Air pollutant emissions management method

Abstract

To provide a method of enabling efficient management including computation of air pollutant emissions by a business operator.SOLUTION: A air pollutant emissions management method involves causing a control unit of a management terminal to receive input data for computing air pollutant emissions from a business operator terminal of a business operator, refer to past input data of the business operator, and recommend data for complementing the received input data on the basis of the past input data.SELECTED DRAWING: Figure 11

Description

Technical Field

[0001] The present invention relates to a method for managing the emissions of air pollutants.

Background Art

[0002] Regarding the greenhouse gas emissions of businesses associated with the use of fuels, electricity, etc., a reporting system targeting SCOPE1 emissions (direct emissions of the company itself) and SCOPE2 emissions (indirect emissions of the company itself) has become widespread, and progress has been made in calculating and reducing emissions in SCOPE1 and SCOPE2.

[0003] In Non-Patent Document 1, with the aim of further reducing the greenhouse gas emissions emitted by businesses, as emissions other than SCOPE1 and SCOPE2, proposals have been made regarding the calculation of SCOPE3 emissions, that is, the emissions of the supply chain (the entire series of processes such as raw material procurement, manufacturing, logistics, sales, waste disposal, etc.) of other related businesses, etc.

Prior Art Documents

Non-Patent Documents

[0004]

Non-Patent Document 1

Summary of the Invention

Problems 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 SCOPE3, etc., for each business operator, especially in enterprises and local governments, etc., a vast amount of data collection and input is required to calculate the emissions, calculate the emissions, and manage the calculation results, which consumes a great deal of labor and time. In particular, in the field of GHG emissions management, the SCOPE of emissions to be calculated is expanding, the data on which the emissions calculation is based varies widely for each SCOPE, and the data management methods also differ for each business operator. Therefore, the introduction of advanced technologies to improve business efficiency has been delayed.

[0006] Therefore, an object of the present invention is to provide a method for efficiently realizing emissions management by reducing the man-hours in the GHG emissions management field such as the calculation of greenhouse gas emissions by business operators by utilizing advanced technologies. Another object is to provide a method for managing air pollutant emissions applying the above emissions management method.

Means for Solving the Problems

[0007] A method for managing air pollutant emissions according to an embodiment of the present invention, wherein a control unit of a management terminal receives input data for calculating air pollutant emissions from a business operator terminal of a business operator, refers to past input data of the business operator, and recommends data for complementing the received input data based on the past input data.

Effects of the Invention

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

Brief Description of the Drawings

[0009]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Figure 10

Figure 11

Mode for Carrying Out the Invention

[0010] The content of the embodiment of the present invention will be listed and described. A greenhouse gas emission 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 air pollutant emissions, The control unit of the management terminal, receives input data for calculating air pollutant emissions from the operator terminal of the operator, refers to the past input data by the operator, and recommends data for complementing the received input data based on the past input data. [Item 2] The input data for calculating the amount of waste is the method described in item 1, including coefficients and units for classification, item, and type. [Item 3] The method described in item 1 of displaying the data to be supplemented via the chat interface of the operator terminal. [Item 4] The method described in item 1, wherein the data to be supplemented includes data for supplementing omissions in the input data or data for correcting errors in the input data.

[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 for explaining a greenhouse gas emission management system according to a first embodiment of the present invention.

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

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

[0015] Further, the management terminal 100 analyzes the received image data of the invoice information by machine learning, extracts necessary items of the invoice information included in the image data, and calculates the amount of greenhouse gas emissions. In addition, the management terminal 100 analyzes the change (for example, increase or decrease) in the greenhouse gas emissions over time calculated by 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. Based on the information regarding the greenhouse gas emissions for each of the above-mentioned predetermined periods, the management terminal 100 generates a single hash value using SHA256 or other hash functions and records it as transaction information in the blockchain network. On the blockchain network, based on the transaction information, the hash value recorded in the previous block, and the nonce value mined by the node, this block is generated, recorded following the previous block, and a blockchain is formed. Here, the above hash generation and / or the recording of the transaction information on the blockchain can also be performed not by the management terminal 100 but via other terminals. 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 regarding the greenhouse gas emissions in the blockchain network as a smart contract. By using the smart contract, based on the above-mentioned information regarding the emissions, a contract regarding the emissions trading with other operators can be automatically generated, approved, and executed without going through a third party. Also, with the smart contract, each operator can refer to the transaction information without going through the management terminal, improving the service convenience and reducing the operation cost. Furthermore, the management terminal 100 connects to a large language model (LLM, for example, GPT) 300 via the network and via an API (Applicaton Progamming Interface) to receive a predetermined question text from other terminals, analyze the question text, and generate an answer text.

