JP2024017421A - Greenhouse gas emissions management method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a method of efficiently performing management of greenhouse gas emissions by a business operator, including computation of the amount of emissions.

SOLUTION: A greenhouse gas emissions management method according to an embodiment of the present invention comprises having a management terminal perform steps of: determining information on energy usage based on input information from a business operator terminal; computing the amount of greenhouse gas emissions based on the information on the energy usage; and storing information required to compute greenhouse emissions as well as information not required to compute greenhouse gas emissions included in the input information.

SELECTED DRAWING: Figure 9

COPYRIGHT: (C)2024,JPO&INPIT

Description

The present invention relates to a method for managing greenhouse gas emissions.

Regarding the greenhouse gas emissions of businesses associated with the use of fuel and electricity, etc., a reporting system targeting SCOPE 1 emissions (direct emissions from the company) and SCOPE 2 emissions (indirect emissions from the company) has become widespread, and SCOPE 1 and SCOPE 2 Efforts to calculate and reduce emissions are making progress.

Non-Patent Document 1 states that with the aim of further reducing greenhouse gases emitted by businesses, SCOPE 3 emissions, that is, emissions from other related businesses, etc., are calculated as emissions other than SCOPE 1 and SCOPE 2. Recommendations have been made regarding the calculation of emissions from the entire series of processes such as raw material procurement, manufacturing, distribution, sales, and disposal.

“How to calculate supply chain emissions”, Ministry of the Environment, November 2017

However, although the technology disclosed in Non-Patent Document 1 discloses the method of calculating greenhouse gas emissions related to SCOPE 3, each business operator, especially companies and local governments, requires a huge amount of effort to calculate emissions. A great deal of effort and time is spent collecting and inputting data, calculating emissions, and managing the calculation results. In particular, in the field of GHG emissions management, the number of SCOPEs for emissions subject to accounting is expanding, the data that serves as the basis for emissions calculation for each SCOPE is diverse, and data management for each business operator is particularly important. Improvements in operational efficiency through the introduction of cutting-edge technology have been delayed due to differences in methods and other factors.

Therefore, the present invention provides a method for efficiently realizing emissions management by reducing operational man-hours by utilizing cutting-edge technology in the field of GHG emissions management, such as calculating greenhouse gas emissions by business operators. The purpose is to provide.

A method for managing greenhouse gas emissions according to an embodiment of the present invention, wherein the management terminal determines information regarding energy usage based on information input by a business terminal. The amount of greenhouse gas emissions is calculated based on the information regarding the usage amount, and in addition to the information included in the input information that is essential for calculating the greenhouse gas, Memorize information that is not available.

According to the present invention, it is possible to provide a method for efficiently realizing management such as calculation of greenhouse gas emissions by a business operator.

BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a diagram illustrating a greenhouse gas emissions management system according to a first embodiment of the present invention. FIG. 2 is a functional block diagram of a management terminal that constitutes a greenhouse gas emissions management system. FIG. 2 is a functional block diagram of a business terminal that constitutes a greenhouse gas emissions management system. FIG. 3 is a diagram illustrating details of company data according to the first embodiment of the present invention. FIG. 3 is a diagram illustrating details of bill information according to the first embodiment of the present invention. FIG. 3 is a diagram illustrating an example of transaction information according to the first embodiment of the present invention. FIG. 7 is a diagram illustrating another example of transaction information according to the first embodiment of the present invention. FIG. 3 is a flowchart diagram illustrating an example of a process for calculating greenhouse gas emissions according to the first embodiment of the present invention. FIG. 3 is a flowchart diagram illustrating an example of a process for predicting the cause of a change in greenhouse gas emissions according to the first embodiment of the present invention. FIG. 3 is a flowchart diagram illustrating an example of transaction processing for greenhouse gas emissions according to the first embodiment of the present invention. FIG. 2 is a flowchart showing an example of a business establishment determination process when calculating greenhouse gas emissions according to the first embodiment of the present invention. FIG. 2 is a flowchart showing an example of a business establishment determination process when calculating greenhouse gas emissions according to the first embodiment of the present invention. FIG. 2 is a flowchart showing an example of a business establishment determination process when calculating greenhouse gas emissions according to the first embodiment of the present invention. FIG. 2 is a flowchart showing an example of a business establishment determination process when calculating greenhouse gas emissions according to the first embodiment of the present invention.

