information (for example, address information for each business establishment, etc.), network name (for example, SSID, IP address, etc.), image information (for example, background image of the business establishment, person image, etc.), industry, contact information, email address, business establishment name, affiliated company name, business operator name related in the supply chain, etc.), input information (for example, image data of invoice information, etc.), analysis information (for example, invoice extracted from image data, greenhouse gas emission amount, information on predicted cause of change in greenhouse gas emission amount, etc.), customer information (for example, customer ID, blockchain address, etc.), and offset report information (for example, TXID, NFTID, etc.).

[0033] FIG. 8 is a flowchart showing an example of the greenhouse gas emission calculation process according to the first embodiment of the present invention.

[0034] First, in the process of step S101, the information acquisition unit 131 of the control unit 130 of the management terminal 100 acquires image data including invoice information collected by the business operator from the business operator terminal 200 via the network NW. The business operator uploads invoices, receipts, slips, etc. (collectively referred to as "invoices" in this embodiment) in file formats such as PDF, Excel, JPG, etc. (collectively referred to as "image data" in this embodiment) via the business operator terminal 200 to the management terminal 100. The image data acquired by the information acquisition unit 131 is stored as input information in the business operator data storage unit 121 of the memory unit 120.

[0035] Next, in the process of step S102, the image analysis unit 132 of the control unit 130 of the management terminal 100 analyzes the image data acquired in the previous step by machine learning. Here, the image analysis uses a technique called OCR, and the image analysis unit 132 of the control unit 130 of the management terminal 100 recognizes text from the image data using a learning model generated in advance by learning image data of a plurality of various formats of invoices stored in the AI ​​model storage unit 122 of the storage unit 120, and extracts items included in the invoice information as structured character string data. Here, the image analysis can also use an image analysis engine (OCR engine, etc.) provided by a business operator other than the management terminal 100 that is linked by API.

[0036] Image analysis is performed by recognizing and extracting text from image data including invoice information, as shown in FIG. 5, for example. As shown in FIG. 5, invoice information includes various items included in an invoice, such as the name of the breakdown of the electricity charge, the amount (yen) for each breakdown, the contracted power (kW), the amount of electricity used for each breakdown (kWh), the total amount (yen), and the date (year and month). In this example, an invoice breakdown for an electricity charge is illustrated, but the invoice may be for charges related to the amount of other energy used, including gas and fuel, in addition to electricity, or may be, for example, a receipt for travel expenses for business trips, a receipt for an employee's commuting expenses, an invoice for transactions with a cargo company, or a breakdown of an invoice for transactions with a waste disposal company. The image analysis unit 132 can extract amount information, the activity amount information described below, and the like included in the invoice information as text by analyzing the image data of the invoice information. The extracted invoice information is stored as analysis information in the business data storage unit 121 of the storage unit 120. In this way, image analysis using machine learning allows businesses to obtain the vast amount of necessary information for calculating greenhouse gas emissions as image data without having to manually enter invoice information, and highly accurate image recognition makes it possible to accurately extract the information necessary for calculating greenhouse gas emissions, thereby making it possible to calculate greenhouse gas emissions more efficient and with greater accuracy.

[0037] Next, in step S103, the image analysis unit 132 of the control unit 130 calculates the greenhouse gas emissions based on the invoice information extracted from the image data. Here, the greenhouse gas emissions are classified into SCOPE 1, SCOPE 2, and SCOPE 3, where SCOPE 1 is direct emissions of greenhouse gases by the business itself (e.g., emissions associated with fuel combustion and industrial processes), SCOPE 2 is indirect emissions associated with the use of electricity, heat, gas, etc. supplied to the business by other companies, and SCOPE 3 is the calculation standard for emissions of an organization's entire supply chain issued by the GHG Protocol, and refers to the emissions of the business's supply chain (the entire series of flows such as raw material procurement, manufacturing, logistics, sales, and disposal). SCOPE3 is further classified into 15 categories (1) purchased products / services, 2) capital goods, 3) fuel and energy-related activities not included in SCOPE1 and SCOPE2, 4) transportation and distribution (upstream), 5) waste generated from business, 6) business trips, 7) employee commuting, 8) leased assets (upstream), 9) transportation and distribution (downstream), 10) processing of sold products, 11) use of sold products, 12) disposal of sold products, 13) leased assets (downstream), 14) franchises, and 15) investments. Here, greenhouse gases include carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulfur hexafluoride (SF6), and nitrogen trifluoride (NF3), but this embodiment will be described using CO2 as an example.

