Carbon regulation response data automatic linkage system and method

Abstract

The present invention relates to an automatic carbon regulation response data linkage system and method that receives corporate activity data from an external server, normalizes and transforms the data, and automates calculation and reporting by regulation. The system includes a communication unit, a storage unit, and a processor, and the processor performs a series of processing steps through data collection, preprocessing, common standard conversion, regulation-specific mapping, report generation, and an administrator interface module. Additionally, by including an energy optimization simulation module, it can explore efficient operating conditions through facility-level digital twin modeling, predictive simulation, and multi-purpose optimization control. Through this, the present invention can enhance a company's sustainable management and global regulatory response capabilities by simultaneously realizing improved data quality, automated regulatory reporting, energy savings, and carbon reduction.

Description

Technology Field

[0001] The present invention relates to an automatic data linkage, calculation, and reporting system for responding to corporate environmental regulations. More specifically, it relates to an automatic data linkage system and method for responding to carbon regulations that collects corporate activity data from external servers, converts it into common reference information according to international standard schemas, and automatically generates calculation results and reporting documents that comply with environmental regulations by country or system. The present invention can comprehensively manage data on energy usage, carbon emissions, raw material procurement, and production activities occurring in various industrial sectors, and support efficient regulatory response and sustainable management through AI-based optimization and simulation functions. Background Technology

[0002] With the recent strengthening of Net Zero policies worldwide, companies are facing various international regulations that mandate the calculation of greenhouse gas emissions and the reporting of reduction implementation. For example, the European Union's Carbon Border Adjustment Mechanism (CBAM), the U.S. SEC Climate Disclosures, ISO 14064, and the GHG Protocol require companies to quantify and report greenhouse gas emissions at the product, process, and facility levels. However, existing systems require manual data extraction from databases such as ERP, MES, FEMS, and legacy systems dispersed across departments, and processing this data using Excel. Consequently, there is a high probability of errors in the calculation process, leading to frequent inconsistencies between reports. Furthermore, existing carbon management solutions are limited to specific regulations or process data, presenting limitations in simultaneously meeting diverse regulatory requirements or managing integrated data across different units.

[0003] Furthermore, conventional regulatory compliance systems have remained at the level of simple report automation, failing to perform sophisticated preprocessing procedures such as data quality verification, unit normalization, and time alignment, and making it difficult to flexibly update calculation logic for each regulation. In particular, inefficiency arose as items had to be manually modified despite periodic changes in emission factors and accounting standards across countries. Additionally, the lack of simulation functions for improving energy efficiency or reducing costs limited regulatory compliance to mere post-management. Therefore, companies urgently require automated data integration systems capable of responding rapidly to changing environmental regulations, as well as an integrated approach that can perform substantial operational optimization. The problem to be solved

[0004] This invention was devised to solve the aforementioned problems and aims to provide a system capable of eliminating manual-based inefficiencies and errors by automatically integrating and normalizing corporate activity data collected from multiple external servers, generating common reference information according to international standard schemas, and automatically calculating results for each regulation based on calculation items and logic. Furthermore, going beyond simple reporting, this invention includes functions capable of performing real-time energy flow and carbon emission forecasts, and deriving a balance between energy consumption, carbon emissions, and operating costs by applying multi-objective optimization algorithms. Moreover, another objective is to provide functions for the automatic generation and electronic submission of report documents to consistently process reports on various international regulations, while simultaneously enabling real-time monitoring, approval, and history management through an administrator interface. In other words, this invention aims to simultaneously improve the accuracy and efficiency of responding to carbon regulations by integrating data quality verification, automatic calculation by regulation, optimization, and reporting functions within a single platform.

[0005] The problems of the present invention are not limited to those mentioned above, and other unmentioned problems will be clearly understood by those skilled in the art from the description below. means of solving the problem

[0006] To achieve the above objective, the present invention provides a main server comprising a communication unit that receives corporate activity data from an external server, a storage unit that stores and manages the data, and a processor that performs data processing, standardization, conversion, and reporting functions in conjunction with the communication unit and the storage unit. The processor may include a data collection module that collects multiple data streams from an external server and performs integrity verification, a data preprocessing module that removes missing values ​​and outliers and standardizes units and time systems, a common reference information conversion module that generates common reference information by mapping the preprocessed data to an international standard schema, a regulation response mapping module that generates results by applying calculation items and calculation logic for each environmental regulation, a report generation module that generates report documents and electronic submission data by combining the calculation results for each regulation into a report template, and an administrator interface module that performs real-time indicator monitoring, approval, and management functions. Furthermore, the present invention may further include an energy optimization simulation module that predicts and simulates energy flow and carbon emissions based on facility, utility, and environmental data, thereby enabling the exploration of a balance point between energy efficiency, carbon emissions, and costs through a multi-purpose optimization controller.

[0007] Specific details of other embodiments are included in the detailed description and drawings. Effects of the invention

[0008] According to the present invention as described above, it has various effects as follows.