The contents of the embodiments of the present invention will be listed and explained. A greenhouse gas emissions management system (hereinafter simply referred to as "system") according to an embodiment of the present invention has the following configuration.
[Item 1]
A method for managing greenhouse gas emissions, the method comprising:
The management terminal is
Determine information regarding energy usage based on information input by the operator terminal,
Calculating greenhouse gas emissions based on the information on the usage amount,
A method for storing information that is included in the input information and that is essential for calculating the greenhouse gas, as well as information that is not essential for calculating the greenhouse gas.
[Item 2]
From the said business terminal, further receives input of business office information regarding the address of the business office of the business from the said business terminal, and receives location information from the said business terminal;
The management method according to item 1, which stores the location information.
[Item 3]
The management method according to item 1, wherein the non-essential information includes any of the location information of the vendor terminal, network information, and information regarding HFC.
[Item 4]
The method according to item 1, wherein the management terminal or the vendor terminal displays an alert asking whether to use the non-essential information for calculating the greenhouse gas.

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

FIG. 1 is a diagram illustrating a greenhouse gas emissions management system according to a first embodiment of the present invention.

As shown in FIG. 1, in the emissions management system 1 according to the present embodiment, a manager terminal 100 and a plurality of business terminals 200A and 200B are interconnected via a communication network NW.

For example, the management terminal 100 receives basic information about the business operator and input information (for example, image data of bill information) for calculating greenhouse gas (for example, CO2) emissions from the business terminals 200A and 200B. do.

Furthermore, the management terminal 100 analyzes the received image data of the bill information by machine learning, extracts necessary items of the bill information included in the image data, and calculates the amount of greenhouse gas emissions. Furthermore, the management terminal 100 analyzes the calculated changes in greenhouse gas emissions over time (for example, increases and decreases) using machine learning, and predicts the cause of the changes.

Furthermore, the management terminal 100 has a wallet and connects to the public blockchain network NW. The management terminal 100 generates a single hash value using SHA256 or another hash function based on the information regarding greenhouse gas emissions for each predetermined period, and records it in the blockchain network as transaction information. On the blockchain network, a main 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 the blockchain. be done. Here, the hash generation and/or recording of transaction information to the blockchain can also be performed not through the management terminal 100 but through another terminal. In this case, the management terminal 100 transmits the greenhouse gas emissions calculated by the matching process to the other terminals. Furthermore, the management terminal 100 can record information regarding greenhouse gas emissions in the blockchain network as a smart contract. By using smart contracts, contracts regarding emissions trading with other businesses can be automatically generated, approved, and executed without going through a third party based on the above emissions information. . In addition, smart contracts allow each business operator to refer to transaction information without going through a management terminal, increasing service convenience and reducing operational costs.

Here, as mentioned above, in public blockchains, transactions are approved not by a specific administrator but by an unspecified number of nodes and miners, so compared to private blockchains, data has a higher degree of non-tampering. In this embodiment, it is preferable to use a public blockchain as a destination for recording power transactions because fault tolerance can be ensured and, therefore, transaction safety can be ensured. Typical public blockchains include Bitcoin, Ethereum, and the like. For example, Ethereum has higher tamperability and reliability than other public blockchains.

In addition, the management terminal 100 can associate information regarding greenhouse gas emissions with an identifier, etc., and record it in a blockchain network as a non-fungible token (hereinafter referred to as "NFT"). . NFT is, for example, a token issued by Etherium, a blockchain network platform, under the ERC721 standard, is a unit of data recorded on the blockchain network, and is non-fungible. . Since NFTs are recorded along with smart contracts on the blockchain and are traceable, it is possible to prove transaction information, including details and history, such as information on businesses that manage greenhouse gas emissions.

FIG. 2 is a functional block diagram of a management terminal that constitutes the emissions management system.

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).

The storage unit 120 stores programs, input data, etc. for executing various control processes and functions within the control unit 130, and is composed of RAM (Random Access Memory), ROM (Read Only Memory), etc. Ru. The storage unit 120 also includes a business data storage unit 121 that stores various data related to the business operator, and an AI model storage unit that stores learning data and a learning model obtained by learning the learning data by AI (artificial intelligence). It has 122. Note that a database (not shown) storing various data may be constructed outside the storage unit 120 or the management terminal 100.

The control unit 130 controls the overall operation of the management terminal 100 by executing programs stored in the storage unit 120, and is composed of a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), etc. be done. As a function of the control unit 130, an information reception unit 131 that receives information from external terminals such as the business terminal 200 analyzes image data such as bill information received from the business terminal and calculates greenhouse gas emissions. an image analysis unit 132 that analyzes the image data, and a cause analysis unit 133 that analyzes the causes of time-series changes in greenhouse gas emissions calculated based on information included in the extracted bill information. , a transaction processing unit 134 that compiles information on greenhouse gas emissions for a predetermined period, generates a hash value, and records it as transaction information on the blockchain network; On the other hand, it has a report generation unit 135 that generates and transmits report data for outputting the results of cause analysis of greenhouse gas emissions and changes in emissions.

