A carbon metering method, device and medium based on measured data
By acquiring and integrating enterprise emissions, energy consumption, and environmental data through sensor networks and blockchain technology, a comparison model is constructed, which solves the problem of insufficient data accuracy in existing carbon measurement methods and achieves precise carbon measurement and emission reduction management.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- INSPUR YUNZHOU (SHANDONG) IND INTERNET CO LTD
- Filing Date
- 2025-10-31
- Publication Date
- 2026-06-05
AI Technical Summary
Existing carbon measurement methods rely on industry average emission factors and historical data, which are difficult to reflect real-time changes in enterprises, resulting in inaccurate emission reduction decisions, affecting emission reduction effectiveness and costs, and fixed formulas cannot be adapted to complex scenarios.
Emissions data, energy consumption data, and environmental data are acquired through sensor networks, integrated into a time coordinate system, and a comparison model is built to determine direct and indirect emissions. Blockchain technology is then used to achieve collaborative measurement and data storage of emissions across the entire industry chain.
It enables precise determination of emission deviations, identification of potential problems, ensures the reliability of results, improves data credibility, meets carbon measurement needs in complex scenarios, and helps enterprises to accurately reduce emissions and manage them.
Smart Images
Figure CN121032004B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of measurement technology, and in particular to a carbon measurement method, device and medium based on measured data. Background Technology
[0002] Currently, mainstream carbon measurement methods primarily rely on emission factor-based accounting systems. Typical approaches include: material balance methods based on industry average emission factors, coefficient extrapolation methods based on historical energy consumption data, and indirect emission calculation models using fixed formulas. While mainstream carbon measurement methods have advantages in data acquisition and operation, they also have limitations such as data accuracy being easily affected by industry averages or historical data deviations, difficulty in reflecting real-time changes and special circumstances within enterprises, and the inability of fixed formulas to adapt to complex scenarios. For enterprises, this can lead to inaccurate emission reduction decisions, affecting emission reduction effectiveness and costs, interfering with carbon pricing mechanisms, and impacting market fairness and efficiency. Summary of the Invention
[0003] To address the aforementioned issues, this application proposes a carbon measurement method based on measured data, comprising: acquiring emission data through a pre-set sensor network, determining the enterprise's energy consumption data, and acquiring environmental data through a pre-set integrated sensor; fusing the emission data, energy consumption data, and environmental data to integrate data from different sources into a pre-set time coordinate system, and spatially aggregating the fused data according to production units to determine a comparison model; determining direct emissions based on the emission data and indirect emissions based on the energy consumption data, comparing the direct emissions and indirect emissions using the comparison model to determine emission deviation, and comparing the emission deviation with a pre-set deviation threshold; if the emission deviation is greater than the deviation threshold, determining the cause of the deviation based on equipment operation logs and the environmental data, determining a verification data point based on the cause of the deviation, and performing verification measurement based on the verification data point.
[0004] In one example, emission data is acquired through a pre-set sensor network, specifically including: collecting data from the production facility using a pre-set laser gas analyzer to obtain greenhouse gas concentrations; acquiring waste gas flow data using a pre-set waste gas flow sensor; and determining the emission data based on the greenhouse gas concentrations and the waste gas flow data.
[0005] In one example, determining a company's energy consumption data specifically includes: connecting to the company via a pre-determined industrial bus to obtain the energy consumption data, which includes electricity consumption data and heat consumption data; determining the company's real-time emission factor; and synchronously associating the energy consumption data based on the real-time emission factor.
[0006] In one example, environmental data is acquired through pre-set integrated sensors, specifically including: the environmental data including temperature, humidity, and air pressure; acquiring the enterprise's process parameters and industry characteristic data; and integrating the environmental data based on the process parameters and industry characteristic data.
[0007] In one example, the fusion of the emission data, the energy consumption data, and the environmental data specifically includes: determining a moving average for the emission data, the energy consumption data, and the environmental data according to a pre-set identification criterion; performing interpolation repair based on the moving average; and aggregating the spatial dimensions of the interpolated data to determine the time coordinate system.
