Highly credible large enterprise carbon emission statistics method and system

Abstract

The present application relates to carbon emission statistics technical field, especially relate to a kind of high trust large-scale enterprise carbon emission statistics method and system, method includes the following steps: creating multilevel enterprise carbon emission data acquisition node;Current level carbon emission data acquisition node obtains carbon emission data, and current level carbon emission data, own unit information, the code of own unit and lower level carbon emission packing data are converted together, obtain the hash value of this level;Current level carbon emission data acquisition node obtains the hash value according to parent node information, and send to upper level carbon emission data acquisition node;The carbon emission data of the present level includes the energy consumption data of carbon emission related energy class in financial number and / or energy consumption data collected in situ, and the lower level carbon emission packing data include child node information.The carbon emission statistics of the large-scale enterprise of complex organization is realized by the method, basically without the possibility of data tampering, improve the accuracy of data.

Description

Technical Field

[0001] This invention relates to the field of carbon emission statistics technology, and in particular to a highly reliable carbon emission statistics method and system for large enterprises. Background Technology

[0002] Currently, many domestic enterprises have conducted research and systematic calculations on carbon emission statistics and calculation methods. For example, Shanghai Baosteel Energy Conservation and Environmental Protection Technology Co., Ltd., in its patent application "A Method, System, Equipment and Medium for Enterprise Carbon Emission Statistics" (application number: CN202411357550.9), proposed real-time acquisition of activity data from current emission sources; screening based on emission factors to obtain target emission factors; calculating carbon emissions; obtaining emission results; comparing the emission results with the target carbon emission balance sheet to obtain target key indicators; and finally generating a target report. This patent focuses on the carbon emission report generation process. Hong Kong and China Gas (Shenzhen) Carbon Asset Management Co., Ltd., in its patent application "A Carbon Emission Statistical Tracking System Based on the Whole Product Life Cycle" (application number: CN202411139379.4), proposed that through the cooperation of a detection module, a remote recording module, and a terminal receiving module, it can effectively track the total carbon emissions of each product throughout its entire life cycle after sale. This patent focuses on constructing a system for tracking the carbon emissions of a specific product. Yunnan Power Grid Co., Ltd. proposed a method for constructing a carbon emission statistical measurement and evaluation system in its patent application "A Method and System for Constructing a Carbon Emission Statistical Measurement and Evaluation System" (application number: CN202311693825.1). The method includes: statistically measuring and calculating the carbon emissions of major carbon sources, and integrating the carbon emissions from these sources; analyzing the carbon emissions from different emission sources within the integrated system, considering the characteristics and influencing factors of different carbon sources, establishing a carbon emission statistical index system, and determining the weights of the carbon emission indexes; determining a comprehensive carbon emission evaluation index based on the weights of the carbon emission indexes; and quantitatively assessing the carbon emission level based on the comprehensive evaluation index. This patent is primarily used for assessing the level of carbon emissions.

[0003] Currently, for large enterprises, simply tracking carbon emissions is insufficient. Carbon emission management for large enterprises is a strategic and systematic project, the core of which lies in establishing an accounting and emission reduction system covering the entire value chain. The primary task of management is to define emission boundaries according to international standards, not only controlling direct emissions and indirect emissions from energy consumption, but also focusing on emissions from the more significant and complex upstream and downstream of the value chain, such as raw material procurement and end-use of products. This requires enterprises to leverage digital tools to ensure data quality and, based on this, set scientific emission reduction targets, such as promoting substantial emission reductions through improving energy efficiency, transitioning to green energy, and optimizing product design.

[0004] Meanwhile, in the construction and implementation of carbon emission management systems for large enterprises, the accuracy and reliability of accounting data constitute the core difficulty and key bottleneck of technical implementation. Existing management processes often face problems such as scattered data sources, inconsistent statistical standards, and high risks of human intervention, resulting in unclear carbon emission baselines and difficulties in tracing sources, making it difficult to support subsequent emission reduction decisions and compliance. In particular, for entities with long value chains and complex emission sources, ensuring the authenticity, consistency, and traceability of multi-dimensional activity data from energy consumption, production operations to upstream and downstream supply chains has become a technical obstacle to improving the effectiveness of carbon management.

[0005] Therefore, it is urgent to establish a rigorous data quality control mechanism. This mechanism should standardize the entire process of data collection, transmission, calculation, and verification, and introduce automated monitoring methods and standardized calculation factors to reduce the risk of errors and information distortion. Simultaneously, it is crucial to ensure that the management process has a complete traceability path, making the source, calculation logic, and adjustment process of each emission data point clearly traceable. This will enhance the credibility and transparency of carbon emission reports, providing a solid data foundation for enterprises to accurately reduce emissions and participate in the carbon trading market. Summary of the Invention

[0006] The purpose of this invention is to overcome the problems existing in the prior art, such as the inability of carbon emission statistics applicable to single enterprises with simple organizational structures to meet the needs of large enterprises, and the inability to achieve high reliability in data reporting. This invention proposes a highly reliable carbon emission statistics method and system for large enterprises.

