System and method for sharing profit of greenhouse gas emission rights on basis of exhaust gas measurement information for transportation means

Abstract

Embodiments propose a system and a method for sharing profit of greenhouse gas emission rights on the basis of exhaust gas measurement information for a transportation means. The method according to an embodiment may comprise the steps of: transmitting, to a user terminal, a sharing request message for requesting sharing of profit of greenhouse gas emission rights; receiving a sharing acceptance message from the user terminal, wherein the sharing acceptance message includes information on a transportation means associated with the user terminal; on the basis of receiving the sharing acceptance message, transmitting, to a measurement device, a measurement initiation message for requesting exhaust gas measurement for the transportation means; receiving, from the measurement device, a measurement completion message including exhaust gas measurement information for the transportation means, wherein the exhaust gas measurement information for the transportation means associated with the user terminal includes first exhaust gas measurement information, second exhaust gas measurement information, and information related to engine cleaning; determining an annual greenhouse gas reduction amount for the transportation means associated with the user terminal on the basis of the information on the transportation means associated with the user terminal, the first exhaust gas measurement information, the second exhaust gas measurement information, and the information related to engine cleaning; determining expected profit of greenhouse gas emission rights for the transportation means associated with the user terminal on the basis of the annual greenhouse gas reduction amount for the transportation means associated with the user terminal; transmitting, to the user terminal, a profit guidance message including the expected profit of greenhouse gas emission rights for the transportation means associated with the user terminal; and sharing, with the user terminal, the profit of greenhouse gas emission rights for the transportation means associated with the user terminal.

Description

System and method for sharing greenhouse gas emission rights revenue based on exhaust gas measurement information for transportation vehicles

[0001] Embodiments of the present disclosure relate to a technology for sharing the revenue of greenhouse gas emission rights, and more particularly, to a technology for sharing the revenue of greenhouse gas emission rights based on exhaust gas measurement information for a means of transportation.

[0002] Meanwhile, various means of transportation, such as cars, ships, and aircraft, have become essential elements of economic activity and daily life, and modern industrial society relies on various forms of transportation. However, because these modes of transportation primarily operate on fossil fuels, their combustion emits massive amounts of greenhouse gases. Greenhouse gases such as carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) accumulate in the atmosphere, accelerating global warming and being identified as a major cause of global climate change.

[0003] To address climate change, the international community is introducing various regulations and systems. Among these, greenhouse gas emissions trading systems (GHG emissions trading systems) are gaining increasing importance as a tool for climate change mitigation. GHG emissions trading systems represent the right for companies or countries to emit a certain amount of greenhouse gases. They are a market-based policy tool that regulates each unit of emissions and contributes to climate change mitigation. GHG emissions trading systems were adopted internationally through the Kyoto Protocol in 1997 and the Paris Agreement in 2015, and are currently actively operating in various countries and regions. Greenhouse gas emissions are primarily traded in trading markets. Major markets include the European Union Emissions Trading System (EU ETS), the world's largest emissions trading market; the Regional Greenhouse Gas Initiative (RGGI), an emissions trading program involving states in the northeastern United States; and China's ETS (Emissions Trading Scheme). These trading markets provide companies with the financial incentives they need to reduce their emissions, while also ensuring the flexibility to buy and sell permits.

[0004] In particular, obtaining greenhouse gas emission credits requires approval through a feasibility assessment and verification from a UN-approved certification body. Specifically, greenhouse gas reduction performance is issued by the UN to CDM (clean development mechanism) project developers in units of Certified Emission Reductions (CERs). CDM project registration and CER issuance require pre-validation and post-validation from the CDM operating body. Proving these greenhouse gas reduction performances requires accurate measurement of greenhouse gas emissions, and various reports, including a business plan, monitoring report, and verification report, based on the measurement data must be submitted and approved. Consequently, it is difficult for individual individuals to apply for and obtain greenhouse gas emission credits.

[0005] Accordingly, a system and method for sharing greenhouse gas emission rights revenue based on exhaust gas measurement information for transportation vehicles are needed.

[0006] Embodiments of the present disclosure may provide a system and method for sharing greenhouse gas emission rights revenue based on exhaust gas measurement information for a means of transportation.

[0007] The technical tasks to be achieved in the embodiments are not limited to those mentioned above, and other technical tasks not mentioned can be considered by a person having ordinary skill in the art from the various embodiments described below.

[0008] A method for sharing the revenue of greenhouse gas emission rights based on exhaust gas measurement information for a means of transportation according to one embodiment comprises: transmitting a sharing request message requesting sharing of the revenue of greenhouse gas emission rights to a user terminal, receiving a sharing acceptance message from the user terminal, wherein the sharing acceptance message includes information on a means of transportation associated with the user terminal, transmitting a measurement initiation message requesting exhaust gas measurement for the means of transportation to a measurement device based on the reception of the sharing acceptance message, receiving a measurement completion message including exhaust gas measurement information for the means of transportation from the measurement device, wherein the exhaust gas measurement information for the means of transportation associated with the user terminal includes first exhaust gas measurement information, second exhaust gas measurement information, and information related to engine cleaning, determining an annual greenhouse gas reduction amount for the means of transportation associated with the user terminal based on the information on the means of transportation associated with the user terminal, the first exhaust gas measurement information, the second exhaust gas measurement information, and the information related to engine cleaning, and determining an expected revenue of greenhouse gas emission rights for the means of transportation associated with the user terminal based on the annual greenhouse gas reduction amount for the means of transportation associated with the user terminal. The method may include a step of transmitting a revenue guidance message including an expected revenue of greenhouse gas emission rights for a means of transportation related to the user terminal to the user terminal, and sharing the revenue of greenhouse gas emission rights for the means of transportation related to the user terminal with the user terminal.

[0009] According to embodiments, the server, upon receiving a sharing acceptance message from a user terminal, initiates a series of procedures related to the acquisition and sale of greenhouse gas emission credits for a vehicle associated with the user terminal, thereby enabling even ordinary individuals to easily acquire and sell greenhouse gas emission credits. Furthermore, the server initiates the acquisition and sale of greenhouse gas emission credits for the vehicle associated with the user terminal and shares the revenue generated from the greenhouse gas emission credits with the user terminal, thereby providing economic incentives to the user terminal and the business operator, while also contributing to climate change mitigation.

[0010] According to embodiments, the server calculates a greenhouse gas reduction amount by considering not only information about the means of transportation related to the user terminal and exhaust gas measurement information, but also cleaning information related to the engine, thereby calculating a more accurate greenhouse gas reduction amount.

[0011] According to embodiments, the server can increase the general public's participation in the greenhouse gas emissions trading system by predicting expected profits according to greenhouse gas reduction amounts and informing user terminals thereof.

[0012] The effects that can be obtained from the examples are not limited to the effects mentioned above, and other effects that are not mentioned can be clearly derived and understood by a person having ordinary skill in the art based on the detailed description below.

[0013] The accompanying drawings, which are included as part of the detailed description to aid understanding of the embodiments, provide various embodiments and, together with the detailed description, describe technical features of the various embodiments.

[0014] FIG. 1 is a diagram showing the configuration of an electronic device according to one embodiment.

[0015] Figure 2 is a diagram showing the configuration of a program according to one embodiment.

[0016] Figure 3 illustrates a system for sharing greenhouse gas emission rights revenue based on exhaust gas measurement information for transportation vehicles.

[0017] FIG. 4 illustrates a method in which a server shares greenhouse gas emission rights revenue with a user terminal based on exhaust gas measurement information for a means of transportation according to one embodiment.

[0018] FIG. 5 is a signal exchange diagram for a method in which a server shares greenhouse gas emission rights revenue with a user terminal based on exhaust gas measurement information for a means of transportation according to one embodiment.

[0019] Figure 6 is an example of a reduction amount calculation model and a profit prediction model according to one embodiment.

[0020] Figure 7 is a block diagram showing the configuration of a server according to one embodiment.

[0021] The following embodiments combine components and features of the embodiments in a predetermined form. Each component or feature may be considered optional unless explicitly stated otherwise. Each component or feature may be implemented without being combined with other components or features. Furthermore, various embodiments may be formed by combining some components and / or features. The order of operations described in various embodiments may be changed. Some components or features of one embodiment may be included in another embodiment or may be replaced with corresponding components or features of another embodiment.

[0022] In the description of the drawings, procedures or steps that may obscure the gist of various embodiments are not described, and procedures or steps that can be understood by a person with ordinary skill in the art are also not described.

[0023] Throughout the specification, when a part is said to "comprising" (or including) a certain component, this does not mean that other components are excluded, but rather that other components can be included, unless specifically stated otherwise. In addition, terms such as "...part," "...unit," and "module" described in the specification mean a unit that processes at least one function or operation, which may be implemented by hardware, software, or a combination of hardware and software. In addition, the words "a" or "an," "one," "the," and similar related words may be used in the singular and plural sense in the context of describing various embodiments (especially in the context of the claims below) unless otherwise indicated herein or clearly contradicted by context.

[0024] Hereinafter, embodiments according to various embodiments will be described in detail with reference to the attached drawings. The detailed description disclosed below, together with the attached drawings, is intended to explain exemplary embodiments of various embodiments and is not intended to represent the only embodiment.

[0025] Additionally, specific terms used in various embodiments are provided to aid understanding of the various embodiments, and the use of such specific terms may be changed in other forms without departing from the technical spirit of the various embodiments.

[0026] FIG. 1 is a diagram showing the configuration of an electronic device according to one embodiment.