[0008] In one example, the method further includes:
[0009] The formula for calculating the direct emissions is as follows:
[0010]
[0011] in, Where C is the direct emission amount, F is the measured gas concentration, and K is the exhaust gas flow rate. tp These are correction factors for temperature and air pressure;
[0012] The formula for calculating the indirect emissions is as follows:
[0013]
[0014] Where Pᵢ represents the real-time power consumption of device i, and EF it Let t be the real-time emission factor of the area where the device is connected to the power grid.
[0015] In one example, the method further includes: using blockchain technology to connect with the measured data interfaces of upstream and downstream enterprises, and combining the GPS trajectory of logistics vehicles and real-time fuel consumption sensor data to determine transportation emissions; integrating direct emissions from the factory area and transportation emissions from suppliers, and performing weighted calculations according to a pre-set procurement ratio to achieve coordinated measurement of emissions across the entire industrial chain.
[0016] In one example, the method further includes: storing key data, including sensor calibration records and emission factor update logs, using blockchain technology to identify data blocks, the data blocks containing timestamps, device numbers, and operator signatures; and when the data is modified, generating a comparison of hash values before and after the modification and synchronizing it to the regulatory node.
[0017] On the other hand, this application also proposes a carbon metering device based on measured data, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the carbon metering device based on measured data to perform: acquiring emission data through a pre-set sensor network, determining the enterprise's energy consumption data, and acquiring environmental data through a pre-set integrated sensor; fusing the emission data, energy consumption data, and environmental data to integrate data from different sources into a pre-set time coordinate system, and spatially aggregating the fused data according to the production unit to determine a comparison model; determining direct emissions based on the emission data, and determining indirect emissions based on the energy consumption data, comparing the direct emissions and indirect emissions through the comparison model to determine emission deviation, and comparing the emission deviation with a pre-set deviation threshold; if the emission deviation is greater than the deviation threshold, determining the cause of the deviation based on the device operation log and the environmental data, determining a verification data point based on the cause of the deviation, and performing verification metering based on the verification data point.
[0018] On the other hand, this application also proposes a non-volatile computer storage medium storing computer-executable instructions, which are configured to: acquire emission data through a pre-set sensor network, determine the enterprise's energy consumption data, and acquire environmental data through a pre-set integrated sensor; fuse the emission data, energy consumption data, and environmental data to integrate data from different sources into a pre-set time coordinate system, and spatially aggregate the fused data according to the production unit to determine a comparison model; determine direct emissions based on the emission data, and determine indirect emissions based on the energy consumption data, compare the direct emissions and indirect emissions through the comparison model to determine emission deviation, and compare the emission deviation with a pre-set deviation threshold; if the emission deviation is greater than the deviation threshold, determine the cause of the deviation based on the equipment operation log and the environmental data, determine a verification data point based on the cause of the deviation, and perform verification measurement based on the verification data point.
[0019] This application acquires emission, energy consumption, and environmental data through multiple channels, including sensor networks and integrated sensors, ensuring broad and accurate data sources and laying a solid foundation for subsequent analysis. By fusing and aggregating multi-source data and constructing a comparison model, it can accurately determine emission deviations and identify potential problems. When emission deviations exceed thresholds, the cause can be determined and the measurement verified based on equipment operation logs and environmental data, ensuring the reliability of the results. Formulas for calculating direct and indirect emissions are provided, considering various factors to make the measurement more accurate. Blockchain technology is used to achieve collaborative emission measurement and key data storage across the entire industry chain, improving data credibility and traceability, meeting carbon measurement needs in complex scenarios, and assisting enterprises in precise emission reduction and management. Attached Figure Description
[0020] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:
[0021] Figure 1 This is a schematic flowchart of a carbon measurement method based on measured data in an embodiment of this application;
[0022] Figure 2 This is a schematic diagram of a carbon metering device based on measured data in an embodiment of this application. Detailed Implementation
[0023] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0024] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.