[0009] According to the present invention, the accuracy and efficiency of data integration can be maximized by automatically linking and normalizing data that existed sporadically across multiple internal systems (ERP, MES, FEMS, etc.). Furthermore, through the conversion of common reference information based on international standard schemas, a data structure capable of responding to various regulatory frameworks can be secured, and calculation results suitable for the reporting formats of each country can be automatically generated through regulation-specific mapping modules. Consequently, problems such as duplicate input, calculation errors, and format inconsistencies in the reporting process are fundamentally eliminated.

[0010] Furthermore, the energy optimization simulation module of the present invention implements facility-level energy flow as a digital twin based on actual operational data and presents optimal conditions that can reduce operating costs while decreasing carbon emissions through prediction and optimization. Accordingly, the present invention can be utilized as a core tool for energy management and ESG strategy formulation, going beyond a simple regulatory compliance system, and can dramatically improve corporate management efficiency through real-time monitoring and automatic reporting functions. In addition, since the model self-corrects through a feedback learning structure, it is capable of continuous adaptation to regulatory changes or fluctuations in environmental conditions.

[0011] The effects according to the present invention are not limited to those exemplified above, and various other effects are included in this specification. Brief explanation of the drawing

[0012] FIG. 1 is a block diagram illustrating the schematic configuration of an automatic carbon regulation response data linkage system according to one embodiment of the present invention. FIG. 2 is a diagram illustrating the main server of an automatic carbon regulation response data linkage system according to one embodiment of the present invention. FIG. 3 is a diagram illustrating the processor of the main server of a carbon regulation response data automatic linkage system according to one embodiment of the present invention. FIG. 4 is a block diagram illustrating a carbon regulation response data automatic linkage method according to an embodiment of the present invention. Specific details for implementing the invention

[0013] Various embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The advantages and features of the present invention, and the methods for achieving them, will become clear by referring to the embodiments described below in detail together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below but may be implemented in various different forms. These embodiments are provided merely to ensure that the disclosure of the present invention is complete and to fully inform those skilled in the art of the scope of the invention, and the present invention is defined only by the scope of the claims.

[0014] Shapes, sizes, ratios, angles, numbers, etc. disclosed in the drawings for explaining embodiments of the present invention are exemplary, and therefore the present invention is not limited to the depicted details. Throughout the specification, the same reference numerals refer to the same components. Furthermore, in describing the present invention, if it is determined that a detailed description of related prior art may unnecessarily obscure the essence of the present invention, such detailed description is omitted. Where terms such as "includes," "has," or "is made up" are used in this specification, other parts may be added unless "only" is used. Where a component is expressed in the singular, it includes cases where it is included in the plural unless specifically stated otherwise.

[0015] In interpreting the components, they are interpreted to include a margin of error even in the absence of a separate explicit statement.

[0016] In the case of describing a positional relationship, for example, when the positional relationship between two parts is described using expressions such as 'on,' 'upper,' 'lower,' or 'next to,' one or more other parts may be located between the two parts unless 'immediately' or 'directly' is used.

[0017] When an element or layer is referred to as "on" another element or layer, it includes cases where another layer or element is placed directly on top of or in between.

[0018] Although terms such as "first," "second," etc., are used to describe various components, these components are not limited by these terms. These terms are used merely to distinguish one component from another. Accordingly, the first component mentioned below may be the second component within the technical scope of the present invention.

[0019] Throughout the specification, the same reference numerals refer to the same components.

[0020] The size and thickness of each component shown in the drawings are illustrated for convenience of explanation, and the present invention is not necessarily limited to the size and thickness of the illustrated components.

[0021] The features of each of the various embodiments of the present invention may be combined or combined with one another, either partially or wholly, and as will be fully understood by those skilled in the art, various technical interlocking and operation are possible, and each embodiment may be implemented independently of one another or together in an interlocking relationship.

[0023] FIG. 1 is a block diagram illustrating the schematic configuration of an automatic carbon regulation response data linkage system according to an embodiment of the present invention. FIG. 2 is a diagram illustrating the main server of an automatic carbon regulation response data linkage system according to an embodiment of the present invention. FIG. 3 is a diagram illustrating the processor of the main server of an automatic carbon regulation response data linkage system according to an embodiment of the present invention. FIG. 4 is a block diagram illustrating a method for automatic carbon regulation response data linkage according to an embodiment of the present invention.

[0024] Referring to FIGS. 1 to 4, the present invention relates to an automatic carbon regulation response data linkage system (1000) that reliably collects corporate activity data generated from distributed sources, converts it into international standard-based common reference information structured according to consistent standards, and then automatically generates and provides regulation-specific calculation results, report documents, and electronic submission data based thereon. In the past, problems could exist where the consistency and traceability of calculation results were reduced due to discrepancies in units and time standards, as the required items and calculation logic of each regulation differed, resulting in the same data being repeatedly collected and processed. In contrast, the present invention can establish a reproducible data flow by having a communication unit (110) stably receive multiple data streams from an external server, a storage unit (120) accumulate and preserve processing results from the raw stage to the result stage, and a processor (130) automatically perform standardization, conversion of common reference information, regulation-specific calculation, and generation of reports using a modularized pipeline. In particular, by systematically maintaining common standard information that serves as input for all calculations, changes to the core dataset can be minimized to respond agilely to revisions in the calculation logic for each regulation or changes in the reporting scope, and the basis of the process can be monitored, approved, and managed in the administrator interface module (136).