[0027] FIG. 1 is a block diagram of an electronic device (101) within a network environment (100) according to various embodiments. Referring to FIG. 1, in the network environment (100), the electronic device (101) may communicate with the electronic device (102) via a first network (198) (e.g., a short-range wireless communication network), or may communicate with at least one of the electronic device (104) or the server (108) via a second network (199) (e.g., a long-range wireless communication network). In one embodiment, the electronic device (101) may communicate with the electronic device (104) via the server (108). According to one embodiment, the electronic device (101) may include a processor (120), a memory (130), an input module (150), an audio output module (155), a display module (160), an audio module (170), a sensor module (176), an interface (177), a connection terminal (178), a haptic module (179), a camera module (180), a power management module (188), a battery (189), a communication module (190), a subscriber identification module (196), or an antenna module (197). In some embodiments, the electronic device (101) may omit at least one of these components (e.g., the connection terminal (178)), or may have one or more other components added. In some embodiments, some of these components (e.g., the sensor module (176), the camera module (180), or the antenna module (197)) may be integrated into one component (e.g., the display module (160)). The electronic device (101) may also be referred to as a client, terminal, or peer.

[0028] The processor (120) may, for example, execute software (e.g., a program (140)) to control at least one other component (e.g., a hardware or software component) of the electronic device (101) connected to the processor (120) and perform various data processing or operations. According to one embodiment, as at least a part of the data processing or operations, the processor (120) may store commands or data received from other components (e.g., a sensor module (176) or a communication module (190)) in a volatile memory (132), process the commands or data stored in the volatile memory (132), and store result data in a non-volatile memory (134). According to one embodiment, the processor (120) may include a main processor (121) (e.g., a central processing unit or an application processor) or an auxiliary processor (123) (e.g., a graphics processing unit, a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor) that can operate independently or together with the main processor (121). For example, when the electronic device (101) includes the main processor (121) and the auxiliary processor (123), the auxiliary processor (123) may be configured to use less power than the main processor (121) or to be specialized for a given function. The auxiliary processor (123) may be implemented separately from the main processor (121) or as a part thereof.

[0029] The auxiliary processor (123) may control at least a portion of functions or states associated with at least one component (e.g., a display module (160), a sensor module (176), or a communication module (190)) of the electronic device (101), for example, on behalf of the main processor (121) while the main processor (121) is in an inactive (e.g., sleep) state, or together with the main processor (121) while the main processor (121) is in an active (e.g., application execution) state. In one embodiment, the auxiliary processor (123) (e.g., an image signal processor or a communication processor) may be implemented as a part of another functionally related component (e.g., a camera module (180) or a communication module (190)). In one embodiment, the auxiliary processor (123) (e.g., a neural network processing unit) may include a hardware structure specialized for processing artificial intelligence models.

[0030] An artificial intelligence model can be generated through machine learning. Such learning can be performed, for example, in the electronic device (101) on which the artificial intelligence model is executed, or can be performed through a separate server (e.g., server (108)). The learning algorithm can include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but is not limited to the examples described above. The artificial intelligence model can include a plurality of artificial neural network layers. The artificial neural network can be one of a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), a deep Q-network, or a combination of two or more of the above, but is not limited to the examples described above. An artificial intelligence model may additionally or alternatively include a software structure in addition to a hardware structure.

[0031] The memory (130) can store various data used by at least one component (e.g., processor (120) or sensor module (176)) of the electronic device (101). The data can include, for example, software (e.g., program (140)) and input data or output data for commands related thereto. The memory (130) can include volatile memory (132) or non-volatile memory (134).

[0032] The program (140) may be stored as software in the memory (130) and may include, for example, an operating system (142), middleware (144), or an application (146).

[0033] The input module (150) can receive commands or data to be used in a component of the electronic device (101) (e.g., a processor (120)) from an external source (e.g., a user) of the electronic device (101). The input module (150) can include, for example, a microphone, a mouse, a keyboard, a key (e.g., a button), or a digital pen (e.g., a stylus pen).

[0034] The audio output module (155) can output audio signals to the outside of the electronic device (101). The audio output module (155) can include, for example, a speaker or a receiver. The speaker can be used for general purposes, such as multimedia playback or recording playback. The receiver can be used to receive incoming calls. In one embodiment, the receiver can be implemented separately from the speaker or as part of the speaker.

[0035] The display module (160) can visually provide information to an external party (e.g., a user) of the electronic device (101). The display module (160) may include, for example, a display, a holographic device, or a projector and a control circuit for controlling the device. According to one embodiment, the display module (160) may include a touch sensor configured to detect a touch, or a pressure sensor configured to measure the intensity of a force generated by the touch.

[0036] The audio module (170) can convert sound into an electrical signal, or vice versa, convert an electrical signal into sound. According to one embodiment, the audio module (170) can acquire sound through the input module (150), output sound through the sound output module (155), or an external electronic device (e.g., electronic device (102)) (e.g., speaker or headphone) directly or wirelessly connected to the electronic device (101).

[0037] The sensor module (176) can detect the operating status (e.g., power or temperature) of the electronic device (101) or the external environmental status (e.g., user status) and generate an electrical signal or data value corresponding to the detected status. According to one embodiment, the sensor module (176) can include, for example, a gesture sensor, a gyro sensor, a barometric pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illuminance sensor.

[0038] The interface (177) may support one or more designated protocols that may be used to directly or wirelessly connect the electronic device (101) with an external electronic device (e.g., the electronic device (102)). In one embodiment, the interface (177) may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

[0039] The connection terminal (178) may include a connector through which the electronic device (101) may be physically connected to an external electronic device (e.g., electronic device (102)). According to one embodiment, the connection terminal (178) may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector).

[0040] The haptic module (179) can convert electrical signals into mechanical stimuli (e.g., vibration or movement) or electrical stimuli that a user can perceive through tactile or kinesthetic sensations. According to one embodiment, the haptic module (179) can include, for example, a motor, a piezoelectric element, or an electrical stimulation device.

[0041] The camera module (180) can capture still images and videos. According to one embodiment, the camera module (180) may include one or more lenses, image sensors, image signal processors, or flashes.

[0042] The power management module (188) can manage power supplied to the electronic device (101). According to one embodiment, the power management module (188) can be implemented as, for example, at least a part of a power management integrated circuit (PMIC).

[0043] A battery (189) may power at least one component of the electronic device (101). In one embodiment, the battery (189) may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

[0044] The communication module (190) may support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between the electronic device (101) and an external electronic device (e.g., electronic device (102), electronic device (104), or server (108)), and the performance of communication through the established communication channel. The communication module (190) may operate independently from the processor (120) (e.g., application processor) and may include one or more communication processors that support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication module (190) may include a wireless communication module (192) (e.g., a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module (194) (e.g., a local area network (LAN) communication module, or a power line communication module). Among these communication modules, the corresponding communication module can communicate with an external electronic device (104) via a first network (198) (e.g., a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network (199) (e.g., a long-range communication network such as a legacy cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., a LAN or WAN)). These various types of communication modules can be integrated into a single component (e.g., a single chip) or implemented as multiple separate components (e.g., multiple chips). The wireless communication module (192) can verify or authenticate the electronic device (101) within a communication network such as the first network (198) or the second network (199) by using subscriber information (e.g., an international mobile subscriber identity (IMSI)) stored in the subscriber identification module (196).

[0045] The wireless communication module (192) can support 5G networks and next-generation communication technologies following the 4G network, such as NR access technology (new radio access technology). The NR access technology can support high-speed transmission of high-capacity data (eMBB (enhanced mobile broadband)), minimization of terminal power and connection of multiple terminals (mMTC (massive machine type communications)), or high reliability and low latency (URLLC (ultra-reliable and low-latency communications)). The wireless communication module (192) can support, for example, a high-frequency band (e.g., mmWave band) to achieve a high data transmission rate. The wireless communication module (192) can support various technologies for securing performance in a high-frequency band, such as beamforming, massive multiple-input and multiple-output (MIMO), full dimensional MIMO (FD-MIMO), array antenna, analog beam-forming, or large scale antenna. The wireless communication module (192) can support various requirements specified in the electronic device (101), an external electronic device (e.g., the electronic device (104)), or a network system (e.g., the second network (199)). According to one embodiment, the wireless communication module (192) can support a peak data rate (e.g., 20 Gbps or more) for eMBB realization, a loss coverage (e.g., 164 dB or less) for mMTC realization, or a U-plane latency (e.g., 0.5 ms or less for downlink (DL) and uplink (UL), or 1 ms or less for round trip) for URLLC realization.

[0046] The antenna module (197) can transmit or receive signals or power to or from an external device (e.g., an external electronic device). In one embodiment, the antenna module (197) may include an antenna including a radiator formed of a conductor or a conductive pattern formed on a substrate (e.g., a PCB). In one embodiment, the antenna module (197) may include a plurality of antennas (e.g., an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network, such as the first network (198) or the second network (199), may be selected from the plurality of antennas, for example, by the communication module (190). A signal or power may be transmitted or received between the communication module (190) and an external electronic device via the at least one selected antenna. In some embodiments, in addition to the radiator, another component (e.g., a radio frequency integrated circuit (RFIC)) may be additionally formed as a part of the antenna module (197).

[0047] According to various embodiments, the antenna module (197) may form a mmWave antenna module. In one embodiment, the mmWave antenna module may include a printed circuit board, an RFIC disposed on or adjacent a first side (e.g., a bottom side) of the printed circuit board and capable of supporting a designated high-frequency band (e.g., a mmWave band), and a plurality of antennas (e.g., an array antenna) disposed on or adjacent a second side (e.g., a top side or a side side) of the printed circuit board and capable of transmitting or receiving signals in the designated high-frequency band.

[0048] At least some of the above components can be interconnected and exchange signals (e.g., commands or data) with each other via a communication method between peripheral devices (e.g., a bus, GPIO (general purpose input and output), SPI (serial peripheral interface), or MIPI (mobile industry processor interface)).