[0025] like Figure 1 As shown, in order to solve the above problems, this application provides a carbon measurement method based on measured data, the method comprising:
[0026] S101. Emission data is acquired through a pre-set sensor network to determine the company's energy consumption data, and environmental data is acquired through a pre-set integrated sensor.
[0027] Multi-source measured data acquisition is achieved through the deployment of distributed sensor networks and interface integration technology, simultaneously acquiring three types of core data. First, direct emission data. Laser gas analyzers are deployed at key nodes in production facilities, such as boiler chimneys and fermenter exhaust outlets, to collect real-time concentrations of greenhouse gases such as CO2 and CH4, with an accuracy controlled within ±1 ppm. Simultaneously, exhaust gas flow sensors are deployed to acquire exhaust gas flow data at a sampling frequency of 1 Hz. The instantaneous emission rate is calculated by combining the greenhouse gas concentration data with the exhaust gas flow data, with the result expressed in kg / h. Second, indirect energy consumption data. Real-time data on energy consumption such as electricity and heat is acquired through industrial bus interfaces, such as the Modbus protocol, connecting to the enterprise's energy management system. This includes minute-level power consumption for each device, and synchronously linking to real-time regional emission factors provided by the power grid company. Third, auxiliary parameter data. This integrates environmental sensor measurements of temperature, humidity, and air pressure; process parameters, such as oxygen supply intensity in steel plant converters; and logistics data, such as GPS tracks and load information of transport vehicles.
[0028] In one embodiment, the data collection frequency is dynamically adjusted based on industry characteristics. For high-emission industries, such as the chemical industry, it is set to once every minute; for low-emission industries, such as office buildings, it is set to once every 15 minutes. This setting ensures the timeliness of critical data while reducing unnecessary energy consumption, providing a foundation for subsequent data processing and metering.
[0029] S102. The emission data, energy consumption data and environmental data are fused to integrate data from different sources into a pre-set time coordinate system, and the fused data is spatially aggregated according to the production unit to determine the comparison model.
[0030] The data preprocessing and fusion stage focuses on comprehensively cleaning and deeply correlating the collected raw data. Regarding outlier handling, the 3σ criterion is used to accurately identify abrupt data fluctuations caused by sensor malfunctions, and interpolation using moving averages from adjacent time periods is employed to correct these fluctuations, thereby ensuring data stability.
[0031] In the spatiotemporal alignment process, data from different sources are unified to the same time coordinate system. Specifically, hourly data of grid emission factors is interpolated and converted into minute-level data. Simultaneously, data is aggregated spatially based on production units, such as workshops and production lines, achieving orderly integration of data in both spatiotemporal dimensions. To ensure data accuracy and consistency, a comparison model between direct measurement and indirect calculation is constructed. When the deviation between direct emission data and values calculated based on fuel consumption exceeds 5%, the system automatically retrieves equipment operation logs for that period, such as burner valve opening information, as well as environmental parameters, to deeply analyze the causes of the deviation and mark data points requiring manual verification. Through this series of operations, reliable and accurate data input is provided for dynamic metering.
[0032] In one embodiment, the data preprocessing and fusion stage primarily involves cleaning and correlating the raw data. For outlier filtering, the 3σ criterion is used to identify jumps in data caused by sensor malfunctions, and interpolation is performed using moving averages from adjacent time periods. For spatiotemporal alignment, data from different sources are unified to the same time coordinate system; for example, hourly data of grid emission factors is interpolated to minute-level data and aggregated spatially by production units such as workshops and production lines. In the cross-validation stage, a comparison model between direct measurement and indirect calculation is established. When the deviation between direct emission data and the estimated value based on fuel consumption exceeds 5%, the equipment operation logs and environmental parameters for that period are automatically retrieved to determine the cause of the deviation and mark data points requiring manual verification. This ensures the accuracy and consistency of the data, providing a reliable input basis for subsequent metering work.
[0033] S103. Determine the direct emissions based on the emission data and the indirect emissions based on the energy consumption data. Compare the direct emissions and the indirect emissions using a comparison model to determine the emission deviation. Compare the emission deviation with a pre-set deviation threshold.