Although not shown, the control unit 130 includes an image generation unit and generates screen information displayed via a user interface of an external terminal such as the business terminal 200. For example, the information displayed on the user interface can be changed by arranging various images and texts in predetermined areas of the user interface based on predetermined layout rules using the image and text data stored in the storage unit 120 as materials. generate. Processing related to the image generation unit can also be executed by a GPU (Graphics Processing Unit).

Furthermore, the management terminal 100 further includes a wallet (not shown) necessary for recording transaction information on the blockchain network. Note that this wallet can also be kept outside the management terminal 100.

FIG. 3 is a functional block diagram of a business terminal that constitutes the emissions management system.

The business terminal 200 includes a communication section 210, a display operation section 220, a storage section 230, and a control section 240.

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.

The display operation unit 220 is a user interface used by the business operator to input instructions and display text, images, etc. in accordance with input data from the control unit 240. If the operator terminal 200 is a smartphone or a tablet terminal, the operator terminal 200 is comprised of a touch panel or the like. This display operation section 220 is activated by a control program stored in the storage section 230 and executed by the business terminal 200, which is a computer (electronic computer).

The storage unit 230 stores programs, input data, etc. for executing various control processes and functions within the control unit 240, and is composed of a RAM, a ROM, and the like. Furthermore, the storage unit 230 temporarily stores the contents of communication with the management terminal 100.

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

FIG. 4 is a diagram illustrating details of business data according to the first embodiment of the present invention.

The business data 1000 shown in FIG. 4 stores various data related to the business acquired from the business through the business terminal 200. In FIG. 4, for convenience of explanation, an example of one business operator (a business operator identified by business ID "10001") is shown, but information on a plurality of business operators can be stored. Various data related to business operators include, for example, basic information of business operators (for example, business name, user name, office information (for example, address information for each business office, etc.), network name (for example, SSID, IP address, etc.), image information (e.g., business background image, person image, etc.), industry, contact information, email address, business name, affiliated company name, name of related business in the supply chain, etc.), input information (e.g., image data of invoice information, etc.), analytical information (e.g., information about invoices extracted from image data, greenhouse gas emissions, predictions of causes of changes in greenhouse gas emissions, etc.), customer information (e.g., customer ID, blockchain address, etc.), and offset report information (e.g., TXID, NFTID, etc.).

FIG. 8 is a flowchart illustrating an example of the greenhouse gas emissions calculation process according to the first embodiment of the present invention.

First, as processing in step S101, the information acquisition unit 131 of the control unit 130 of the management terminal 100 acquires image data including bill information collected by the business operator from the business terminal 200 via the network NW. do. The business operator sends invoices, receipts, slips, etc. (in this embodiment, these are collectively referred to as "invoices") via the business terminal 200 in a file format such as PDF, Excel, or JPG (this embodiment These are collectively referred to as "image data") and are uploaded to the management terminal 100. The image data acquired by the information acquisition unit 131 is stored as input information in the business data storage unit 121 of the storage unit 120.

Subsequently, as processing in 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 using machine learning. Here, in the image analysis, a method called OCR is used, and the image analysis section 132 of the control section 130 of the management terminal 100 analyzes a plurality of various various A learning model generated by learning the image data of the invoice format recognizes text from the image data and extracts items included in the invoice information as structured character string data. Here, for image analysis, it is also possible to use an image analysis engine (such as an OCR engine) provided by a business other than the management terminal 100 and linked through an API.

Image analysis is performed, for example, by recognizing and extracting text from image data that includes bill information, as shown in FIG. As shown in Figure 5, bill information includes various items included in the bill. Items include power consumption (kWh), total amount (yen), date (year/month), etc. In this example, the breakdown of the electricity bill is shown as an example, but in addition to electricity, it may also be a bill for other energy usage including gas and fuel. It may be a breakdown of receipts for expenses, receipts for commuting expenses for employers, invoices for transactions with cargo companies, and invoices for transactions with waste disposal companies. The image analysis unit 132 can extract amount information, activity amount information, etc. included in the bill information as text by analyzing the image data of the bill information. The extracted bill information is stored in the business data storage section 121 of the storage section 120 as analysis information. In this way, image analysis using machine learning allows businesses to acquire a huge amount of necessary information in the form of image data to calculate greenhouse gas emissions without having to input billing information manually. The information necessary for calculating greenhouse gas emissions can be accurately extracted through highly accurate image recognition, which makes calculating greenhouse gas emissions more efficient and highly accurate. I can do it.

