A method, system, device, medium and product for tracing and checking carbon emissions of an enterprise

Abstract

The application discloses an enterprise carbon emission traceability checking method, system, device, medium and product, relates to the technical field of carbon emission accounting, and achieves the technical leap of multi-source data from mutual fragmentation to collaborative analysis by acquiring multi-source data of a target enterprise park and preprocessing, respectively performing theoretical accounting on park power consumption data and carbon emission data, performing real measurement inversion on unmanned aerial vehicle observation data and meteorological data, and cross-checking time-sharing and period carbon emission results obtained through two independent paths, effectively solving the technical problems that carbon emission accounting results are difficult to be finely attributed and lack independent verification in the prior art.

Description

Technical Field

[0001] This invention relates to the field of carbon emission accounting technology, and in particular to a method, system, equipment, medium and product for enterprise carbon emission traceability and verification. Background Technology

[0002] As key emission sources, the accuracy, traceability, and verifiability of carbon emission data from energy-intensive industries have become the core foundation for emission management and compliance assessment. Currently, relevant enterprises generally adopt traditional accounting methods based on material consumption statistics and emission factor estimation. This method heavily relies on manually entered production data and preset calculation parameters. However, in actual production processes, material consumption data is often scattered across multiple independent business information systems, resulting in inconsistent data definitions, long aggregation cycles, and a lack of effective measurement methods for a large amount of non-process-related fugitive emissions, making it difficult to fully include them in the statistical scope. Furthermore, production facilities are widely distributed, and many high-altitude facilities, enclosed spaces, and restricted areas cannot be fully covered by manual inspections or ground-based fixed equipment. The existence of monitoring blind spots makes it difficult to accurately grasp the overall emission situation. Monthly or quarterly manual data aggregation is not only inefficient but also carries the risks of data misreporting, omissions, and human interference. Carbon emission data generally lacks effective independent verification methods.

[0003] In recent years, aerial mobile monitoring technology, with its unique advantages of mobility, ability to traverse complex terrain, and wide coverage, has begun to be explored for application in emission monitoring in industrial parks. Equipped with appropriate sensors, aerial mobile platforms can collect atmospheric gas concentration data over factory areas through patrols or fixed-point operations, providing a direct reflection of the actual emission status of the area from an airborne perspective, effectively compensating for the shortcomings of traditional ground monitoring and manual inspections in terms of spatial coverage and accessibility. Simultaneously, the widespread deployment of energy metering systems in industrial parks enables the accurate collection of energy consumption data by region and time period. However, in current practice, these two types of monitoring data systems still operate independently, lacking an effective collaborative analysis mechanism. This fragmentation of data systems makes it difficult for any single monitoring method to independently support complete emission analysis needs. Atmospheric concentration-based monitoring methods can reflect the overall emission level of the region, but it is difficult to attribute the total result to specific emission stages; while estimation methods based on consumption data can obtain emission estimates for each stage, they lack independent measured data from the atmospheric medium for comparison and verification. The data from multiple sources cannot form a mutually supportive and cross-validation relationship, and the emission accounting results lack both refined attribution capabilities and effective external validation. Summary of the Invention

[0004] This invention provides a method, system, equipment, medium, and product for enterprise carbon emission traceability and verification, which solves the technical problems in the prior art where emission accounting data from multiple sources are fragmented and cannot be analyzed collaboratively, resulting in difficulty in finely attributing emission results and a lack of independent verification.

[0005] The first aspect of this invention provides a method for verifying corporate carbon emissions, comprising:

[0006] The target enterprise park is acquired and preprocessed to obtain target multi-source data, which includes UAV observation data, park electricity consumption data, carbon emission data and meteorological data;

[0007] Theoretical carbon emissions are calculated using the park's electricity consumption data and carbon emission data to obtain the enterprise's time-of-use theoretical carbon emissions, and carbon emissions are summarized based on preset time periods to obtain the periodic theoretical carbon emissions.

[0008] By using the UAV observation data and the meteorological data to perform measured carbon emission inversion, the company's time-sharing measured carbon emissions are obtained, and carbon emissions are summarized based on the preset time period to obtain periodic measured carbon emissions.

[0009] The time-sharing theoretical carbon emissions of the enterprise are compared with the time-sharing measured carbon emissions of the enterprise, and the total carbon emissions are compared with the periodic theoretical carbon emissions and the periodic measured carbon emissions to generate the verification result of the target enterprise park.

[0010] Optionally, the park's electricity consumption data includes the time-of-use electricity consumption of each electricity-consuming unit within the target enterprise park, and the carbon emission data includes the fuel consumption, fuel carbon content, fuel oxidation rate, process raw material consumption, and process emission factor of each electricity-consuming unit within the target enterprise park. The theoretical carbon emission calculation is performed using the park's electricity consumption data and the carbon emission data to obtain the enterprise's time-of-use theoretical carbon emission, and carbon emissions are summarized based on a preset time period to obtain the periodic theoretical carbon emission, including:

[0011] The time-of-use grid factor published by the regional power grid is retrieved and combined with the time-of-use electricity consumption of each of the aforementioned power-consuming units to determine the unit time-of-use electricity carbon emissions of each of the aforementioned power-consuming units;

[0012] The unit-time fuel combustion carbon emissions of each power consumption unit are determined based on the fuel consumption, fuel carbon content, and fuel oxidation rate of each power consumption unit.

[0013] Based on the amount of process raw materials used and the process emission factor of each power-consuming unit, the unit time-sharing process carbon emissions of each power-consuming unit are determined.

[0014] The unit time-sharing carbon emissions of fuel combustion associated with each of the power consumption units and the unit time-sharing carbon emissions of the process are summed to determine the unit time-sharing direct carbon emissions of each of the power consumption units.

[0015] The unit time-of-use electricity carbon emissions associated with each of the aforementioned electricity consumption units are summed with the unit time-of-use direct carbon emissions to determine the unit time-of-use theoretical carbon emissions of each of the aforementioned electricity consumption units.

[0016] The summation of all the unit time-sharing theoretical carbon emissions is used to obtain the enterprise time-sharing theoretical carbon emissions of the target enterprise park.

[0017] The theoretical carbon emissions of the enterprise at different times within the preset monitoring period are summed to obtain the theoretical carbon emissions of the target enterprise park over the period.

[0018] Optionally, the UAV observation data includes the original observed concentration and altitude of multiple preset spatial points within the target enterprise park, and the meteorological data includes wind speed, wind direction, temperature, humidity, and surface roughness of the target enterprise park. The step of using the UAV observation data and the meteorological data to perform measured carbon emission inversion to obtain the enterprise's time-sharing measured carbon emissions, and summarizing carbon emissions based on the preset time periods to obtain periodic measured carbon emissions, includes:

[0019] Based on the wind speed and the surface roughness, the original observed concentrations at different heights of each preset spatial point are corrected by wind profiles to determine the corrected wind speed and diffusion parameters at the corresponding heights of each preset spatial point.

[0020] Based on the wind direction, the upwind background point of the target enterprise park is determined. The observed concentration at the upwind background point is used as the background concentration. The difference between each original observed concentration and the background concentration is calculated to determine the background correction concentration increment of each preset spatial point.

[0021] Based on the temperature and humidity, temperature and pressure corrections are applied to each of the background correction concentration increments to determine the temperature and pressure correction concentrations at each of the preset spatial points.

[0022] Based on the preset three-dimensional grid model of the park, the temperature and pressure corrected concentrations are corrected by building shading, the corrected concentrations of each preset spatial point are determined, and the corrected three-dimensional carbon dioxide concentration field is constructed.

[0023] Based on the corrected wind speed and the diffusion parameters, combined with the corrected three-dimensional carbon dioxide concentration field, the source strength of each elevated point source is inverted using the Gaussian plume model, and the time-period point source emission flux of each of the elevated point sources is determined.

[0024] Based on the corrected three-dimensional carbon dioxide concentration field, a gridded surface source is established using a control target model and the source intensity is inverted to determine the total flux of the surface source over a period of time.

[0025] The total emission flux of point sources during all the time periods is obtained by summing the emission fluxes of all the aforementioned time periods.

[0026] The time-sharing measured carbon emissions of enterprises in the target enterprise park are obtained by summing the total area source flux and the total point source emission flux during the time period.

[0027] The periodic measured carbon emissions of the target enterprise park are obtained by summing the time-sharing measured carbon emissions of the enterprise in all time periods within the preset monitoring period.

[0028] Optionally, the step of performing time-sharing verification using the enterprise's time-sharing theoretical carbon emissions and the enterprise's time-sharing measured carbon emissions, and performing total verification using the periodic theoretical carbon emissions and the periodic measured carbon emissions, to generate the verification result for the target enterprise park, includes:

[0029] The deviation rate for each time period is obtained by calculating the deviation between the actual carbon emissions of the enterprise at each time period within the preset monitoring period and the corresponding theoretical carbon emissions of the enterprise at each time period.

[0030] The total deviation rate is obtained by calculating the deviation between the measured carbon emissions of the period and the theoretical carbon emissions of the period.

[0031] Based on the preset deviation judgment interval in which each of the time-sharing deviation rates falls, the deviation level of the target enterprise park in each of the time periods is determined;

[0032] Based on the preset deviation judgment interval in which the total deviation rate falls, the deviation level of the target enterprise park in the preset time period is determined;

[0033] The verification results include the deviation level of the target enterprise park during the preset time period, and the deviation level during each of the time periods.

[0034] Optionally, the park's electricity consumption data also includes the equipment start / stop status of each power consumption unit, and further includes:

[0035] Based on the total deviation rate and the time-specific deviation rate for each time period, the carbon emission anomaly scenario of the target enterprise park is identified, and corresponding early warning information is generated and pushed; the carbon emission anomaly scenario includes data anomaly scenario, leakage anomaly scenario and operating condition anomaly scenario.

[0036] When the total deviation rate exceeds the severe deviation threshold in the preset deviation judgment interval, it is determined to be the data abnormal scenario, and a data abnormality warning message is generated and pushed.