[0025] Next, the system may include a main server (100), an external server (200) that transmits data to the main server, and a user terminal (300) that receives results from the main server. The main server is composed of a communication unit (110), a storage unit (120), and a processor (130), and the communication unit may operate as a data input / output that receives and transmits corporate activity data from the external server. The storage unit may partition and store the received corporate activity data and the processing results of each stage according to the collection time, processing time, and calculation time, thereby enabling verification, reprocessing, and reproduction in subsequent stages. The processor serves as the center of the pipeline, where a data collection module (131) collects multiple data streams flowing in from the external server and verifies their integrity, and a data preprocessing module (132) performs missing value interpolation, outlier detection, unit standardization, and time alignment to assign quality indicators. Next, the common standard information conversion module (133) maps the preprocessed data to an international standard schema to generate common standard information, and the regulation response mapping module (134) processes this according to the calculation items and calculation logic for each environmental regulation to generate and record the calculation results for each regulation. The report generation module (135) combines the calculation results for each regulation with a report template to automatically generate report documents and electronic submission data, and the administrator interface module (136) provides real-time indicator monitoring, approval, and management functions to control the overall operation. If necessary, an energy optimization simulation module (137) may be further included to derive and reflect reduction execution scenarios through a digital twin based on facility, utility, and environmental data and a multi-purpose optimization controller.

[0026] The carbon regulation response data automatic linkage system (1000) of the present invention organically links an external server (200), a main server (100), and a user terminal (300), so that the collection, preprocessing, standardization, regulation response, and report generation of corporate activity data are performed in a sequential and consistent data flow. Through this linkage structure, heterogeneous data generated from various information systems within the company can be integrated, and calculation results and report documents matching the required format for each regulation can be automatically generated.

[0027] First, the external server (200) is composed of an ERP server (210), an MES server (220), a FEMS server (230), and a Legacy server (240) as data sources. Each server transmits corporate activity data to the main server (100) in the form of multiple data streams through the communication unit (110). The ERP server (210) provides organizational and product unit data such as procurement, cost, and product structure; the MES server (220) provides process and facility-specific operating status and quality data; the FEMS server (230) provides energy and utility usage data; and the Legacy server (240) provides historical calculation results and authentication basis data. These data are received through the communication unit (110) via authentication and encryption channels and transmitted to the data collection module (131).

[0028] Next, the data collection module (131) performs integrity verification on the received multiple data streams. It checks for data tampering and omission based on file format, checksum, transmission timestamp, etc., and records only verified data in its raw state in the storage unit (120). At this time, the source identifier, reception time, and integrity verification result are stored together with each data so that it can be traced during subsequent processing.

[0029] In the next step, the data preprocessing module (132) retrieves raw data from the storage unit (120) and performs missing value interpolation, outlier removal, unit conversion, and time alignment. This process ensures reliability in subsequent steps by calculating completeness, accuracy, and consistency indices as described in claim 2 and assigning a quality grade. The preprocessed data is converted into a standardized form and transmitted to the next module in the main server, the common reference information conversion module (133).

[0030] The common standard information conversion module (133) maps data preprocessed according to international standard schemas (e.g., ISO, GHG Protocol) to generate common standard information for organizations, processes, and facilities. Product and procurement information from the ERP server (210), process and facility information from the MES server (220), and energy usage information from the FEMS server (230) are integrated at this stage. Additionally, basic indicators required for calculating emissions are calculated by applying country-specific emission factors, and the results are recorded as standardized data in the storage unit (120). The common standard information is utilized as a key input for subsequent regulation-specific calculation steps.

[0031] Subsequently, the regulation response mapping module (134) automatically maps the calculation items and calculation logic for each environmental regulation using common standard information as input. For example, since the calculation method differs depending on regulations such as CBAM, OCF, and PCF, this module calculates the emissions, energy consumption, and carbon emission coefficients for each regulation by referring to predefined rules and mapping tables. The calculated results for each regulation are recorded in the regulation data storage area of ​​the storage unit (120), and the calculation basis and application rules for each result are stored together as processing results, thereby ensuring verifiability when reporting.

[0032] In the next step, the report generation module (135) retrieves the calculation results by regulation and combines them with the report template required by each regulatory agency. Based on the calculation results, the module automatically generates tables, graphs, and explanatory text, and the report includes data sources, quality grades, and calculation basis. Additionally, it is converted into XML or JSON format for electronic submission to perform automatic signing and submission history management functions. Finally, the generated report document and electronic submission data are transmitted to a user terminal (300) or an external submission agency via the communication unit (110).