[0038] Greenhouse gas emissions are calculated by defining a business's electricity usage, cargo transport volume, waste disposal volume, and various transaction amounts as activity volume, and multiplying the activity volume by the CO2 emissions per kWh of electricity used, CO2 emissions per ton of cargo transported, and CO2 emissions per ton of waste incinerated as emissions units.Greenhouse gas emissions are calculated for the above-mentioned SCOPE 1, SCOPE 2, and SCOPE 3 (SCOPE 3 is divided into 15 categories), and the total emissions are calculated as supply chain emissions.

[0039] In this embodiment, the image analysis unit 132 extracts related invoice information by SCOPE1, SCOPE2, and SCOPE3 (and further by category for SCOPE3), and calculates the emission amount based on, for example, the electricity consumption amount kWh from the invoice information, using the above calculation method. The calculated emission amount is stored as analysis information in the business entity data storage unit 121 of the memory unit 120.

[0040] Next, as processing in step S104, the report generation unit 135 of the control unit 130 generates a visualized report based on the information regarding the calculated emissions, showing a breakdown of the emissions over time by SCOPE (and further by category for SCOPE 3).

[0041] FIG. 9 is a flowchart showing an example of the process of predicting the cause of changes in greenhouse gas emissions according to the first embodiment of the present invention.

[0042] First, in the process of step S201, the cause analysis unit 133 of the control unit 130 of the management terminal 100 refers to information related to the greenhouse gas emissions of the business operator, calculated in step S103 of FIG. 8. Here, for the greenhouse gas emissions, the amount of emissions by SCOPE (and further by category for SCOPE 3) is referred to. In addition, the cause analysis unit 133 can check the change (increase or decrease) in the amount of greenhouse gas emissions by referring to the past emission amount data of the same business operator. As described above, the emission amount is stored as analysis information in the business operator data storage unit 121 of the memory unit 120.

[0043] Next, in the process of step S202, the cause analysis unit 133 analyzes and predicts the cause of the change in emission amount by machine learning based on the emission amount information referenced above. Here, in analyzing the cause, the cause analysis unit 133 of the control unit 130 of the management terminal 100 predicts the cause of the change in emission amount by SCOPE (and further by category for SCOPE 3) by using the emission amount information referenced above, factors that affect the change (increase or decrease) in emission amount, and a learning model that is generated by learning data related to factors that affect the change (increase or decrease) in emission amount that is stored in the AI ​​model storage unit 122 of the memory unit 120 in advance.

[0044] Here, factors that affect the change (increase or decrease) in the amount of output include, for example, weather, temperature, product demand and / or factory operation, store or factory business hours or operating hours, device or facility changes, software measures, energy saving actions, fuel conversion, energy menu changes, changes in business trips or commuting, and the amount of electricity generated by private power generation. Each of these factors affects the amount of emissions in one of the SCOPEs. For example, the factor of weather affects precipitation, wind volume, hours of sunshine, and temperature, and precipitation affects the amount of water power generation, wind volume affects the amount of wind power generation, hours of sunshine affect the amount of solar power generation, and temperature affects air conditioning. Furthermore, the amount of electricity generated affects the amount of private power generation, and the amount of private power generation affects the amount of CO2 emissions due to electricity, thereby affecting the change in emissions in SCOPE 2, while air conditioning affects the amount of gas usage, and the amount of gas usage affects the amount of CO2 emissions due to gas combustion, thereby affecting the change in emissions in SCOPE 1. In addition, energy saving activities, factory operation due to product demand, and business hours affect electricity usage, which affects SCOPE 2. In addition, EMS, replacement of refrigeration equipment, introduction of energy-saving equipment, and automobile usage also affect electricity usage and thus affect SCOPE 2. In addition, automobile usage, fuel efficiency, boiler usage, and boiler efficiency affect fuel usage and thus affect CO2 emissions from fuel, which in turn affects SCOPE 1.

