Carbon metering method and device based on measured data and medium

Abstract

The invention discloses a carbon metering method and device based on measured data and a medium, and the method comprises the steps: obtaining emission data through a preset sensor network, determining the energy consumption data of an enterprise, and obtaining environment data through a preset integrated sensor; fusing the emission data, the energy consumption data and the environment data to integrate data from different sources into a preset time coordinate system, and performing spatial aggregation on the fused data according to a production unit to determine a comparison model; determining a direct emission amount according to the emission data, determining an indirect emission amount according to the energy consumption data, comparing the direct emission amount with the indirect emission amount through a comparison model to determine an emission deviation, and comparing the emission deviation with a preset deviation threshold value; and if the emission deviation is greater than a deviation threshold value, determining a deviation reason according to the equipment operation log and the environment data, determining a re-check data point according to the deviation reason, and performing re-check metering according to the re-check data point.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of measurement, in particular to a carbon metering method based on measured data, a device and a medium. BACKGROUND

[0002] At present, the mainstream carbon metering method mainly relies on an accounting system based on emission factors, typical schemes including: material balance algorithm relying on industry average emission factors, coefficient calculation algorithm based on enterprise historical energy consumption data, and indirect emission calculation model using fixed formula. Although the mainstream carbon metering method has advantages in data acquisition and operation, it has limitations such as data accuracy being easily affected by industry average value or historical data deviation, being difficult to reflect real-time changes and special circumstances of enterprises, and fixed formula being unable to adapt to complex scenarios. For enterprises, it will lead to inaccurate emission reduction decision, affect emission reduction effect and cost, interfere with carbon price formation mechanism, and affect market fairness and efficiency. SUMMARY

[0003] In order to solve the above problems, the present application provides a carbon metering method based on measured data, which comprises: obtaining emission data through a pre-set sensor network, determining energy consumption data of an enterprise, and obtaining environmental data through a pre-set integrated sensor; fusing the emission data, energy consumption data and environmental data to integrate data of 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 emission according to the emission data, and determining indirect emission according to the energy consumption data, comparing the direct emission and the indirect emission 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 deviation reasons according to equipment operation logs and the environmental data, determining review data points according to the deviation reasons, and performing review metering according to the review data points.

[0004] In one example, the emission data is obtained through a pre-set sensor network, specifically including: data acquisition of production facilities through a pre-set laser gas analyzer to obtain greenhouse gas concentration; obtaining waste gas flow data through a pre-set waste gas flow sensor, and determining the emission data according to the greenhouse gas concentration and the waste gas flow data.

[0005] In one example, the energy consumption data of the enterprise is determined, specifically including: interfacing with the enterprise through a pre-determined industrial bus to obtain the energy consumption data, the energy consumption data including power consumption data and heat consumption data; determining real-time emission factors of the enterprise, and synchronously associating the energy consumption data according to the real-time emission factors.

[0006] In one example, the environmental data is obtained by a pre-set integrated sensor, specifically including: the environmental data includes temperature, humidity, air pressure; the process parameters and industry characteristic data of the enterprise are obtained, and the environmental data is integrated according to the process parameters and the industry characteristic data.

[0007] In one example, the emission data, the energy consumption data and the environmental data are fused, specifically including: a moving average value of the emission data, the energy consumption data and the environmental data is determined according to a pre-set identification criterion, interpolation repair is performed according to the moving average value, and spatial dimension aggregation is performed on the data after interpolation repair to determine the time coordinate system.

[0008] In one example, the method further comprises: The calculation formula of the direct emission amount is:

[0009] Wherein, The direct emission amount is C, the measured gas concentration is F, the waste gas flow is K tp The correction coefficient of temperature and air pressure is The calculation formula of the indirect emission amount is:

[0010] Wherein, Pᵢ is the real-time power consumption of equipment i, EF it The real-time emission factor of the area where the equipment accesses the power grid at t is

[0011] In one example, the method further comprises: determining the transportation emission by a blockchain technology to interface the measured data of the upstream and downstream enterprises, combining the GPS track of the logistics vehicle and the real-time fuel consumption sensor data; integrating the direct emission of the factory area and the transportation emission of the supplier, and performing weighted calculation according to the pre-set procurement proportion, so as to realize the collaborative measurement of the whole industry chain emission.

[0012] In one example, the method further comprises: storing the key data by a blockchain technology, including sensor calibration records and emission factor update logs, to determine data blocks, the data blocks containing time stamps, equipment numbers and operator signatures; when the data is modified, the hash values before and after the modification are compared and synchronized to the supervision node.