[0033] Finally, the entire data flow starts from the communication unit (110) and proceeds sequentially through the data collection module (131), data preprocessing module (132), common reference information conversion module (133), regulatory response mapping module (134), and report generation module (135). Each module is interconnected via the storage unit (120) to share input data and processing results step by step, and the user terminal (300) can monitor and approve these results in real time. Through such an organic interconnected structure, the system can automate the entire process from data collection to reporting while simultaneously ensuring consistency and traceability that meet international regulatory requirements.

[0035] Next, referring to FIG. 4, the operation process of the system is examined. First, the communication unit (110) receives corporate activity data from an external server (200) (S110). The ERP server (210), MES server (220), FEMS server (230), and Legacy server (240) transmit data in parallel according to their respective roles. The ERP server transmits procurement, cost, and product structure information; the MES server transmits process and equipment operation data; the FEMS server transmits energy usage and utility data; and the Legacy server transmits past calculation and authentication history. The communication unit (110) converts the received data stream into an internal standard format and performs sender authentication and integrity verification procedures. The verified data is then transmitted to the data collection module (131).

[0036] Next, the data collection module (131) performs integrity verification on the received multiple data streams and assigns a sender identifier, reception time, and verification status to each data and records it in the storage unit (120) (S115). During the integrity verification process, the sender's hash value and the receiver's calculated value are compared to determine whether tampering has occurred. If an error is found, the data is classified into a temporary hold area, and a retransmission request may be automatically generated. Data that passes the verification is stored in its raw form and is used as input to the data preprocessing module (132) in a subsequent step. At this time, the storage unit (120) stores the collection log, verification results, and error status together as processing results, which can be utilized for subsequent quality evaluation and audit response.

[0037] Next, the data preprocessing module (132) retrieves the stored raw data and performs missing value interpolation, outlier removal, unit standardization, and time alignment (S120). Data from ERP, MES, FEMS, and Legacy are processed in parallel by source, and a dynamic time series alignment algorithm may be applied during time alignment. During the preprocessing process, completeness indicators, accuracy indicators, and consistency indicators are calculated and a quality grade is assigned, and this quality information is recorded in the storage unit (120) in the form of metadata. The preprocessed data is transmitted as input to the common reference information conversion module (133), and at the same time, the quality grade can be reused as a reliability weight of the calculation result in the subsequent regulatory response mapping module (134).

[0038] The common reference information conversion module (133) generates common reference information by mapping preprocessed data to an international standard schema (e.g., ISO 14064, GHG Protocol) (S130). This module automatically configures a hierarchical structure by organization, process, equipment, and product unit, and links emission factors and activity data for each item. Process-specific operation data provided by the MES server (220) is converted into process activity volume items, product information from the ERP server (210) is converted into product structure items, and energy usage data from the FEMS server (230) is combined with country-specific indirect emission factors to be converted into items for Scope 2 calculation. The generated common reference information is recorded as a standard dataset in the storage unit (120) and is directly transmitted to the regulation response mapping module (134) to be used as input for regulation-specific calculations. Additionally, the organization structure and equipment metadata included in the common reference information can be automatically referenced by the report generation module (135) in conjunction with the unit notation of the report document.

[0039] The regulation response mapping module (134) receives common standard information and generates regulation-specific calculation results according to the calculation items and calculation logic for each environmental regulation (S140). This module can automatically apply activity boundaries, emission factors, and correction factors for each regulation by referring to mapping tables and calculation formulas for various regulations such as CBAM, OCF, and PCF. For example, in CBAM, input raw materials and transportation routes for each product, and in OCF, energy usage and reduction plans at the organizational level are reflected in the calculation formula. The calculated regulation-specific calculation results are stored in the storage unit (120) by regulation classification, and the applied calculation formula, emission factor, data source, and assumption conditions remain together as processing results. The regulation-specific calculation results are transmitted to the administrator interface module (136) so that real-time review and approval procedures can be performed, and the approved results are input back into the report generation module (135) and used as basic data for generating the final report document.

[0040] Subsequently, the report generation module (135) retrieves the calculation results and common standard information for each regulation recorded in the storage unit (120) and generates report documents and electronic submission data according to the reporting format for each regulation (S150). The report generation module (135) automatically arranges tables, graphs, and explanatory text based on predefined templates for each regulation, and each item specifies the data source, quality grade, and basis for calculation. The generated report documents and electronic submission data are stored along with electronic signatures and submission history, and are transmitted to a user terminal (300) or an external regulatory agency via the communication unit (110). The user terminal (300) can review and approve them and send an approval log or a modification request signal back to the administrator interface module (136) of the main server (100). The approved documents are recorded as the final version in the storage unit (120) and can be referenced again in subsequent reporting cycles.

[0041] Finally, the system manages the history of submitted report documents and electronic submission data, and performs a step (S160) of storing verification and approval status based on the submission results. In this step, the storage unit (120) accumulates and stores the submission path, approver, submission time, version information, and feedback log of the report document in the form of processing results, which can be used as supporting data for future re-reporting, revised submission, and audit response. Additionally, the administrator interface module (136) visualizes the implementation status for each regulation based on the approved report and feeds the results back to the common standard information conversion module (133) and the regulation response mapping module (134). Through this, the results of the previous reporting cycle are reused as a comparison standard with the initial boundary value of the next calculation cycle, so that the entire system operates in a circular data flow structure.