[0049] According to one embodiment, commands or data may be transmitted or received between the electronic device (101) and an external electronic device (104) via a server (108) connected to a second network (199). Each of the external electronic devices (102 or 104) may be the same or a different type of device as the electronic device (101). According to one embodiment, all or part of the operations executed in the electronic device (101) may be executed in one or more of the external electronic devices (102, 104, or 108). For example, when the electronic device (101) is to perform a certain function or service automatically or in response to a request from a user or another device, the electronic device (101) may, instead of or in addition to executing the function or service itself, request one or more external electronic devices to perform the function or at least a part of the service. One or more external electronic devices that receive the request may execute at least a portion of the requested function or service, or an additional function or service related to the request, and transmit the result of the execution to the electronic device (101). The electronic device (101) may process the result as is or additionally and provide it as at least a portion of a response to the request. For this purpose, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used, for example. The electronic device (101) may provide an ultra-low latency service by using distributed computing or mobile edge computing, for example. In another embodiment, the external electronic device (104) may include an Internet of Things (IoT) device. The server (108) may be an intelligent server utilizing machine learning and / or a neural network. According to one embodiment, the external electronic device (104) or the server (108) may be included in the second network (199).The electronic device (101) can be applied to intelligent services (e.g., smart home, smart city, smart car, or healthcare) based on 5G communication technology and IoT-related technology.

[0050] The server (108) is connected to an electronic device (101) and can provide services to the connected electronic device (101). In addition, the server (108) can process a membership registration process, store and manage various information of users who have registered as members, and provide various purchase and payment functions related to the service. In addition, the server (108) can share in real time the execution data of service applications running on each of a plurality of electronic devices (101) so that services can be shared among users. This server (108) may have the same hardware configuration as a typical web server or service server. However, in terms of software, it may include program modules that are implemented in any language such as C, C++, Java, Python, Golang, or Kotlin and perform various functions. In addition, the server (108) generally refers to a computer system and computer software (server program) installed therefor that is connected to an unspecified number of clients and / or other servers through an open computer network such as the Internet, and that receives a request to perform a task from a client or other server and provides the result of the task accordingly. In addition, the server (108) should be understood as a broad concept that includes, in addition to the above-mentioned server program, a series of application programs running on the server (108) and, in some cases, various databases (DB: Database, hereinafter referred to as “DB”) built internally or externally. Accordingly, the server (108) classifies member registration information and various information and data about the game and stores and manages them in the DB, and this DB can be implemented internally or externally to the server (108).In addition, the server (108) can be implemented using a variety of server programs provided according to operating systems such as Windows, Linux, UNIX, and Macintosh on general server hardware, and representative examples include IIS (Internet Information Server) used in a Windows environment and CERN, NCSA, APPACH, TOMCAT, etc. used in a UNIX environment, which can implement a web service. In addition, the server (108) can also be linked with an authentication system and a payment system for user authentication of a service or purchase payment related to a service.

[0051] The first network (198) and the second network (199) refer to a connection structure that enables information exchange between each node, such as terminals and servers, or a network that connects the server (108) and electronic devices (101, 104). The first network (198) and the second network (199) include, but are not limited to, the Internet, a Local Area Network (LAN), a Wireless Local Area Network (Wireless LAN), a Wide Area Network (WAN), a Personal Area Network (PAN), 3G, 4G, LTE, 5G, Wi-Fi, etc. The first network (198) and the second network (199) may be closed first networks (198) and second networks (199) such as LANs and WANs, but are preferably open such as the Internet. The Internet refers to a worldwide open computer network (198) and second network (199) structure that provides protocols such as TCP / IP protocol, TCP, UDP (user datagram protocol), and various services existing at their upper layers, namely HTTP (HyperText Transfer Protocol), Telnet, FTP (File Transfer Protocol), DNS (Domain Name System), SMTP (Simple Mail Transfer Protocol), SNMP (Simple Network Management Protocol), NFS (Network File Service), and NIS (Network Information Service).

[0052] A database can have a general data structure implemented in the storage space (hard disk or memory) of a computer system using a database management program (DBMS). The database can have a data storage form that allows free searching (extracting), deleting, editing, adding, etc. of data. The database can be implemented to suit the purpose of one embodiment of the present disclosure using a relational database management system (RDBMS) such as Oracle, Informix, Sybase, or DB2, an object-oriented database management system (OODBMS) such as Gemston, Orion, or O2, and an XML native database such as Excelon, Tamino, or Sekaiju, and can have appropriate fields or elements to achieve its own function.

[0053] Figure 2 is a diagram showing the configuration of a program according to one embodiment.

[0054] FIG. 2 is a block diagram (200) illustrating a program (140) according to various embodiments. According to one embodiment, the program (140) may include an operating system (142), middleware (144), or an application (146) executable on the operating system (142) for controlling one or more resources of the electronic device (101). The operating system (142) may include, for example, Android™, iOS™, Windows™, Symbian™, Tizen™, or Bada™. At least some of the programs (140) may be preloaded onto the electronic device (101), for example, during manufacturing, or may be downloaded or updated from an external electronic device (e.g., the electronic device (102 or 104), or a server (108)) when used by a user. All or part of the program (140) may include a neural network.

[0055] The operating system (142) may control the management (e.g., allocation or retrieval) of one or more system resources (e.g., processes, memory, or power) of the electronic device (101). The operating system (142) may additionally or alternatively include one or more driver programs for driving other hardware devices of the electronic device (101), for example, an input module (150), an audio output module (155), a display module (160), an audio module (170), a sensor module (176), an interface (177), a haptic module (179), a camera module (180), a power management module (188), a battery (189), a communication module (190), a subscriber identification module (196), or an antenna module (197).

[0056] Middleware (144) can provide various functions to the application (146) so that functions or information provided from one or more resources of the electronic device (101) can be used by the application (146). Middleware (144) can include, for example, an application manager (201), a window manager (203), a multimedia manager (205), a resource manager (207), a power manager (209), a database manager (211), a package manager (213), a connectivity manager (215), a notification manager (217), a location manager (219), a graphics manager (221), a security manager (223), a call manager (225), or a voice recognition manager (227).

[0057] The application manager (201) can manage, for example, the life cycle of the application (146). The window manager (203) can manage, for example, one or more GUI resources used on the screen. The multimedia manager (205) can, for example, identify one or more formats required for playing media files, and perform encoding or decoding of a corresponding media file among the media files using a codec suitable for the corresponding format selected among the formats. The resource manager (207) can manage, for example, the source code of the application (146) or the memory space of the memory (130). The power manager (209) can manage, for example, the capacity, temperature, or power of the battery (189), and determine or provide related information necessary for the operation of the electronic device (101) using the corresponding information. According to one embodiment, the power manager (209) can be linked with the basic input / output system (BIOS) (not shown) of the electronic device (101).

[0058] The database manager (211) can, for example, create, search, or modify a database to be used by the application (146). The package manager (213) can, for example, manage the installation or update of an application distributed in the form of a package file. The connectivity manager (215) can, for example, manage a wireless connection or direct connection between the electronic device (101) and an external electronic device. The notification manager (217) can, for example, provide a function for notifying a user of the occurrence of a specified event (e.g., an incoming call, a message, or an alarm). The location manager (219) can, for example, manage location information of the electronic device (101). The graphics manager (221) can, for example, manage one or more graphic effects to be provided to the user or a user interface related thereto.

[0059] The security manager (223) may provide, for example, system security or user authentication. The telephony manager (225) may manage, for example, a voice call function or a video call function provided by the electronic device (101). The voice recognition manager (227) may, for example, transmit the user's voice data to the server (108) and receive, from the server (108), a command corresponding to a function to be performed in the electronic device (101) based at least in part on the voice data, or text data converted based at least in part on the voice data. In one embodiment, the middleware (244) may dynamically delete some of the existing components or add new components. In one embodiment, at least a portion of the middleware (144) may be included as a part of the operating system (142) or implemented as separate software different from the operating system (142).

[0060] The application (146) may include, for example, a home (251), a dialer (253), an SMS / MMS (255), an instant message (IM) (257), a browser (259), a camera (261), an alarm (263), a contact (265), a voice recognition (267), an email (269), a calendar (271), a media player (273), an album (275), a watch (277), a health (279) (e.g., measuring biometric information such as the amount of exercise or blood sugar), or an environmental information (281) (e.g., measuring barometric pressure, humidity, or temperature information) application. According to one embodiment, the application (146) may further include an information exchange application (not shown) that can support information exchange between the electronic device (101) and an external electronic device. The information exchange application may include, for example, a notification relay application configured to transmit designated information (e.g., a call, a message, or an alarm) to an external electronic device, or a device management application configured to manage an external electronic device. The notification relay application may, for example, transmit notification information corresponding to a designated event (e.g., receipt of an email) that occurred in another application (e.g., an email application (269)) of the electronic device (101) to the external electronic device. Additionally or alternatively, the notification relay application may receive notification information from the external electronic device and provide the information to the user of the electronic device (101).

[0061] A device management application may, for example, control the power (e.g., turning on or off) or the function (e.g., brightness, resolution, or focus) of an external electronic device or a component thereof (e.g., a display module or a camera module of the external electronic device) that communicates with the electronic device (101). The device management application may additionally or alternatively support the installation, deletion, or update of an application running on the external electronic device.

[0062] Throughout this specification, the terms "neural network," "neural network," and "network function" may be used interchangeably. A neural network may be comprised of a set of interconnected computational units, generally referred to as "nodes." These "nodes" may also be referred to as "neurons." A neural network comprises at least two nodes. The nodes (or neurons) comprising a neural network may be interconnected by one or more "links."

[0063] Within a neural network, two or more nodes connected via links can form a relationship between input and output nodes. The concepts of input and output nodes are relative, meaning that any node that is in an output relationship with one node can also be in an input relationship with another node, and vice versa. As described above, input-to-output node relationships can be created based on links. One input node can be connected to one or more output nodes via links, and vice versa.

[0064] In a relationship between input nodes and output nodes connected through a single link, the value of the output node can be determined based on the data input to the input node. Here, the node interconnecting the input nodes and output nodes can have a weight. The weight can be variable and can be varied by a user or an algorithm so that the neural network can perform a desired function. For example, when one or more input nodes are interconnected to one output node through each link, the output node can determine the output node value based on the values ​​input to the input nodes connected to the output node and the weight set for the link corresponding to each input node.