[0017] Here, as described above, in a public blockchain, since the approval of transactions is carried out by an unspecified number of nodes or miners rather than a specific administrator, higher data immutability and fault tolerance can be ensured compared to a private blockchain. Therefore, since the security of transactions is ensured, it is preferable to use a public blockchain as the destination for recording power transactions in this embodiment. Representative public blockchains include Bitcoin and Ethereum. For example, Ethereum has higher immutability and reliability among public blockchains.

[0018] In addition, the management terminal 100 can associate information regarding greenhouse gas emissions with an identifier or the like and record it as a non-fungible token (Non-Fungible Token, hereinafter referred to as "NFT") in the blockchain network. An NFT is, for example, a token issued according to the "ERC721" standard of Etherium, which is a platform of the blockchain network, and is a unit of data recorded in the blockchain network and has a non-fungible nature. Since the NFT is recorded on the blockchain together with a smart contract and is traceable, it can prove transaction information including details and history such as the information of the operator managing the greenhouse gas emissions.

[0019] FIG. 2 is a functional block diagram of a management terminal constituting an 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 each function within the control unit 130, input data, etc., and is composed of a RAM (Random Access Memory), a ROM (Read Only Memory), and the like. Further, the storage unit 120 has a business operator data storage unit 121 that stores various data related to business operators, and an AI model storage unit 122 that stores learning data and a learning model learned by AI (Artificial Intelligence) from the learning data. Note that a database (not shown) storing 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), and the like. As functions of the control unit 130, there are an information reception unit 131 that receives information from external terminals 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 the greenhouse gas emissions, a cause analysis unit 133 that analyzes the cause of the time-series change of the greenhouse gas emissions calculated based on the information included in the extracted invoice information by analyzing the image data, a transaction processing unit 134 that generates a hash value by summarizing information related to the greenhouse gas emissions for a predetermined period and performs a process of recording it as transaction information in the blockchain network, and a report generation unit 135 that generates and transmits report data for outputting the greenhouse gas emissions and the results of the cause analysis of the change in the emissions to the business operator every predetermined period.

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

[0024] Further, the management terminal 100 further has a wallet (not shown) necessary for recording transaction information in the blockchain network. Note that this wallet can also be provided outside the management terminal 100.

[0025] FIG. 3 is a functional block diagram of the 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 an operator to input instructions and display texts, images, etc. according to input data from the control unit 240. When the operator terminal 200 is configured by a personal computer, it is composed of a display, a keyboard, and a mouse. When the operator terminal 200 is configured by a smartphone or a tablet terminal, it is composed of a touch panel or the like. This display operation unit 220 is activated by a control program stored in the storage unit 230 and executed by the operator terminal 200 which is a computer (electronic computer).

[0029] The storage unit 230 stores programs for executing various control processes and each function within the control unit 240, input data, etc., and is composed of a RAM, a ROM, etc. Further, the storage unit 230 temporarily stores the communication content with the management terminal 100.

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

[0031] FIG. 4 is a diagram for explaining the details of business operator 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 the sake of convenience of explanation, an example of one business operator (the business operator identified by the business operator ID "10001") is shown, but information of a plurality of business operators can be stored. As various data related to the business operator, for example, basic information of the business operator (for example, corporate name of the business operator, user name, business office information (for example, address information for each business office, etc.), network name (for example, SSID, IP address, etc.), image information (for example, background image of the business office, portrait image, etc.), business type, contact information, email address, business office name, related company name, business operator name related on the supply chain, etc.), input information (for example, image data of invoice information, etc.), analysis information (for example, information related to invoices extracted from image data, greenhouse gas emissions, prediction of the cause of changes in greenhouse gas emissions, etc.), customer information (for example, customer ID, blockchain address, etc.), and offset report information (for example, TXID, NFTID, etc.), activity information (credit information held, information related to the type / origin of credit assigned to a project (product) / raw material and the offset amount) can be included.

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

[0034] First, as the process of step S101, the information acquisition unit 131 of the control unit 130 of the management terminal 100 acquires, from the business operator terminal 200 via the network NW, image data including invoice information collected on the business operator side. The business operator uploads invoices, receipts, vouchers, 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) to the management terminal 100 via the business operator terminal 200. 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 storage unit 120.