[0007] To achieve the above-mentioned objectives, the present invention provides the following technical solution:

[0008] On the one hand, this invention discloses a highly reliable method for carbon emission statistics of large enterprises, specifically including the following steps: Create multi-level enterprise carbon emission data collection nodes. The information of the data collection node includes parent node information, child node information, its own unit information, and its own unit code. The current level carbon emission data collection node acquires carbon emission data, and converts the current level carbon emission data, its own unit information, its own unit code, and the lower level carbon emission package data together to obtain the hash value of this level; The current level carbon emission data collection node sends the hash value obtained from the parent node to the next higher level carbon emission data collection node based on the parent node information; The carbon emission data at this level includes energy consumption data related to carbon emissions from financial data and / or energy consumption data collected on-site. The carbon emission packaged data at the next lower level includes sub-node information.

[0009] Furthermore, the formula for calculating the current level of carbon emission data is as follows:

[0010] in, This represents energy consumption data collected on-site; This refers to energy consumption data related to carbon emissions in financial data; It is the carbon emission factor corresponding to the energy consumption data collected on-site; This represents the carbon emission factor corresponding to energy consumption data related to carbon emissions in financial data. This indicates the deviation of energy consumption data related to carbon emissions in the financial data during the previous statistical period.

[0011] Furthermore, the formula for calculating the current level of carbon emission data is as follows:

[0012] in, This represents energy consumption data collected on-site; This refers to energy consumption data related to carbon emissions in financial data; It is the carbon emission factor corresponding to the energy consumption data collected on-site; This represents the carbon emission factor corresponding to energy consumption data related to carbon emissions in financial data. This indicates the deviation of energy consumption data related to carbon emissions in the financial data during the previous statistical period. This indicates the deviation of energy consumption data related to carbon emissions in the financial data for the current statistical period.

[0013] Furthermore, the information about the organization itself includes the organization's full name, abbreviation, industry, province, and business segment.

[0014] Furthermore, the energy consumption data related to carbon emissions in the financial data includes energy consumption data from expense invoices, which includes consumption data for gasoline, diesel, kerosene, electricity, water, and natural gas.

[0015] Furthermore, the steps also include: the current level carbon emission data collection node verifies the acquired lower-level carbon emission packaged data, and calculates the hash value of this level only after the verification is successful.

[0016] Furthermore, the steps also include: when the difference between the energy consumption data related to carbon emissions in the financial data and the energy consumption data collected on-site is greater than an error threshold, a verification step is initiated, the verification step including: On the other hand, a highly reliable carbon emission statistics system for large enterprises is also provided, including a network, a carbon emission data acquisition terminal, and a hash module; The carbon emission data acquisition terminal is used to acquire carbon emission data and is configured with information about the data acquisition nodes, including parent node information, child node information, its own unit information, and its own unit code. The network is used to realize data transmission of multi-level enterprise carbon emission data collection nodes; The hash module performs the following steps based on the carbon emission data acquired by the carbon emission data acquisition terminal: Obtain carbon emission data, and convert the current level carbon emission data, its own unit information, its own unit code, and the lower level carbon emission data together to obtain the hash value of this level; The current level carbon emission data collection node sends the hash value obtained from the parent node to the next higher level carbon emission data collection node based on the parent node information; The carbon emission data at this level includes energy consumption data related to carbon emissions from financial data and / or energy consumption data collected on-site. The carbon emission packaged data at the next lower level includes sub-node information.

[0017] Furthermore, the carbon emission data acquisition terminal includes intelligent metering terminals for energy media such as electricity, water, and gas.

[0018] Furthermore, the energy consumption data related to carbon emissions in the financial data is obtained through an interface connected to the financial system.

[0019] Compared with the prior art, the beneficial effects of the present invention are as follows: the method realizes the carbon emission statistics of large enterprises with complex organizational structures, and if the data of any unit needs to be tampered with, it will cause all the summary data involving that unit to be incorrect, so there is basically no possibility of data tampering, thus improving the accuracy of the data. Attached Figure Description

[0020] Figure 1 This is a flowchart of a highly reliable carbon emission statistics method for large enterprises provided in Embodiment 1 of the present invention; Figure 2 This is a flowchart of the mechanism construction process provided in Embodiment 3 of the present invention; Figure 3 This is the distributed structured organization and organizational topology diagram provided in Embodiment 3 of the present invention; Figure 4 This is a schematic diagram of the association relationship of unit information nodes provided in Embodiment 3 of the present invention; Figure 5 This is the verification flowchart provided in Embodiment 3 of the present invention; Figure 6 This is an example flowchart of the data aggregation method provided in Embodiment 3 of the present invention. Detailed Implementation

[0021] The present invention will now be described in further detail with reference to specific embodiments. However, this should not be construed as limiting the scope of the present invention to the following embodiments; all technologies implemented based on the content of the present invention fall within the scope of the present invention.