[0013] In another aspect, the application also provides a carbon metering device based on measured data, comprising: at least one processor; and a memory in communication connection with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the carbon metering device based on measured data to perform: obtaining emission data through a pre-set sensor network, determining energy consumption data of an enterprise, and obtaining environmental data through a pre-set integrated sensor; fusing the emission data, the energy consumption data, and the environmental data to integrate data of different sources into a pre-set time coordinate system, and performing spatial aggregation on the fused data according to a production unit to determine a comparison model; determining direct emission according to the emission data, and determining indirect emission according to the energy consumption data, comparing the direct emission and the indirect emission through the comparison model to determine emission deviation, comparing the emission deviation with a pre-set deviation threshold; if the emission deviation is greater than the deviation threshold, determining deviation reasons according to equipment operation logs and the environmental data, determining review data points according to the deviation reasons, and performing review metering according to the review data points.

[0014] In another aspect, the application also provides a non-volatile computer storage medium storing computer executable instructions, which are configured to: obtain emission data through a pre-set sensor network, determine energy consumption data of an enterprise, and obtain environmental data through a pre-set integrated sensor; fuse the emission data, the energy consumption data, and the environmental data to integrate data of different sources into a pre-set time coordinate system, and perform spatial aggregation on the fused data according to a production unit to determine a comparison model; determine direct emission according to the emission data, and determine indirect emission according to the energy consumption data, compare the direct emission and the indirect emission through the comparison model to determine emission deviation, compare the emission deviation with a pre-set deviation threshold; if the emission deviation is greater than the deviation threshold, determine deviation reasons according to equipment operation logs and the environmental data, determine review data points according to the deviation reasons, and perform review metering according to the review data points.

[0015] The application obtains emission, energy consumption and environmental data through a sensing network, integrated sensors and other channels, ensures that the data sources are extensive and accurate, and lays a solid foundation for subsequent analysis. The fusion and aggregation of multi-source data can accurately determine the emission deviation and find potential problems. When the emission deviation is greater than the threshold, the cause can be determined according to the equipment operation log and environmental data, and the metering is reviewed to ensure the reliability of the results. Direct and indirect emission calculation formulas are given, considering various factors to make the metering more accurate. The use of blockchain technology realizes collaborative metering of the whole industry chain and key data storage, improves the data credibility and traceability, meets the carbon metering demand in complex scenarios, and helps enterprises to accurately reduce emissions and management. BRIEF DESCRIPTION OF DRAWINGS

[0016] The drawings described herein are used to provide further understanding of the application, and form a part of the application. The illustrative embodiments of the application and their descriptions are used to explain the application, and do not constitute an improper limitation on the application. In the drawings: Figure 1 A flowchart of a carbon metering method based on measured data in an embodiment of the application is shown. Figure 2 A schematic diagram of a carbon metering device based on measured data in an embodiment of the application is shown. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical scheme and advantages of the application clearer, the technical scheme of the application will be described in detail below with reference to the specific embodiments of the application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the application.

[0018] The technical scheme provided by each embodiment of the application will be described in detail below with reference to the drawings.

[0019] As shown in Figure 1 In order to solve the above problems, the application provides a carbon metering method based on measured data, which comprises: S101, obtaining emission data through a pre-set sensing network, determining energy consumption data of an enterprise, and obtaining environmental data through a pre-set integrated sensor.

[0020] The multi-source real measurement data collection work is realized through the deployment of distributed sensing network and interface integration technology, and synchronously acquires three types of core data. Firstly, direct emission data. Laser gas analyzers are deployed at key nodes of production facilities, such as boiler chimneys and fermentation tank exhaust ports, to collect greenhouse gas concentrations such as CO2 and CH4 in real time, with an accuracy of ±1 ppm. At the same time, waste gas flow sensors are deployed to obtain waste gas flow data at a sampling frequency of 1 Hz. The instantaneous emission amount is calculated by combining the greenhouse gas concentration data and the waste gas flow data, and the calculation result is in kg / h. Secondly, indirect energy consumption data. Through industrial bus, such as Modbus protocol, the enterprise energy management system is connected to obtain real-time data of energy consumption such as electricity and heat, for example, the minute-level power consumption of each device, and synchronously associate the regional real-time emission factor provided by the power grid enterprise. Thirdly, auxiliary parameter data. Temperature, humidity, and air pressure data measured by environmental sensors, process parameters such as converter oxygen supply intensity in steelmaking plants, and logistics data such as GPS trajectory and load information of transport vehicles are integrated.