[0035] Subsequently, as 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, when performing image analysis, a method called so-called OCR is used. The image analysis unit 132 of the control unit 130 of the management terminal 100 uses a learning model generated by previously learning image data of a plurality of various formats of invoices stored in the AI model storage unit 122 of the storage unit 120 to recognize text from the image data and extract items included in the invoice information as structured string data. Here, when performing image analysis, it is also possible to use an image analysis engine (such as an OCR engine) provided by a business operator other than the management terminal 100 and linked by an API.

[0036] Regarding image analysis, for example, as shown in FIG. 5, it is performed by recognizing and extracting text from image data containing claim information. As shown in FIG. 5, examples of claim information include various items included in the claim. For example, items such as the breakdown name of the electricity bill, the amount (yen) for each breakdown, the contract power (kW), the electricity consumption (kWh) for each breakdown, the total amount (yen), and the date (year and month) can be mentioned. In this example, the breakdown of the electricity bill is illustrated, but it may be a claim for the usage fee of other energies including gas and fuel in addition to electricity, or other examples such as the receipt for transportation expenses for business trips, the receipt for commuting expenses of employees, the claim accompanying transactions with freight forwarders, and the breakdown of the claim accompanying transactions with waste operators. The image analysis unit 132 can extract the amount information, the following activity amount information, etc. included in the claim information as text by analyzing the image data of these claim information. The extracted claim information is stored as analysis information in the business operator data storage unit 121 of the storage unit 120. In this way, through image analysis by machine learning, without the business operator inputting claim information manually or otherwise, a vast amount of necessary information for calculating greenhouse gas emissions can be obtained as image data, and through highly accurate image recognition, the information necessary for calculating greenhouse gas emissions can be accurately extracted. Therefore, the efficiency and high accuracy of calculating greenhouse gas emissions can be achieved.

[0037] Next, in step S103, the image analysis unit 132 of the control unit 130 calculates the greenhouse gas emissions based on the claim information extracted from the image data. Here, the greenhouse gas emissions are classified into SCOPE1, SCOPE2, and SCOPE3. SCOPE1 refers to the direct emissions of greenhouse gases by the operator itself (for example, emissions associated with fuel combustion and industrial processes), SCOPE2 refers to the indirect emissions associated with the use of electricity, heat, gas, etc. supplied by other companies to the operator, and further, SCOPE3 is the calculation standard for the emissions of the entire supply chain of an organization issued by the GHG Protocol, referring to the emissions of the operator's supply chain (the entire series of flows such as raw material procurement, manufacturing, logistics, sales, and waste 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, distribution (upstream), (5) waste from the business, (6) business trips, (7) commuting of employees, (8) leased assets (upstream), (9) transportation, distribution (downstream), (10) processing of sold products, (11) use of sold products, (12) disposal of sold products, (13) leased assets (downstream), (14) franchises, (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), and in this embodiment, CO2 will be used as an example for explanation.

[0038] Also, the greenhouse gas emissions are defined with the operator's electricity consumption, volume of goods transported, volume of waste processed, and various transaction amounts as activity volumes, and the emissions are calculated by multiplying the activity volumes by the emission factors per unit of activity, namely, the CO2 emissions per 1 kWh of electricity used, the CO2 emissions per 1 ton of goods transported, and the CO2 emissions per 1 ton of waste incinerated. The greenhouse gas emissions are calculated separately for the above SCOPE1, SCOPE2, and SCOPE3 (for SCOPE3, by category of the 15 categories), and the total emissions are calculated as the supply chain emissions.

[0039] In this embodiment, the image analysis unit 132 extracts relevant claim information for each of SCOPE1, SCOPE2, and SCOPE3 (and for SCOPE3, further categorized by category), and among the claim information, for example, based on the electricity consumption in kWh, the emission amount is calculated based on the above calculation method. The calculated emission amount is stored as analysis information in the operator data storage unit 121 of the storage unit 120.

[0040] Subsequently, as the process of step S104, the report generation unit 135 of the control unit 130 generates a visualized report showing the breakdown of the emission amount over time for each SCOPE (and for SCOPE3, further categorized by category) based on the information regarding the calculated emission amount.

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

[0042] First, as the process of step S201, the cause analysis unit 133 of the control unit 130 of the management terminal 100 refers to the information regarding the greenhouse gas emission amount of the operator calculated in step S103 of FIG. 8. Here, regarding the greenhouse gas emission amount, the emission amounts for each SCOPE (and for SCOPE3, further categorized by category) are referred to. Also, the cause analysis unit 133 can confirm the change (increase or decrease) in the emission amount by referring to the past emission amount data of the same operator for the greenhouse gas emission amount. As described above, the emission amount is stored as analysis information in the operator data storage unit 121 of the storage unit 120.