[0022] While some methods exist for corporate carbon emission statistics, they typically only involve data acquisition or equipment matching, failing to consider the complexities of large enterprises, the dispersed data sources, inconsistent statistical standards, and the high risk of human intervention. This leads to unclear carbon emission baselines, difficulties in tracing sources, and challenges in supporting subsequent emission reduction decisions and compliance. This invention provides a highly reliable method for carbon emission statistics in large enterprises. First, it creates a multi-level corporate carbon emission data collection node, forming a topology structure consistent with the actual architecture of large enterprises. Each unit has a corresponding data collection node, and several data collection nodes are connected to form a topological network. The information of each data collection node includes parent node information, child node information, its own unit information, and its own unit code. The specific data flow process is as follows: The current-level carbon emission data collection node acquires carbon emission data, converts the current-level carbon emission data, its own unit information, its own unit code, and the lower-level carbon emission packaged data together to obtain the hash value of this level; the current-level carbon emission data collection node sends the obtained hash value to the higher-level carbon emission data collection node according to the parent node information; the current-level carbon emission data includes energy consumption data related to carbon emissions in financial data and / or energy consumption data collected on-site, and the lower-level carbon emission packaged data includes child node information. Through layer-by-layer calculation and packaging, a standardized management of the entire data collection and transmission process is formed, introducing automated monitoring methods and standardized accounting factors to reduce the risk of errors and information distortion.

[0023] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0024] Example 1 A highly reliable method for carbon emission statistics of large enterprises includes the following steps: Create multi-level enterprise carbon emission data collection nodes. The information of the data collection node includes parent node information, child node information, its own unit information, and its own unit code. The current level carbon emission data collection node acquires carbon emission data, and converts the current level carbon emission data, its own unit information, its own unit code, and the lower level carbon emission package data together to obtain the hash value of this level; The current level carbon emission data collection node sends the hash value obtained from the parent node to the next higher level carbon emission data collection node based on the parent node information; The carbon emission data at this level includes energy consumption data related to carbon emissions from financial data and / or energy consumption data collected on-site. The carbon emission packaged data at the next lower level includes sub-node information.

[0025] As a specific implementation, multi-level enterprise carbon emission data collection nodes constitute a topological network. For example, the enterprise's units are divided into multiple levels, including first-level units, second-level units, third-level units, and lower-level units belonging to the higher-level units. Since there are more lower-level units, an inverted tree-like topological structure is formed, and carbon emission data is aggregated upwards according to the levels.

[0026] Each unit has a corresponding data acquisition node, and several data acquisition nodes are connected to form a topology network, creating a topology structure that conforms to the actual architecture of large enterprises. The information of each data acquisition node includes parent node information, child node information, its own unit information, and its own unit code. For example, parent node information includes the name and ID of the superior unit corresponding to this level of data acquisition node; child node information includes the name and ID of the subordinate unit corresponding to this level of data acquisition node; and its own unit information includes the full name of the unit, its abbreviation, its industry, its province, and its business segment, used for querying and tracing carbon emission data.

[0027] As a specific implementation, the energy consumption data related to carbon emissions in the financial data mainly includes energy consumption data such as gasoline, diesel, kerosene, electricity, water, and gas from the reimbursement invoices of various units. Within a certain period (e.g., each quarter), the energy consumption unit is determined based on the name of the invoice purchaser, and the energy consumption type and specific quantity are determined based on the specifications and quantities in the invoice, thereby obtaining detailed data for each unit.

[0028] As a specific implementation, energy consumption data collected on-site is obtained through smart metering terminals of energy media deployed in various units. Further, the steps include initiating a verification step to eliminate erroneous information when the difference between the energy consumption data related to carbon emissions in the financial data and the energy consumption data collected on-site exceeds an error threshold. Because financial data lags behind directly collected on-site data, it is preferable to use the real-time on-site data. However, some sites may not have smart metering terminals for energy media, in which case the financial data will be used. Preferably, user-manual data modification is also supported, but the modified data can only increase relative to the on-site collected data and financial data, and cannot be less than the on-site collected data and financial data, and data addition vouchers must be uploaded.

[0029] Furthermore, the current-level carbon emission data collection node verifies the acquired lower-level carbon emission packaged data. Only after successful verification is the hash value calculated for this level. As a specific implementation, the method is as follows: For each unit, the statistical period and carbon emission data are processed using a hash algorithm to generate a 192-bit fixed-length hash value, named "carbon emission verification data." This hash value is synchronized to all terminals via the network topology. Each time data is aggregated, the data submitted by lower-level sub-units and their grandchildren is verified to prevent lower-level organizations from modifying the data during the data submission process.

[0030] Furthermore, the formula for calculating the current level of carbon emission data is as follows:

[0031] in, This represents energy consumption data collected on-site; This refers to energy consumption data related to carbon emissions in financial data; It is the carbon emission factor corresponding to the energy consumption data collected on-site; This represents the carbon emission factor corresponding to energy consumption data related to carbon emissions in financial data. This indicates the deviation of energy consumption data related to carbon emissions in the financial data during the previous statistical period.