[0065] As described above, a neural network is a network in which two or more nodes are interconnected through one or more links, forming input and output node relationships within the network. The characteristics of a neural network can be determined based on the number of nodes and links within the network, the relationships between the nodes and links, and the weight values ​​assigned to each link. For example, if two neural networks have the same number of nodes and links but different weight values ​​between the links, the two neural networks can be perceived as different from each other.

[0066] Figure 3 illustrates a system for sharing greenhouse gas emission rights revenue based on exhaust gas measurement information for a vehicle. One embodiment of Figure 3 can be combined with various embodiments of the present disclosure.

[0067] Referring to FIG. 3, a system (300) (hereinafter, revenue sharing system (300)) for sharing greenhouse gas emission rights revenue based on exhaust gas measurement information for a means of transportation may include a server (310), a user terminal (320), a measurement device (330), and an exchange server (340).

[0068] For example, the revenue sharing system (300) may be a system in which the server (310) determines an annual greenhouse gas reduction amount based on exhaust gas information measured by a measuring device (330) for a means of transportation associated with a user terminal (320), acquires greenhouse gas emission rights for the means of transportation associated with the user terminal (320) based on the annual greenhouse gas reduction amount, and sells the greenhouse gas emission rights for the means of transportation associated with the user terminal (320) through an exchange server (340) and shares the profits between the server (310) and the user terminal (320). Here, the means of transportation is a means of transportation that uses an engine, and may include various types of means of transportation such as, for example, vehicles, trains, ships, and airplanes that emit exhaust gas through engines.

[0069] The server (310) may be a server that provides a service that acquires greenhouse gas emission rights for a transportation means associated with a user terminal (320) and shares the revenue from the greenhouse gas emission rights for the transportation means. For example, the server (310) may receive information about the transportation means from a user terminal (320) that has accepted sharing of the revenue from the greenhouse gas emission rights, and may receive exhaust gas measurement information about the transportation means from a measurement device (330) linked to the user terminal (320). For example, the server (310) may determine an annual greenhouse gas reduction amount for the transportation means based on the exhaust gas measurement information for the transportation means. For example, the server (310) may acquire greenhouse gas emission rights for the transportation means based on the annual greenhouse gas reduction amount of the transportation means. For example, the server (310) may share the revenue from the greenhouse gas emission rights for the transportation means with the user terminal. For example, the server (310) may include the server (108) of FIG. 1.

[0070] The user terminal (320) may be a terminal utilizing a service that shares the profits from greenhouse gas emission rights for a means of transportation. For example, the user terminal (320) may include any type of device that includes a video display function and a user input interface. For example, the user terminal (320) may be installed with an application that provides a service that shares the profits from greenhouse gas emission rights for a means of transportation. For example, the user terminal (320) may receive a sharing request message from the server (310) through the application, requesting sharing of the profits from greenhouse gas emission rights. For example, the user terminal (320) may transmit a sharing acceptance message containing information about the means of transportation associated with the user terminal (320) to the server (310) through the application. For example, the user terminal (320) may include a smartphone, a tablet PC, a PC, a smart TV, a mobile phone, a personal digital assistant (PDA), a laptop, a media player, a micro server, a global positioning system (GPS) device, an e-book reader, a digital broadcasting terminal, a navigation device, a kiosk, an MP3 player, a digital camera, home appliances, and other mobile or non-mobile computing devices. For example, the user terminal (330) may include the electronic device (101) of FIG. 1.

[0071] The measuring device (330) may be a device for measuring exhaust gas from an engine of a vehicle. For example, the measuring device (330) may measure the concentration of nitrogen oxides and oxygen emitted from the engine of the vehicle. For example, the measuring device (330) may include a processor, a communication unit, and a memory in addition to a measuring unit for measuring the concentration of exhaust gas. For example, the measuring device (330) may transmit exhaust gas measurement information for the vehicle to a server (310) and / or a user terminal (320) through the communication unit. For example, the measuring device (330) may store exhaust gas measurement information for the vehicle and information related to engine cleaning for the vehicle through the memory. For example, the measuring device (330) may measure nitrogen oxide emissions from an engine of the vehicle. For example, the measuring device (330) may measure nitrogen oxide emissions after carbon deposits have been removed from the engine of the vehicle using hydrogen molecules and after a lubricant additive has been injected. For example, the measuring device (330) may transmit nitrogen oxide emissions before and after cleaning the engine of a vehicle to the server (310). Thus, the server (310) may use the nitrogen oxide emissions before and after cleaning the engine of the vehicle as basic data for obtaining greenhouse gas emission credits. For example, the server (310) may obtain greenhouse gas emission credits based on nitrogen oxide reduction.

[0072] For example, the server (310) can determine the amount of change in the concentration of nitrous oxide in the exhaust gas by multiplying the amount of change in the concentration of nitrogen oxide in the exhaust gas measured by the measuring device (330) by a preset first coefficient. Furthermore, the server (310) can determine the amount of change in the concentration of carbon dioxide in the exhaust gas by multiplying the amount of change in the concentration of nitrous oxide in the exhaust gas by a preset second coefficient. The server (310) can determine the annual greenhouse gas reduction amount for the transportation vehicle based on the amount of change in the concentration of carbon dioxide in the exhaust gas.

[0073] The exchange server (340) may be a server that provides an interface related to trading greenhouse gas emission rights and manages trading for greenhouse gas emission rights. For example, the exchange server (340) may transmit transaction information for greenhouse gas emission rights to the server (310) or the user terminal (320). Once the server (310) and / or the user terminal (320) complete an authentication process with the exchange server (330), the exchange server (330) may transmit transaction information for greenhouse gas emission rights to the server (310) or the user terminal (320). For example, the server (310) may register greenhouse gas emission rights in the greenhouse gas emission rights trading market via the exchange server (340) and sell the greenhouse gas emission rights. For example, the exchange server (330) may include the server (108) of FIG. 1 .

[0074] As illustrated in FIG. 3, components of the revenue sharing system (300) may be connected via a network. According to one embodiment, the network refers to a connection structure that enables information exchange between each node, such as a plurality of terminals and servers. Examples of such networks include, but are not limited to, RF, 3GPP (3rd Generation Partnership Project) network, LTE (Long Term Evolution) network, 5GPP (5th Generation Partnership Project) network, WIMAX (World Interoperability for Microwave Access) network, Internet, LAN (Local Area Network), Wireless LAN (Wireless Local Area Network), WAN (Wide Area Network), PAN (Personal Area Network), Bluetooth network, NFC network, satellite broadcasting network, analog broadcasting network, DMB (Digital Multimedia Broadcasting) network, etc.

[0075] Below, a specific method is exemplified in which a server (310) shares the profits from greenhouse gas emission rights with a user terminal (320) based on exhaust gas measurement information for a means of transportation.

[0076] FIG. 4 illustrates a method in which a server shares greenhouse gas emission rights revenue with a user terminal based on exhaust gas measurement information for a vehicle according to one embodiment. The embodiment of FIG. 4 may be combined with various embodiments of the present disclosure.

[0077] Referring to FIG. 4, in step S410, the server may transmit a sharing request message requesting sharing of greenhouse gas emission rights revenue to the user terminal.

[0078] A sharing request message may be a message from the server requesting the user terminal to share in the revenue from greenhouse gas emissions. This sharing may involve receiving a preset percentage of the revenue from greenhouse gas emissions. For example, the sharing request message may include the percentage of the revenue from greenhouse gas emissions. The percentage of revenue from greenhouse gas emissions may be preset on the server. For example, the preset percentage may be 50%.

[0079] For example, a user terminal may receive a sharing request message from a server through an application that provides a service for sharing the revenue from greenhouse gas emission rights for a means of transportation.

[0080] In step S420, the server can receive a sharing acceptance message from the user terminal.

[0081] A sharing acceptance message may be a message from a user terminal to a server indicating that the user terminal accepts the sharing of greenhouse gas emission rights revenue. For example, the sharing acceptance message may include information about a vehicle associated with the user terminal. The information about the vehicle associated with the user terminal may be information about the vehicle for which the user terminal seeks to acquire greenhouse gas emission rights revenue. For example, the information about the vehicle associated with the user terminal may include information about the type and size of the vehicle, information about the type of fuel, information about the amount of movement of the vehicle, and information about the vehicle's age. The information about the type and size of the vehicle may include values ​​for the type of the vehicle and values ​​for the weight of the vehicle. For example, values ​​for the types of multiple vehicles and values ​​for the weight of multiple vehicles may be preset on the server. The information about the type of fuel may include values ​​for the type of fuel used by the vehicle. For example, values ​​for the types of multiple fuels may be preset on the server. The information about the amount of movement of the vehicle may include values ​​for the distance traveled by the vehicle. Information about the vehicle's model year may include a value for the year the vehicle was manufactured.

[0082] For example, a user terminal may send a sharing acceptance message to a server through an application that provides a service for sharing the revenue from greenhouse gas emission rights for a means of transportation.

[0083] For example, the server may provide a service of acquiring and selling greenhouse gas emission rights for a means of transportation associated with the user terminal based on receiving a sharing request message from the user terminal, thereby sharing a portion of the revenue from the greenhouse gas emission rights with the user terminal.

[0084] In step S430, the server may transmit a measurement initiation message requesting the measurement device to measure exhaust gas for the vehicle based on the shared acceptance message received.

[0085] The measuring device may be pre-connected with the user terminal. For example, the user terminal may be pre-connected with the measuring device through an application that provides a service for sharing greenhouse gas emission rights revenue for transportation vehicles. For example, the user terminal may establish a pre-connection with the measuring device via wireless communication. For example, the user terminal may transmit a discovery message requesting a pre-connection to the measuring device through an application that provides a service for sharing greenhouse gas emission rights revenue for transportation vehicles. For example, the user terminal may establish a pre-connection with the measuring device by receiving a message from the measuring device accepting the pre-connection. For example, the message accepting the pre-connection may include identification information about the measuring device. The identification information may include at least one of an identifier (ID) or an identification number. Here, the ID may be an ID for identifying the device. The identification number may be composed of at least one of the device's model name or serial number.