[0045] In addition, the factor of product sales volume affects categories 1, 9, 10, 11, and 12 in SCOPE 3, capital investment affects category 2, renewable energy ratio and amount of energy procured affects category 3, number of transports and changes in transport routes affects categories 4 and 9, product loss rate affects category 5, business trips and number of people coming to the office affects category 6, number of people commuting and number of employees coming to the office affects category 7, power consumption affects category 8, reduction in processing through product improvements affects category 10, improvements to energy-efficient products affects category 11, increase in recycling rate affects category 12, office electricity for tenants affects category 13, franchise emissions affects category 14, and emissions from investment destinations affects category 15.

[0046] In this way, machine learning is used to learn which factors affect which SCOPE or category, and by obtaining information on emissions and information on each factor from business operators, it is possible to predict the causes of changes in emissions. Here, by predicting the causes of emissions using machine learning, it is possible to efficiently and accurately predict factors that affect changes in greenhouse gas emissions for each business operator and for each SCOPE.

[0047] Next, as processing in step S203, the report generation unit 135 of the control unit 130 generates a visualized report on the causes of changes in emissions by SCOPE (and further by category for SCOPE 3) based on the information regarding the predicted causes of changes in emissions analyzed above.

[0048] FIG. 10 is a flow chart diagram showing an example of a transaction process of greenhouse gas emissions according to the first embodiment of the present invention.

[0049] First, in the process of step S301, the transaction processing unit 134 of the control unit 130 of the management terminal 100 refers to the business operator data stored in the business operator data storage unit 121 of the memory unit 120. Here, the business operator data to be referred to includes business operator analysis information (emission amount of greenhouse gases for each SCOPE) and the like.

[0050] Next, in the process of step S302, the transaction processing unit 134 generates a hash value based on the business data referenced in step S301. That is, the transaction processing unit 134 generates one row of hash values ​​for greenhouse gas emissions for a predetermined period using a hash function, and records the hash value as transaction information in the public blockchain. On the blockchain network, this block is generated based on the transaction information, the hash value recorded in the previous block, and the nonce value mined by the node, and is recorded following the previous block, forming a blockchain. Here, in this example, in order to reduce the cost of blockchain recording, it is recorded in a layer 2 (e.g., a side chain) different from the main blockchain (so-called layer 1).

[0051] Furthermore, the transaction processing unit 134 can assign and manage an NFTID by linking it to the blockchain record of the greenhouse gas emissions of the business operator. More specifically, as shown in Fig. 4, the business operator's customer ID can be assigned as customer information to the business operator data 1000, a reference blockchain address can be stored, and an NFTID and a TXID can be assigned as offset report information.

[0052] As shown in FIG. 6, on the blockchain network, a blockchain address is associated with each NFTID, and the management terminal 100 manages the NFTID and the customer ID. For example, information on greenhouse gas emissions related to a business corresponding to customer ID "2" can be read out by referring to the blockchain address for each NFTID, such as NFTID "13" and "14", as shown in FIG. 7. FIG. 7 shows information on an offset report associated with NFTID "14", in which a TXID is assigned in association with the offset report, and the offset report includes CO2 emissions by SCOPE, the target year and month, and the issue date of the report. In addition to the CO2 emissions for the target year and month in this example, the most recent CO2 emissions, the reduced CO2 emissions, and the offset CO2 emissions can also be converted into NFTs. In this way, by managing CO2 emissions as NFTs, businesses can trade NFT-converted certificates while ensuring non-tampering and reliability of the transaction, and can also certify the emissions to third parties.

[0053] 11 is a flow chart showing an example of waste activity estimation processing of the greenhouse gas emission calculation processing according to the first embodiment of the present invention. When calculating the greenhouse gas emission amount, the business operator uploads information on a report on the management of waste of the product (for example, a report on proper management of waste including a carrier for transporting the waste, the contents / type of waste, the waste disposal location, the disposal method, etc., also called a "manifest") to the management terminal 100, and estimates waste activities including the contents / type of waste, the waste emission intensity, the waste (emission intensity) and / or information on the activity base of the product estimated from the application location of the manifest, the disposal location, and / or the disposal method based on the extraction of each item included in the manifest information through image analysis, and calculates the amount of activity, and then multiplies the emission intensity to calculate the amount of greenhouse gas (for example, CO2 gas) emission.

[0054] First, in the process of step S401, the information acquisition unit 131 of the control unit 130 of the management terminal 100 acquires image data including manifest information collected by the business operator from the business operator terminal 200 via the network NW. The business operator uploads the manifest information to the management terminal 100 via the business operator terminal 200 in a file format such as PDF, Excel, JPG, etc. (in this embodiment, these are collectively referred to as "image data"). The image data acquired by the information acquisition unit 131 is stored as input information in the business operator data storage unit 121 of the memory unit 120.