Here, in this embodiment, bill information and other input information input or acquired from the business terminal 200 include predetermined items (for example, the name of the breakdown of electricity charges, the amount (in yen) for each breakdown, the contract power (kW), breakdown power usage (kWh), total amount (yen), date (year/month), etc.), as well as items that are required for greenhouse gas emissions as described below, as well as items that are not required at this time. (For example, location information of the operator terminal 200 described below, network information, information regarding dozens of types of HFCs (hydrofluorocarbons) (for example, HFCs with different global warming coefficients such as HFC23, HFC32, HFC125, HFC134), etc. ) is also stored in the storage unit 120 as business data 1000. These non-essential items may become essential items in the future when calculating greenhouse gas emissions, and by storing these items, it will be easier to calculate greenhouse gas emissions. It can be referenced as a basis for calculation when it becomes an essential item. In addition, after a predetermined period of time has passed after storing the non-essential items in the storage unit 120, or every predetermined period, the management terminal 100 or the business terminal 200 is checked to see if they will be used for calculating greenhouse gas emissions. It is also possible to display or send an alert for this purpose. Furthermore, the non-essential items can be stored for a predetermined period of time and deleted after the predetermined period has elapsed. Even in this case, an alert can be displayed or sent to the management terminal 100 or the business terminal 200 to confirm whether the non-essential items can be deleted.

Next, in step S103, the image analysis unit 132 of the control unit 130 calculates greenhouse gas emissions based on the bill information extracted from the image data. Here, greenhouse gas emissions are classified into SCOPE 1, SCOPE 2, and SCOPE 3. SCOPE 1 is the direct emission of greenhouse gases by the business itself (e.g., emissions from fuel combustion and industrial processes), and SCOPE 2 is the emissions from other companies. In addition, SCOPE 3 is a standard for calculating emissions from the entire supply chain of an organization published by the GHG Protocol. This refers to emissions from the entire series of processes such as raw material procurement, manufacturing, distribution, sales, and disposal. SCOPE 3 further includes 15 categories (1) purchased products/services, 2) capital goods, 3) fuel and energy-related activities not included in SCOPE 1 and SCOPE 2, 4) transportation and distribution (upstream), and 5) from operations. Waste generated, 6) Business trips, 7) Commuting of employees, 8) Leased assets (upstream), 9) Transportation and delivery (downstream), 10) Processing of sold products, 11) Use of sold products, 12) 13) Leased assets (downstream), 14) Franchises, and 15) Investments). Here, greenhouse gases include carbon dioxide (CO2), methane (CH4), dinitrogen monoxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulfur hexafluoride (SF6), This includes nitrogen fluoride (NF3), but in this embodiment, CO2 will be explained as an example.

In addition, greenhouse gas emissions are calculated by defining the amount of electricity used by a business, the amount of cargo transported, the amount of waste processed, and the amount of various transactions as the amount of activity, and adding 1kWh of electricity to the amount of activity as the emission intensity. It is calculated by multiplying the CO2 emissions per use, the CO2 emissions per ton of freight transported, and the CO2 emissions per ton of waste incineration. Greenhouse gas emissions are calculated separately for SCOPE 1, SCOPE 2, and SCOPE 3 (15 categories for SCOPE 3), and the total emissions are calculated as supply chain emissions.

In the present embodiment, the image analysis unit 132 extracts related bill information for each SCOPE 1, SCOPE 2, and SCOPE 3 (further by category for SCOPE 3), and extracts related bill information based on, for example, electric power consumption kWh of the bill information. Then, calculate the emissions based on the above calculation method. The calculated emissions amount is stored in the business data storage section 121 of the storage section 120 as analysis information.

Subsequently, as processing in step S104, the report generation unit 135 of the control unit 130 generates a time-series record of emissions by SCOPE (further by category for SCOPE3) based on the information regarding the calculated emissions. Generate a visualized report showing the breakdown.

FIG. 9 is a flowchart illustrating an example of a process for predicting the cause of a change in greenhouse gas emissions according to the first embodiment of the present invention.

First, as processing in step S201, the cause analysis unit 133 of the control unit 130 of the management terminal 100 refers to information regarding the greenhouse gas emissions of the business operator calculated in step S103 of FIG. Here, regarding greenhouse gas emissions, refer to emissions by SCOPE (further by category for SCOPE 3). Furthermore, the cause analysis unit 133 can confirm changes (increases and decreases) in greenhouse gas emissions by referring to past emissions data of the same company. As described above, the amount of emissions is stored in the business data storage section 121 of the storage section 120 as analysis information.