[0032] Considering the time discrepancy between actual carbon emission data and financial and collected data, which may lead to biases in carbon emission data aggregation, a certain margin of error needs to be reserved for each emission source. Therefore, the current level of carbon emission data is optimized. The optimized formula for calculating the current level of carbon emission data is as follows:

[0033] in, This represents energy consumption data collected on-site; This refers to energy consumption data related to carbon emissions in financial data; It is the carbon emission factor corresponding to the energy consumption data collected on-site; This represents the carbon emission factor corresponding to energy consumption data related to carbon emissions in financial data. This indicates the deviation of energy consumption data related to carbon emissions in the financial data during the previous statistical period. This indicates the deviation of energy consumption data related to carbon emissions in the financial data for the current statistical period.

[0034] Example 2 Based on the same concept, a highly reliable carbon emission statistics system for large enterprises is also provided, including a network, a carbon emission data acquisition terminal, and a hash module; The carbon emission data acquisition terminal is used to acquire carbon emission data and is configured with information about the data acquisition nodes, including parent node information, child node information, its own unit information, and its own unit code. The network is used to realize data transmission of multi-level enterprise carbon emission data collection nodes; The hash module performs the following steps based on the carbon emission data acquired by the carbon emission data acquisition terminal: Obtain carbon emission data, and convert the current level carbon emission data, its own unit information, its own unit code, and the lower level carbon emission data together to obtain the hash value of this level; The current level carbon emission data collection node sends the hash value obtained from the parent node to the next higher level carbon emission data collection node based on the parent node information; The carbon emission data at this level includes energy consumption data related to carbon emissions from financial data and / or energy consumption data collected on-site. The carbon emission packaged data at the next lower level includes sub-node information.

[0035] Furthermore, the carbon emission data acquisition terminal includes intelligent metering terminals for energy media such as electricity, water, and gas.

[0036] Furthermore, the energy consumption data related to carbon emissions in the financial data is obtained through an interface connected to the financial system. Typically, finance departments have their own management systems, such as integrated enterprise financial platforms; therefore, energy consumption-related data is retrieved according to the corresponding interface.

[0037] The above content is an explanation of the various technical features of this application. To more clearly demonstrate how the solution operates in practice, the following will describe the parts described above in conjunction with a preferred embodiment. This specific embodiment serves as an instance of the foregoing description and helps those skilled in the art to understand and apply the method provided by this application to a wider range of scenarios.

[0038] Example 3 This invention discloses a highly reliable method and system for carbon emission statistics of large enterprises. The method includes an organizational structure construction process, an organizational solidification process, a data acquisition process, a data entry process, and a data aggregation process.

[0039] The organizational structure construction process adopts a step-by-step approach, building from first-level, second-level, third-level, and fourth-level units. First-level units create second-level unit information, and second-level units are added as nodes; second-level units create third-level unit information, and third-level units are added as nodes; third-level units create fourth-level unit information, and fourth-level units are added as nodes. The flowchart of the organizational structure construction process is as follows: Figure 2 As shown. Finally, a distributed, structured organizational and institutional topology diagram is constructed, as shown in the diagram. Figure 3 As shown.

[0040] More specifically, each unit information node consists of "parent ID", "unit information" and "ID" (i.e., identity information). The unit information includes the full name of the unit, the abbreviation of the unit, the industry to which it belongs, the province to which it belongs, the business segment, the full name of the superior unit, and the hash code of the subordinate unit.

[0041] The organizational solidification process involves converting the unit information of each unit node and all its corresponding child nodes into a hash code using a hash algorithm. This hash code is then sent to the next higher-level unit and incorporated into the higher-level unit's information. As a specific implementation, the hash code is calculated using the formula: Hash value = (Previous round hash value × base + Current character ASCII code) % Large prime number. The corresponding code is as follows: def bkdr_hash(s: str, mod: int = 10 192 + 7, base: int = 131) ->int: "":param s: Input string :param mod: Take the modulo of a large prime number (to avoid overflow) :param base: The base number (131 / 31 / 13131) :return: The hash value (integer) of the string. """ hash_val = 0 # Initialize hash value for char in s: # Core Formula: New Hash = Old Hash × Radix + Character ASCII Code hash_val = (hash_val base + ord(char)) % mod return hash_val # Test Example if __name__ == "__main__": print(bkdr_hash("hello")) # Output: 66183237 print(bkdr_hash("world")) # Output: 83543160 print(bkdr_hash("hello")) # The hash values ​​of the same strings must be the same This method ensures that after the distributed, structured organizational and institutional topology is constructed, no unit at any level can modify its own information or that of its subordinate units. Any modification will inevitably result in a hash code verification failure, meaning that the preceding unit's information has been modified, and data saving and submission are not permitted. This prevents arbitrary modifications to unit information from affecting data authenticity. If the hash verification fails, it means that the preceding unit's information has been modified, and data saving and submission are not permitted. After the organization is solidified, all units' reporting terminal software will form a P2P network, and subsequent reporting data will be synchronized within this P2P network. The relationship between unit information nodes is as follows: Figure 4 As shown.