[0042] Consequently, all modules of the system exchange inputs and outputs centered around the storage unit (120), and data is circulated as follows. Raw data collected from an external server (200) passes through the communication unit (110) and the data collection module (131) to become the input of the preprocessing module (132), and the preprocessing result is transmitted to the common reference information conversion module (133). The output of the conversion module enters as the input of the regulation response mapping module (134), and the calculation result moves to the report generation module (135). The report generation result is transmitted back to the storage unit (120) and the administrator interface module (136) for approval and feedback, and the approval log and verification data are reused as the initial input of the common reference information conversion module (133) during the next cycle calculation. By circulating the inputs and outputs of each module through the storage unit in this way, the system can continuously learn and update data, and simultaneously ensure the accuracy and traceability of the report.

[0044] According to the carbon regulation response data automatic linkage system and method according to another embodiment of the present invention, the energy optimization simulation module (137) is an analysis and control expansion module optionally provided within the processor (130), and can perform digital twin-based prediction, optimization, and feedback loops by referring to corporate activity data preprocessed and input through the communication unit (110), common reference information generated by the common reference information conversion module (133), and regulation-specific calculation results of the regulation response mapping module (134). The module is subdivided into a digital twin generator, a real-time data synchronizer, a prediction simulation engine, a multi-purpose optimization controller, a feedback learning manager, an energy flow visualization and risk analyzer, and the inputs and outputs of each sub-component are recorded as processing results in the storage unit (120) to ensure reproducibility and traceability.

[0045] Next, the digital twin generator models equipment, utilities, and environmental objects within the workplace as a node-edge graph to construct state vectors such as equipment efficiency (η), heat loss coefficient (k), load, output, and power flow / heat flow. At this time, the node and edge definitions and initial parameter values ​​are derived from common standard information related to processes, equipment, and utilities and equipment metadata (rated capacity, year of installation, maintenance history, etc.) loaded in the storage unit (120), and mapping information with BIM / CAD / PLC tag maps can be accumulated together as a processing result. The generated twin is used as a common workspace for the prediction, optimization, and visualization stages to maintain data alignment between subsequent modules.

[0046] Subsequently, the real-time data synchronizer can continuously update the twin state using the preprocessing results received via the communication unit (110). Sensor-specific sampling heterogeneity is time-matched using Dynamic Time Warping (DTW), and measurement noise and intermittent missing values ​​can be estimated and corrected using a Kalman filter (including EKF). In this process, the error range between the twin variables and the actual variables is calculated as a management indicator, and the operator can adjust the correction cycle in the administrator interface module (136) to converge the state reproduction error within a set threshold (e.g., a policy value of ±1~2%). The synchronization results are version-managed by time in the twin state area of ​​the storage unit (120) and can serve as input for prediction and optimization.

[0047] Next, the prediction simulation engine can perform short-term and long-term predictions in a hybrid manner. In short-term prediction, load, power consumption, heat loss, etc., in the range of 1 hour to 24 hours can be calculated using an LSTM / GRU-based time series model from the preprocessing results and auxiliary variables such as twin status, ambient temperature, and production plan. In long-term prediction, behavior at the level of 1 day to 1 week can be calculated using an energy balance equation / finite element approximation using twin parameters. The engine can expand combinations of control parameters (e.g., chiller operating rate, air conditioning temperature, boiler pressure, etc.) in multiple dimensions through a scenario manager, and can tabulate energy usage (E), carbon emissions (CO2eq), and costs for each scenario and record them in a storage unit (120). The source and validity period of the emission factor and tariff policy referenced in the prediction stage can be left together with the processing results to limit the recalculation range when regulations change.

[0048] Next, the multi-objective optimization controller takes the prediction result table as input and the objective function

[0049] It is possible to explore the optimal operating conditions that minimize the cost J = w1Х E + w2 Х CO₂ + w₃ Х. Here, E is the total energy consumption calculated based on simulation, and is a value obtained by normalizing the electricity, gas, and steam consumption for each facility into a common unit (kWh, etc.). CO is the carbon emissions under the same scenario, calculated by applying emission factors based on fuel, electricity, and process data, and includes Scope 1 items by default. Cost is the total operating cost reflecting energy unit prices, operating efficiency, peak charges, and maintenance costs.

[0050] The weights w1, w2, and w₃ can be dynamically set in the administrator interface module (136) according to the company's policy or regulatory priorities, and the three items become comparable through a unit normalization process. Optimization search is performed using genetic algorithms (GA), particle swarm optimization (PSO), or Bayesian optimization techniques, and facility safety ranges, quality conditions, and production constraints are applied as hard constraints. The algorithm generates a Pareto Frontier to visualize the balance relationship between energy, carbon, and cost, and the calculated candidate solution is recorded in the storage unit (120) and promoted to an execution plan upon operator approval.