[0021] In one embodiment, the data collection frequency is dynamically adjusted according to industry characteristics. For high-emission industries, such as the chemical industry, it is set to 1 minute / time; for low-emission industries, such as office buildings, it is set to 15 minutes / time. Such settings can not only ensure the timeliness of key data, but also reduce unnecessary energy consumption, providing a foundation for subsequent data processing and measurement.

[0022] S102, 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 production units to determine a comparison model.

[0023] The data preprocessing and fusion link is committed to comprehensive cleaning and deep correlation of the collected raw data. In terms of abnormal value processing, the 3σ criterion is used to accurately identify jump data caused by sensor failure, and the moving average of adjacent time periods is used for interpolation repair to ensure the stability of the data.

[0024] In the spatiotemporal alignment operation, data from different channels is unified to the same time coordinate system. Specifically, the hourly data of the power grid emission factor is converted to minute-level data by interpolation, and is aggregated in the spatial dimension according to production units such as workshops and production lines, so that the data is orderly integrated in the spatiotemporal dimension. To ensure the accuracy and consistency of the data, a comparison model of direct measurement and indirect calculation is constructed. When the deviation between the direct emission data and the value calculated based on fuel consumption exceeds 5%, the system automatically retrieves the device operation log of the period, such as the burner valve opening degree and other information, as well as the environmental parameters, in-depth analyzes the causes of the deviation, and marks the data points that need to be manually reviewed. Through this series of operations, reliable and accurate data input is provided for dynamic measurement.

[0025] In one embodiment, the data preprocessing and fusion link mainly carries out cleaning and correlation of the original data. In the abnormal value filtering, the 3σ criterion is used to identify jump data caused by sensor failure, and the moving average value of the adjacent period is used for interpolation repair. In the spatiotemporal alignment, data from different sources is unified to the same time coordinate system, such as converting the hourly data of the power grid emission factor to minute-level data, and aggregating in the spatial dimension according to production units such as workshops and production lines. In the cross-validation link, a comparison model of direct measurement and indirect calculation is established. When the deviation between the direct emission data and the value calculated based on fuel consumption exceeds 5%, the device operation log and environmental parameters of the period are automatically retrieved, the causes of the deviation are judged, and the data points that need to be manually reviewed are marked, so as to ensure the accuracy and consistency of the data and provide reliable input basis for subsequent measurement work.

[0026] S103, determining a direct emission amount according to the emission data, and determining an indirect emission amount according to the energy consumption data, comparing the direct emission amount and the indirect emission amount through a comparison model to determine an emission deviation, and comparing the emission deviation with a pre-set deviation threshold.

[0027] In one embodiment, the direct emission calculation work is carried out based on the preprocessed direct emission data, and a real-time concentration-flow coupling formula is used:

[0028] wherein, is the direct emission amount, C is the measured gas concentration (kg / m 3 ), F is the exhaust gas flow (m 3 / h), K tp is the temperature (t) and pressure (p) correction coefficient, which is derived based on the ideal gas state equation, and is integrated to calculate the instantaneous emission amount according to the measurement period, realizing high-precision real-time measurement of direct emission.

[0029] The direct emission calculation link uses a real-time concentration-flow coupling formula to carry out dynamic calculation. The coefficient of the formula is determined by the ideal gas state equation, and the integral interval is set as the measurement period. The formula can integrate gas concentration, waste gas flow and temperature and pressure correction coefficients in real time, thereby realizing high-precision real-time measurement of direct emissions and ensuring that the calculation results can truly and accurately reflect the actual situation of instantaneous emissions in the production process.

[0030] In one embodiment, the indirect emission calculation uses pre-processed indirect energy consumption data, introduces a dynamic factor library, and uses the power consumption carbon emission formula:

[0031] wherein, is the indirect emission amount, P i is the real-time power consumption of device i (kWh), EF it is the real-time emission factor of the area where the device is connected to the power grid at time t, which is pushed by the power grid enterprise API in real time, and completes the dynamic calculation of indirect emissions such as purchased energy. The factor is pushed by the power grid enterprise API in real time. By obtaining the device power consumption and the power grid emission factor in real time, the indirect emissions such as purchased energy are dynamically calculated, ensuring that the results are updated synchronously with the real-time changes of the power grid emission factor, and improving the accuracy and timeliness of indirect emission measurement.