[0042] The data collection process involves extracting energy consumption data (gasoline, diesel, kerosene, electricity, water, gas, etc.) from reimbursement invoices of various units through an interface with the enterprise's integrated financial platform. Each quarter, the energy-consuming unit is identified based on the invoice purchaser's name, and the energy consumption type and specific quantity are determined based on the specifications and quantities on the invoice, thus obtaining detailed data for each unit. In the financial system, each reimbursement item has a category and quantity; for example, electricity is listed in kilowatt-hours. Besides obtaining on-site energy consumption data through the enterprise's integrated financial platform, electricity, water, and gas sensors are deployed in key energy-consuming units. Real-time data collected by these sensors is compared and verified with financial data. The verification flowchart is shown below. Figure 5 As shown, if a significant data discrepancy is detected, a prompt will be given, asking the user to confirm further.

[0043] The data entry process adopts a bottom-up approach. Personnel in each unit can access data collected from the enterprise's integrated financial platform and data collected by on-site sensors. Because financial data lags behind directly collected on-site data, real-time on-site data is preferred. However, in areas where sensors are not installed, financial data will be used. This method supports manual data modification, but modifications can only increase the amount of data compared to the on-site and financial data, and cannot be less than the combined on-site and financial data. Supporting documentation for any data additions must also be uploaded.

[0044] The formula for calculating the recommended carbon emission reporting values ​​is as follows:

[0045] To account for potential carbon emission deviations caused by time discrepancies between actual reporting and financial data collection, a certain deviation value needs to be considered for each emission source. The deviation from the previous period mainly considers the surplus or excess from the previous period. The deviation for the current period is used to input financial data that has occurred in the current period but has not yet been recorded. This data may introduce statistical bias. Therefore, the final carbon emission calculation formula is as follows:

[0046] in, This represents energy consumption data collected on-site; This refers to energy consumption data related to carbon emissions in financial data; It is the carbon emission factor corresponding to the energy consumption data collected on-site; This represents the carbon emission factor corresponding to energy consumption data related to carbon emissions in financial data. This indicates the deviation of energy consumption data related to carbon emissions in the financial data during the previous statistical period. This indicates the deviation of energy consumption data related to carbon emissions in the financial data for the current statistical period. The statistical period is generally y, with one quarter (or one month) as a period.

[0047] The data aggregation process involves generating a fixed-length 192-bit hash value for each unit name, statistical period, and reported data using a hash algorithm. This hash value is named "Carbon Emission Verification Data." The hash value is set to a longer length to avoid hash collisions. The hash value will be synchronized to all terminals via a P2P network created during the institutional solidification process. Each data aggregation will verify the data submitted by subordinate and sub-sub-organizations to prevent them from modifying the data during the submission process. If the verification fails, the data cannot be saved and must be resubmitted. An example flowchart of the data aggregation method is shown below. Figure 6 As shown.

[0048] This method allows for the complete calculation of carbon emissions for large enterprises. Each unit's data submission generates carbon emission verification data, which is then sent to all units' terminals via a P2P network. Before data aggregation at higher levels, the carbon emission verification data of sub-units (lower-level units) and grandchildren (sub-sub-sub-units) are verified. Because the integrity of the data from the next two levels is checked during each aggregation process, data corruption due to external factors is prevented during the hierarchical aggregation process.

[0049] Example 4 b. The method of the present invention collects data through an interface with an integrated enterprise financial platform. This embodiment specifically describes the process of collecting data through the interface.