[0034] In one embodiment, direct emission calculations are based on preprocessed direct emission data and employ a real-time concentration-flow coupling formula:
[0035]
[0036] in, Where C is the direct emission amount, and C is the measured gas concentration (kg / m³). 3 F is the exhaust gas flow rate (m³ / s). 3 / h), K tp It is a correction coefficient for temperature (t) and air pressure (p), which is derived based on the ideal gas law. It calculates the instantaneous emission by integrating over the metering period, thus achieving high-precision real-time metering of direct emissions.
[0037] The direct emission calculation process utilizes a real-time concentration-flow coupling formula for dynamic calculations. The coefficients of this formula are derived from the ideal gas law, and the integration interval is set as the metering period. This formula can integrate gas concentration, exhaust flow rate, and temperature and pressure correction coefficients in real time, thereby achieving high-precision real-time metering of direct emissions and ensuring that the calculation results accurately reflect the actual instantaneous emissions during the production process.
[0038] In one embodiment, indirect emissions calculation utilizes preprocessed indirect energy consumption data, incorporates a dynamic factor library, and employs the electricity consumption carbon emission formula:
[0039]
[0040] in, For the aforementioned indirect emissions, P i EF represents the real-time power consumption (kWh) of device i. it The real-time emission factor for the region where the device is connected to the power grid at time t is pushed in real-time by the power grid company's API, enabling dynamic calculation of indirect emissions such as purchased energy. This factor is pushed in real-time by the power grid company's API. By acquiring the device's power consumption and the power grid emission factor in real time, indirect emissions such as purchased energy are dynamically calculated, ensuring that the results are updated synchronously with the real-time changes in the power grid emission factor, thus improving the accuracy and timeliness of indirect emission measurement.
[0041] S104. If the emission deviation is greater than the deviation threshold, the cause of the deviation is determined based on the equipment operation log and the environmental data, and a verification data point is determined based on the cause of the deviation, and a verification measurement is performed based on the verification data point.
[0042] In one embodiment, collaborative metering across the supply chain leverages blockchain technology to connect with measured data interfaces of upstream and downstream enterprises, broadly integrating data from multiple sources to achieve emission metering across the entire supply chain. Specifically, for calculating emissions from logistics transportation, the mileage is first accurately calculated based on the GPS trajectory of the transport vehicles. Then, combined with data obtained from real-time fuel consumption sensors, the transportation emissions are precisely calculated based on the correlation between vehicle fuel consumption and mileage. The total emissions of the automaker are obtained by adding the direct emissions from the factory to the transportation emissions from each component supplier. The transportation emissions from each component supplier are weighted according to their procurement percentage. Blockchain technology effectively ensures the credibility of data during transmission and sharing, thereby achieving collaborative metering of emissions across all links of the supply chain. The collaborative metering process utilizes blockchain technology to connect with measured data interfaces of upstream and downstream enterprises, integrating data from various parties to achieve full-chain emission metering. For calculating emissions from logistics transportation, the mileage is first determined by the GPS trajectory of the transport vehicles, and then combined with real-time fuel consumption sensor data to calculate the emissions based on the correlation between vehicle fuel consumption and mileage. Automakers calculate total emissions by summing direct emissions from their factories with emissions from transportation by all component suppliers, with transportation emissions from each supplier weighted according to their procurement proportion. Blockchain technology can ensure the credibility of data during transmission and sharing, enabling collaborative measurement of emissions across the entire supply chain.