[0037] When the measured carbon emissions at any preset spatial point within the target enterprise park are consistently higher than the theoretical carbon emissions, and the time-of-use electricity consumption of the application unit does not increase, it is determined to be the leakage abnormal scenario, and leakage abnormal warning information is generated and pushed.

[0038] When the time-of-use deviation rate of any power consumption unit in the target enterprise park exceeds the severe deviation threshold in the preset deviation judgment interval, and the corresponding equipment start-up and shutdown status does not match the carbon emission change trend, it is determined as the abnormal operating condition scenario, and an abnormal operating condition warning message is generated and pushed; the carbon emission change trend is determined by the enterprise's time-of-use theoretical carbon emissions and the enterprise's time-of-use measured carbon emissions in each time period arranged in chronological order.

[0039] The early warning information includes the type of abnormal scenario, the time period of the abnormality, and the corresponding preset spatial location or power unit identifier.

[0040] Optionally, the step of acquiring and preprocessing multi-source data of the target enterprise park to obtain target multi-source data includes:

[0041] Acquire multi-source data from the target enterprise park;

[0042] Outlier cleaning and time alignment are performed on the multi-source data to obtain the target multi-source data.

[0043] The second aspect of this invention provides an enterprise carbon emission traceability and verification system, comprising:

[0044] The multi-source data acquisition and preprocessing module is used to acquire multi-source data of the target enterprise park and preprocess it to obtain target multi-source data, which includes UAV observation data, park electricity consumption data, carbon emission data and meteorological data.

[0045] The theoretical carbon emission calculation module is used to calculate the theoretical carbon emission using the park's electricity consumption data and the carbon emission data, to obtain the enterprise's time-of-use theoretical carbon emission, and to summarize the carbon emission based on a preset time period to obtain the periodic theoretical carbon emission.

[0046] The measured carbon emission inversion module is used to perform measured carbon emission inversion using the UAV observation data and the meteorological data to obtain the enterprise's time-sharing measured carbon emissions, and to summarize the carbon emissions based on the preset time period to obtain the periodic measured carbon emissions.

[0047] The cross-verification module is used to perform time-sharing verification using the enterprise's time-sharing theoretical carbon emissions and the enterprise's time-sharing measured carbon emissions, and to perform total verification using the periodic theoretical carbon emissions and the periodic measured carbon emissions, thereby generating the verification results for the target enterprise park.

[0048] A third aspect of the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the enterprise carbon emission traceability verification method described above.

[0049] The fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed, it implements the enterprise carbon emission traceability verification method as described above.

[0050] The fifth aspect of the present invention provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein when the program instructions are executed by a computer, the computer performs the enterprise carbon emission traceability verification method as described above.

[0051] As can be seen from the above technical solutions, the present invention has the following advantages:

[0052] This invention provides a method, system, equipment, medium, and product for enterprise carbon emission traceability and verification. By acquiring and preprocessing multi-source data from the target enterprise's industrial park, various data originally scattered across different collection systems are unified in time alignment and spatial correlation, forming a standardized data foundation with a consistent spatiotemporal benchmark. Based on this, two independent and parallel calculation paths are used to estimate carbon emissions. The first uses industrial park electricity consumption and carbon emission data to perform theoretical calculations from the perspective of energy consumption and material balance, obtaining time-of-use theoretical carbon emissions and their periodic summaries reflecting the enterprise's own statistical standards. The second uses UAV observation data and meteorological data to perform measured inversion from the perspective of atmospheric environmental concentration, obtaining independent measured carbon emissions and their periodic summaries based on actual atmospheric sampling data. Since the data sources of the two calculation paths are completely different and their calculation logic is independent, there is no mutual referencing or cross-influence between them, thus forming a theoretically mutually verifiable set of comparative data. Based on this, the time-series theoretical carbon emissions and time-series measured carbon emissions at the same time dimension are verified one by one, and the total amount of periodic theoretical carbon emissions and periodic measured carbon emissions is verified. The final verification result of the target enterprise park is generated by comparing the deviations of the two levels. In the whole process, the theoretical accounting path provides complete time-series data for emission estimation on the ledger side, and the measured inversion path provides atmospheric concentration observation data independent of the ledger for actual emission status. The time-series verification and the total amount verification constitute a multi-level cross-validation mechanism. The time-series verification can identify the distribution characteristics of emission deviations in the time dimension, while the total amount verification verifies the credibility of the overall emission level from a macro level. The combination of the two makes the final verification result have both the ability to track changes in each time period and the comprehensive judgment basis at the total amount level.

[0053] Therefore, this invention achieves a technological leap from fragmented to collaborative analysis of multi-source data. It enables emission accounting results to not only be mutually verified through two independent paths, but also to achieve precise deviation location and attribution analysis through multi-level verification. This effectively solves the technical problems in existing technologies where fragmented data systems lead to difficulties in fine-grained attribution of emission results and a lack of independent verification. Furthermore, since the two accounting paths share the same pre-processed standardized data foundation, and the verification process is based on a clear time-dimensional correspondence, no additional assumptions or empirical parameters are required. The entire method exhibits good reproducibility and engineering applicability, and can be directly deployed in an enterprise's existing data acquisition and management environment. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is a flowchart illustrating the steps of a corporate carbon emission traceability verification method provided in Embodiment 1 of the present invention.

[0056] Figure 2 This is a flowchart illustrating the steps of a corporate carbon emission traceability verification method provided in Embodiment 2 of the present invention.

[0057] Figure 3 This is a structural block diagram of an enterprise carbon emission traceability and verification system provided in Embodiment 3 of the present invention;

[0058] Figure 4 This is a structural block diagram of an electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0059] This invention provides a method, system, equipment, medium, and product for enterprise carbon emission traceability and verification, which solves the technical problem in the prior art that emission accounting data from multiple sources are fragmented and cannot be analyzed collaboratively, resulting in difficulty in finely attributing emission results and a lack of independent verification.

[0060] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0061] Please see Figure 1 , Figure 1 The flowchart illustrates the steps of a corporate carbon emission traceability and verification method provided in Embodiment 1 of the present invention.

[0062] This invention provides a method for verifying corporate carbon emissions, comprising:

[0063] Step 101: Obtain multi-source data of the target enterprise park and preprocess it to obtain target multi-source data, which includes UAV observation data, park electricity consumption data, carbon emission data and meteorological data.

[0064] In this embodiment of the invention, multi-source data of the target enterprise park is first acquired. This multi-source data specifically includes drone observation data, park electricity consumption data, carbon emission data, and meteorological data. The drone observation data is collected by a drone carbon monitoring terminal deployed at the take-off and landing points of the target enterprise park. Specifically, multiple preset spatial points and their corresponding flight routes are planned in advance within the three-dimensional space of the target enterprise park. These preset spatial points are distributed in the downwind and upwind background areas surrounding the emission sources and at multiple altitude levels. The drone performs cruise flight and fixed-point hovering sampling according to the preset routes, collecting the original observed concentration, GPS coordinates, altitude, and collection time at each preset spatial point using its onboard carbon dioxide sensor and carbon flux module, and transmitting this data back to the edge computing gateway in real time via a wireless link. Electricity consumption data for the industrial park is collected by electricity acquisition terminals deployed in various power distribution rooms, workshop circuits, and major electrical equipment locations within the target enterprise park. This data includes time-of-use electricity consumption, load curves, and equipment start-up / shutdown status for each electricity unit. Electricity units are categorized by power distribution circuit or electrical equipment. Time-of-use electricity consumption for each unit is read by smart meters in 15-minute increments. Equipment start-up / shutdown status is determined in real-time by a load monitoring module. Carbon emission data is imported from the target enterprise park's existing enterprise resource planning (ERP) system, fuel management system, or process management system via a data interface. This data includes fuel consumption, carbon content, and oxidation rate of various fuels consumed by each electricity unit, as well as the amount of process raw materials and emission factors for various processes involved in each electricity unit. Meteorological data is simultaneously collected by meteorological acquisition terminals deployed at open, high points within the target enterprise park. This data includes wind speed, wind direction, temperature, humidity, and ground roughness, and is transmitted wirelessly to an edge computing gateway.

[0065] After acquiring the aforementioned multi-source data, the data is preprocessed, specifically including: cleaning outliers from the original observed concentrations at each preset spatial point, removing instantaneous spikes caused by equipment failure or environmental interference; resampling the UAV observation data, park electricity consumption data, carbon emission data, and meteorological data to the same time segment, ensuring all data are aligned to a uniform 15-minute time granularity; mapping each preset spatial point to its corresponding grid in the preset 3D grid model of the park based on its GPS coordinates and altitude, establishing spatial relationships between each grid and each electricity consumption unit, and completing spatial binding; and dividing the preprocessed data into an electronic set for ledger purposes and a UAV meteorological subset. The electronic set for ledger purposes includes park electricity consumption data and carbon emission data for theoretical carbon emission calculation, while the UAV meteorological subset includes UAV observation data and meteorological data.

[0066] Step 102: Use the park's electricity consumption data and carbon emission data to calculate the theoretical carbon emission, obtain the enterprise's time-of-use theoretical carbon emission, and summarize the carbon emission based on the preset time period to obtain the periodic theoretical carbon emission.

[0067] In this embodiment of the invention, each power consumption unit established in step 101 is used as the basic accounting unit, and the calculation is performed in time intervals with a uniform time granularity of 15 minutes.

[0068] First, the backend analysis server retrieves the time-of-use grid factors for each time period within the current monitoring cycle from the regional power grid via an external data interface. These time-of-use grid factors are dynamically updated and published by the power grid operator based on the real-time power structure. For any power consumption unit, the server multiplies its time-of-use electricity consumption within the time period by the corresponding time-of-use grid factor to obtain the unit's time-of-use electricity carbon emissions for that time period. Second, for the same power consumption unit, the server multiplies its corresponding fuel consumption by the product of fuel carbon content and fuel oxidation rate, and then multiplies this product by the carbon-to-carbon dioxide molecular weight conversion factor to obtain the unit's time-of-use fuel combustion carbon emissions for that time period. Simultaneously, the server multiplies its corresponding process raw material consumption by the process emission factor to obtain the unit's time-of-use process carbon emissions for that time period. Adding the above time-of-use fuel combustion carbon emissions and time-of-use process carbon emissions together yields the unit's time-of-use direct carbon emissions for that time period.