[0050] # ============================================== # Configuration of Interface for Enterprise Carbon Emission Management System & Integrated Financial Platform (Energy Project) # Configuration Version: V1.0.0 # Core Data Transmission: Gasoline, Diesel, Kerosene, Electricity, Water, and Natural Gas Consumption / Cost / Carbon Emissions # Applicable scenarios: Energy data synchronization, carbon emission accounting, energy cost accounting, and voucher callback. # Maintainers: Carbon Emission System R&D Department | Financial Platform R&D Department # ============================================== # ------------ 1. Global Basic Configuration-------------------------- global: env: prod# Runtime environment: dev (development) / test (testing) / prod (production) effective_time: "2026-03-24 00:00:00"# Effective time charset: UTF-8# Unified encoding timeout: 30# Interface timeout (seconds) request_id_prefix: CARBON_ENERGY_FIN_# Request identifier prefix # ------------ 2. Security Authentication Configuration -------------------------- auth: auth_mode: api_key_sign# Authentication mode: API key + digital signature api_key: carbon_system: "CARBON_ENERGY_2026"# Carbon emission system identifier finance_system: "FIN_ENERGY_2026"# Financial platform identifier sign: Algorithm: SHA256# Signature algorithm (tamper-proof) sort_rule: ascii_asc# Signature field sorting rules secret_key: "${ENERGY_SIGN_SECRET}" # Signature key (for production cryptographic injection) token_expire: 7200# Token expiration time (seconds) # ------------ 3. System Connection Configuration-------------------------- system: # Carbon Emission Management System (Data Provider: Outputs energy consumption / carbon emission data) carbon_emission_system: Name: "Enterprise Carbon Emission Management System" code: "CARBON_ENERGY_SYS domain: dev: "http: / / dev-carbon-energy.enterprise.com" test: "http: / / test-carbon-energy.enterprise.com" prod: "https: / / carbon-energy.enterprise.com" version: v1.0 # Integrated Financial Platform (Data Recipient: Recording Energy Costs / Generating Vouchers) finance_integration_platform: Name: "Integrated Enterprise Financial Platform" code: "FIN_ENERGY_SYS domain: dev: "http: / / dev-finance-energy.enterprise.com" test: "http: / / test-finance-energy.enterprise.com" prod: "https: / / finance-energy.enterprise.com" version: v3.0 # ------ 4. Core Business Interfaces (including 6 types of energy transmission fields) -------------------- interface: # ============================================== # Interface 1: Energy Consumption & Carbon Cost Synchronization Interface (Core: Transmits gasoline / diesel / kerosene / electricity / water / gas data) # Direction: Financial Platform → Carbon Emission System # Transmitted content: Consumption, unit price, total cost, and carbon emissions of 6 types of energy # ============================================== - interface_id: ENERGY_FIN_001 interface_name: Energy consumption and carbon cost synchronization interface request_method: POST request_path: / api / finance / energy / carbon / sync data_format: JSON trigger_type: auto # Triggering method: Automatic (1 AM daily) # Core field mapping (carbon emission system fields → financial platform fields) # Includes: Full transmission fields for gasoline, diesel, kerosene, electricity, water, and natural gas. field_mapping: # Basic Public Fields - carbon_field: enterprise_id# Enterprise ID finance_field: company_code is_required: true data_type: string - carbon_field: account_period# Accounting period (YYYY-MM) finance_field: account_date is_required: true data_type: string # -------------------------- Gasoline Transmission Field -------------------------- - carbon_field: gasoline_consumption# Gasoline consumption (L) finance_field: gasoline_qty is_required: true data_type: decimal(18,2) - carbon_field: gasoline_unit_price# Gasoline unit price (yuan / L) finance_field: gasoline_price is_required: true data_type: decimal(18,4) - carbon_field: gasoline_total_cost# Total cost of gasoline (yuan) finance_field: gasoline_amount is_required: true data_type: decimal(18,2) # -------------------------- Diesel Transmission Field -------------------------- - carbon_field: diesel_consumption# Diesel consumption (L) finance_field: diesel_qty is_required: true data_type: decimal(18,2) - carbon_field: diesel_unit_price# Diesel unit price (yuan / L) finance_field: diesel_price is_required: true data_type: decimal(18,4) - carbon_field: diesel_total_cost# Total cost of diesel fuel (RMB) finance_field: diesel_amount is_required: true data_type: decimal(18,2) # -------------------------- Kerosene Transmission Field -------------------------- - carbon_field: kerosene_consumption# Kerosene consumption (L) finance_field: kerosene_qty is_required: false # Optional (can be 0 if no cost is incurred) data_type: decimal(18,2) - carbon_field: kerosene_unit_price# Kerosene unit price (yuan / L) finance_field: kerosene_price is_required: false data_type: decimal(18,4) - carbon_field: kerosene_total_cost# Total cost of kerosene (yuan) finance_field: kerosene_amount is_required: false data_type: decimal(18,2) # -------------------------- Power Transmission Field -------------------------- - carbon_field: electricity_consumption# Electricity consumption (kWh) finance_field: electricity_qty is_required: true data_type: decimal(18,2) - carbon_field: electricity_unit_price# Electricity unit price (yuan / kWh) finance_field: electricity_price is_required: true data_type: decimal(18,4) - carbon_field: electricity_total_cost# Total electricity cost (yuan) finance_field: electricity_amount is_required: true data_type: decimal(18,2) # -------------------------- Water Transport Field-------------------------- - carbon_field: water_consumption# Water consumption (tons) finance_field: water_qty is_required: true data_type: decimal(18,2) - carbon_field: water_unit_price# Water unit price (yuan / ton) finance_field: water_price is_required: true data_type: decimal(18,4) - carbon_field: water_total_cost# Total water cost (yuan) finance_field: water_amount is_required: true data_type: decimal(18,2) # -------------------------- Gas Transmission Field -------------------------- - carbon_field: gas_consumption# Gas consumption (m³) finance_field: gas_qty is_required: true data_type: decimal(18,2) - carbon_field: gas_unit_price# Gas unit price (yuan / m³) finance_field: gas_price is_required: true data_type: decimal(18,4) - carbon_field: gas_total_cost# Total cost of gas (RMB) finance_field: gas_amount is_required: true data_type: decimal(18,2) # Summary Fields - carbon_field: total_energy_cost# Total energy cost (yuan) finance_field: total_energy_amount is_required: true data_type: decimal(18,2) - carbon_field: total_carbon_emission# Total carbon emissions (tons of CO2) finance_field: total_carbon is_required: true data_type: decimal(18,2) # Response Standard response: success_code: 200 success_msg: "Energy data synchronization successful" fail_code: 500 fail_msg: "Energy data synchronization failed" # ============================================== # Interface 2: Financial Voucher Callback Interface # Direction: Financial Platform → Carbon Emission System # Scenario: After the finance department generates energy cost vouchers, a callback is made to update the carbon emission system status. # ============================================== - interface_id: ENERGY_FIN_002 interface_name: Energy cost voucher callback interface request_method: POST request_path: / api / carbon / energy / voucher / callback data_format: JSON trigger_type: auto field_mapping: - finance_field: voucher_no# financial voucher number carbon_field: finance_voucher_no is_required: true data_type: string - finance_field: voucher_status# Voucher status (1: Generated 2: Posted) carbon_field: voucher_status is_required: true data_type: int - finance_field: account_period# Related accounting period carbon_field: account_period is_required: true data_type: string - finance_field: total_energy_amount # Total amount of vouchers carbon_field: total_energy_cost is_required: true data_type: decimal(18,2) # ============================================== # Interface 3: Energy Data Reconciliation Interface # Direction: Cross-system query # Scenario: Monthly reconciliation of six types of energy consumption, costs, and carbon emissions data # ============================================== - interface_id: ENERGY_FIN_003 interface_name: Energy data reconciliation interface request_method: GET request_path: / api / finance / energy / reconciliation data_format: JSON trigger_type: manual common_params: [enterprise_id, account_period] # ------- 5. Data Transmission Security Configuration -------------------------- transfer: sensitive_encrypt: enable: true # Encrypt sensitive data Algorithm: AES-128-CBC encrypt_fields: [total_energy_cost, gasoline_total_cost, secret_key] # Encrypted monetary fields IP whitelist: [10.0.0.0 / 8, 172.16.0.0 / 12, 192.168.0.0 / 16]# Internal network whitelist # ------ 6. Fault Tolerance and Retry Mechanisms -------------------------- retry: max_retry_times: 3 # Maximum number of retries retry_interval: [3, 5, 10] # Retry interval (seconds) circuit breaker: enable: true fail_count: 5# Circuit breaker triggered after 5 consecutive failures. break_time: 300# Circuit breaker will be activated for 5 minutes. dead_letter_queue: enable: true storage_type: redis expire_days: 90 # ------7. Log and Monitoring Configuration-------------------------- log: level: info path: / logs / carbon-energy-finance / rotate: daily# Split logs daily monitor: enable: true alarm_type: email# alarm method alarm_receiver: "energy-fin@enterprise.com" fail_rate_threshold: 0.01# Alert if failure rate exceeds 1%. # ------ 8. Exception Error Code Mapping -------------------------- error_code: 1001: 4001# Parameter error 1002: 4002# Authentication failed 1003: 4003# Signature failed 2001: 4004# Energy data missing 5000: 5000# System error The beneficial effects of this invention are as follows: Data collected using this method is so sensitive that any alteration to the data of any single unit will result in inaccurate aggregated data for that unit, virtually eliminating the possibility of data tampering. This method is also applicable to large, complex enterprises. This is a characteristic currently lacking in carbon emission calculation methods and systems.