[0043] In one embodiment, automatic calibration ensures sensor measurement accuracy through a dual mechanism. Every morning at dawn, a zero-point drift correction operation is performed on the sensor using a standard gas of known concentration, such as CO2, to eliminate accumulated errors during long-term operation. Simultaneously, an error prediction model is trained based on nearly 30 days of historical data. When environmental parameters deviate from standard conditions, such as when the air pressure drops below 101 kPa, the system automatically adjusts the correction coefficients in the measurement formula to dynamically compensate for the impact of environmental changes on sensor measurement accuracy, thereby ensuring long-term stable operation and maintaining high measurement accuracy. The automatic calibration process utilizes both standard gas calibration and environmental parameter correction mechanisms to ensure measurement accuracy. Every morning at dawn, a zero-point drift correction is performed on the sensor using a standard gas of known concentration, such as CO2, to eliminate accumulated errors during operation. Simultaneously, an error prediction model is trained based on nearly 30 days of historical data. When environmental parameters deviate from standard conditions, such as when the air pressure drops below 101 kPa, the correction coefficients in the measurement formula are automatically adjusted to dynamically compensate for the impact of environmental changes on sensor measurement accuracy, ensuring long-term stable and accurate operation of the sensor.
[0044] In one embodiment, end-to-end traceability utilizes blockchain technology to store key data, ensuring the traceability of the measurement process and the immutability of the data. The stored key data includes sensor calibration records, emission factor update logs, and other content. Each data block contains a timestamp, equipment number, and operator signature, thus forming a complete and clear audit trail. When key parameters are modified, such as fuel calorific value data, the system automatically generates a hash value comparison before and after the modification and synchronizes this information to the regulatory node. This ensures that data modifications are traceable and verifiable, effectively improving the credibility of carbon data in scenarios such as carbon trading. The end-to-end traceability process uses blockchain technology to store key data, ensuring the traceability of the measurement process and the immutability of the data. The stored content includes key data such as sensor calibration records and emission factor update logs. Each data block contains a timestamp, equipment number, and operator signature, constructing a complete audit trail. When key parameters such as fuel calorific value are modified, the system automatically generates a comparison of hash values before and after the modification and synchronizes them to the regulatory node to ensure that the data modification is verifiable and verifiable, thereby improving the credibility of carbon data in scenarios such as carbon trading.
[0045] In one embodiment, real-time monitoring output relies on metering results to accurately push minute-level emission curves to the enterprise's operations team, providing a real-time and intuitive reflection of emission dynamics during the production process. The system performs in-depth analysis of historical emission data to set scientifically reasonable emission peak thresholds, using the 90th percentile of historical data as an example. Once real-time emission data exceeds this set threshold, the system immediately triggers an early warning mechanism, promptly prompting the enterprise to adjust its production strategy. This approach quickly captures anomalies in instantaneous emissions, providing strong real-time decision support for enterprises to achieve dynamic emission reduction. The real-time monitoring output pushes minute-level emission curves to the enterprise's operations team based on metering results, presenting real-time changes in emissions during the production process. The system analyzes historical emission data to determine emission peak thresholds, such as the 90th percentile of historical data. When real-time emission data exceeds this threshold, the system automatically triggers an early warning, reminding the enterprise to adjust its production strategy in a timely manner, effectively capturing anomalies in instantaneous emissions, and providing real-time and accurate decision-making basis for dynamic emission reduction.
[0046] In one embodiment, the periodic accounting and emission reduction analysis work aims to provide enterprises with comprehensive and accurate carbon emission management support. This work generates carbon measurement reports compliant with ISO 14064 standards on a daily, monthly, and yearly basis. In the reports, the system automatically distinguishes between direct emissions (referred to as Scope 1), emissions from purchased energy (referred to as Scope 2), and other indirect emissions (referred to as Scope 3), ensuring that the report content meets the stringent standardization requirements of regulation and carbon trading. Simultaneously, combined with production process data, the system meticulously compares the emission-to-output ratios of different shifts. Through this analysis, high-emission, low-output production periods can be accurately identified, thereby uncovering the enterprise's emission reduction potential. Based on these analysis results, the system recommends optimal production scheduling schemes for enterprises, helping them optimize production processes and effectively achieve refined emission reduction targets. The periodic accounting and emission reduction analysis process generates carbon measurement reports compliant with ISO 14064 standards on a daily, monthly, and yearly basis. The reports automatically distinguish between Scope 1 direct emissions, Scope 2 purchased energy emissions, and Scope 3 other indirect emissions, complying with the standardized specifications of regulation and carbon trading. At the same time, by combining production process data to compare the emission-output ratio of different shifts, the system identifies production periods with high emissions and low output, recommends the optimal production scheduling plan, and helps enterprises optimize production processes to achieve refined emission reduction goals.