[0069] Subsequently, the server adds the unit-time electricity carbon emissions and unit-time direct carbon emissions of the same power consumption unit in the same time period to obtain the unit-time theoretical carbon emissions for that power consumption unit in that time period. The unit-time theoretical carbon emissions of all power consumption units in the same time period are summed to obtain the enterprise-time theoretical carbon emissions for the target enterprise park in that time period. This unit-by-unit, time-by-time calculation process is repeated until all time periods within the preset monitoring period are covered, resulting in the enterprise-time theoretical carbon emission sequence for each time period. Finally, the server sums and aggregates the enterprise-time theoretical carbon emissions for all time periods within the preset monitoring period to obtain the periodic theoretical carbon emissions for the target enterprise park. All the above unit-time electricity carbon emissions, unit-time fuel combustion carbon emissions, unit-time process carbon emissions, unit-time direct carbon emissions, unit-time theoretical carbon emissions, enterprise-time theoretical carbon emissions, and periodic theoretical carbon emissions are expressed in tons of carbon dioxide and are uniformly stored in the database of the backend analysis server after calculation.

[0070] Step 103: Use UAV observation data and meteorological data to perform real-time carbon emission inversion, obtain the enterprise's time-sharing real-time carbon emission, and summarize the carbon emissions based on the preset time period to obtain the periodic real-time carbon emission.

[0071] In this embodiment of the invention, firstly, the background analysis server performs wind profile correction on the original observed concentrations at different heights of each preset spatial point based on the surface roughness and wind speed in the meteorological data. Specifically, the server applies the logarithmic wind profile formula to each spatial point, and calculates the actual wind speed and diffusion parameters at the height of each preset spatial point based on the surface roughness and wind speed, thereby eliminating the influence of different sampling heights on the observed concentration values. Then, based on the wind direction in the meteorological data, the server determines the upwind area of ​​the target enterprise park, and uses the observed concentration at the upwind background points as the regional atmospheric background concentration for that period. The original observed concentrations at each preset spatial point are subtracted from this background concentration to obtain the background-corrected concentration increments for each preset spatial point, thereby eliminating the interference of the regional atmospheric background concentration on the monitoring results. Subsequently, based on the temperature and humidity in the meteorological data, the server performs temperature and pressure correction on each background-corrected concentration increment, correcting the concentration values ​​measured under different temperature and humidity conditions to a standard state, and obtaining the temperature and pressure corrected concentrations for each preset spatial point. Then, based on the preset three-dimensional mesh model of the park, the building obstruction between each preset spatial point and each emission source is identified. The local turbulence factor compensation is applied to the temperature and pressure corrected concentration of the obstructed points to complete the building obstruction correction and obtain the corrected concentration of each preset spatial point. The corrected concentrations of all preset spatial points are arranged in the three-dimensional mesh model according to their respective spatial coordinates to form a corrected three-dimensional carbon dioxide concentration field covering the entire target enterprise park.

[0072] After obtaining the corrected 3D carbon dioxide concentration field, the server performs flux inversion for both elevated point sources and area sources. For elevated point sources, the server extracts the location, height, and emission outlet parameters of each elevated point source from the park's 3D mesh model. Based on the wind speed and diffusion parameters at each height obtained from the wind profile correction, and combined with the corrected 3D carbon dioxide concentration field, a Gaussian plume model is used to invert the source intensity of each elevated point source. Mass flow rate integration is then performed on the downstream cross-section of each elevated point source to obtain the time-period point source emission flux for that time period. For area sources, the server extracts the regional extent of each area source from the park's 3D mesh model. Based on the corrected 3D carbon dioxide concentration field, a gridded area source is established using a control target model. The observed concentration at each receiver point is represented as a linear superposition of the contributions from each source grid. The source intensity of each grid is calculated through inversion, and the total time-period area source flux is obtained by summing these values. The server sums the point source emission fluxes of all elevated point sources for each time period to obtain the total point source emission flux for that time period; then it sums the total point source emission flux for that time period with the total area source emission flux for that time period to obtain the time-sharing measured carbon emissions of enterprises in the target enterprise park for that time period.

[0073] The above inversion process is repeated until all time periods within the preset monitoring period are covered, resulting in a sequence of time-sharing measured carbon emissions for each period. Finally, the server sums and aggregates the time-sharing measured carbon emissions for all time periods within the preset monitoring period to obtain the periodic measured carbon emissions for the target enterprise park. Both the time-sharing and periodic measured carbon emissions are expressed in tons of carbon dioxide and are stored uniformly in the database of the backend analysis server after the inversion is completed.

[0074] Step 104: Perform time-sharing verification using the enterprise's time-sharing theoretical carbon emissions and the enterprise's time-sharing measured carbon emissions, and perform total verification using the periodic theoretical carbon emissions and the periodic measured carbon emissions, generating the verification results for the target enterprise park.

[0075] In this embodiment of the invention, the verification is divided into two levels: time-sharing verification and total verification. The two levels are independent of each other and complementary, together constituting the final verification result.

[0076] First, time-sharing verification is performed. The server reads the theoretical carbon emission sequence for each time period obtained in step 102 and the measured carbon emission sequence for each time period obtained in step 103 from the database. Both have the same time period division granularity (15 minutes) and the same total number of time periods. For each time period within the preset monitoring cycle, the server subtracts the theoretical carbon emission for that time period from the measured carbon emission for that time period, and then divides the difference by the theoretical carbon emission for that time period to obtain the time-sharing deviation rate for that time period. The time-sharing deviation rate reflects the degree of deviation between the inversion results based on atmospheric measurements and the theoretical calculation results compiled by the enterprises themselves within that specific time period. When the time-sharing deviation rate is positive, it indicates that the actual carbon emission inversion from atmospheric monitoring for that time period is higher than the theoretical value calculated by the enterprise's ledger; when the time-sharing deviation rate is negative, it indicates that the measured inversion result is lower than the theoretical calculation value. The server calculates the time-sharing deviation rate for all time periods sequentially, forming a time-sharing deviation rate sequence covering the entire monitoring cycle.

[0077] Simultaneously, a total emission verification is performed. The server reads the theoretical carbon emissions obtained in step 102 and the measured carbon emissions obtained in step 103 from the database. The theoretical carbon emissions are subtracted from the measured carbon emissions, and the difference is divided by the theoretical carbon emissions to obtain the total emission deviation rate. The total emission deviation rate reflects, from a macro perspective, the overall degree of deviation between the total carbon emissions obtained from the two independently calculated paths over the entire monitoring period.

[0078] After obtaining the time-specific deviation rate and total deviation rate for each time period, the server combines the two to generate the verification result for the target enterprise park. This verification result includes the total deviation rate, the time-specific deviation rate sequence for each time period, and the corresponding numerical value for each deviation rate. The server stores the verification result in a database for subsequent steps and also displays it through the user's terminal.

[0079] Target Enterprise Park: The production area of ​​a coal, oil, and gas enterprise to be monitored, calculated, and verified for carbon emissions, including multiple power consumption units, elevated point sources, area sources, and buildings. Multi-Source Data: A collective term for various types of data from different acquisition systems, including at least UAV observation data, park power consumption data, carbon emission data, and meteorological data. UAV Observation Data: Data collected by sensors mounted on UAVs at various preset spatial points above the target enterprise park, including at least the original observed concentration, GPS coordinates, altitude, and acquisition time. Park Power Consumption Data: Data collected by power consumption acquisition terminals deployed in various power distribution rooms, workshop circuits, and major electrical equipment locations within the target enterprise park, including at least the time-of-use power consumption, load curves, and equipment start-up / shutdown status for each power consumption unit. Carbon Emission Data: Data imported from the target enterprise park's existing enterprise resource planning system, fuel management system, or process management system via data interfaces, including at least the fuel consumption, fuel carbon content, fuel oxidation rate, process raw material usage, and process emission factors for each power consumption unit. Meteorological data: Data collected synchronously by meteorological acquisition terminals deployed at open, high points within the target enterprise park, including at least wind speed, wind direction, temperature, humidity, and surface roughness. Enterprise time-of-use theoretical carbon emissions: The theoretical carbon emissions of the target enterprise park, calculated using an emissions inventory system based on the park's electricity consumption and carbon emission data, with a preset time period (15 minutes) as the time granularity. Preset time period: The basic unit for dividing the monitoring period; in this method, it is 15 minutes. Periodic theoretical carbon emissions: The total theoretical carbon emissions of the target enterprise park within the complete monitoring period, obtained by summing the enterprise time-of-use theoretical carbon emissions for all time periods within the preset monitoring period. Enterprise time-of-use measured carbon emissions: The measured carbon emissions of the target enterprise park, with a preset time period as the time granularity, obtained by inverting atmospheric CO2 concentration based on UAV observation data and meteorological data. Periodic measured carbon emissions: The total measured carbon emissions of the target enterprise park within the complete monitoring period, obtained by summing the enterprise time-of-use measured carbon emissions for all time periods within the preset monitoring period. Time-based verification: The process of comparing the theoretical carbon emissions of enterprises at different times with the actual carbon emissions at different times for each corresponding period. Total verification: The process of comparing the theoretical carbon emissions over a period with the actual carbon emissions over a period. Verification results: The output data generated after the time-based and total verifications include at least the total deviation rate, the time-based deviation rate for each period, and the corresponding deviation level.

[0080] Please see Figure 2 , Figure 2 The flowchart illustrates the steps of a corporate carbon emission traceability verification method provided in Embodiment 2 of the present invention.

[0081] This invention provides a method for verifying corporate carbon emissions, comprising:

[0082] Step 201: Obtain multi-source data of the target enterprise park.

[0083] In this embodiment of the invention, the specific implementation process of step 201 is similar to that of step 101, and will not be repeated here.