[0051] It should be understood that the various modules of the highly reliable carbon emission statistics method and system for large enterprises provided in the above embodiments are only illustrated by the division of each functional module in the above description. In practical applications, the above functions can be assigned to different functional modules as needed. That is, the internal structure of the system can be divided into different functional modules to complete all or part of the functions described above.

[0052] The functional modules in the above embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of the embodiments of this application.

[0053] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A highly reliable method for calculating carbon emissions from large enterprises, characterized in that, Includes the following steps: Create enterprise carbon emission data collection nodes with a multi-level tree topology structure that corresponds one-to-one with the enterprise's administrative affiliation. The multi-level tree topology structure is an inverted tree structure, in which the upper-level node corresponds to the upper-level management unit and the lower-level node corresponds to the lower-level subordinate unit. The information of the data acquisition node includes parent node information, child node information, its own unit information, and its own unit's unique code; The current level carbon emission data collection node acquires the carbon emission data of its own level, and takes the current level carbon emission data, its own unit information, its own unit's unique code and the lower level carbon emission package data as input, and transforms them through a hash algorithm to obtain the unique hash value of its own level; The current-level carbon emission data collection node sends the hash value obtained from the parent node to the next-level carbon emission data collection node so that the next-level node can generate its own hash value. The carbon emission data at this level includes energy consumption data related to carbon emissions from financial data and / or energy consumption data collected on-site. The carbon emission packaged data at the lower level includes the hash value of the lower-level node itself and the complete hash value chain of all its subordinate child nodes. Specifically, when the carbon emission data of any node in the multi-level tree topology is tampered with, the hash value corresponding to that node changes, which causes all its parent nodes to change the hash values ​​generated by the lower-level carbon emission packaged data containing the changed hash value, thereby realizing global data anti-tampering linkage verification.