Subsequently, as processing in step S202, the cause analysis unit 133 uses machine learning to analyze and predict the cause of the change in the amount of emissions based on the referenced information regarding the amount of emissions. Here, when performing the cause analysis, the cause analysis unit 133 of the control unit 130 of the management terminal 100 uses the above-referenced emission amount information, factors that affect changes (increases and decreases) in the emission amount, and the AI stored in the storage unit 120. A learning model stored in the model storage unit 122 that is generated by learning data related to factors that affect changes (increases and decreases) in emissions in advance is used to analyze data by SCOPE (further by category for SCOPE 3). Predict the causes of changes in emissions.

Here, factors that affect changes (increases and decreases) in output include, for example, weather, temperature, product demand and/or factory operation, store or factory business hours or operating hours, changes in equipment or equipment, software changes, etc. Factors include measures, power-saving actions, fuel conversion, changes in energy menu, changes in business trip or commuting volume, and amount of power generated by in-house power generation. Each of these factors is a factor that influences the output of any SCOPE. For example, the weather factor affects precipitation, wind volume, sunshine hours, and temperature; precipitation affects small water power generation, wind volume affects wind power generation, sunshine duration affects solar power generation, and temperature affects air conditioning. In addition, the amount of power generation affects the amount of in-house power generation, and the amount of in-house power generation affects the amount of CO2 emissions from electricity, thereby affecting the change in SCOPE2 emissions, while air conditioning This affects the amount of gas used, and the amount of gas used affects the amount of CO2 emissions from gas combustion, thereby affecting the change in the amount of emissions of SCOPE1. In addition, power-saving activities, factory operation due to product demand, and business hours affect power consumption and affect SCOPE 2. In addition, EMS, replacement of refrigeration equipment, introduction of energy-saving equipment, and amount of automobiles used also affect electricity consumption, which affects SCOPE2. This affects CO2 emissions from the fuel, thereby affecting SCOPE1.

In addition, the factor of product sales affects categories 1, 9, 10, 11, and 12 of SCOPE 3, capital investment is in category 2, renewable energy ratio and amount of energy procured is in category 3, number of transportation, transportation route, etc. Changes to categories 4 and 9, product loss rate to category 5, number of business trips and office workers to category 6, number of commuters and office employees to category 7, power consumption to category 8, processing reduction due to product improvement. is in category 10, improvements to energy-saving products are in category 11, increases in recycling rates are in category 12, tenant office electricity is in category 13, franchise emissions are in category 14, and investee emissions are in category 15. have an impact on each.

In this way, by using machine learning to learn which factors affect which SCOPEs or categories, and obtaining information on emissions and each factor from businesses, we can identify the causes of changes in emissions. predictions can be made. Here, by predicting the causes of emissions using machine learning, it is possible to efficiently and accurately predict factors that influence changes in greenhouse gas emissions for each business operator and each SCOPE.

Subsequently, as processing in step S203, the report generation unit 135 of the control unit 130 generates emissions by SCOPE (further by category for SCOPE 3) based on the information regarding the analyzed cause prediction of the change in emissions. Generate a visualized report on the causes of change.

FIG. 10 is a flowchart diagram illustrating an example of transaction processing for greenhouse gas emissions according to the first embodiment of the present invention.

First, as processing in step S301, the transaction processing unit 134 of the control unit 130 of the management terminal 100 refers to the company data stored in the company data storage unit 121 of the storage unit 120. Here, the business data to be referred to includes business analysis information (greenhouse gas emissions for each SCOPE) and the like.

Next, as processing in step S302, the transaction processing unit 134 generates a hash value based on the business entity data referenced in step S301. That is, the transaction processing unit 134 uses a hash function to generate one row of hash values for greenhouse gas emissions over a predetermined period, and records the hash values in the public blockchain as transaction information. On the blockchain network, a main 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 the blockchain. be done. Here, in this example, in order to reduce the cost related to blockchain recording, it is assumed that data is recorded on a layer 2 (for example, a side chain) that is different from the main blockchain (so-called layer 1).

Furthermore, the transaction processing unit 134 can assign and manage an NFTID in association with a blockchain record of greenhouse gas emissions of a business operator. More specifically, as shown in FIG. 4, the business data 1000 is given the customer ID of the business as customer information, stores the blockchain address to refer to, and stores the NFTID and NFT ID as the offset report information. A TXID can be assigned.

As shown in FIG. 6, blockchain addresses are associated with each NFTID on the blockchain network, and the NFTID and customer ID are managed on the management terminal 100. Information regarding greenhouse gas emissions related to business operators can be retrieved by referring to the blockchain address for each NFTID, such as NFTID "13" and "14", to read the details of the emissions information as shown in Figure 7. I can do it. Figure 7 shows information regarding the offset report that is associated with NFTID "14". A TXID is assigned in association with the offset report, and the offset report includes CO2 emissions by SCOPE, target year and month, and report publication date. It will be done. In addition to the CO2 emissions for the target year and month in this example, it is also possible to convert the most recent year's CO2 emissions, reduced CO2 emissions, and offset CO2 emissions into NFT.In this way, CO2 emissions can be managed using NFT. This allows businesses to trade NFT-based certificates while ensuring non-tampering and transaction reliability, and also makes it possible to prove emissions to third parties. can.