[0084] Step 202: Perform outlier cleaning and time alignment on the multi-source data to obtain the target multi-source data. The target multi-source data includes UAV observation data, park electricity consumption data, carbon emission data, and meteorological data.

[0085] In this embodiment of the invention, the specific implementation process of step 202 is similar to that of step 101, and will not be repeated here.

[0086] Outlier cleaning: The process of identifying and removing instantaneous spikes in multi-source data caused by equipment failure or environmental interference. Time alignment: The process of resampling multi-source data with different acquisition frequencies and time bases to the same time segment (15-minute granularity).

[0087] Step 203: Use the park's electricity consumption data and carbon emission data to calculate the theoretical carbon emission, obtain the enterprise's time-of-use theoretical carbon emission, and summarize the carbon emission based on the preset time period to obtain the periodic theoretical carbon emission.

[0088] Furthermore, the park's electricity consumption data includes the time-of-use electricity consumption of each electricity-consuming unit within the target enterprise's park, and the carbon emission data includes the fuel consumption, fuel carbon content, fuel oxidation rate, process raw material consumption, and process emission factor of each electricity-consuming unit within the target enterprise's park. Step 203 may include the following sub-steps:

[0089] S11. Retrieve the time-of-use grid factors published by the regional power grid, and combine them with the time-of-use electricity consumption of each power consumption unit to determine the unit's time-of-use electricity carbon emissions.

[0090] In this embodiment of the invention, the background analysis server sends a data retrieval request to the regional power grid carbon emission factor publishing platform through an external data interface, according to the time granularity of each time period within a preset monitoring period, to obtain the time-of-use power grid factors for each time period within the current monitoring period. The time-of-use power grid factors are dynamically calculated and published by the power grid operator based on the real-time output ratio of various power sources such as thermal power, wind power, and photovoltaic power in the region, and their time granularity is consistent with the 15-minute electricity consumption data collection granularity in step 101. After obtaining the time-of-use power grid factors, the server reads the time-of-use electricity consumption of each power consumption unit in each time period from the electronic ledger. For each power consumption unit and time period, the server multiplies the time-of-use electricity consumption by the corresponding time-of-use power grid factor to obtain the unit's time-of-use electricity carbon emission in that time period. The calculation formula is as follows:

[0091] ;

[0092] In the formula, For the first Each electricity unit during the time period Carbon emissions from time-of-use electricity, expressed in tons of carbon dioxide; For the first Each electricity unit during the time period Time-of-use electricity consumption, in megawatt-hours; For time period The regional grid time-of-use grid factor is expressed in tons of carbon dioxide per megawatt-hour.

[0093] S12. Determine the unit-time fuel combustion carbon emissions of each power consumption unit based on the fuel consumption, fuel carbon content and fuel oxidation rate of each power consumption unit.

[0094] In this embodiment of the invention, the background analysis server reads the fuel consumption of each power-consuming unit at each time period, as well as the fuel carbon content and fuel oxidation rate of the corresponding fuel for each power-consuming unit, from the electronic ledger. (The subscript is omitted as it is not part of the main text.) Indicates the electrical unit number, subscript This indicates the fuel type number. The server assigns it to each power-consuming unit within a specific time period. The first internal consumption Multiply the consumption of one type of fuel by its carbon content, then by its oxidation rate, and finally by the molecular weight conversion factor from carbon to carbon dioxide to obtain the result. The carbon emissions from the combustion of a particular fuel in a given power consumption unit during a given time period are calculated by summing the carbon emissions from the combustion of all fuel types in the same power consumption unit during the same time period. The calculation formula is as follows:

[0095] ;

[0096] In the formula, For the first Each electricity unit during the time period Carbon emissions from time-of-use fuel combustion, expressed in tons of carbon dioxide; For the first The first power consumption unit fuel during time period Fuel consumption, expressed in tons or ten thousand standard cubic meters; For the first The first power consumption unit The carbon content of a fuel, expressed in tons of carbon per ton or tons of carbon per 10,000 standard cubic meters; For the first The first power consumption unit The fuel oxidation rate of this fuel is a dimensionless constant, ranging from 0 to 1; This is the molecular weight conversion factor from carbon to carbon dioxide. Number the fuel type , This represents the total number of different types of fuel consumed by each electrical unit. Number the electricity consumption unit. , This represents the total number of power-consuming units within the target enterprise park.

[0097] S13. Determine the unit-time carbon emissions of each power-consuming unit based on the amount of process raw materials used and the process emission factor.

[0098] In this embodiment of the invention, the background analysis server reads the process raw material consumption and corresponding process emission factors of each power-consuming unit at each time period from the electronic ledger. (The subscript is omitted as it is not part of the main text.) Indicates the electrical unit number, subscript Indicates the process source number. The server assigns it to each power-consuming unit within a specific time period. Inner Multiplying the amount of process raw materials used by a process source by the corresponding process emission factor yields the result of the first process source. The carbon emissions from a specific process source within a given power consumption unit during a given time period are calculated as follows: The carbon emissions from all process sources within the same power consumption unit during the same time period are summed to obtain the unit-time carbon emissions for that power consumption unit during that time period. The calculation formula is as follows:

[0099] ;

[0100] In the formula, For the first Each electricity unit during the time period Carbon emissions from unit-time process, expressed in tons of carbon dioxide; For the first The first power consumption unit The source of the process in time period The amount of raw materials used in the process, in tons or ten thousand standard cubic meters; For the first The first power consumption unit The process emission factor of a process source is expressed in tons of carbon dioxide per ton or tons of carbon dioxide per 10,000 standard cubic meters. For process source number, , This represents the total number of process source types involved in each power consumption unit.

[0101] S14. Sum the unit time-of-use fuel combustion carbon emissions associated with each power consumption unit and the unit time-of-use process carbon emissions to determine the unit time-of-use direct carbon emissions of each power consumption unit.

[0102] In this embodiment of the invention, the background analysis server, for each power consumption unit, divides it into time periods. The unit-time fuel combustion carbon emissions and unit-time process carbon emissions are summed to obtain the unit-time direct carbon emissions for that electricity consumption unit during that period. The calculation formula is as follows:

[0103] ;

[0104] In the formula, For the first Each electricity unit during the time period The unit time-sharing direct carbon emissions are measured in tons of carbon dioxide.

[0105] S15. Sum the unit time-of-use electricity carbon emissions associated with each power consumption unit and the unit time-of-use direct carbon emissions to determine the unit time-of-use theoretical carbon emissions of each power consumption unit.

[0106] In this embodiment of the invention, the background analysis server, for each power consumption unit, divides it into time periods. The unit's time-of-use carbon emissions are summed with its direct time-of-use carbon emissions to obtain the theoretical time-of-use carbon emissions for that electricity-consuming unit during that time period. The calculation formula is as follows:

[0107] ;

[0108] In the formula, For the first Each electricity unit during the time period The unit time-sharing theoretical carbon emissions, in tons of carbon dioxide.

[0109] S16. Sum the time-sharing theoretical carbon emissions of all units to obtain the time-sharing theoretical carbon emissions of the target enterprise park.

[0110] In this embodiment of the invention, the background analysis server sums the unit-time theoretical carbon emissions of all electricity-consuming units within the target enterprise park for each time period, thus obtaining the enterprise-time theoretical carbon emissions of the target enterprise park for that time period. The calculation formula is as follows:

[0111] ;

[0112] In the formula, For the target enterprise park during the time period The enterprise's time-of-use theoretical carbon emissions, in tons of carbon dioxide; For the first Each period, , This represents the total number of time periods within the preset monitoring period.

[0113] S17. Sum the theoretical carbon emissions of enterprises at all times within the preset monitoring period to obtain the theoretical carbon emissions of the target enterprise park during the period.

[0114] In this embodiment of the invention, the background analysis server sums the time-sharing theoretical carbon emissions of enterprises for all time periods within a preset monitoring period to obtain the periodic theoretical carbon emissions of the target enterprise park within that preset monitoring period. The calculation formula is as follows:

[0115] ;

[0116] In the formula, The periodic theoretical carbon emissions of the target enterprise park within the preset monitoring period are expressed in tons of carbon dioxide.

[0117] Electricity Consumption Unit: An independent electricity consumption statistical unit within the target enterprise park, divided by power distribution circuit, workshop, or electrical equipment, used to break down the total electricity consumption of the park to each production area or equipment. Time-of-Use Electricity Consumption: Electricity consumption data for each electricity consumption unit within a preset time period, collected by smart meters in 15-minute increments. Fuel Consumption: The quantity of various fuels (including coal, oil, gas, etc.) consumed by each electricity consumption unit within the preset time period. Fuel Carbon Content: The mass fraction of carbon in the various fuels consumed by each electricity consumption unit, expressed as per ton of carbon or per 10,000 standard cubic meters. Fuel Oxidation Rate: The proportion of carbon oxidized during the combustion of various fuels consumed by each electricity consumption unit, a dimensionless constant ranging from 0 to 1. Process Raw Material Consumption: The quantity of raw materials consumed by each electricity consumption unit in various processes within the preset time period. Process Emission Factor: The carbon dioxide emissions per unit of raw material consumption in each process of each electricity consumption unit. Time-of-use grid factor: A benchmark value for carbon emissions per unit of electricity consumption in each time period, dynamically calculated and published by the regional power grid based on the real-time power structure, expressed as tons of carbon dioxide per megawatt-hour. Unit time-of-use electricity carbon emissions: Indirect carbon emissions from purchased electricity generated by a single electricity unit within a preset time period. Unit time-of-use fuel combustion carbon emissions: Direct carbon emissions from various fuel combustion processes generated by a single electricity unit within a preset time period. Unit time-of-use process carbon emissions: Direct carbon emissions from various processes generated by a single electricity unit within a preset time period. Unit time-of-use direct carbon emissions: The sum of fuel combustion carbon emissions and process carbon emissions of a single electricity unit within a preset time period. Unit time-of-use theoretical carbon emissions: The sum of electricity carbon emissions and direct carbon emissions of a single electricity unit within a preset time period. Preset monitoring period: The complete time span for carbon emission monitoring, calculation, and verification performed by this method, such as one day, one week, or one month.