[0043] Subsequently, as the process of step S202, based on the information regarding the emission amount referred to above, the cause analysis unit 133 analyzes and predicts the cause of the change in the emission amount by machine learning. Here, in the cause analysis, the cause analysis unit 133 of the control unit 130 of the management terminal 100 uses the information on the emission amount referred to above, the factors affecting the change (increase or decrease) in the emission amount, and the learning model generated by learning the data regarding the factors affecting the change (increase or decrease) in the emission amount, which is stored in the AI model storage unit 122 of the storage unit 120 in advance, to predict the cause of the change in the emission amount for each SCOPE (for SCOPE3, further categorized by category).

[0044] Here, examples of the factors affecting the change (increase or decrease) in the emission amount include weather, temperature, product demand and / or factory operation, store or factory business or operation hours, changes in equipment or facilities, measures by software, power-saving actions, fuel conversion, energy menu changes, changes in business trips or commuting volume, and the power generation amount of self-generation. Each of these factors is a factor affecting the emission amount of any SCOPE. For example, the factor of weather affects precipitation, wind volume, sunshine duration, and temperature. Precipitation affects the small hydropower generation amount, wind volume affects the wind power generation amount, sunshine duration affects the solar power generation amount, temperature affects air conditioning, and furthermore, the power generation amount affects the self-generation amount, and the self-generation amount affects the CO2 emission amount by electricity, thereby affecting the change in the emission amount of SCOPE2. On the other hand, air conditioning affects the gas usage amount, and the gas usage amount affects the CO2 emission amount by gas combustion, thereby affecting the change in the emission amount of SCOPE1. Also, power-saving activities, factory operation due to product demand, and business hours affect the power consumption amount and affect SCOPE2. Also, EMS, replacement of refrigeration equipment, introduction of energy-saving equipment, and automobile usage amount also affect the power consumption amount and affect SCOPE2. In addition, automobile usage amount, fuel consumption, boiler usage amount, and boiler efficiency affect the fuel usage amount and affect the CO2 emission amount by fuel, thereby affecting SCOPE1.

[0045] In addition, the factor of product sales volume affects Categories 1, 9, 10, 11, and 12 of SCOPE3. Facility investment affects Category 2, the renewable energy ratio and the amount of procured energy affect Category 3, the number of transportation trips and transportation route changes affect Categories 4 and 9, the product loss rate affects Category 5, the number of business trips and the number of employees going to the office affect Category 6, the number of commuting employees and the number of employees going to the office affect Category 7, the power consumption affects Category 8, the reduction of processing due to product improvement affects Category 10, the improvement to energy-saving products affects Category 11, the increase in the recycling rate affects Category 12, the office power of tenants affects Category 13, the emissions of franchises affect Category 14, and the emissions of investment destinations affect Category 15, respectively.

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

[0047] Subsequently, as the process of step S203, the report generation unit 135 of the control unit 130 generates a visualized report on the cause of the change in emissions for each SCOPE (and further for each category in the case of SCOPE3) based on the information on the predicted cause of the change in emissions analyzed above.

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

[0049] First, as 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 storage unit 120. Here, the business operator data to be referred to includes the analysis information of the business operator (greenhouse gas emissions for each SCOPE), etc.

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

[0051] In addition, the transaction processing unit 134 can assign and manage an NFT ID in association with the blockchain record of the operator's greenhouse gas emissions amount. More specifically, as shown in FIG. 4, to the operator data 1000, as customer information, the customer ID of the operator is assigned and the blockchain address to be referred to is stored, and as offset report information, the NFT ID and TX ID can be assigned.

[0052] As shown in FIG. 6, on the blockchain network, a blockchain address is associated with each NFT ID, and in the management terminal 100, the NFT ID and the customer ID are managed. Therefore, for example, information on the greenhouse gas emissions of an operator corresponding to the customer ID "2" can be obtained by referring to the blockchain address for each NFT ID, such as NFT IDs "13" and "14", and as shown in FIG. 7, the details of the emissions information can be read out. FIG. 7 shows information on the offset report associated with the NFT ID "14". A TXID is assigned to the offset report, and the CO2 emissions by SCOPE, the target year and month, and the report issuance date are included in the offset report. In addition to the CO2 emissions in the target year and month of this example, the CO2 emissions in the most recent years, the reduced CO2 emissions, and the offset CO2 emissions can also be NFTized. In this way, by managing the CO2 emissions as NFTs, the operator can conduct transactions of NFTized certificates while ensuring non-repudiation and transaction reliability, and can also prove the emissions to a third party.