[0086] The measurement initiation message may be a message from the server requesting a measurement device pre-connected to the user terminal to measure exhaust emissions from the vehicle. For example, identification information about the measurement device pre-connected to the user terminal may be pre-stored on the server.

[0087] In step S440, the server can receive a measurement completion message including exhaust gas measurement information for the vehicle from the measuring device.

[0088] The measurement completion message may be a message that the measurement device notifies the server that it has completed measuring exhaust gases for the vehicle.

[0089] For example, exhaust gas measurement information for a vehicle may include first exhaust gas measurement information, second exhaust gas measurement information, and information related to engine cleaning. The first exhaust gas measurement information may be information about exhaust gas measured before performing engine cleaning on the vehicle. For example, the first exhaust gas measurement information may include concentrations of nitrogen oxides and concentrations of oxygen. The second exhaust gas measurement information may be information about exhaust gas measured after performing engine cleaning on the vehicle. For example, the second exhaust gas measurement information may include concentrations of nitrogen oxides and concentrations of oxygen.

[0090] Information related to engine cleaning may be information regarding engine cleaning performed on the engine of a vehicle. For example, information related to engine cleaning may include cleaning information regarding an HHO generator and cleaning information regarding a lubricant additive. For example, cleaning information regarding an HHO generator may include values ​​regarding the amount of HHO gas generated per minute and the time for which HHO gas was injected into the engine. An HHO generator is a device that generates HHO gas by electrolyzing water and may be a device that connects a hose to an engine combustion chamber to clean the engine using the HHO gas. HHO gas refers to a mixed gas of hydrogen (H) and oxygen (O) generated by electrolyzing water. For example, by combusting HHO gas together with fuel remaining in the engine, accumulated fuel inside the engine can be removed. For example, cleaning information regarding a lubricant additive may include values ​​indicating the type and product name of the lubricant additive and values ​​regarding the amount of the lubricant additive injected into the engine. Values ​​representing the types and product names of multiple lubricant additives may be preset on the server. For example, the types of multiple lubricant additives may include antioxidants, corrosion inhibitors, rust inhibitors, detergents, extreme pressure additives, viscosity index improvers, pour point depressants, antifoamers, emulsifiers, tackifiers, preservatives, and metal deactivators. For example, values ​​representing multiple product names for each type of multiple lubricant additives may be preset on the server.

[0091] In step S450, the server can determine an annual greenhouse gas reduction amount for the vehicle based on information about the vehicle, first exhaust gas measurement information, second exhaust gas measurement information, and information related to engine cleaning.

[0092] In one embodiment, the server can determine the annual greenhouse gas reduction amount for a vehicle using a reduction calculation model using a neural network.

[0093] For example, the server can generate a vehicle vector that includes values ​​for the type and weight of the vehicle, values ​​for the type of fuel, values ​​for the distance traveled by the vehicle, and values ​​for the model year of the vehicle through data preprocessing on information about the vehicle. For example, if the vehicle is a 1-ton diesel truck manufactured in 2021 and has traveled 100,000 km, the values ​​for the type and weight of the vehicle in the vehicle vector may be set to values ​​indicating the truck and 1 ton, the value for the type of fuel may be set to values ​​indicating diesel, the value for the distance traveled by the vehicle may be set to values ​​indicating 100,000 km, and the value for the model year of the vehicle may be set to values ​​indicating 2021.

[0094] For example, the server can generate a measurement vector including a value for a change in the concentration of nitrogen oxides and a value for a change in the concentration of oxygen through data preprocessing of the first exhaust gas measurement information and the second exhaust gas measurement information. For example, in the first exhaust gas measurement information, the concentration of nitrogen oxides may be 400 ppm and the concentration of oxygen may be 1%, and in the second exhaust gas measurement information, the concentration of nitrogen oxides may be 0 ppm and the concentration of oxygen may be 2%. At this time, the value for the change in the concentration of nitrogen oxides in the measurement vector may be set to a value representing 400 ppm, and the value for the change in the concentration of oxygen may be set to a value representing 1%. For example, it may be determined that engine cleaning has been performed normally based on a decrease in the concentration of nitrogen oxides and a slight increase in the concentration of oxygen.

[0095] For example, the server can generate a cleaning vector including a value for the amount of HHO gas generated per minute, a value for the time that the HHO gas was injected into the engine, a value indicating the type and product name of a lubricant additive, and a value for the amount of the lubricant additive injected into the engine through data preprocessing of information related to engine cleaning. For example, the value for the amount of HHO gas generated per minute can be expressed in units of cc of HHO gas generated per minute. For example, the value for the time that the HHO gas was injected into the engine can be expressed in units of minutes. For example, the value for the type and product name of the lubricant additive can include a value indicating at least one type among a plurality of types of lubricant additives and a value indicating any one product name among a plurality of product names for at least one type. For example, when extreme pressure additive A is used as the lubricant additive, the value for the type and product name of the lubricant additive can include a value indicating the extreme pressure additive and a value indicating A. The value for the amount of the lubricant additive injected into the engine can be expressed for each type of at least one lubricant additive injected into the engine. At this time, the value for the amount of lubricant additive injected into the engine can be expressed in units of cc.

[0096] For example, the annual greenhouse gas reduction for a vehicle can be determined based on the vehicle vector, measurement vector, and cleaning vector input into the reduction calculation model. For example, the annual greenhouse gas reduction for a vehicle can be expressed in tons. For example, the target of the annual greenhouse gas reduction could be carbon dioxide. That is, the server can estimate the annual carbon dioxide reduction through the reduction calculation model based on information about the vehicle, changes in the concentrations of nitrogen oxides and oxygen, and information related to engine cleaning.

[0097] For example, a reduction amount estimation model can be trained based on multiple transportation vectors, multiple measurement vectors, multiple cleaning vectors, multiple reference vectors, and multiple correct greenhouse gas reduction amounts.

[0098] In step S460, the server can determine an expected revenue from greenhouse gas emission credits for the vehicle based on the annual greenhouse gas reduction amount for the vehicle.

[0099] In one embodiment, the server can determine the expected revenue from greenhouse gas emission credits for transportation using a revenue prediction model utilizing a long short-term memory network (LSTM) model. Recurrent neural networks (RNNs) are typically capable of effectively modeling time-series information because hidden layer values ​​for previously stored inputs are considered in the output for the next input. However, because RNNs rely on past observations, they can suffer from problems such as vanishing gradients or exploding gradients. LSTMs address this issue. By replacing nodes within LSTMs with memory cells, they can accumulate information or delete portions of past information, thereby complementing the aforementioned RNN issues.

[0100] For example, a server may receive transaction information for a preset period from an exchange server. For example, the transaction information for the preset period may include the transaction price for greenhouse gas emission rights, the ask price for greenhouse gas emission rights, the bid price for greenhouse gas emission rights, the range of ask prices, the range of bid prices, the executed transaction volume for each range, the order volume for each range, and the canceled transaction volume for each range. The transaction price is the price at which a transaction for greenhouse gas emission rights is concluded. The ask price is the price set by the seller to sell greenhouse gas emission rights. The bid price is the price set by the buyer to purchase greenhouse gas emission rights. The ask price range is the interval between different ask prices. The bid price range is the interval between different bid prices. The executed transaction volume for each range is the transaction volume for greenhouse gas emission rights concluded within the ask price range and the bid price range. The order volume by segment represents the number of orders submitted to the exchange for greenhouse gas emission rights in the sell and buy price segments. The canceled volume by segment represents the number of orders submitted to the exchange for greenhouse gas emission rights in the sell and buy price segments and then canceled.

[0101] For example, the preset period may be a period of time prior to the time the server receives a measurement completion message from the measuring device. For example, the preset period may be one year prior to the time the server receives exhaust gas measurement information for a vehicle associated with the user terminal.

[0102] For example, the server can generate a transaction vector containing values ​​for the daily fair price of greenhouse gas emission rights and the daily exchange rate for the preset period based on transaction information and exchange rate information. For example, the daily fair price of greenhouse gas emission rights can be determined based on the average of the opening and closing prices of greenhouse gas emission rights on a given date within the preset period. The exchange rate value can be a value for the exchange rate between two preset countries. For example, it can be a value for the exchange rate between the South Korean won and the European euro. For example, the exchange rate information can include values ​​for the daily exchange rate for the preset period. For example, the exchange rate information can be pre-stored on the server.

[0103] Additionally, for example, the appropriate price can be determined by the following mathematical formula 1.

[0104] [Mathematical Formula 1]

[0105]

[0106] In the above mathematical expression 1, the P fair is the above fair price, N is the transaction volume of greenhouse gas emissions generated during the day, and Pa is i is the sum of the price fluctuations including the amount of transactions canceled after the transaction was ordered on the exchange for each of the above N transactions, and the above Pb i is the sum of the actual price fluctuation values ​​for each of the above N transactions, and the above Pv i is the sum of the valid bids for each of the above N transactions, and the Pr i is the sum of the closing prices for each of the above N transactions, and P d is the default value for each of the above N transactions, and P open is the market price on that date, and the above P close may be the closing price on that date.

[0107] Here, the valid bid price may be the expected price when a transaction is concluded at the market price equivalent to the average trading volume. Here, the market price is the price at which immediate trading is possible at the current point in time. For example, the valid bid price may be a price determined based on the trading volume and average trading volume for each section at the time of the transaction. For example, P d can be preset on the server.

[0108] For example, the greater the sum of the price fluctuations reflecting the volume of cancelled transactions after a transaction was ordered and the actual price fluctuations, the lower the reliability of the greenhouse gas emission trading market is determined. In addition, the greater the value of the effective bid price minus the executed price, the lower the reliability of the greenhouse gas emission trading market is determined. Therefore, the server can determine the reliability of the trading market by reflecting the impact of the greenhouse gas emission trading market on the range of price fluctuations.