[0118] Step 204: Use UAV observation data and meteorological data to perform real-time carbon emission inversion, obtain the enterprise's time-sharing real-time carbon emission, and summarize the carbon emissions based on the preset time period to obtain the periodic real-time carbon emission.

[0119] Furthermore, the drone observation data includes the raw observation concentration and altitude of multiple preset spatial points within the target enterprise park, and the meteorological data includes wind speed, wind direction, temperature, humidity, and surface roughness within the target enterprise park. Step 204 may include the following sub-steps:

[0120] S21. Based on wind speed and surface roughness, perform wind profile correction on the original observed concentrations at different heights of each preset spatial point, and determine the corrected wind speed and diffusion parameters at the corresponding heights of each preset spatial point.

[0121] In this embodiment of the invention, the background analysis server reads the altitude data of each preset spatial point and the wind speed and surface roughness from the meteorological data in the UAV meteorological subset. Wind profile correction is used to eliminate the influence of different sampling altitudes on wind speed and diffusion conditions. Its physical basis is that in the near-surface atmospheric boundary layer, wind speed exhibits a logarithmic distribution with altitude. For each preset spatial point, the server substitutes its altitude into the logarithmic wind profile formula, and combines the surface roughness and friction velocity to calculate the corrected wind speed at that altitude. The friction velocity is dynamically calculated by the server based on the measured wind speed and surface roughness using an iterative method. Specifically, the logarithmic wind profile formula (i.e., the wind profile correction formula) is expanded at the standard altitude (usually 10 meters) of the meteorological acquisition terminal. Using the measured wind speed at that altitude as a constraint, the friction velocity is solved inversely. Then, the friction velocity is substituted back into the formula to calculate the corrected wind speed at the altitude of each preset spatial point. The wind profile correction formula is as follows:

[0122] ;

[0123] In the formula, For height Corrected wind speed at the location, in meters per second; The friction speed is measured in meters per second and is dynamically calculated by the server based on the measured wind speed at the standard altitude where the meteorological data acquisition terminal is located. The height of the preset spatial point, in meters; 0.4 represents the surface roughness in meters, obtained from meteorological data; 0.4 is the von Kármán constant.

[0124] Based on the corrected wind speeds obtained at various altitudes, the server further determines the atmospheric stability level (A to F) according to GB / T3840-1991, using the total cloud cover, solar altitude angle, and wind speed at a height of 10 meters from the meteorological data. Then, based on the stability level, the corresponding coefficients of the empirical formula for diffusion parameters are obtained, and the lateral diffusion parameters at the corresponding altitudes of each preset spatial point are calculated using the following formula. With vertical diffusion parameters :

[0125] ;

[0126] ;

[0127] In the formula, The downwind distance from the preset spatial point location to the corresponding elevated point source, in meters; This is the lateral diffusion parameter, in meters; This is the vertical diffusion parameter, in meters; For empirical coefficients related to atmospheric stability level and terrain type, the coefficient values ​​for each stability level are as follows for open rural terrain:

[0128]

[0129] The server will calculate the corrected wind speed at the corresponding height of each preset spatial point. With diffusion parameters , It is temporarily stored in memory for use in subsequent Gaussian plume model inversion steps.

[0130] S22. Determine the upwind background points of the target enterprise park based on the wind direction. Use the observed concentration at the upwind background points as the background concentration. Perform a difference calculation between each original observed concentration and the background concentration to determine the background correction concentration increment for each preset spatial point.

[0131] In this embodiment of the invention, the background analysis server reads wind direction data and the original observed concentrations of each preset spatial point from the meteorological data in the UAV meteorological subset. Wind direction data is used to determine the direction of atmospheric transport, thereby identifying the background area least affected by emissions from the industrial park. Based on the wind direction, the server identifies preset spatial points located upwind of the target enterprise park and sufficiently far from the main emission sources (usually 1 to 2 kilometers) in the three-dimensional grid model of the industrial park as upwind background points. The original observed concentration of these points during the same time period is used as the regional atmospheric background concentration for that time period. After obtaining the background concentration, the server performs a difference calculation between the original observed concentration and the background concentration for each preset spatial point to obtain the background correction concentration increment for each preset spatial point. The purpose of background correction is to eliminate the interference of the regional atmospheric background concentration on the monitoring results, ensuring that the concentration signal used for subsequent inversion calculations mainly originates from the contribution of emission sources within the industrial park. The calculation formula is as follows:

[0132] ;

[0133] In the formula, The background correction concentration increment for preset spatial points, in parts per million; The original observed concentration at the preset spatial location, in parts per million; This refers to the concurrent observation concentration of the upwind background points determined by the server based on wind direction, i.e., the regional atmospheric background concentration, expressed in parts per million.

[0134] S23. Based on temperature and humidity, perform temperature and pressure correction on each background correction concentration increment to determine the temperature and pressure corrected concentration at each preset spatial point.

[0135] In this embodiment of the invention, the background analysis server reads temperature and air pressure from meteorological data in the UAV meteorological subset and performs temperature and pressure correction on the background correction concentration increment at each preset spatial point. The CO2 sensor on the UAV has an integrated temperature and humidity compensation algorithm, and the server directly reads the concentration value output by the sensor after internal compensation. Based on this, the server corrects the concentration value output by the sensor from the on-site temperature and pressure conditions to standard conditions (standard temperature 298.15K, standard atmospheric pressure 101.325kPa) according to the ideal gas law, eliminating concentration fluctuations caused by differences in environmental temperature and pressure during different monitoring periods. The correction formula is as follows:

[0136] ;

[0137] In the formula, The concentration at the preset spatial location after temperature and pressure correction (i.e., the equivalent concentration increment contributed by the emission source), in parts per million; The atmospheric pressure was measured on-site, and the unit is kilopascal, obtained from meteorological data. Standard atmospheric pressure is taken as 101.325 kPa; The standard temperature is 298.15K. The temperature is measured on-site, in Kelvin, and obtained from meteorological data. After the server performs the above corrections on each preset spatial point, the temperature and pressure corrected concentrations for each preset spatial point are obtained.

[0138] S24. Based on the preset three-dimensional mesh model of the park, the concentrations after temperature and pressure correction are corrected by building shading, the corrected concentrations at each preset spatial point are determined, and the corrected three-dimensional carbon dioxide concentration field is constructed.

[0139] In this embodiment of the invention, the background analysis server reads a preset 3D mesh model of the industrial park. This model includes the 3D contour data of various buildings, equipment frames, storage tanks, pipe corridors, and other structures within the target enterprise park. The server maps each preset spatial point to the 3D mesh model and identifies whether there are buildings or large equipment obstructing the line of sight between each preset spatial point and each emission source. For spatial points determined to be obstructed, the server calculates an obstruction correction coefficient based on the geometric dimensions of the obstructing object, and multiplies the temperature and pressure corrected concentration by the obstruction correction coefficient to obtain the corrected concentration at that point. For unobstructed spatial points, the obstruction correction coefficient is set to 1. equal The mathematical expression for building occlusion correction is as follows:

[0140] ;

[0141] ;

[0142] In the formula, The corrected concentration after building shading correction, in parts per million; The occlusion correction factor is dimensionless. This is an empirical constant, ranging from 0.3 to 0.5; The height of the building is to be obscured, and the unit is meters; The effective height of the emission source, in meters; The thickness of the obstruction along the line of sight, expressed in meters; This represents the horizontal distance from the spatial location to the emission source, expressed in meters. or hour, Take 1. After the server completes the building occlusion correction for all preset spatial points, it obtains the corrected concentration for each preset spatial point. The corrected concentrations of all preset spatial points are then arranged and interpolated in a three-dimensional mesh model according to their respective spatial coordinates to form a corrected three-dimensional carbon dioxide concentration field covering the entire target enterprise park. .

[0143] S25. Based on the corrected wind speed and diffusion parameters, and combined with the corrected three-dimensional carbon dioxide concentration field, the source strength of each elevated point source is inverted using the Gaussian plume model to determine the time-period point source emission flux of each elevated point source.

[0144] In this embodiment of the invention, the background analysis server extracts the location and effective source height of each elevated point source from the three-dimensional mesh model of the park. With emission outlet parameters. Elevated point sources are centralized emission outlets with a certain emission height, such as chimneys or flares. For each elevated point source, the server establishes a Gaussian plume model coordinate system with its location as the origin. It extracts the corrected concentration at each preset spatial point downwind of the elevated point source from the corrected three-dimensional carbon dioxide concentration field, and combines this with the corrected wind speed and diffusion parameters at the corresponding height obtained in S21, substituting them into the Gaussian plume concentration formula. This formula describes the theoretical concentration distribution at any spatial location downwind under known source strength. The Gaussian plume concentration formula is as follows:

[0145] ;

[0146] In the formula, Spatial location The theoretical concentration at that location, in parts per million; , where is the source strength of the elevated point source (i.e., the emission per unit time), and is the quantity to be inverted, in grams per second; The distance from the spatial point to the central axis of the plume is expressed in meters. These are the vertical height coordinates of a spatial point, in meters. The effective source height of the elevated point source, i.e., the geometric height of the chimney or flare plus the flue gas rise height, is obtained from the 3D mesh model of the park. The server uses a least squares fitting method to fit the corrected measured concentration values ​​at each preset spatial point with the theoretical concentration values ​​calculated by the Gaussian plume formula. Through iterative optimization, the sum of squared errors between the two is minimized, thereby retrieving the source strength of the elevated point source. After obtaining the source strength, the server calculates the time-period point source emission flux of the elevated point source using the following formula:

[0147] ;

[0148] In the formula, For the first The elevated point source during the time period Point source emission fluxes per time period, expressed in tons of carbon dioxide per time period; The source strength obtained from the inversion is expressed in grams per second. The duration is 900 seconds. This is the conversion factor from grams to tons. Repeat the above process until the inversion and flux calculation of all elevated point sources are completed.