[0032] S104, if the emission deviation is greater than the deviation threshold, determine the deviation reason according to the device operation log and the environmental data, determine the review data point according to the deviation reason, and perform review measurement according to the review data point.

[0033] In one embodiment, in the industry chain collaborative metering work, the measured data interface of upstream and downstream enterprises is connected through blockchain technology, and the data from multiple parties is widely integrated to realize the emission metering of the whole industry chain. Specifically, for the calculation of logistics transportation emissions, the driving distance is first calculated accurately according to the GPS track of the transportation vehicle, and then combined with the data obtained by the real-time fuel consumption sensor, the transportation emissions are calculated accurately according to the correlation between vehicle fuel consumption and mileage. The total emissions of automobile manufacturers are obtained by adding the direct emissions of the factory and the transportation emissions of each parts supplier. Among them, the transportation emissions of each parts supplier are weighted according to the procurement proportion. Through the blockchain technology, the credibility of the data in the transmission and sharing process can be effectively guaranteed, and the collaborative metering of the emissions of each link of the industry chain can be realized. The industry chain collaborative metering link uses the measured data interface of upstream and downstream enterprises through blockchain technology, integrates data from all parties, and achieves whole-link emission metering. The calculation of logistics transportation emissions first determines the driving distance through the GPS track of the transportation vehicle, and then combines the real-time fuel consumption sensor data to calculate the emissions according to the corresponding relationship between vehicle fuel consumption and mileage. The total emission calculation method of automobile manufacturers is the sum of the direct emissions of the factory and the transportation emissions of each parts supplier, and the transportation emissions of each parts supplier are weighted according to the procurement proportion. With the help of blockchain technology, the credibility of the data in the transmission and sharing process can be ensured, and the collaborative metering of the emissions of each link of the industry chain can be realized.

[0034] In one embodiment, the automatic calibration work guarantees the metering accuracy of the sensor through a double mechanism. At dawn every day, a standard gas with a known concentration, such as CO2 standard gas, is used to correct the zero drift of the sensor, so as to eliminate the cumulative error of the device during long-term operation. At the same time, an error prediction model is trained based on the historical data of the past 30 days. When the environmental parameters deviate from the standard state, such as when the air pressure is lower than 101 kPa, the system will automatically adjust the correction coefficient in the metering formula to dynamically compensate for the influence of environmental changes on the measurement accuracy of the sensor, so as to ensure that the sensor can operate stably for a long time and maintain high metering accuracy. The automatic calibration link uses two mechanisms, standard gas calibration and environmental parameter correction, to guarantee the metering accuracy. At dawn every day, a standard gas with a known concentration, such as CO2 standard gas, is used to correct the zero drift of the sensor, so as to eliminate the cumulative error of the device during operation. At the same time, an error prediction model is trained based on the historical data of the past 30 days. When the environmental parameters deviate from the standard state, such as when the air pressure is lower than 101 kPa, the system will automatically adjust the correction coefficient in the metering formula to dynamically compensate for the influence of environmental changes on the measurement accuracy of the sensor, so as to ensure that the sensor can operate stably for a long time and maintain high metering accuracy.

[0035] In one embodiment, the full-link traceability work records key data by means of blockchain technology to ensure that the metering process is traceable and the data is tamper-proof. The recorded key data includes sensor calibration records, emission factor update logs, etc. Each data block contains a timestamp, device number, and operator signature, thus forming a complete and clear audit trail. When a key parameter is modified, for example, the fuel heat value data is modified, the system will automatically generate a comparison of the hash values before and after the modification and synchronize this information to the regulatory node. In this way, the data modification is traceable and verifiable, effectively improving the credibility of carbon data in carbon trading and other scenarios. The full-link traceability link uses blockchain technology to record key data, ensuring that the metering process is traceable and the data cannot be tampered with. The recorded content includes key data such as sensor calibration records and emission factor update logs. Each data block contains a timestamp, device number, and operator signature, building a complete audit trail. When a key parameter such as fuel heat value data is modified, the system automatically generates a comparison of the hash values before and after the modification and synchronizes it to the regulatory node, ensuring that the data modification is verifiable and verifiable, and improving the credibility of carbon data in carbon trading and other scenarios.