[0051] Next, the feedback learning manager can continuously correct the model by calculating the error between the results measured after actual execution and the prediction and optimization outputs, namely the difference in energy consumption (E_before - E_after), the difference in carbon emissions (CO2_before - CO₂_after), and the difference in operating costs (Cost_before - Cost_after). The weights of the time series model are updated in real time through online learning, and the efficiency and loss parameters of the physics-based model can be corrected through an iterative estimate-validation loop. In addition, by applying feature alignment and adversarial learning techniques to reduce the deviation between the actual data distribution and the simulation data distribution, high generalization performance can be maintained even in various facility environments. These feedback results are accumulated as processing logs in the storage unit (120), which can gradually improve the accuracy of the subsequent periodic prediction and optimization processes.

[0052] Afterward, the energy flow visualization and risk analyzer can integrate the outputs of the twin and optimization to represent the energy flow between processes using Sankey diagrams, and display the loss rate, overload rate, and inefficiency points for each facility using color steps (green-yellow-red). Additionally, potential risks can be identified using anomaly detection logic to alert the operator, and the difference between the current state and the optimal state KPI can be provided on a dashboard. The visualization output can be transmitted to a report generation module (135) and attached as an analysis part or appendix to a report document.

[0053] The above sub-components can be organically linked to operate as a single integrated data flow. First, in the input collection and normalization stage, a twin input set can be constructed by selecting items necessary for operational optimization from the results produced by the data preprocessing module (132), the common reference information conversion module (133), and the regulatory response mapping module (134). The input set thus constructed may include energy consumption at the facility level, carbon emissions by process, operating costs, and operating status information.

[0054] Next, in the twin modeling stage, the digital twin generator uses the aforementioned input set to form a node-edge graph between the physical facility and the virtual model, and can reflect the dynamic behavior of the system by updating the state vector of each node in real time. In the subsequent predictive simulation stage, the prediction engine calculates changes in energy consumption (E), carbon emissions (CO2), and operating costs (Cost) for short-term and long-term scenarios, thereby simulating performance under various operating conditions.

[0055] In the multi-objective optimization stage, the controller uses the prediction results as input to derive candidate optimal operating conditions that minimize the objective function J = w1Х E + w2 Х CO₂ + w₃ Х Cost, and generates a Pareto Frontier to visualize the balance relationship between energy, carbon, and cost. Subsequently, in the execution and feedback stage, the approved optimal operating conditions are reflected in actual plant operation, and actual data measured after execution is fed into the feedback learning manager to periodically correct the model parameters and simulation accuracy.

[0056] Finally, in the visualization and reporting stage, the optimization and feedback results are transmitted to the administrator interface module (136) and visualized on a real-time monitoring screen, and simultaneously linked to the report generation module (135) so that they can be automatically reflected as report documents for regulatory compliance and analysis data for internal decision-making. Through this, the entire process from input to execution, verification, and reporting is integrated into a single closed loop, allowing for continuous autonomous optimization.

[0057] In addition, to ensure alignment with the regulatory compliance framework, this module may provide, as an auxiliary output, the estimated variation in calculation results by regulation caused by the calculated optimal operating conditions. That is, E / CO before and after optimization execution. 2 / By estimating the impact of cost changes on activity and emission items in common reference data, it is possible to provide managers with a preliminary direction for expected regulation-specific calculation results when reflected in the next calculation cycle. This enables execution and reporting to be linked in a closed loop within the reporting cycle, thereby supporting the simultaneous achievement of operational optimization and regulatory compliance.

[0058] Finally, all intermediate outputs and grounds of the energy optimization simulation module (137) are version-managed as processing results in the storage unit (120), and operational control can be unified through real-time indicator monitoring, approval, and management in the administrator interface module (136). With the introduction of this module, the main server (100) can be expanded beyond a monitoring and calculation-centered system into a self-improving operational platform where prediction, optimization, execution, and learning are connected by a consistent data flow.

[0060] The carbon regulation response data automatic linkage system according to the present invention can realize integrated and autonomous data management and optimization through the interaction between the aforementioned series of steps and modules. First, the system automatically collects corporate activity data from an external server and can maintain data quality above a certain level by performing missing value correction, outlier removal, unit normalization, and time alignment through a data preprocessing module (132). This quality management system prevents the accumulation of errors caused by incomplete source data and enables the derivation of reliable results in subsequent common standard information conversion and regulation-specific calculation processes.

[0061] Second, the common standard information conversion module (133) based on international standard schemas can ensure interoperability between country-specific and regulatory reporting systems by automatically mapping various internal corporate format data into international standard structures such as ISO and GHG Protocol. This allows companies to easily generate multiple regulatory reports using the same dataset and reduce duplicate calculations or human errors. Furthermore, this structural conversion can be extended to various utilization scenarios, such as internal ESG evaluation and verification of supply chain partners, in addition to regulatory compliance.

[0062] Third, through the regulatory response mapping module (134) and the report generation module (135), the company can automatically apply calculation items and formulas for each regulation, thereby ensuring the consistency and format compliance of the report. By automating complex manual calculation and editing procedures, this significantly reduces the time required to write reports and lowers the workload of the person in charge. In addition, the report generation module includes electronic signature management and submission history tracking functions, allowing for a rapid response to requirements from external verification bodies or post-audits.