[0149] S26. Based on the corrected three-dimensional carbon dioxide concentration field, a gridded surface source is established using the control target model, and the source intensity is inverted to determine the total flux of the surface source during the time period.

[0150] In this embodiment of the invention, the background analysis server extracts the regional range of each surface source from the 3D mesh model of the park. The server discretizes each surface source region into several regular meshes in the horizontal direction, with each mesh serving as an independent source mesh, possessing the source intensity per unit area to be inverted. The server uses the corrected concentration at each preset spatial point. Using the observed values ​​and the source strength of each source grid as the unknown, an optimal estimation method is used for inversion to obtain the source strength per unit area for each source grid. After obtaining the source strength of each source grid, the server calculates the total area source flux for that time period using the following formula:

[0151] ;

[0152] In the formula, For time period Total area source flux during the time period, expressed in tons of carbon dioxide per time period; For the first Source strength per unit area of ​​each source grid, expressed in grams per square meter per second; For the first The area of ​​each source grid, in square meters.

[0153] S27. The total point source emission flux for each time period is obtained by summing the point source emission fluxes for all time periods.

[0154] In this embodiment of the invention, the background analysis server sums the time-period point source emission fluxes of all elevated point sources obtained in S25 to obtain the total time-period point source emission flux for that time period. The calculation formula is as follows:

[0155] ;

[0156] In the formula, For time period Total point source emission flux during the time period, expressed in tons of carbon dioxide per time period; This refers to the total number of elevated points within the target enterprise park.

[0157] S28. The total flux of area sources and the total flux of point sources during the time period are summed to obtain the time-sharing measured carbon emissions of enterprises in the target enterprise park.

[0158] In this embodiment of the invention, the background analysis server sums the total area source emission flux obtained in S26 with the total point source emission flux obtained in S27 to obtain the time-sharing measured carbon emissions of enterprises in the target enterprise park during that time period. The calculation formula is as follows:

[0159] ;

[0160] In the formula, For the target enterprise park during the time period The company's time-of-use carbon emissions are measured in tons of carbon dioxide.

[0161] S29. Sum the measured carbon emissions of enterprises at different times within the preset monitoring period to obtain the periodic measured carbon emissions of the target enterprise park.

[0162] In this embodiment of the invention, the background analysis server sums the time-sharing measured carbon emissions of enterprises for all time periods within a preset monitoring period to obtain the periodic measured carbon emissions of the target enterprise park within that preset monitoring period. The calculation formula is as follows:

[0163] ;

[0164] In the formula, This represents the periodic measured carbon emissions of the target enterprise park within the preset monitoring period, expressed in tons of carbon dioxide. At this point, the backend analysis server has completed the inversion and summarization of measured carbon emissions for all time periods. The resulting time-sharing measured carbon emission sequences and periodic measured carbon emission volumes for each enterprise are stored in the database for subsequent cross-verification steps.

[0165] Preset Spatial Locations: Before executing the UAV flight mission, the sampling spatial locations of the UAV are pre-planned based on the 3D mesh model of the target enterprise park. Each location has specific GPS coordinates and altitude, distributed in the downwind and upwind background areas and multiple altitude layers surrounding the emission source. Raw Observed Concentration: The uncorrected atmospheric CO2 concentration value directly measured by the CO2 sensor onboard the UAV at each preset spatial location. Altitude: The vertical distance of the preset spatial location from the ground. Wind Speed: The horizontal movement speed of the atmosphere in the target enterprise park during a preset time period. Wind Direction: The direction of horizontal movement of the atmosphere in the target enterprise park during a preset time period. Temperature: The ambient temperature of the atmosphere in the target enterprise park during a preset time period. Humidity: The ambient humidity of the atmosphere in the target enterprise park during a preset time period. Surface Roughness: A parameter reflecting the geometric characteristics of the underlying surface of the target enterprise park (building height distribution, terrain undulation, etc.), in meters. Wind Profile Correction: Based on the logarithmic distribution of wind speed with altitude in the near-surface atmospheric boundary layer, the raw observed concentrations at different altitudes of each preset spatial location are corrected to a unified wind speed benchmark, while simultaneously calculating diffusion parameters at each altitude. Corrected wind speed: The wind speed value at the corresponding height of each preset spatial point after correction by the wind profile. Diffusion parameters: Parameters reflecting atmospheric diffusion capacity, including lateral and vertical diffusion parameters, used in Gaussian plume models to calculate pollutant concentration distribution. Upwind background points: Preset spatial points located upwind of the target enterprise park, sufficiently far from the main emission sources and unaffected by park emissions, used to obtain regional atmospheric background concentrations. Background concentration: The concurrent atmospheric CO2 concentration measured at the upwind background points, representing the regional atmospheric background value unpolluted by emissions from the target enterprise park. Background correction concentration increment: The difference between the original observed concentration and the background concentration at the preset spatial points, representing the concentration signal contributed by the emission sources. Temperature and pressure correction: The process of correcting the background correction concentration increment at each preset spatial point from the on-site temperature and pressure conditions to standard conditions (standard temperature 298.15K, standard atmospheric pressure 101.325kPa) based on the ideal gas law. Temperature and pressure corrected concentration: The equivalent concentration increment under standard conditions obtained after temperature and pressure correction. 3D Mesh Model of the Industrial Park: A 3D digital spatial model of the target enterprise industrial park, dividing the park space into several regular grids. It includes the 3D outline data of various buildings, equipment frames, storage tanks, pipe corridors, and other structures, as well as spatial information such as the location and height of each elevated point source and the area range of each surface source. Building Obstruction Correction: Based on the 3D mesh model of the industrial park, this process identifies whether there are buildings or large equipment obstructing the line of sight between each preset spatial point and each emission source, and compensates for the temperature and pressure correction of the concentration at the obstructed points. Corrected Concentration: The final concentration value of each preset spatial point obtained after background correction, temperature and pressure correction, and building obstruction correction.Corrected 3D CO2 Concentration Field: A 3D concentration distribution covering the entire target enterprise park, formed by interpolating the corrected concentrations of all preset spatial points according to their respective spatial coordinates in a 3D grid model. Gaussian Plume Model: A mathematical model simulating the diffusion of emissions from elevated point sources in the atmosphere, assuming that pollutant concentrations follow a Gaussian distribution in both horizontal and vertical directions and are transported downstream along the wind direction. Elevated Point Source: A concentrated emission outlet with a certain emission height, including chimneys, flares, etc. Source Strength: The amount of CO2 emitted per unit time by an elevated point source or area source; it is the core solution object of the inversion algorithm. Time-Period Point Source Emission Flux: The amount of CO2 emitted by a single elevated point source within a preset time period. Control Target Model: The mathematical model used in area source strength inversion, representing the corrected concentration of each receptor point as a linear superposition of the contributions of each source grid, and solving for the source strength of each grid through inversion. Gridded Area Source: A set of source grids formed by discretizing the area source region horizontally into several regular grids; each grid is an independent emission unit with the source strength to be inverted. Total area source flux during a given time period: The total CO2 emissions from an area source region within a preset time period. Total point source emission flux during a given time period: The total CO2 emissions from all elevated point sources within the target enterprise park within a preset time period. Measured carbon emissions per enterprise at different times: The total measured carbon emissions from the target enterprise park within a preset time period, equal to the sum of the total point source emission flux during the time period and the total area source emission flux during the time period. Measured carbon emissions per period: The sum of the measured carbon emissions per enterprise at different times within a preset monitoring period.

[0166] Step 205: Perform time-sharing verification using the enterprise's time-sharing theoretical carbon emissions and the enterprise's time-sharing measured carbon emissions, and perform total verification using the periodic theoretical carbon emissions and the periodic measured carbon emissions, generating the verification results for the target enterprise park.

[0167] Furthermore, the verification results include the deviation level of the target enterprise park during the preset time period, and the deviation level in each time period. Step 203 may include the following sub-steps:

[0168] S31. The deviation between the actual carbon emissions of enterprises at different times within a preset monitoring period and the corresponding theoretical carbon emissions at different times is calculated to obtain the time-sharing deviation rate for each time period.

[0169] In this embodiment of the invention, the background analysis server reads from the database the theoretical carbon emissions of enterprises for each time period obtained in step 203, and the measured carbon emissions of enterprises for each time period obtained in step 204. Both have the same time period division granularity (15 minutes) and the same total number of time periods. For each time period within the preset monitoring cycle, the server subtracts the theoretical carbon emissions of enterprises for that time period from the measured carbon emissions of enterprises for that time period, and then divides the difference by the theoretical carbon emissions of enterprises for that time period to obtain the time-sharing deviation rate for that time period. The time-sharing deviation rate reflects the degree of deviation between the inversion results based on atmospheric measurements and the theoretical calculation results calculated by the enterprises themselves within that specific time period. When the time-sharing deviation rate is positive, it indicates that the actual carbon emissions inverted by atmospheric monitoring for that time period are higher than the theoretical value calculated by the enterprise's ledger; when the time-sharing deviation rate is negative, it indicates that the measured inversion results are lower than the theoretical calculation value. The server calculates the time-sharing deviation rate for all time periods in sequence, forming a time-sharing deviation rate sequence covering the entire monitoring cycle. The calculation formula is as follows:

[0170] ;

[0171] In the formula, For time period The time-sharing deviation rate is dimensionless and expressed as a percentage.

[0172] S32. The deviation between the periodically measured carbon emissions and the periodically theoretical carbon emissions is calculated to obtain the total deviation rate.

[0173] In this embodiment of the invention, the background analysis server reads the theoretical carbon emissions obtained in step 203 and the measured carbon emissions obtained in step 204 from the database. The server subtracts the theoretical carbon emissions from the measured carbon emissions and then divides the difference by the theoretical carbon emissions to obtain the total deviation rate. The total deviation rate reflects, from a macro perspective, the overall degree of deviation between the total carbon emissions obtained from the two independently calculated paths over the entire monitoring period. The calculation formula is as follows:

[0174] ;

[0175] In the formula, The total deviation rate is dimensionless and expressed as a percentage.