[0053] FIG. 11 is a flowchart showing an example of recommending data for complementing input data for calculating air pollutants according to the first embodiment of the present invention. Conventionally, for calculating the air pollutant emissions of an operator, there was a mechanism of multiplying the emission method by the emission factor for calculation. However, for the calculation, it was necessary to manually input the classification (such as facility exhaust, vehicle exhaust, other exhaust, etc.), item (such as factory, ordinary vehicle, light vehicle, diesel vehicle, etc.), emission factor (such as factory name, vehicle type, etc.), and unit amount (such as g / vehicle km, g / km, etc.) as IDs in a calculation format such as CSV or Excel. Furthermore, when manually inputting, there was a problem that if there was an input error, an error message would be displayed and the correct emissions could not be calculated. Therefore, in this embodiment, it is characterized in that the AI function and the notification function are utilized to automatically complete the input when manually inputting, as follows.

[0054] First, as the process of step S401, the information acquisition unit 131 of the control unit 130 of the management terminal 100 receives input data for calculating air pollutants from the business operator terminal 200. The person in charge of the business operator inputs classification (general waste, industrial waste, paper scraps, wood scraps, etc.), item (incineration, landfill, recycling, etc.), emission factor (including the transportation stage, excluding the transportation stage, etc.), and unit quantity (kg, ton, m3, Mm3, etc.) via CSV or Excel displayed on the business operator terminal 200. Also, as another example, the person in charge of the business operator should originally input all of classification (facility exhaust, vehicle exhaust, other exhaust, etc.), item (factory, ordinary vehicle, light vehicle, diesel vehicle, etc.), emission factor (factory name, vehicle type, etc.), and unit quantity (g / unit km, g / km, etc.) via CSV or Excel displayed on the business operator terminal 200. However, input data in a state where any one of these is not input is accepted. Further, this process can also be used to send the input data to the LLM 300 via the chat interface displayed on the business operator terminal 200.

[0055] Subsequently, as the process of step S402, the report generation unit 135 of the control unit 130 of the management terminal 100 refers to the past input data of the business operator data 1000 in the business operator data storage unit 121 of the storage unit 120, and for example, when the emission factor or the unit quantity associated with the emission factor is insufficient in the input data, estimates the emission factor or unit quantity to be supplemented. Similarly, referring to the past input data of the business operator, it is estimated whether there are any abnormalities or deficiencies in the numerical values and contents input by the business operator this time. For example, when there is a misrecording in the input data, the correct data is estimated. Further, this process can also be executed by the management terminal 100 via the LLM 300. The LLM 300 refers to the data related to the past input data of the business operator as described above, for example, estimates the emission factor, or estimates whether there are any abnormalities or deficiencies in the numerical value input by the business operator this time, and sends the result to the management terminal 100.

[0056] Subsequently, as the process of step S403, the report generation unit 135 of the control unit 130 of the management terminal 100 transmits the data generated in the management terminal 100 or the LLM 300 in the process of S402 to the business operator terminal 200. The person in charge of the business operator can confirm the emission unit, the unit quantity associated with the unit, and the recommended information based on the verification result received by the business operator terminal 200 in a form displayed via the interface or pop-up display of the business operator terminal 200. After receiving the above data, the person in charge of the business operator complements the input content based on the received content. The above interface includes being displayed in ways such as selection by natural conversation on the chat screen, selection by button display on the chat screen, individual selection by the pre-registration pop-up on the system screen, and batch selection by the pre-registration pop-up on the system screen.

[0057] As described above, in the person in charge of the business operator, the automatic completion function of the input data for calculating the amount of waste can realize accurate and efficient input without mistakes.

[0058] The above-described embodiments are merely examples for facilitating the understanding of the present invention, and are not for limiting and interpreting the present invention. It goes without saying that the present invention can be changed and improved without departing from its gist, and equivalents of the present invention are included in the present invention.

Explanation of Signs

[0059] 100 Management terminal 200 Business operator terminal

Claims

1. A method for managing air pollutant emissions, wherein the control unit of the management terminal receives input data for calculating air pollutant emissions from the operator terminal of the operator, refers to the past input data by the operator, and recommends data for complementing the received input data based on the past input data.

2. The method according to claim 1, wherein the input data for calculating the air pollutant emissions includes coefficients and units of classification, item, and type.

3. The method according to claim 1, wherein the data to be complemented is displayed via the chat interface of the operator terminal.

4. The method according to claim 1, wherein the data to be complemented includes data for supplementing omissions in the input data or data for correcting errors in the input data.

Images