FIG. 11 is a flowchart showing an example of a business establishment determination process when calculating greenhouse gas emissions according to the first embodiment of the present invention. Conventionally, when calculating greenhouse gas emissions, a business operator accesses by logging into an application provided by the management terminal 100, etc. When accessing from a different address than usual, in order to perform the greenhouse gas emissions calculation process corresponding to that address, manually set the base address of the business office from which the current access is being made. There was a need. In contrast, in the following embodiment, an efficient method for automatically setting a business address will be exemplified.

First, as processing in step S401, the information acquisition unit 131 of the control unit 130 of the management terminal 100 receives information regarding the address of the business office as the business office information of the business operator via the network NW from the business terminal 200. The registration is performed by storing the information in the business data storage unit 121 of the storage unit 120 as business office information. Here, the address of the business office refers to the address of the business's main office, branch office, etc., both in Japan and overseas, and it is possible for the business to register multiple business addresses. For example, the address of the Tokyo head office can be registered as "Marunouchi, Chiyoda-ku, Tokyo..." and the address of the Beijing branch can be registered as "Beijing City, People's Republic of China...".

Next, at a predetermined timing, such as when the business operator logs in from the business terminal 200 to use the application provided by the management terminal 100, or when performing some input processing when calculating greenhouse gas emissions, etc. In step S402, the information acquisition unit 131 of the control unit 130 of the management terminal 100 receives the position information of the business terminal 200 from the business terminal 200 via the network NW. Here, the location information can be received, for example, via a GPS built into the operator terminal 200.

Subsequently, as processing in step S403, the information acquisition unit 131 of the control unit 130 of the management terminal 100 obtains the address of the business operator registered as the business office information based on the location information received from the business terminal 200. It is checked whether the location information matches any business address registered by the business operator, and the business address is determined as the business information. For example, if the location information received from the business terminal 200 indicates “Beijing City,” the management terminal 100 refers to the business office information of the business stored in the business data storage unit 121. , is determined to match "Beijing City, People's Republic of China," which indicates the address of the Beijing branch office, and the location where the business operator is currently logging in via the business terminal 200 is determined as the "Beijing branch office." Thereby, it is possible to correctly determine the base address of the business office where the business operator is logged in, and to efficiently execute the process of calculating the amount of greenhouse gas emissions corresponding to the determined business office address.

FIG. 12 is a flowchart showing an example of a business establishment determination process when calculating greenhouse gas emissions according to the first embodiment of the present invention. Conventionally, when calculating greenhouse gas emissions, a business operator accesses by logging into an application provided by the management terminal 100, etc. When accessing from a different address than usual, especially when the user's business trip or assignment is in a foreign country, the management terminal 100 uses the invoice image data when executing the greenhouse gas emissions calculation process. When analyzing images using machine learning, etc., if the language used for analysis is set to a different language (e.g., Japanese) than that of the foreign country (e.g., China), the image analysis may not be performed properly. Harmful effects may occur. In contrast, in the following embodiments, an efficient method for automatically setting a business address and properly performing image analysis will be exemplified.

First, as processing in step S501, the information acquisition unit 131 of the control unit 130 of the management terminal 100 receives information regarding the address of the business office as the business office information of the business operator via the network NW from the business terminal 200. The registration is performed by storing the information in the business data storage unit 121 of the storage unit 120 as business office information. Here, the address of the business office refers to the address of the business's main office, branch office, etc., both in Japan and overseas, and it is possible for the business to register multiple business addresses. For example, the address of the Tokyo head office can be registered as "Marunouchi, Chiyoda-ku, Tokyo..." and the address of the Beijing branch can be registered as "Beijing City, People's Republic of China...".

Next, at a predetermined timing, such as when the business operator logs in from the business terminal 200 to use the application provided by the management terminal 100, or when performing some input processing when calculating greenhouse gas emissions, etc. In step S502, the information acquisition unit 131 of the control unit 130 of the management terminal 100 receives position information of the business terminal 200 from the business terminal 200 via the network NW. Here, the location information can be received, for example, via a GPS built into the operator terminal 200.