[0055] Next, in the process of step S402, the image analysis unit 132 of the control unit 130 of the management terminal 100 analyzes the image data acquired in the previous step by machine learning. Here, the image analysis uses a so-called OCR technique, and the image analysis unit 132 of the control unit 130 of the management terminal 100 recognizes text from the image data using a learning model generated in advance by learning image data of multiple manifest information in various formats stored in the AI ​​model storage unit 122 of the storage unit 120, and extracts items included in the invoice information as structured character string data. Here, the image analysis can also use an image analysis engine (OCR engine, etc.) provided by a business operator other than the management terminal 100 that is linked by an API.

[0056] The image analysis is performed by recognizing and extracting text from image data including the manifest information. As described above, the manifest information includes, for example, items such as a carrier for transporting the waste, the contents / type of the waste, the waste disposal location, and the disposal method. The image analysis unit 132 can extract information on each item included in the manifest information as text by analyzing the image data of the manifest information. The extracted invoice information is stored as analysis information in the business data storage unit 121 of the storage unit 120. In this way, the image analysis using machine learning can obtain a huge amount of necessary information for calculating greenhouse gas emissions as image data without the business operator having to manually input information on the disposal activities of the product, and the information necessary for calculating greenhouse gas emissions can be accurately extracted by highly accurate image recognition, thereby realizing efficient and high-precision calculation of greenhouse gas emissions.

[0057] Therefore, in step S403, the image analysis unit 132 of the control unit 130 executes estimation of activities related to the disposal of the product based on the manifest information. The image analysis unit 132 can directly or indirectly estimate the contents / types of waste (e.g., iron waste, concrete rubble, rock crushing, etc.), raw materials of the waste (e.g., iron, tin, etc.) and the emission intensity of the raw materials, activity bases related to the product (e.g., nearby factories, construction / demolition sites, etc.), and disposal treatment methods of the waste (e.g., incineration, landfill, etc.) by using image-recognized text and / or machine learning. Here, in machine learning, a learning model capable of estimating information on the base (factory, construction site, etc.) of the product related to the waste, activity information at the base, disposal site, disposal method, etc., based on the application date of the manifest, the application location of the manifest (prefecture, city, town, village, etc.), the base setting information of the business operator, etc., is generated, and the estimated information can be optimized by weighting the data of the base, application location, etc.

[0058] Next, in step S404, the image analysis unit 132 of the control unit 130 calculates the greenhouse gas emission amount based on the manifest information extracted from the image data and the estimated disposal information. According to the above example, the image analysis unit 132 estimates the raw material of the waste (e.g., waste containing tin) based on the input manifest information, estimates the base where the raw material was used as an activity (e.g., a demolition site of an accommodation facility close to the disposal application location in the manifest), estimates the means of transportation (e.g., truck, etc.) from the activity base to the disposal site, estimates the disposal method (e.g., incineration, landfill, etc.), and can calculate the emission amount based on these activity amounts and emission unit values.

[0059] In this way, waste disposal activities that businesses were previously unable to trace can now be quantified and visualized through manifest information, enabling proper waste management and making greenhouse gas emissions calculations more efficient.

[0060] The above-described embodiment is merely an example for facilitating understanding of the present invention, and is not intended to limit the present invention. The present invention can be modified or improved without departing from the spirit of the present invention, and it goes without saying that the present invention includes equivalents thereof. [Explanation of symbols]

[0061] 100 Management terminal 200 Operator terminal

Claims

1. 1. A method for managing greenhouse gas emissions, comprising: Accept image data of manifest information from the business operator's terminal, The image data is analyzed using machine learning to estimate product disposal activities; calculating greenhouse gas emissions based on information about the disposal activities.

2. The method according to claim 1 , wherein the manifest information includes any one of information on a transport company of the waste of the product, information on the type of the waste, information on a disposal location of the waste, and information on a disposal method of the waste.

3. The method according to claim 1 , wherein the waste activity includes any one of information on a raw material of the waste, an emission intensity of the raw material, an activity base related to the product, and a waste disposal method of the waste.

4. The method of claim 3 , wherein the estimation includes estimating a base of operations for the product based on a disposal application location in the manifest information.

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