2. The highly reliable carbon emission statistics method for large enterprises as described in claim 1, characterized in that, The formula for calculating the current level of carbon emissions data is: in, This represents energy consumption data collected on-site; This refers to energy consumption data related to carbon emissions in financial data; It is the carbon emission factor corresponding to the energy consumption data collected on-site; This represents the carbon emission factor corresponding to energy consumption data related to carbon emissions in financial data. This indicates the deviation of energy consumption data related to carbon emissions in the financial data during the previous statistical period; This reflects the surplus or excess from the previous cycle; Based on real-time data collected on-site, and since there are no smart metering terminals for energy media on-site, financial data shall prevail.

3. The highly reliable carbon emission statistics method for large enterprises as described in claim 1, characterized in that, The formula for calculating the current level of carbon emissions data is: in, This represents energy consumption data collected on-site; This refers to energy consumption data related to carbon emissions in financial data; It is the carbon emission factor corresponding to the energy consumption data collected on-site; This represents the carbon emission factor corresponding to energy consumption data related to carbon emissions in financial data. This indicates the deviation of energy consumption data related to carbon emissions in the financial data during the previous statistical period. This indicates the deviation of energy consumption data related to carbon emissions in the financial data for the current statistical period; This refers to the data corresponding to financial vouchers that have occurred in this period but have not yet been recorded. Based on real-time data collected on-site, and since there are no smart metering terminals for energy media on-site, financial data shall prevail.

4. The highly reliable carbon emission statistics method for large enterprises as described in claim 1, characterized in that, The information about the organization itself includes the organization's full name, abbreviation, industry, province, and business segments.

5. The highly reliable carbon emission statistics method for large enterprises as described in claim 1, characterized in that, The energy consumption data related to carbon emissions in the financial data includes energy consumption data from expense invoices, which includes consumption data for gasoline, diesel, kerosene, electricity, water, and natural gas.

6. A highly reliable carbon emission statistics method for large enterprises as described in any one of claims 1-5, characterized in that, The steps also include: the current level carbon emission data collection node verifies the acquired lower-level carbon emission packaged data, and calculates the hash value of this level after the verification is successful.

7. A highly reliable carbon emission statistics method for large enterprises as described in any one of claims 1-5, characterized in that, The steps also include: when the difference between the energy consumption data related to carbon emissions in the financial data and the energy consumption data collected on-site is greater than the error threshold, the verification step is initiated.

8. A highly reliable carbon emission statistics system for large enterprises, characterized in that, This includes the network, carbon emission data collection terminal, and hash module; The carbon emission data acquisition terminal is used to acquire carbon emission data and is configured with information about the data acquisition node, including parent node information, child node information, its own unit information, and a unique code of its own unit. The network is used to realize data transmission of multi-level enterprise carbon emission data collection nodes; the multi-level enterprise carbon emission data collection nodes are multi-level tree topology enterprise carbon emission data collection nodes that correspond one-to-one with the administrative affiliation of enterprises. The multi-level tree topology is an inverted tree structure, in which the upper-level node corresponds to the upper-level management unit and the lower-level node corresponds to the lower-level subordinate unit. The hash module performs the following steps based on the carbon emission data acquired by the carbon emission data acquisition terminal: To obtain the carbon emission data for this level, the current level's carbon emission data, its own unit information, its own unit's unique code, and the lower level's carbon emission data are taken as input and converted through a hash algorithm to obtain the unique hash value for this level. The current level carbon emission data collection node sends the obtained hash value to the next level carbon emission data collection node based on the parent node information, so that the next level node can generate its own hash value. The carbon emission data at this level includes energy consumption data related to carbon emissions from financial data and / or energy consumption data collected on-site. The carbon emission packaged data at the lower level includes the hash value of the lower-level node itself and the complete hash value chain of all its subordinate child nodes. Specifically, when the carbon emission data of any node in the multi-level tree topology is tampered with, the hash value corresponding to that node changes, which causes all its parent nodes to change the hash values ​​generated by the lower-level carbon emission packaged data containing the changed hash value, thereby realizing global data anti-tampering linkage verification.

9. A highly reliable carbon emission statistics system for large enterprises as described in claim 8, characterized in that, The carbon emission data acquisition terminal includes intelligent metering terminals for energy media such as electricity, water, and gas.

10. A highly reliable carbon emission statistics system for large enterprises as described in claim 8, characterized in that, Energy consumption data related to carbon emissions in the financial data is obtained through an interface connected to the financial system.

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