[0063] Fourth, the energy optimization simulation module (137) can construct a digital twin of the actual facility to predict changes in energy flow, carbon emissions, and operating costs, and can explore the most efficient operating conditions through a multi-purpose optimization controller. Through this, the system can not only comply with regulations but also provide practical economic effects such as energy savings and cost efficiency. In addition, the simulation results are visualized in real time through the manager interface module (136), allowing the decision-maker to immediately review the optimization results and determine whether to implement them.

[0064] Finally, the entire system is equipped with a feedback learning structure, enabling continuous model calibration based on the difference between execution results and predicted values. This self-learning capability provides scalability for rapid adaptation to changes in the operating environment or the introduction of new regulations, and enables the establishment of a data-driven carbon management system in the long term. Therefore, the present invention goes beyond a simple regulatory response automation system and holds technical significance as a core infrastructure for realizing corporate sustainability management.

[0066] It goes without saying that various embodiments of the present invention can be implemented as new embodiments by combining one or more of them.

[0068] A carbon regulation response data automatic linkage system according to various embodiments of the present invention includes a communication unit that receives corporate activity data from an external server, a storage unit that stores corporate activity data and processing results, and a main server including a processor that is linked to the communication unit and the storage unit to standardize corporate activity data, convert it into common reference information based on international standards, and generate regulation-specific calculation and reporting data; an external server that transmits data to the main server; and a user terminal that receives reporting documents and analysis results from the main server. The processor may include a data collection module that collects multiple data streams from the external server and performs integrity verification; a data preprocessing module that preprocesses corporate activity data to remove missing values ​​and outliers and standardizes unit and time systems; a common reference information conversion module that maps the preprocessed data to an international standard schema to generate common reference information; a regulation response mapping module that converts the common reference information according to calculation items and calculation logic for each environmental regulation to generate regulation-specific calculation results; a report generation module that combines the regulation-specific calculation results with a report template to generate reporting documents and electronic submission data; and an administrator interface module that performs real-time indicator monitoring, approval, and management functions.

[0069] According to another feature of the present invention, the data preprocessing module performs missing value interpolation, outlier detection, unit unification, and time alignment on the collected business activity data, and can assign a quality grade by calculating a completeness indicator, an accuracy indicator, and a consistency indicator for the business activity data.

[0070] According to another feature of the present invention, the common reference information conversion module can structure data preprocessed according to an international standard schema into organization, process, and facility units, and generate common reference information by applying country-specific emission factors.

[0071] According to another feature of the present invention, the regulation response mapping module can automatically map calculation items and calculation formulas for each environmental regulation based on common reference information to derive emission amounts and calculation results for each regulation, and record the regulation-specific dataset in the storage unit.

[0072] According to another feature of the present invention, the report generation module can automatically generate a report including tables, graphs, and explanatory text by combining a regulation-specific report template and regulation-specific calculation results calculated by a regulation response mapping module, and can manage electronic signatures and submission history.

[0073] According to another feature of the present invention, the processor further includes an energy optimization simulation module, and the energy optimization simulation module can predict and simulate energy flow and carbon emissions by constructing a digital twin based on facility, utility, and environmental data.

[0074] According to another feature of the present invention, the energy optimization simulation module includes a multi-purpose optimization controller, and the multi-purpose optimization controller can automatically adjust control parameters and derive optimal operating conditions by setting energy consumption, carbon emissions, and cost items as objective functions.

[0075] A method for automatically linking carbon regulation response data according to an embodiment of the present invention may include: (a) a communication unit receiving corporate activity data from an external server; (b) a data preprocessing module of a processor removing missing and outlier values ​​from corporate activity data with verified integrity and standardizing unit and time systems; (c) a common reference information conversion module of a processor mapping the preprocessed data to an international standard schema to generate common reference information; (d) a regulation response mapping module of a processor converting the common reference information according to calculation items and calculation logic for each environmental regulation to generate calculation results for each regulation; (e) a report generation module of a processor combining the calculation results for each regulation with a report template to generate a report document and electronic submission data; and (f) a communication unit providing the report document and electronic submission data to a user terminal or transmitting them to an external server.

[0077] Although the present invention has been described above with specific details such as specific components, limited embodiments, and drawings, this is provided only to aid in a more comprehensive understanding of the invention and the invention is not limited to the above embodiments, and a person skilled in the art to which the invention belongs can make various modifications and variations from this description.