[0109] For example, the server can periodically obtain transaction information on greenhouse gas emission rights from the exchange server. For example, the server can obtain transaction information on greenhouse gas emission rights from the exchange server daily.

[0110] Through this, the server can determine a reliable expected revenue for greenhouse gas emission rights by determining an appropriate price for greenhouse gas emission rights by considering the reliability of the trading market for greenhouse gas emission rights on that date.

[0111] For example, the server may generate a configuration vector that includes values ​​for annual greenhouse gas reductions for transportation vehicles and values ​​for the expected time required to acquire greenhouse gas emission credits (hereinafter, "expected time required"). For example, the expected time required may be determined as the average time required for an emission group that includes a transportation vehicle vector, a measurement vector, and a cleaning vector among multiple emission groups.

[0112] Additionally, for example, the server can create multiple emission groups through clustering based on multiple sample vectors. The sample vectors may be vectors composed of a vehicle vector, a measurement vector, and a cleaning vector for a vehicle that has actually acquired greenhouse gas emission credits. Each sample vector may be matched with a value for an actual required period. The actual required period may be the period actually taken to acquire greenhouse gas emission credits for the corresponding vehicle. Here, clustering may refer to unsupervised learning that groups data with similar attributes into a certain number of clusters. Specifically, the multiple sample vectors may be reduced to vectors of three dimensions or less using various dimensionality reduction techniques. For example, the server may reduce the dimensionality of the multiple sample vectors to three dimensions or less using various dimensionality reduction techniques. For example, the server may reduce the dimensionality of the sample vectors to three dimensions or less using principal component analysis (PCA). For example, the server can determine the axis of data with the highest variance when projecting multiple sample vectors onto the principal component axes, and reduce the dimensionality along the determined axis. For example, the server can create the first axis based on the highest variance among the multiple sample vectors, and the second axis can be created as a vector that is orthogonal to the first vector axis. Afterwards, the server can create the third axis as a vector that is orthogonal to the second axis. When the server projects the original data onto the three generated vector axes, the server can reduce the dimensionality of the original data to the number of dimensions of the vector axes. Hereinafter, a vector that is obtained by reducing the dimensionality of multiple sample vectors through various dimensionality reduction techniques may be referred to as a dimensionality reduction vector.For example, multiple emission groups can be determined using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) technique based on multiple dimensionality reduction vectors. For example, DBSCAN assumes that if a specific point belongs to a cluster, it must be located close to many other elements within that cluster. For this calculation, a radius and minimum points can be used. For example, the diameter can be a radius around a specific data point, which can be referred to as a dense area. For example, the minimum point can indicate the number of elements required around a core point to designate a core point. Furthermore, each element in the data set can be classified as a core, a border, or an outlier point. For example, the server can check the size of the diameter for each element and explore the number of elements surrounding it. Then, if there are k or more elements within the diameter range, the server can determine that element as a core element. Furthermore, the server can determine elements within the diameter range from the core element as boundary elements. Furthermore, the server can determine elements outside the diameter range from the core element as outlier elements, and these outlier elements can be excluded from the cluster. Furthermore, if the distance between core elements is less than the diameter, the server can classify the elements into the same cluster.

[0113] For example, the server may average the actual duration values ​​for each sample vector within each emission group for each emission group. The server may then determine the average of the actual duration values ​​as the average duration for that emission group.

[0114] For example, the server may determine an emission group that includes sample vectors composed of a transportation vector, an emission vector, and a cleaning vector for the user terminal from among a plurality of emission groups. At this time, the server may determine a dimensionality reduction vector for the sample vectors composed of the transportation vector, the emission vector, and the cleaning vector for the user terminal, and may determine an emission group that is closest to the dimensionality reduction vector from among the plurality of emission groups as the emission group for the user terminal. The server may determine the expected period required for the user terminal to acquire greenhouse gas emission rights as the average period matched to the emission group for the user terminal.

[0115] For example, the server can determine the expected revenue from greenhouse gas emission credits for a vehicle by inputting transaction vectors and configuration vectors into a revenue prediction model.

[0116] For example, a revenue prediction model can be trained based on multiple transaction vectors, multiple setup vectors, and multiple correct predicted revenues.

[0117] In step S470, the server may transmit a revenue guidance message including the expected revenue of greenhouse gas emission rights for the means of transportation to the user terminal.

[0118] The revenue guidance message may be a message from the server informing the user terminal of the expected revenue from greenhouse gas emission rights for the means of transportation associated with the user terminal.

[0119] For example, a server can acquire greenhouse gas emission credits for a vehicle based on the vehicle's annual greenhouse gas reductions. For example, a document form for applying for greenhouse gas emission credits can be preset on the server. For example, the server can generate a document for applying for greenhouse gas emission credits by combining the preset document form with information about the vehicle and exhaust gas measurement information proving the vehicle's annual greenhouse gas reductions. For example, the server can transmit an approval request message containing information about the document for applying for greenhouse gas emission credits to an external server that manages greenhouse gas emission credits. For example, the server can receive information related to the acquisition of greenhouse gas emission credits from the external server. In this case, if information related to the acquisition of greenhouse gas emission credits is received, the server can determine that the vehicle has acquired greenhouse gas emission credits. For example, the information related to the acquisition of greenhouse gas emission credits can include information certifying the greenhouse gas emission credits.

[0120] For example, a server can generate revenue from greenhouse gas emission credits for a vehicle by selling the emission credits for the vehicle through an exchange server.

[0121] For example, a server may share the revenue from greenhouse gas emission credits for transportation with user terminals.

[0122] For example, when revenue from greenhouse gas emission rights for a means of transportation is obtained, a portion of the revenue from greenhouse gas emission rights for the means of transportation corresponding to a preset ratio may be shared with the user terminal.

[0123] FIG. 5 is a signal exchange diagram illustrating a method in which a server shares greenhouse gas emission rights revenue with a user terminal based on exhaust gas measurement information for a vehicle according to one embodiment. The embodiment of FIG. 5 may be combined with various embodiments of the present disclosure.

[0124] Referring to FIG. 5, in step S501, the server can transmit a sharing request message to the user terminal.

[0125] For example, a share request message may include a percentage for sharing the revenue from greenhouse gas emissions.

[0126] In step S502, the server can receive a sharing acceptance message from the user terminal.

[0127] For example, a sharing acceptance message may include information about the vehicle associated with the user terminal. For example, information about the vehicle associated with the user terminal may include information about the type and size of the vehicle, information about the type of fuel it uses, information about the vehicle's travel time, and information about the vehicle's age.

[0128] In step S503, the server may transmit a measurement initiation message to the measurement device.

[0129] For example, the server may transmit a measurement initiation message to a measurement device pre-connected with the user terminal. For example, the server may identify the measurement device based on identification information about the measurement device pre-connected with the user terminal and transmit the measurement initiation message to the measurement device. For example, the sharing acceptance message may further include identification information about the measurement device pre-connected with the user terminal.

[0130] In step S504, the server can receive a measurement completion message from the measurement device.

[0131] For example, the measurement completion message may include exhaust gas measurement information for the vehicle associated with the user terminal. For example, the exhaust gas measurement information for the vehicle associated with the user terminal may include first exhaust gas measurement information, second exhaust gas measurement information, and information related to engine cleaning.

[0132] For example, the server can determine the annual greenhouse gas reduction amount for the vehicle through a reduction amount calculation model using a neural network based on information about the vehicle associated with the user terminal, first exhaust gas measurement information, second exhaust gas measurement information, and information related to engine cleaning.

[0133] In step S505, the server may request first transaction information from the exchange server.

[0134] For example, a server may request first transaction information from an exchange server based on the receipt of a measurement completion message. The first transaction information may be transaction information for a first preset period. The first preset period may be one year prior to the receipt of the measurement completion message.

[0135] In step S506, the server can receive first transaction information from the exchange server.

[0136] In step S507, the server can determine the expected revenue of greenhouse gas emission credits for the vehicle based on the annual greenhouse gas reduction amount for the vehicle.

[0137] For example, the server can determine the expected revenue of greenhouse gas emission rights for a transportation means through a revenue prediction model using an LSTM model based on the annual greenhouse gas reduction amount for the transportation means, the first input vector for the user terminal, the first transaction information, and the first exchange rate information. Here, the first input vector for the user terminal may include the transportation means vector, the measurement vector, and the cleaning vector input to the reduction amount calculation model. That is, the transportation means vector, the measurement vector, and the cleaning vector input to the reduction amount calculation model may be reused in the revenue prediction model. For example, the server can determine an emission group for the user terminal based on the first input vector among multiple emission groups, thereby determining the average required period matched to the emission group as the expected period required for the user terminal to acquire greenhouse gas emission rights. The first exchange rate information may be exchange rate information for a preset first period. In this case, the first exchange rate information may be preset in the server.

[0138] In step S508, the server can transmit a profit guidance message to the user terminal.

[0139] For example, a revenue guidance message may include the annual greenhouse gas reduction for the vehicle, the expected revenue from the greenhouse gas credits for the vehicle, and the expected time it will take to acquire the greenhouse gas credits for the vehicle.

[0140] In step S509, the server may request second transaction information from the exchange server.

[0141] For example, based on the completion of approval for a greenhouse gas emission permit, the server may request second transaction information from the exchange server. The second transaction information may be transaction information for a preset second period. The preset second period may be a period of one year prior to the completion of approval for the greenhouse gas emission permit. For example, the completion of approval for the greenhouse gas emission permit may mean that the server has determined that the greenhouse gas emission permit has been acquired. For example, the server may transmit an approval request message containing information about the documents required to apply for the greenhouse gas emission permit to an external server that manages the greenhouse gas emission permit. Thereafter, the server may receive information related to the acquisition of the greenhouse gas emission permit from the external server. If information related to the acquisition of the greenhouse gas emission permit is received, the server may determine that the approval for the greenhouse gas emission permit has been completed.

[0142] In step S510, the server can receive second transaction information from the exchange server.