[0176] S33. Determine the deviation level of the target enterprise park in each time period based on the preset deviation judgment interval where each time deviation rate is located.

[0177] In this embodiment of the invention, the background analysis server has a preset deviation judgment interval, which divides the deviation rate into three level intervals: when the absolute value of the deviation rate is... When the deviation rate is within acceptable limits, it is considered to be at the normal level, indicating that the difference between the theoretical calculation value and the measured inversion value is within an acceptable range, and the enterprise's carbon emission data is basically consistent with the measured data; when the absolute value of the deviation rate meets the following conditions... When the deviation rate is determined to be at a slight deviation level, it indicates that there is a certain deviation between the two accounting paths, and it is recommended to pay attention to and review the relevant ledgers or monitoring equipment; when the absolute value of the deviation rate is... When the deviation is classified as severe, it indicates a significant discrepancy between the two accounting paths, requiring close investigation and potentially indicating data distortion or hidden emissions risks. The server will calculate the time-sharing deviation rate for each time period in S31. Each time period is compared with the preset deviation judgment interval to determine the deviation level corresponding to each time period, and the deviation level label is marked for each time period.

[0178] S34. Determine the deviation level of the target enterprise park within the preset time period based on the preset deviation judgment interval where the total deviation rate is located.

[0179] In this embodiment of the invention, the background analysis server compares the total deviation rate calculated in S32 with the same preset deviation judgment interval in S33: when When the total deviation level of the target enterprise park within the preset monitoring period is determined to be normal; when When, it is judged as a slight deviation; when If a deviation is detected, it is considered a serious deviation. The server combines the total deviation level with the time-specific deviation levels for each time period to generate the verification results for the target enterprise park within the preset monitoring period. These verification results include: the total deviation rate and its corresponding deviation level, and the time-specific deviation rate sequence for each time period and its corresponding time-specific deviation level. The server stores the generated verification results in the database for subsequent steps and also displays them through the user's terminal.

[0180] Time-of-use deviation rate: The degree of deviation between the enterprise's actual time-of-use carbon emissions and its theoretical time-of-use carbon emissions for each time period, expressed as a percentage. Total deviation rate: The degree of deviation between the actual carbon emissions for a period and its theoretical carbon emissions for that period, expressed as a percentage. Preset deviation judgment range: A pre-defined deviation rate grading standard that divides the deviation rate into three levels: normal (≤5%), slight deviation (5%~15%), and severe deviation (>15%). Deviation level: The qualitative evaluation result determined based on the deviation rate's position within the preset deviation judgment range, including normal, slight deviation, and severe deviation.

[0181] Furthermore, the early warning information includes the type of abnormal scenario, the time period of the abnormality, and the corresponding preset spatial location or power unit identifier. The power consumption data of the park also includes the start / stop status of the equipment in each power unit, and may also include the following steps:

[0182] A1. Based on the total deviation rate and the time-specific deviation rate for each period, identify abnormal carbon emission scenarios in the target enterprise park, generate and push corresponding early warning information; abnormal carbon emission scenarios include abnormal data scenarios, abnormal leakage scenarios, and abnormal operating conditions scenarios.

[0183] In this embodiment of the invention, after the background analysis server completes the verification result generation in step 205, it further calls the early warning analysis module to automatically identify abnormal carbon emission scenarios in the target enterprise park based on the total deviation rate and the time-specific deviation rate for each time period. The server has a built-in rule engine with pre-configured trigger conditions for three abnormal scenarios. The server combines the identified abnormal scenario type, the time period of the abnormality, and the corresponding preset spatial location or power consumption unit identifier to generate corresponding early warning information, and pushes the early warning information to the user's operating terminal for display.

[0184] A2. When the total deviation rate exceeds the severe deviation threshold in the preset deviation judgment range, it is judged as a data abnormality scenario, and a data abnormality warning message is generated and pushed.

[0185] In this embodiment of the invention, the background analysis server reads the total deviation rate obtained in step 205 and compares its absolute value with a severe deviation threshold in a preset deviation judgment interval. The severe deviation threshold is 15%. When the server determines that a data anomaly has occurred within the current monitoring period, it triggers a data anomaly warning. The logic behind this determination is that if the total deviation rate exceeds the severe deviation threshold and there is no corresponding increase in electricity consumption or fuel consumption to explain it, it indicates a significant and inexplicable deviation between the theoretically calculated value and the measured inversion value. After triggering the warning, the server generates data anomaly warning information, including: the anomaly scenario type – “data anomaly”, the anomaly occurrence period – the start and end times of the current preset monitoring period, and the corresponding total deviation rate value, which is displayed through the user's terminal.

[0186] A3. When the measured carbon emissions at any preset spatial point within the target enterprise park are consistently higher than the theoretical carbon emissions, and the time-of-use electricity consumption of the application unit does not increase, it is determined to be an abnormal leakage scenario, and an abnormal leakage warning message is generated and pushed.

[0187] In this embodiment of the invention, for each preset spatial location, the background analysis server obtains the measured carbon emissions of that location in each time period from the partition statistics results in step 204, and obtains the theoretical time-of-use carbon emissions of the applied power unit for that preset spatial location in each time period from step 203. Time-of-use electricity consumption The server chronologically judges multiple consecutive time periods within a preset monitoring cycle: if the measured carbon emissions of a preset spatial location are higher than its corresponding theoretical carbon emissions for three or more consecutive time periods, then... The data is continuously established, and the electricity consumption of the power consumption unit corresponding to the preset spatial location is within the same time period. When the electricity consumption is not increased compared to adjacent historical periods (i.e., the current period's electricity consumption does not exceed 105% of the previous period's electricity consumption), the server determines that a leakage anomaly scenario exists at the preset spatial location and triggers a leakage anomaly warning. Monitoring conditions consistently exceeding theoretical values ​​are used to eliminate false judgments caused by instantaneous disturbances. After determining the trigger, the server generates leakage anomaly warning information, which includes: the anomaly scenario type—"Leakage Anomaly," the time period of the anomaly, and the corresponding preset spatial location identifier and power consumption unit identifier, and is displayed through the user's operating terminal.

[0188] A4. When the time-of-use deviation rate of any power consumption unit in the target enterprise park exceeds the severe deviation threshold in the preset deviation judgment interval, and the corresponding equipment start-up and shutdown status does not match the carbon emission change trend, it is judged as an abnormal operating condition scenario, and an abnormal operating condition warning information is generated and pushed. The carbon emission change trend is determined by the enterprise's time-of-use theoretical carbon emissions and the enterprise's time-of-use measured carbon emissions in chronological order for each time period.

[0189] In this embodiment of the invention, the background analysis server reads the equipment start-up and shutdown status of each power consumption unit from step 203. The equipment start-up and shutdown status is the operating status of each power consumption unit as determined in real time by the load monitoring module in step 101, including both running and stopped states. For each power consumption unit, the server obtains the time-sharing deviation rate of the power consumption unit in each time period from step 205, and obtains the equipment start-up and shutdown status of the power consumption unit in each time period from step 203, and arranges the time-sharing deviation rates of each time period in chronological order to form the carbon emission change trend of the power consumption unit. The server performs time-series correlation analysis on the equipment start-up and shutdown status of the power consumption unit and the carbon emission change trend: when the absolute value of the time-sharing deviation rate of a certain power consumption unit exceeds the severe deviation threshold, i.e. When the carbon emission trend of a power-consuming unit is inconsistent with the trend of the equipment's start-up and shutdown status, the server determines that the power-consuming unit has an abnormal operating condition and triggers an abnormal operating condition warning. The specific determination method for "inconsistency" is: after the equipment switches from running to shut-down status, the carbon emissions of the power-consuming unit (represented by the time-sharing deviation rate) do not show a corresponding decreasing trend; or after the equipment switches from shut-down to running status, the carbon emissions of the power-consuming unit do not show a corresponding increasing trend. The time-sharing deviation rate itself reflects the degree of deviation between theoretical calculations and actual monitoring. When it exceeds the severe deviation threshold and is inconsistent with the trend of equipment operating status changes, it indicates that the carbon emission changes cannot be explained by normal production operating condition changes. After the server determines that an abnormality has been triggered, it generates an abnormal operating condition warning message, which includes: the abnormal scenario type—"abnormal operating condition," the time period of the abnormality and the corresponding power-consuming unit identifier, and comparison data of the equipment's start-up and shutdown status and carbon emission change trends, and displays it through the user's operating terminal.

[0190] The carbon emission trend is determined by calculating the time-sharing deviation rate of each period based on the enterprise's theoretical and measured time-sharing carbon emissions in chronological order. If the time-sharing deviation rate of a later period is greater than that of a previous period, the carbon emissions for that period are considered to be increasing; if the time-sharing deviation rate of a later period is less than that of a previous period, the carbon emissions for that period are considered to be decreasing. If carbon emissions do not decrease after equipment switches from operating to shut-down mode, or do not increase after equipment switches from shut-down to operating mode, then the equipment's start-up / shut-down status is considered to be mismatched with the carbon emission trend.