[0036] In one embodiment, real-time monitoring output relies on metering results to accurately push minute-level emission curves to enterprise operation end to reflect the emission dynamics in the production process in real time and intuitively. The system will analyze historical emission data in depth and then set a scientific and reasonable emission peak threshold, taking the historical same period 90% quantile value as an example. Once the real-time emission data exceeds the set threshold, the system will immediately trigger the early warning mechanism and timely remind the enterprise to adjust the production strategy. In this way, instantaneous emission anomalies can be quickly captured, providing strong real-time decision support for dynamic emission reduction for enterprises. The real-time monitoring output link pushes minute-level emission curves to the enterprise operation end based on metering results to present the emission changes in the production process in real time. The system determines the emission peak threshold by analyzing historical emission data, such as the historical same period 90% quantile value. When the real-time emission data exceeds the threshold, the system automatically triggers an early warning to remind the enterprise to adjust the production strategy in a timely manner, effectively capturing instantaneous emission anomalies and providing real-time and accurate decision-making basis for dynamic emission reduction for enterprises.

[0037] In one embodiment, the periodic accounting and emission reduction analysis work is committed to providing comprehensive and accurate carbon emission management support for enterprises. This work will generate carbon measurement reports in accordance with the ISO 14064 standard on a daily, monthly, and annual basis. In the report, the system can automatically distinguish the scope of direct emissions (referred to as Scope 1), the scope of purchased energy emissions (referred to as Scope 2), and other indirect emissions (referred to as Scope 3), ensuring that the report content meets the strict requirements of regulation and carbon trading for standardization. At the same time, combined with production process data, the system will make a detailed comparison of the emission-output ratio of different shifts. Through this analysis, it can accurately identify high-emission, low-output production periods and thus tap the emission reduction potential of the enterprise. Based on these analysis results, the system will recommend the optimal production scheduling scheme for the enterprise, helping the enterprise optimize production processes and effectively achieve the goal of fine-tuned emission reduction. The periodic accounting and emission reduction analysis section generates carbon measurement reports in accordance with the ISO 14064 standard on a daily, monthly, and annual basis. The report automatically distinguishes Scope 1 direct emissions, Scope 2 purchased energy emissions, and Scope 3 other indirect emissions, meeting the standardization specifications of regulation and carbon trading. At the same time, combined with production process data, it compares the emission-output ratio of different shifts to find high-emission, low-output production periods, recommends the optimal production scheduling scheme, helps the enterprise optimize production processes, and achieves the goal of fine-tuned emission reduction.

[0038] In one embodiment, the laser gas analyzer is deployed at key nodes of the production facility to collect greenhouse gas concentrations such as CO2 and CH4 in real time, with an accuracy controlled within ±1 ppm; the waste gas flow sensor synchronously obtains waste gas flow data at a sampling frequency of 1 Hz, and both are used to calculate instantaneous emissions, with the calculation result in units of kg / h.

[0039] In one embodiment, energy consumption data is obtained through industrial bus interface with enterprise energy management system: specifically including real-time data of power, heat and other consumptions, such as minute-level power consumption of each device, and synchronously associated with regional real-time emission factors provided by the power grid enterprise; the regional real-time emission factors are pushed by the power grid enterprise API in real time, used for dynamic calculation of indirect emissions such as purchased energy.

[0040] In one embodiment, environmental sensors measure temperature, humidity, and air pressure; process parameter sensors collect key process parameters such as converter oxygen supply intensity in a steel mill; logistics data sensors record the GPS track and load information of transport vehicles; all data are dynamically adjusted in accordance with industry characteristics, with a setting of 1 minute / time for high-emission industries and 15 minutes / time for low-emission industries.

[0041] In one embodiment, interpolation repair is performed by moving average of adjacent time periods; at the same time, different source data is unified to the same time coordinate system, such as interpolation of hour-level data of power grid emission factors to minute-level, and spatial dimension aggregation according to production units such as workshops and production lines.

[0042] In one embodiment, the measured data interface of upstream and downstream enterprises is connected through blockchain technology, and the transportation emissions are calculated by combining the GPS trajectory of logistics vehicles and real-time fuel consumption sensor data; the direct emissions of the factory area and the transportation emissions of the suppliers are integrated, weighted according to the procurement proportion, and the collaborative measurement of the whole industry chain emissions is realized.

[0043] In one embodiment, blockchain technology is used to store key data, including sensor calibration records and emission factor update logs, and each data block contains a timestamp, a device number and an operator signature; when a key parameter is modified, the system automatically generates a comparison of the hash values before and after the modification, and synchronizes it to the regulatory node, ensuring that the data is tamper-proof and traceable.