[0078] Accordingly, the scope of the present invention is not limited to the embodiments described above, and all variations equivalent to or equivalent to the claims set forth below, as well as the claims described below, shall be considered to fall within the scope of the concept of the present invention. Explanation of the symbols

[0079] 1000: Automatic Data Linkage System for Carbon Regulation Response 100: Main Server 110: Communications Department 120: Storage section 130: Processor 131: Data Collection Module 132: Data Preprocessing Module 133: Common Reference Information Conversion Module 134: Regulatory Response Mapping Module 135: Report Generation Module 136: Administrator Interface Module 137: Energy Optimization Simulation Module 200: External server 210: ERP Server 220: MES Server 230: FEMS Server 240: Legacy Server 300: User terminal

Claims

Claim 1 The system includes a main server comprising a communication unit that receives corporate activity data from an external server, a storage unit that stores the corporate activity data and processing results, and a processor that is linked with the communication unit and the storage unit to standardize the corporate activity data, convert it into common reference information based on international standards, and generate regulation-specific calculation and reporting data; an external server that transmits data to the main server; and a user terminal that receives report documents and analysis results from the main server. The processor includes a data collection module that collects multiple data streams from the external server and performs integrity verification; a data preprocessing module that preprocesses the corporate activity data to remove missing values ​​and outliers and standardizes unit and time systems; a common reference information conversion module that maps the preprocessed data to an international standard schema to generate the common reference information; a regulation response mapping module that converts the common reference information according to calculation items and calculation logic for each environmental regulation to generate regulation-specific calculation results; a report generation module that combines the regulation-specific calculation results with a report template to generate report documents and electronic submission data; and an administrator interface module that performs real-time indicator monitoring, approval, and management functions. The processor further includes an energy optimization simulation module, and the energy optimization simulation module A digital twin is constructed based on facility, utility, and environmental data to predict and simulate energy flow and carbon emissions; the energy optimization simulation module includes a multi-objective optimization controller; the multi-objective optimization controller sets energy consumption, carbon emissions, and cost items as objective functions to automatically adjust control parameters and derive optimal operating conditions; the objective function is calculated according to the following mathematical formula: [Mathematical Formula] J = w1 Х E + w2 Х CO₂ + w₃ Х Cost (where J is the objective function, E is the total energy consumption calculated based on simulation, CO₂ is the carbon emissions under the same scenario,Cost is the total operating cost, and w1, w2, and w₃ are weights. The above energy optimization simulation module estimates the impact of changes in energy consumption, carbon emissions, and operating costs before and after the execution of the derived optimal operating conditions on the activity and emission items of the above common reference information, and provides an estimated change in the calculation results by regulation when reflected in the next calculation cycle, thereby enabling operational optimization and regulatory compliance to be achieved simultaneously. This is an automatic carbon regulation response data linkage system. Claim 2 A carbon regulation response data automatic linkage system, wherein the data preprocessing module performs missing value interpolation, outlier detection, unit unification, and time alignment on the collected corporate activity data, and calculates completeness indicators, accuracy indicators, and consistency indicators on the corporate activity data to assign a quality grade. Claim 3 In claim 1, the common standard information conversion module structures the preprocessed data into organization, process, and facility units according to the international standard schema and generates the common standard information by applying country-specific emission factors, thereby forming an automatic carbon regulation response data linkage system. Claim 4 A carbon regulation response data automatic linkage system, wherein the regulation response mapping module automatically maps calculation items and calculation formulas for each environmental regulation based on the common standard information to derive emission amounts for each regulation and calculation results for each regulation, and records a dataset for each regulation in a storage unit. Claim 5 A carbon regulation response data automatic linkage system according to claim 1, wherein the report generation module automatically generates a report containing tables, graphs, and explanatory text by combining a regulation-specific report template and regulation-specific calculation results calculated by the regulation response mapping module, and manages electronic signatures and submission history. Claim 6 delete Claim 7 delete Claim 8 (a) a step in which a communications unit receives corporate activity data from an external server; (b) a step in which a data preprocessing module of the processor removes missing and outlier values ​​from the integrity-verified corporate activity data and standardizes the unit and time system; (c) a step in which a common reference information conversion module of the processor maps the preprocessed data to an international standard schema to generate common reference information; (d) a step in which a regulatory response mapping module of the processor converts the common reference information according to the calculation items and calculation logic for each environmental regulation to generate calculation results for each regulation; (e) a step in which a report generation module of the processor combines the calculation results for each regulation into a report template to generate a report document and electronic submission data; and (f) a step in which an energy optimization simulation module of the processor constructs a digital twin based on facility, utility, and environmental data to predict and simulate energy flow and carbon emissions, and a multi-purpose optimization controller included in the energy optimization simulation module sets energy consumption, carbon emissions, and cost items as objective functions to automatically adjust control parameters and derive optimal operating conditions; (g) a step in which the energy optimization simulation module of the processor estimates the impact of changes in energy consumption, carbon emissions, and operating costs before and after the execution of the derived optimal operating conditions on the activity and emission items of the common reference information, and provides an estimated value of the expected change in the calculation results by regulation when reflected in the next calculation cycle; and (h) a step in which the communication unit provides the report document and the electronic submission data to a user terminal or transmits them to an external server, wherein in step (f), the objective function is calculated according to the following mathematical formula.[Mathematical Formula] J = w1 / XE + w2 / XCO2 + w₃ / XCost(where J is the objective function, E is the total energy consumption calculated based on simulation, CO₂ is the carbon emissions under the same scenario, Cost is the total operating cost, and w1, w₂, and w₃ are weights).

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