[0143] In step S511, the server can determine the trading time of greenhouse gas emission rights for the means of transportation based on the second transaction information and the second exchange rate information.

[0144] Additionally, for example, the server can determine the timing of greenhouse gas emission rights transactions for transportation vehicles through a transaction prediction model utilizing an autoencoder model based on secondary transaction information, secondary exchange rate information, and information on greenhouse gas emissions rights. The autoencoder model can be an unsupervised learning algorithm that can express linear and nonlinear relationships between data, and is strong in removing noise and inferring patterns in data. The information on greenhouse gas emissions rights can be information on greenhouse gas emission rights acquired for transportation vehicles. For example, the information on greenhouse gas emissions rights can include a value for the greenhouse gas emissions rights. The value for the greenhouse gas emissions rights can be expressed in units of 1 tonne of carbon dioxide equivalent (tCO2-eq). The information on greenhouse gas emissions rights can be set on the server as the greenhouse gas emissions rights are acquired.

[0145] In step S512, the server may request the exchange server to sell greenhouse gas emission rights based on the transaction time being reached.

[0146] For example, a server may request the exchange server to sell greenhouse gas emission rights based on the transaction time, along with a bid price. The bid price may be the price with the highest order volume at the time of the transaction.

[0147] In step S513, the server may receive a sale completion message from the exchange server notifying that the sale of greenhouse gas emission rights has been completed.

[0148] In step S514, the server may transmit a revenue sharing message to the user terminal.

[0149] For example, a server may send a revenue sharing message to a user terminal based on a sale completion message received from the exchange server. The revenue sharing message may be a message informing the user terminal of the actual revenue from the greenhouse gas emission rights to be shared. For example, the revenue sharing message may include the revenue from the sale of the greenhouse gas emission rights and the revenue to be provided to the user terminal. For example, the revenue to be provided to the user terminal may be a pre-set percentage of the revenue from the sale of the greenhouse gas emission rights.

[0150] FIG. 6 illustrates an example of a reduction calculation model and a revenue prediction model according to one embodiment. The embodiment of FIG. 6 can be combined with various embodiments of the present disclosure.

[0151] Referring to FIG. 6, the reduction amount estimation model may use a CNN-based neural network (610). For example, the CNN-based neural network (610) may include a first input layer (611), one or more first hidden layers (612), and a first output layer (613).

[0152] For example, learning data consisting of a plurality of transportation vectors, a plurality of measurement vectors, a plurality of cleaning vectors, a plurality of reference vectors, and a plurality of correct greenhouse gas reduction amounts is input to a first input layer (611), passes through one or more first hidden layers (612) and a first output layer (613), and is output as a first output vector. The first output vector is input to a first loss function layer connected to the first output layer (613), and the first loss function layer outputs a first loss value using a first loss function that compares the first output vector with the first correct answer vector for each learning data, and the parameters of the CNN-based neural network (610) can be learned in a direction in which the first loss value decreases.

[0153] One or more first hidden layers (612) may include one or more convolutional layers and one or more pooling layers. For example, a plurality of movement vectors, a plurality of measurement vectors, and a plurality of cleaning vectors may be filtered in the convolutional layers, and a feature map may be formed through the convolutional layers.

[0154] For example, by selecting fixed vectors related to features for dimensionality reduction based on the feature map formed in the pooling layer and performing sub-sampling on the formed feature map, a plurality of reference vectors and a plurality of features related to correct greenhouse gas reduction amounts can be extracted from the vectorized data. For example, the pooling layer may be a max pooling layer that extracts the largest value. For example, the pooling layer may be an average pooling layer that extracts an average value. For example, at this time, the parameters of the CNN-based neural network (610) may include parameters (size of the feature map, size of the filter, depth, stride, zero padding) related to the convolutional layer and the pooling layer.

[0155] For example, one vehicle vector, one measurement vector, and one cleaning vector can constitute one reference vector, one correct greenhouse gas reduction amount, and one training data set. Multiple training data sets can be pre-saved.

[0156] The multiple reference vectors may be the vehicle vector, measurement vector, and cleaning vector for multiple vehicles for which actual greenhouse gas emission credits have been acquired. The multiple correct greenhouse gas reduction amounts may be the annual greenhouse gas reduction amounts for multiple vehicles for which actual greenhouse gas emission credits have been acquired.

[0157] Therefore, the reduction estimation model can determine the reference vector with the highest similarity to the input transportation vector, measurement vector, and cleaning vector, and output the annual greenhouse gas reduction for the transportation means by applying the similarity to the correct greenhouse gas reduction amount consisting of a set with the reference vector. In other words, the reduction estimation model can be trained to determine the transportation means with the highest similarity to the input transportation means vector, measurement vector, and cleaning vector among multiple transportation means that have actually acquired greenhouse gas emission rights, and determine the annual greenhouse gas reduction amount for the transportation means based on the similarity based on the annual greenhouse gas reduction amount of the transportation means with the highest similarity. For example, the similarity can be a value greater than 0 and less than 1. For example, the annual greenhouse gas reduction amount for the transportation means can be determined as a value obtained by multiplying the correct greenhouse gas reduction amount by the similarity.

[0158] This allows the server to more accurately calculate annual greenhouse gas reductions by taking into account not only the characteristics of the vehicle and the amount of exhaust gas it produces, but also engine-related cleaning information.

[0159] For example, a revenue prediction model can use an LSTM model (620).

[0160] For example, an LSTM model (620) may include a second input layer (621), one or more second hidden layers (622), and a second output layer (623).

[0161] For example, training data composed based on multiple transaction vectors, multiple setting vectors, and multiple correct expected profits is input to a second input layer (621), passes through one or more second hidden layers (622) and a second output layer (623), and is output as a second output vector. The second output vector is input to a second loss function layer connected to the second output layer (623). The second loss function layer outputs a second loss value using a second loss function that compares the second output vector with the second correct answer vector for each training data. The parameters of the LSTM model (620) can be trained in a direction in which the second loss value decreases.

[0162] For example, one transaction vector and one configuration vector can constitute one correct expected return and one training data set. Multiple training data sets can be pre-saved. The multiple correct expected returns can represent actual greenhouse gas emission credits for multiple transportation modes.

[0163] For example, one or more of the second hidden layers (622) may include one or more LSTM blocks, and one LSTM block may include a memory cell, an input gate, a forget gate, and an output gate.

[0164] For example, a memory cell is a node that outputs a result through an activation function, and a memory cell can perform a recursive operation that uses the value output from the memory cell at the immediately previous time point as its input at the current time point. For example, if the current time point is t, the value output by the memory cell at the current time point t can be influenced by the values ​​of the past memory cells. A memory cell has a cell state (C t ) value and hidden state (h t) can output the value. That is, the memory cell can output the cell state value (C) transmitted by the memory cell at time t-1. t-1 ) and hidden state value (h t-1 ) can be used as input values ​​to calculate the cell state value and hidden state value at time t.

[0165] For example, the input gate, delete gate, and output gate all contain sigmoid layers, which can indicate how much of the input information is passed through the sigmoid layer. For example, the sigmoid layer uses the sigmoid function ( ) can be a layer with an activation function. In addition, for example, the cell state is controlled through input gates, delete gates, and output gates, and there can be weights according to each gate and input.

[0166] For example, the server may generate a transaction vector containing values ​​for the daily fair price of greenhouse gas emissions credits and the daily exchange rate for the first preset period based on the first transaction information and the first exchange rate information. The server may also generate a configuration vector containing values ​​for the annual greenhouse gas reduction amount for a transportation vehicle and values ​​for the expected required period.

[0167] For example, multiple transaction vectors and multiple setting vectors are input to the second input layer (621), and the deletion gate is generated based on the multiple transaction vectors and the multiple setting vectors. t-1 (hidden state at time t-1) and x t C is a value between 0 and 1 based on the input value at time t. t-1 can be transmitted. Here, the value between 0 and 1 is the amount of information that has gone through the deletion process. The closer it is to 0, the more information is deleted, and the closer it is to 1, the more complete information can be transmitted.

[0168] For example, the input gate determines the values ​​to update through a sigmoid layer, and a tanh layer determines the new candidate values ​​C. t You can create a vector and store it in the cell state. tanh stands for nonlinear activation function (hyperbolic tangent function).

[0169] For example, the past cell state C t-1 With the update, a new cell state, C t can be generated. That is, for the cell state, the above f t Information can be deleted by multiplying the cell state, and a scaled value of the update value can be added.

[0170] For example, the output gate can determine a portion of the cell state to be output based on multiple transaction vectors and multiple setting vectors in the sigmoid layer, and the output gate can multiply the cell state determined in the sigmoid layer by a value output as a value between -1 and 1 through the tanh layer.

[0171] For example, an LSTM model could be a combination of a delete gate and an input gate. In this case, only previous information is deleted as new information is added, allowing for faster computation.

[0172] Therefore, the server can use the parameters of the neural network learned through the revenue prediction model, and through this, the server can determine the expected revenue of greenhouse gas emission rights through the revenue prediction model using the appropriate price and exchange rate of greenhouse gas emission rights and the expected period required to acquire the greenhouse gas emission rights.

[0173] Additionally, according to one embodiment, the transaction prediction model may use an autoencoder model.

[0174] For example, a server can determine the transaction timing for greenhouse gas emission rights for a transportation vehicle using a transaction prediction model utilizing an autoencoder model based on secondary transaction information, secondary exchange rate information, and information about greenhouse gas emission rights. Here, the transaction timing for greenhouse gas emission rights for a transportation vehicle may include the transaction date.

[0175] For example, the autoencoder model is a structure that combines two neural network layers in a symmetrical manner, and determines feature values ​​for data input through the third input layer through the encoding layer, and restores the feature values ​​in the decoding layer, thereby enabling the third output layer to learn to be as similar as possible to the correct data.

[0176] For example, the server may generate a target vector including a daily fair price of greenhouse gas emissions for a second period included in a preset sales period, a value for the daily exchange rate for the preset second period, and a value for greenhouse gas emissions based on second transaction information, second exchange rate information, and information about greenhouse gas emissions.