[0044] As shown in Figure 2 The embodiment of the present application also provides a carbon measurement device based on measured data, which comprises: at least one processor; and a memory in communication connection with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the carbon measurement device based on measured data to perform: acquire emission data through a pre-set sensor network, determine energy consumption data of an enterprise, and acquire environmental data through a pre-set integrated sensor; fuse the emission data, the energy consumption data and the environmental data to integrate data of different sources into a pre-set time coordinate system, and perform spatial aggregation on the fused data according to production units to determine a comparison model; determine direct emissions according to the emission data, determine indirect emissions according to the energy consumption data, compare the direct emissions and the 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 deviation reasons according to equipment operation logs and the environmental data, determine review data points according to the deviation reasons, and perform review measurement according to the review data points.

[0045] The embodiment of the present application also provides a non-volatile computer storage medium, which stores computer executable instructions, and the computer executable instructions are set to: The emission data is acquired through the preset sensor network, the energy consumption data of the enterprise is determined, and the environmental data is acquired through the preset integrated sensor; The emission data, the energy consumption data and the environmental data are fused to integrate the data of different sources into a preset time coordinate system, and the fused data is spatially aggregated according to the production unit to determine a comparison model; The direct emission amount is determined according to the emission data, the indirect emission amount is determined according to the energy consumption data, the direct emission amount and the indirect emission amount are compared through the comparison model to determine an emission deviation, and the emission deviation is compared with a preset deviation threshold value; If the emission deviation is greater than the deviation threshold value, the deviation reason is determined according to the equipment operation log and the environmental data, the review data point is determined according to the deviation reason, and the review metering is performed according to the review data point.

[0046] Each of the embodiments in the present application is described in a progressive manner, and the same and similar parts between the embodiments can be referred to each other. Each of the embodiments mainly describes the difference from other embodiments. In particular, the device and medium embodiments are described simply because they are basically similar to the method embodiments, and the related parts can be referred to the part of the method embodiments.

[0047] The device and medium provided by the embodiments of the present application are one-to-one corresponding to the method, and therefore, the device and medium also have the similar beneficial technical effects as the method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the device and medium will not be described here.

[0048] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can be in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can be in the form of a computer program product implemented 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.

[0049] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or combination thereof. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or combination thereof.

[0050] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or combination thereof. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or combination thereof.

[0051] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or combination thereof. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or combination thereof.

[0052] In one typical configuration, the computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0053] The memory can include non-persistent memory and / or volatile memory, such as a random access memory (RAM) including a cache area for the temporary storage of data. A

[0054] Computer-readable media includes permanent and non-permanent, movable and non-movable media that can implement information storage by any method or technology. The information can be computer-readable instructions, data structures, program modules 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, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer-readable media does not include transitory media such as modulated data signals and carriers.

[0055] It should also be noted that the terms "comprising", "containing", or any other variant thereof are intended to cover a non-exclusive inclusion, such that a process, method, article or apparatus that comprises a list of elements does not only include those elements, but can also include other elements not expressly listed or inherent to such process, method, article or apparatus. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article or apparatus that includes the element.

[0056] The above only describes the embodiments of the present application and is not intended to limit the present application. Those skilled in the art can make various changes and modifications to the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application shall be included in the scope of the claims of the present 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, and the fused data is spatially aggregated according to the production unit to determine the comparison model. 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.

2. The method according to claim 1, characterized in that, 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.

3. The method according to claim 1, characterized in that, 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. The real-time emission factor of the enterprise is determined, and the energy consumption data is synchronously correlated based on the real-time emission factor.

4. The method according to claim 1, characterized in that, Environmental data is acquired through pre-configured integrated sensors, specifically including: The environmental data includes temperature, humidity, and air pressure; The process parameters and industry characteristic data of the enterprise are obtained, and the environmental data are integrated based on the process parameters and industry characteristic data.

5. The method according to claim 1, characterized in that, 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.

6. 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, Where Pᵢ is the indirect emission amount, Pᵢ is the real-time power consumption of device i, and EF is the indirect emission amount. it Let t be the real-time emission factor of the area where the device is connected to the power grid.

7. 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.

8. 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.

9. 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 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: 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, and the fused data is spatially aggregated according to the production unit to determine the comparison model. 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.

10. A non-volatile computer storage medium storing computer-executable instructions, characterized in that, The computer-executable instructions are set as follows: 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, and the fused data is spatially aggregated according to the production unit to determine the comparison model. 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.

Images