[0047] In one embodiment, a laser gas analyzer is deployed at key nodes of the production facility to collect the concentrations of greenhouse gases such as CO2 and CH4 in real time with an accuracy controlled within ±1ppm; an exhaust gas flow sensor acquires exhaust gas flow data synchronously at a sampling frequency of 1Hz, and the two are combined to calculate the instantaneous emissions, with the calculation result in kg / h.
[0048] In one embodiment, energy consumption data is obtained by connecting to the enterprise's energy management system via an industrial bus. Specifically, this includes real-time data on electricity and heat consumption, such as minute-level power consumption per device, and is synchronously associated with the regional real-time emission factor provided by the power grid company. The regional real-time emission factor is pushed in real-time by the power grid company's API and is used to dynamically calculate indirect emissions such as purchased energy.
[0049] In one embodiment, environmental sensors measure temperature, humidity, and air pressure; process parameter sensors collect key process parameters such as oxygen supply intensity in steelmaking converters; and logistics data sensors record the GPS trajectory and load information of transport vehicles. The collection frequency of all data is dynamically adjusted according to industry characteristics, with high-emission industries set to 1 minute / time and low-emission industries set to 15 minutes / time.
[0050] In one embodiment, interpolation is performed using moving averages of adjacent time periods; simultaneously, data from different sources are unified to the same time coordinate system, such as interpolating hourly data of power grid emission factors to minute-level data, and aggregating them spatially by production units such as workshops and production lines.
[0051] In one embodiment, blockchain technology is used to connect with the measured data interfaces of upstream and downstream enterprises, and the transportation emissions are calculated by combining the GPS trajectory of logistics vehicles and real-time fuel consumption sensor data; the direct emissions from the factory area and the transportation emissions from suppliers are integrated and weighted according to the proportion of procurement to achieve collaborative measurement of emissions across the entire industrial chain.
[0052] In one embodiment, blockchain technology is used to store key data, including sensor calibration records and emission factor update logs. Each data block contains a timestamp, equipment number, and operator signature. When key parameters are modified, the system automatically generates a comparison of hash values before and after the modification and synchronizes them to the regulatory node to ensure that the data is tamper-proof and traceable.
[0053] like Figure 2 As shown in the illustration, this application also provides a carbon metering device based on measured data, comprising:
[0054] At least one processor; and,
[0055] A memory communicatively connected to the at least one processor; wherein,
[0056] The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the carbon metering device based on measured data to perform the following:
[0057] Emissions data is acquired through a pre-set sensor network to determine the company’s energy consumption data, and environmental data is acquired through pre-set integrated sensors.
[0058] The emission data, energy consumption data, and environmental data are fused to integrate data from different sources into a pre-set time coordinate system, and the fused data is spatially aggregated according to the production unit to determine the comparison model.
[0059] The direct emissions are determined based on the emission data, and the indirect emissions are determined based on the energy consumption data. The direct emissions and the indirect emissions are compared using a comparison model to determine the emission deviation. The emission deviation is then compared with a pre-set deviation threshold.
[0060] If the emission deviation is greater than the deviation threshold, the cause of the deviation is determined based on the equipment operation log and the environmental data, and a verification data point is determined based on the cause of the deviation, and a verification measurement is performed based on the verification data point.
[0061] This application embodiment also provides a non-volatile computer storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured as follows:
[0062] Emissions data is acquired through a pre-set sensor network to determine the company’s energy consumption data, and environmental data is acquired through pre-set integrated sensors.
[0063] The emission data, energy consumption data, and environmental data are fused to integrate data from different sources into a pre-set time coordinate system, and the fused data is spatially aggregated according to the production unit to determine the comparison model.
[0064] The direct emissions are determined based on the emission data, and the indirect emissions are determined based on the energy consumption data. The direct emissions and the indirect emissions are compared using a comparison model to determine the emission deviation. The emission deviation is then compared with a pre-set deviation threshold.