[0177] For example, the server may generate an evaluation vector containing daily evaluation scores for a preset second period based on secondary transaction information, secondary exchange rate information, and information about greenhouse gas emission rights. For example, the evaluation score may be determined based on the change in the price of greenhouse gas emission rights over the preset second period.

[0178] Additionally, for example, the evaluation score can be determined by the following mathematical expression 2.

[0179] [Equation 2]

[0180]

[0181] In the above mathematical expression 2, the S value is the above evaluation score, and the above P totalis the total return for the preset second period, TDay is the period up to that date, and P sd(+) is the standard deviation of the rate of return for transactions that generated profits during the period from the second preset period to the corresponding date, and P sd(-) may be the standard deviation of the loss rate for transactions that incurred losses during the period from the above-mentioned second preset period to the corresponding date.

[0182] For example, the total yield may be the current value of the greenhouse gas emission rights (i.e., the transaction price minus the initial transaction price of the greenhouse gas emission rights) multiplied by the value of the greenhouse gas emission rights. In this case, the transaction price may be the fair value determined by the mathematical equation 1 described above.

[0183] Through this, the server can use the data that determines the evaluation score for the preset second period as initial data for predicting the timing of trading greenhouse gas emission rights.

[0184] For example, it can be configured based on an autoencoder model and include a third input layer, two encoding layers, two decoding layers, and a third output layer. The ReLU (Rectified Linear Unit) function can be used as the activation function of the encoding layer and the decoding layer, and the activation function of the third output layer can be a linear function. Here, the ReLU function is a function that returns the value 0 for negative values ​​and the corresponding value for positive values. At this time, the autoencoder model can determine the weights of the encoder layer and the decoder layer symmetrically. This can improve the learning speed and reduce the risk of overfitting by reducing the learning amount for the autoencoder neural network by half.

[0185] For example, the server can input the target vector and evaluation vector into the third input layer, pass them through the encoding layer to determine feature values, and restore the feature values ​​in the decoding layer to determine an evaluation score that is as similar as possible to the correct evaluation score in the output layer. In other words, the server can determine the evaluation score for each day within a preset trading period based on past evaluation scores and multiple target vectors that determined past evaluation scores. For example, the server can determine the date with the highest evaluation score among the daily evaluation scores within the preset trading period as the trading date for greenhouse gas emission rights for transportation. For example, the preset trading period may be a period of one year after the time the server generated the target vector.

[0186] Through this, the server can determine the most profitable trading date using a transaction prediction model that utilizes an autoencoder model and conduct transactions for greenhouse gas emission rights on that date.

[0187] FIG. 7 is a block diagram illustrating a configuration of a server according to one embodiment. The embodiment of FIG. 7 can be combined with various embodiments of the present disclosure.

[0188] As illustrated in FIG. 7, the server (700) may include a processor (710), a communication unit (720), and a memory (730). However, not all of the components illustrated in FIG. 7 are essential components of the server (700). The server (700) may be implemented with more components than the components illustrated in FIG. 7, or may be implemented with fewer components than the components illustrated in FIG. 7. For example, the server (700) according to some embodiments may further include a user input interface (not illustrated), an output unit (not illustrated), and the like, in addition to the processor (710), the communication unit (720), and the memory (730).

[0189] The processor (710) typically controls the overall operation of the server (700). The processor (710) may include one or more processors and control other components included in the server (700). For example, the processor (710) may control the communication unit (720) and the memory (730) in general by executing programs stored in the memory (730). In addition, the processor (710) may perform the functions of the server (700) described in FIGS. 3 to 6 by executing programs stored in the memory (730).

[0190] The communication unit (720) may include one or more components that enable the server (700) to communicate with other devices (not shown) and the server (not shown). The other devices (not shown) may be computing devices such as the server (700) or sensing devices, but are not limited thereto. The communication unit (720) may receive user input from other electronic devices or receive data stored in an external device from an external device via a network.

[0191] For example, the communication unit (720) can transmit and receive messages for establishing a connection with at least one device. The communication unit (720) can transmit information generated by the processor (710) to at least one device connected to the server. The communication unit (720) can receive information from at least one device connected to the server. The communication unit (720) can transmit information related to the received information in response to the information received from at least one device.

[0192] The memory (730) can store programs for processing and controlling the processor (710). For example, the memory (730) can store information input to the server or information received from another device via a network. In addition, the memory (730) can store data generated by the processor (710). The memory (730) can also store information input to the server (700) or output from the server (700).

[0193] The memory (730) may include at least one type of storage medium among a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (e.g., SD or XD memory, etc.), a RAM (Random Access Memory), a SRAM (Static Random Access Memory), a ROM (Read-Only Memory), an EEPROM (Electrically Erasable Programmable Read-Only Memory), a PROM (Programmable Read-Only Memory), a magnetic memory, a magnetic disk, and an optical disk.

[0194] The embodiments described above may be implemented using hardware components, software components, and / or a combination of hardware components and software components. For example, the devices, methods, and components described in the embodiments may be implemented using one or more general-purpose computers or special-purpose computers, such as, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a programmable logic unit (PLU), a microprocessor, or any other device capable of executing instructions and responding to them. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to the execution of the software. For ease of understanding, the processing device is sometimes described as being used alone; however, one of ordinary skill in the art will recognize that the processing device may include multiple processing elements and / or multiple types of processing elements. For example, a processing unit may include multiple processors, or a processor and a controller. Other processing configurations, such as parallel processors, are also possible.

[0195] Software may include a computer program, code, instructions, or a combination of one or more of these, and may configure a processing device to perform a desired operation or, independently or collectively, command the processing device. The software and / or data may be permanently or temporarily embodied in any type of machine, component, physical device, virtual equipment, computer storage medium or device, or transmitted signal wave, for interpretation by the processing device or for providing instructions or data to the processing device. The software may also be distributed over networked computer systems and stored or executed in a distributed manner. The software and data may be stored on one or more computer-readable recording media.

[0196] The method according to the embodiment may be implemented in the form of program commands that can be executed through various computer means and recorded on a computer-readable medium. The computer-readable medium may include program commands, data files, data structures, etc., alone or in combination. The program commands recorded on the medium may be those specially designed and configured for the embodiment or may be those known and available to those skilled in the art of computer software. Examples of the computer-readable recording medium include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and hardware devices specially configured to store and execute program commands, such as ROMs, RAMs, and flash memories. Examples of the program commands include not only machine language codes generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter, etc. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiment, and vice versa.

[0197] Although the embodiments described above have been described with limited drawings, those skilled in the art will appreciate that various technical modifications and variations can be applied based on the above. For example, appropriate results can still be achieved even if the described techniques are performed in a different order than described, and / or components of the described systems, structures, devices, circuits, etc. are combined or combined in a different manner than described, or are replaced or substituted with other components or equivalents.

[0198] Therefore, other implementations, other embodiments, and equivalents to the claims also fall within the scope of the claims described below.

[0199]

Claims

1. A method for a server to share greenhouse gas emission rights revenue with a user terminal based on exhaust gas measurement information for a means of transportation, A step of receiving exhaust gas measurement information for a means of transportation related to the user terminal from a measuring device; A step of determining an annual greenhouse gas reduction amount for the means of transportation based on exhaust gas measurement information for the means of transportation; A step of acquiring greenhouse gas emission rights for the above transportation means based on the annual greenhouse gas reduction amount for the above transportation means; and Including a step of sharing the profits from greenhouse gas emission rights for the above means of transportation with the user terminal, A step of transmitting a sharing request message requesting sharing of the profits of the greenhouse gas emission rights to the user terminal; A step of receiving a sharing acceptance message from the user terminal; The above sharing acceptance message includes information about the means of transportation associated with the user terminal, A step of transmitting a measurement initiation message requesting the measurement device to measure exhaust gas for the moving means based on receiving the above sharing acceptance message; A measurement completion message including exhaust gas measurement information for the means of transportation is received from the measuring device, The exhaust gas measurement information for the means of transportation related to the user terminal includes first exhaust gas measurement information, second exhaust gas measurement information, and information related to engine cleaning, The annual greenhouse gas reduction amount for the above means of transportation is determined based on information about the above means of transportation, the first exhaust gas measurement information, the second exhaust gas measurement information, and information related to the engine cleaning. The expected revenue from greenhouse gas emission rights for the above means of transportation is determined based on the annual greenhouse gas reduction amount for the above means of transportation, Further comprising a step of transmitting a revenue guidance message including the expected revenue of greenhouse gas emission rights for the above means of transportation to the user terminal, When the revenue from greenhouse gas emission rights for the above means of transportation is acquired, a preset ratio of the revenue from greenhouse gas emission rights for the above means of transportation is shared with the user terminal. method.

2. In paragraph 1, The annual greenhouse gas reduction amount for the above means of transportation is determined through a reduction calculation model using a neural network, Through data preprocessing for the information on the above means of transportation, a transportation vector is created that includes values ​​for the type and weight of the means of transportation, values ​​for the type of fuel, values ​​for the distance traveled by the means of transportation, and values ​​for the year of the means of transportation. Through data preprocessing of the first exhaust gas measurement information and the second exhaust gas measurement information, a measurement vector including a value for a change in the concentration of nitrogen oxide and a value for a change in the concentration of oxygen is generated. Through data preprocessing for the information related to the above engine cleaning, a cleaning vector is generated that includes values ​​for the amount of HHO gas generated per minute, values ​​for the time at which HHO gas was injected into the engine, values ​​indicating the type and product name of the lubricant additive, and values ​​for the amount of lubricant additive injected into the engine. The annual greenhouse gas reduction amount for the transportation means is determined based on the above transportation means vector, the measurement vector, and the cleaning vector being input into the reduction amount calculation model, The above reduction amount calculation model is learned based on multiple transportation vectors, multiple measurement vectors, multiple cleaning vectors, multiple reference vectors, and multiple correct greenhouse gas reduction amounts. method.

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