Subsequently, as processing in step S503, the information acquisition unit 131 of the control unit 130 of the management terminal 100 obtains the address of the business operator registered as the business office information based on the location information received from the business terminal 200. It is checked whether the location information matches any business address registered by the business operator, and the business address is determined as the business information. For example, if the location information received from the business terminal 200 indicates "Beijing", the management terminal 100 refers to the business office information of the business stored in the business data storage unit 121. , is determined to match "Beijing City, People's Republic of China," which indicates the address of the Beijing branch office, and the location where the business operator is currently logging in via the business terminal 200 is determined as the "Beijing branch office."

Subsequently, as processing in step S504, the image analysis unit 132 of the control unit 130 of the management terminal 100 searches for the address based on the location information received from the business terminal 200 or the office information determined in the previous step. Based on this, determine the language to use for image analysis. For example, if the location information received from the business terminal 200 indicates "Beijing City", or the location where the business operator is currently logged in via the business terminal 200 is determined as the "Beijing branch office". In this case, when analyzing the image data received from the operator terminal 200, the image analysis unit 132 sets the image reading language by AI to "Chinese" and executes the image analysis process. Thereby, it is possible to correctly determine the base address of the business office where the business operator is logged in, and to efficiently execute the process of calculating the amount of greenhouse gas emissions corresponding to the determined business office address.

FIG. 13 is a flowchart showing an example of a business establishment determination process when calculating greenhouse gas emissions according to the first embodiment of the present invention. Conventionally, when calculating greenhouse gas emissions, a business operator accesses by logging into an application provided by the management terminal 100, etc. When accessing from a different address than usual, in order to perform the greenhouse gas emissions calculation process corresponding to that address, manually set the base address of the business office from which the current access is being made. There was a need. For example, if a business user's business trip destination is China, the business office should be entered as "Beijing Branch", but even if it is incorrectly entered as "Japan Branch" or "Beijing Branch", A case may occur in which a person who illegally obtains the business terminal 200 attempts to access from an address different from Beijing. In contrast, in the following embodiment, an efficient method for automatically detecting a business address and appropriately calculating greenhouse gas emissions will be exemplified.

First, as processing in step S601, the information acquisition unit 131 of the control unit 130 of the management terminal 100 acquires information about the address of the business and the network as the business office information of the business from the business terminal 200 via the network NW. Registration is performed by receiving the information and storing it as business office information in the business data storage unit 121 of the storage unit 120. Here, the address of the business office refers to the address of the business's main office, branch office, etc., both in Japan and overseas, and it is possible for the business to register multiple business addresses. For example, the address of the Tokyo head office can be registered as "Marunouchi, Chiyoda-ku, Tokyo..." and the address of the Beijing branch can be registered as "Beijing City, People's Republic of China...". Further, as the network information, information regarding the network that uniquely identifies the connection location of the operator terminal 200, such as a network name (SSID) and an IP address, can be registered.

Next, at a predetermined timing, such as when the business operator logs in from the business terminal 200 to use the application provided by the management terminal 100, or when performing some input processing when calculating greenhouse gas emissions, etc. In step S602, the information acquisition unit 131 of the control unit 130 of the management terminal 100 receives network information of the business terminal 200 from the business terminal 200 via the network NW. Here, the network information can be received via a network connected to the operator terminal 200, for example, and uniquely identifies the connection location of the operator terminal 200, such as a network name (SSID), IP address, etc. Contains information about the network.

Subsequently, as processing in step S603, the information acquisition unit 131 of the control unit 130 of the management terminal 100 determines the address or address of the business entity registered as the business information, based on the network information received from the business terminal 200. Refer to the network information, check whether the network information matches any business address or network information registered by the business operator, and determine the business address as the business information. For example, if the network information received from the operator terminal 200 is the SSID "ABCCORP_BEIJING" or the IP address indicates access from "Beijing City", the management terminal 100 121 and determines that the address matches "Beijing City, People's Republic of China," which indicates the address of the Beijing branch, and that the business operator is currently logging in via the business terminal 200. The location of the Beijing branch will be determined as the "Beijing branch".

Here, as the process in step S604, if the received network information is different from the registered office information, for example, the registered office information is "Tokyo Head Office". , if the SSID included in the received network information is "ABCCORP_BEIJING" or the IP address indicates access from "Beijing City", the management terminal 100 determines that the registered "Tokyo Head Office" is connected to the network. It is determined that the address information is different from the address information specified from the information, and an alert is sent to the business terminal 200 to the effect that the registered office information and the office information inferred from the network information are different. As a result, by referring to the network information received from the business terminal 200 in addition to or in place of the location information, the base address of the business office where the business operator is logged in can be determined more accurately. It is possible to efficiently perform calculation processing of greenhouse gas emissions corresponding to a business address.