[0065] If the emission deviation is greater than the deviation threshold, the cause of the deviation is determined based on the equipment operation log and the environmental data, and a verification data point is determined based on the cause of the deviation, and a verification measurement is performed based on the verification data point.
[0066] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device and medium embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the description of the method embodiments.
[0067] The devices and media provided in this application are one-to-one with the methods. Therefore, the devices and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.
[0068] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0069] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0070] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0071] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0072] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.
[0073] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.
[0074] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.
[0075] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0076] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.
Claims
1. A carbon measurement method based on measured data, characterized in that, include: Emissions data is acquired through a pre-set sensor network to determine the company’s energy consumption data, and environmental data is acquired through pre-set integrated sensors. The emission data, energy consumption data, and environmental data are fused to integrate data from different sources into a pre-set time coordinate system. The fused data is then spatially aggregated according to the production unit to determine the comparison model. The production unit is a workshop or production line. The direct emissions are determined based on the emission data, and the indirect emissions are determined based on the energy consumption data. The direct emissions and the indirect emissions are compared using a comparison model to determine the emission deviation. The emission deviation is then compared with a pre-set deviation threshold. If the emission deviation is greater than the deviation threshold, the cause of the deviation is determined based on the equipment operation log and the environmental data, and a verification data point is determined based on the cause of the deviation, and a verification measurement is performed based on the verification data point; Emissions data is acquired through a pre-configured sensor network, specifically including: Data on greenhouse gas concentrations are collected from production facilities using a pre-set laser gas analyzer. The exhaust gas flow rate data is obtained by a pre-set exhaust gas flow sensor, and the emission data is determined based on the greenhouse gas concentration and the exhaust gas flow rate data. Determine the company's energy consumption data, specifically including: The system interfaces with the enterprise via a pre-defined industrial bus to obtain the energy consumption data, which includes electricity consumption data and heat consumption data. Determine the real-time emission factor of the enterprise, and synchronously correlate the energy consumption data based on the real-time emission factor; Environmental data is acquired through pre-configured integrated sensors, specifically including: The environmental data includes temperature, humidity, and air pressure; Obtain the process parameters and industry characteristic data of the enterprise, and integrate the environmental data based on the process parameters and industry characteristic data; The fusion of the emission data, the energy consumption data, and the environmental data specifically includes: The emission data, energy consumption data, and environmental data are determined according to pre-set identification criteria, and interpolation is performed based on the moving averages. The interpolated data is then aggregated in terms of spatial dimensions to determine the time coordinate system.
2. The method according to claim 1, characterized in that, The method further includes: The formula for calculating the direct emissions is as follows: in, Where C is the direct emission amount, F is the measured gas concentration, and K is the exhaust gas flow rate. tp These are correction factors for temperature and air pressure; The formula for calculating the indirect emissions is as follows: in, For the aforementioned indirect emissions, P i For the real-time power consumption of device i, EF it Let t be the real-time emission factor of the area where the device is connected to the power grid.
3. The method according to claim 1, characterized in that, The method further includes: By connecting the measured data interface of upstream and downstream enterprises through blockchain technology, and combining the GPS trajectory of logistics vehicles and real-time fuel consumption sensor data, transportation emissions can be determined. By integrating direct emissions from the plant area with emissions from supplier transportation, and weighting them according to a pre-set procurement ratio, collaborative measurement of emissions across the entire industrial chain can be achieved.
4. The method according to claim 1, characterized in that, The method further includes: Key data, including sensor calibration records and emission factor update logs, are stored using blockchain technology to identify data blocks. These data blocks contain timestamps, device numbers, and operator signatures. When data is modified, a comparison of the hash values before and after the modification is generated and synchronized to the monitoring node.
5. A carbon metering device based on measured data, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the carbon metering device based on measured data to perform the method as described in claim 1.
6. A non-volatile computer storage medium storing computer-executable instructions, characterized in that, The computer-executable instructions are configured as described in claim 1.