FIG. 14 is a flowchart showing an example of a business establishment determination process when calculating greenhouse gas emissions according to the first embodiment of the present invention. Conventionally, when calculating greenhouse gas emissions, a business operator accesses by logging into an application provided by the management terminal 100, etc. When accessing from a different address than usual, in order to perform the greenhouse gas emissions calculation process corresponding to that address, manually set the base address of the business office from which the current access is being made. There was a need. In contrast, in the following embodiment, an efficient method for automatically setting a business address will be exemplified. In this example, a method of automatically setting a business address while further increasing security by referring to information other than location information and network information will be exemplified.

First, as processing in step S701, the information acquisition unit 131 of the control unit 130 of the management terminal 100 receives information regarding the address of the business office as the business office information of the business operator from the business terminal 200 via the network NW. The registration is performed by storing the information in the business data storage unit 121 of the storage unit 120 as business office information. Here, the address of the business office refers to the address of the business's main office, branch office, etc., both in Japan and overseas, and it is possible for the business to register multiple business addresses. For example, the address of the Tokyo head office can be registered as "Marunouchi, Chiyoda-ku, Tokyo..." and the address of the Beijing branch can be registered as "Beijing City, People's Republic of China...". In addition, in this example, image data of the scenery of the office that is the base of the business is received from the business terminal 200, and is registered as business information in association with the address of the base of the business, Can be stored. Further, as image data, information regarding facial images of employees working at the office that is the base of the company can be registered in advance.

Next, at a predetermined timing, such as when the business operator logs in from the business terminal 200 to use the application provided by the management terminal 100, or when performing some input processing when calculating greenhouse gas emissions, etc. In step S702, the information acquisition unit 131 of the control unit 130 of the management terminal 100 receives image information of the business terminal 200 from the business terminal 200 via the network NW. Here, the image information is, for example, an image captured via a camera built into the business terminal 200, and includes information regarding a background image of an office serving as a base of the business operator. It can also include information regarding facial images of employees who work at the office that is the base of the company.

Subsequently, as processing in step S703, the information acquisition unit 131 of the control unit 130 of the management terminal 100 determines the address or address of the business registered as the business information, based on the image information received from the business terminal 200. Referring to the image information, it is confirmed whether the image information matches any business address or image information registered by the business operator, and the business address is determined as the business information. For example, if the location information received from the operator terminal 200 indicates "Beijing City" or if the registered image information matches image data regarding the office scenery of the Beijing branch office, the management terminal 100 refers to the business office information of the business operator stored in the business data storage unit 121, and matches the address of the Beijing branch office in "Beijing City, People's Republic of China" or the office scenery of the "Beijing branch office" The location where the business operator is currently logging in via the business terminal 200 is determined as the "Beijing branch office." Thereby, it is possible to correctly determine the base address of the business office where the business operator is logged in, and to efficiently execute the process of calculating the amount of greenhouse gas emissions corresponding to the determined business office address. Here, the management terminal 100 refers to the facial image data of employees working at each business office, which is registered in advance, based on the facial image of the person included in the image information received from the business terminal 200. , it is also possible to determine the base address of the business office where the business operator is logged in.

Here, as the process of step S704, if the received image information is different from the registered office information or image information, for example, the registered office information is "Tokyo Head Office". On the other hand, if the background of the office included in the received image information is related to the Beijing branch, or if the person included in the image information is a person working at the Beijing branch, the management terminal 100 It is determined that "Tokyo Head Office" is different from the office information specified from the image information, and an alert is sent to the business terminal 200 that the registered office information and the office information inferred from the image information are different. Send. As a result, by referring to the image information received from the business terminal 200 in addition to or in place of the location information and network information, the base address of the business office where the business is logged in can be determined more accurately. It is possible to efficiently perform calculation processing of greenhouse gas emissions corresponding to the determined business address.

The embodiments described above are merely illustrative to facilitate understanding of the present invention, and are not intended to be interpreted as limiting the present invention. It goes without saying that the present invention can be modified and improved without departing from its spirit, and that the present invention includes equivalents thereof.

100 Management terminal 200 Business terminal

Claims (4)

A method for managing greenhouse gas emissions, the method comprising:
The management terminal is
Determine information regarding energy usage based on information input by the operator terminal,
Calculating greenhouse gas emissions based on the information on the usage amount,
A method for storing information that is included in the input information and that is essential for calculating the greenhouse gas, as well as information that is not essential for calculating the greenhouse gas. From the said business terminal, further receives input of business office information regarding the address of the business office of the business from the said business terminal, and receives location information from the said business terminal;
The management method according to claim 1, wherein the location information is stored. 2. The management method according to claim 1, wherein the non-essential information includes any one of location information of the vendor terminal, network information, and information regarding HFC. 2. The method according to claim 1, wherein the management terminal or the vendor terminal displays an alert asking whether to use the non-essential information for calculating the greenhouse gas.