[0191] Equipment Start-up / Stop Status: The operating status of the main electrical equipment in each power consumption unit, including "Running" and "Stopped" states, determined in real time by the load monitoring module. Carbon Emission Anomaly Scenarios: Specific situations where carbon emission data or emission behavior deviates from normal conditions, including data anomaly scenarios, leakage anomaly scenarios, and operating condition anomaly scenarios. Data Anomaly Scenarios: An anomaly detected when the total deviation rate exceeds the severe deviation threshold (>15%), indicating a significant and inexplicable deviation between the theoretical calculation value and the measured inversion value. Leakage Anomaly Scenarios: An anomaly detected when the measured carbon emissions at a preset spatial point are consistently higher than the theoretical carbon emissions without an increase in the electricity consumption of the power application unit, indicating potential pipeline leaks, illegal venting, or fugitive emissions at that point. Operating Condition Anomaly Scenarios: An anomaly detected when the time-of-use deviation rate of a power consumption unit exceeds the severe deviation threshold and the equipment start-up / stop status is inconsistent with the carbon emission change trend, indicating that carbon emission changes cannot be explained by normal production operating condition changes. Severe Deviation Threshold: The boundary value used to distinguish between minor and severe deviations within the preset deviation judgment interval; in this method, it is 15%. Data Anomaly Warning Information: A warning notification generated when a data anomaly scenario is identified, including the anomaly scenario type, the time period of the anomaly, and the corresponding deviation rate value. Leakage Anomaly Warning Information: A warning notification generated when a leakage anomaly scenario is identified, including the anomaly scenario type, the time period of the anomaly, and the corresponding preset spatial location identifier and power consumption unit identifier. Operating Condition Anomaly Warning Information: A warning notification generated when an operating condition anomaly scenario is identified, including the anomaly scenario type, the time period of the anomaly, the corresponding power consumption unit identifier, and a comparison of equipment start / stop status and carbon emission change trend data. Carbon Emission Change Trend: A sequence of rising and falling directions formed by arranging the carbon emission data of each power consumption unit (characterized by the time-sharing deviation rate) in chronological order. This trend is determined by calculating the time-sharing deviation rate of each time period after arranging the enterprise's theoretical and measured time-sharing carbon emissions in chronological order: a higher time-sharing deviation rate in a later time period indicates an upward trend, and vice versa. Warning Information: Notification data generated by the backend analysis server, containing the type of abnormal scenario, the time period of the abnormality, and the corresponding preset spatial location or power consumption unit identifier. Abnormal Scenario Type: The specific category of the carbon emission abnormal scenario, including data abnormality, leakage abnormality, and operating condition abnormality. Abnormality Occurrence Time: The specific time period during which the carbon emission abnormal scenario was identified and determined to trigger the warning. Power Consumption Unit Identifier: A unique number or name used to identify each power consumption unit.

[0192] Please see Figure 3 , Figure 3 This is a structural block diagram of an enterprise carbon emission traceability and verification system provided in Embodiment 3 of the present invention.

[0193] This invention provides an enterprise carbon emission traceability and verification system, comprising:

[0194] The multi-source data acquisition and preprocessing module 301 is used to acquire multi-source data of the target enterprise park and preprocess it to obtain target multi-source data, which includes UAV observation data, park electricity consumption data, carbon emission data and meteorological data.

[0195] The theoretical carbon emission calculation module 302 is used to calculate the theoretical carbon emission using the park's electricity consumption data and carbon emission data, obtain the enterprise's time-of-use theoretical carbon emission, and summarize the carbon emission based on the preset time period to obtain the periodic theoretical carbon emission.

[0196] The measured carbon emission inversion module 303 is used to perform measured carbon emission inversion using UAV observation data and meteorological data to obtain the enterprise's time-sharing measured carbon emissions, and to summarize carbon emissions based on preset time periods to obtain periodic measured carbon emissions.

[0197] The cross-verification module 304 is used to perform time-sharing verification using the enterprise's time-sharing theoretical carbon emissions and the enterprise's time-sharing measured carbon emissions, and to perform total verification using the periodic theoretical carbon emissions and the periodic measured carbon emissions, thereby generating the verification results for the target enterprise park.

[0198] Since the above is a system corresponding to one of the enterprise carbon emission traceability verification methods, its implementation principle is the same as that of an enterprise carbon emission traceability verification method. For the sake of convenience and brevity, those skilled in the art can clearly understand that the specific working process of the system and modules described above can be referred to the corresponding process in the aforementioned method embodiments, and will not be repeated here.

[0199] Please see Figure 4 , Figure 4 This is a structural block diagram of an electronic device provided in Embodiment 4 of the present invention.

[0200] An electronic device according to an embodiment of the present invention includes: a memory 401 and a processor 402. The memory 401 stores a computer program. When the computer program is executed by the processor 402, the processor 402 performs the enterprise carbon emission traceability verification method as described in the above embodiment.

[0201] Memory 401 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Memory 401 has storage space 403 for program code 413 for performing any of the method steps described above. For example, storage space 403 for program code may include individual program codes 413 for implementing the various steps in the methods described above. This program code may be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, CDs, memory cards, or floppy disks. The program code may be compressed, for example, in a suitable form. When run by a computing processing device, this code causes the computing processing device to perform the various steps in the methods described above. This program code may be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, CDs, memory cards, or floppy disks. The program code may be compressed, for example, in a suitable form. When this code is run by a computing device, it causes the device to perform the various steps in the enterprise carbon emission traceability verification method described above.

[0202] Embodiment 5 of the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the enterprise carbon emission traceability verification method as described in the above embodiments.

[0203] Embodiment 6 of the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer performs the enterprise carbon emission traceability verification method as described in the above embodiments.

[0204] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0205] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0206] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0207] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0208] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0209] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for verifying corporate carbon emissions, characterized in that, include: The target enterprise park is acquired and preprocessed to obtain target multi-source data, which includes UAV observation data, park electricity consumption data, carbon emission data and meteorological data; Theoretical carbon emissions are calculated using the park's electricity consumption data and carbon emission data to obtain the enterprise's time-of-use theoretical carbon emissions, and carbon emissions are summarized based on preset time periods to obtain the periodic theoretical carbon emissions. By using the UAV observation data and the meteorological data to perform measured carbon emission inversion, the company's time-sharing measured carbon emissions are obtained, and carbon emissions are summarized based on the preset time period to obtain periodic measured carbon emissions. The time-sharing theoretical carbon emissions of the enterprise are compared with the time-sharing measured carbon emissions of the enterprise, and the total carbon emissions are compared with the periodic theoretical carbon emissions and the periodic measured carbon emissions to generate the verification result of the target enterprise park.

2. The enterprise carbon emission traceability verification method according to claim 1, characterized in that, The park's electricity consumption data includes the time-of-use electricity consumption of each electricity-consuming unit within the target enterprise park. The carbon emission data includes the fuel consumption, fuel carbon content, fuel oxidation rate, process raw material consumption, and process emission factor of each electricity-consuming unit within the target enterprise park. The theoretical carbon emission calculation is performed using the park's electricity consumption data and the carbon emission data to obtain the enterprise's time-of-use theoretical carbon emission. Carbon emissions are then aggregated based on a preset time period to obtain the periodic theoretical carbon emission, including: The time-of-use grid factor published by the regional power grid is retrieved and combined with the time-of-use electricity consumption of each of the aforementioned power-consuming units to determine the unit time-of-use electricity carbon emissions of each of the aforementioned power-consuming units; The unit-time fuel combustion carbon emissions of each power consumption unit are determined based on the fuel consumption, fuel carbon content, and fuel oxidation rate of each power consumption unit. Based on the amount of process raw materials used and the process emission factor of each power-consuming unit, the unit time-sharing process carbon emissions of each power-consuming unit are determined. The unit time-sharing carbon emissions of fuel combustion associated with each of the power consumption units and the unit time-sharing carbon emissions of the process are summed to determine the unit time-sharing direct carbon emissions of each of the power consumption units. The unit time-of-use electricity carbon emissions associated with each of the aforementioned electricity consumption units are summed with the unit time-of-use direct carbon emissions to determine the unit time-of-use theoretical carbon emissions of each of the aforementioned electricity consumption units. The summation of all the unit time-sharing theoretical carbon emissions is used to obtain the enterprise time-sharing theoretical carbon emissions of the target enterprise park. The theoretical carbon emissions of the enterprise at different times within the preset monitoring period are summed to obtain the theoretical carbon emissions of the target enterprise park over the period.

3. The enterprise carbon emission traceability verification method according to claim 1, characterized in that, The UAV observation data includes the original observed concentration and altitude at multiple preset spatial points within the target enterprise park. The meteorological data includes wind speed, wind direction, temperature, humidity, and surface roughness within the target enterprise park. The UAV observation data and the meteorological data are used to perform measured carbon emission inversion to obtain the enterprise's time-series measured carbon emissions. Based on the preset time periods, carbon emissions are summarized to obtain periodic measured carbon emissions, including: Based on the wind speed and the surface roughness, the original observed concentrations at different heights of each preset spatial point are corrected by wind profiles to determine the corrected wind speed and diffusion parameters at the corresponding heights of each preset spatial point. Based on the wind direction, the upwind background point of the target enterprise park is determined. The observed concentration at the upwind background point is used as the background concentration. The difference between each original observed concentration and the background concentration is calculated to determine the background correction concentration increment of each preset spatial point. Based on the temperature and humidity, temperature and pressure corrections are applied to each of the background correction concentration increments to determine the temperature and pressure correction concentrations at each of the preset spatial points. Based on the preset three-dimensional grid model of the park, the temperature and pressure corrected concentrations are corrected by building shading, the corrected concentrations of each preset spatial point are determined, and the corrected three-dimensional carbon dioxide concentration field is constructed. Based on the corrected wind speed and the diffusion parameters, combined with the corrected three-dimensional carbon dioxide concentration field, the source strength of each elevated point source is inverted using the Gaussian plume model, and the time-period point source emission flux of each of the elevated point sources is determined. Based on the corrected three-dimensional carbon dioxide concentration field, a gridded surface source is established using a control target model and the source intensity is inverted to determine the total flux of the surface source over a period of time. The total emission flux of point sources during all the time periods is obtained by summing the emission fluxes of all the aforementioned time periods. The time-sharing measured carbon emissions of enterprises in the target enterprise park are obtained by summing the total area source flux and the total point source emission flux during the time period. The periodic measured carbon emissions of the target enterprise park are obtained by summing the time-sharing measured carbon emissions of the enterprise in all time periods within the preset monitoring period.

4. The enterprise carbon emission traceability verification method according to claim 1, characterized in that, The process involves time-sharing verification using the enterprise's time-sharing theoretical carbon emissions and the enterprise's time-sharing measured carbon emissions, and total verification using the periodic theoretical carbon emissions and the periodic measured carbon emissions, generating the verification results for the target enterprise park, including: The deviation rate for each time period is obtained by calculating the deviation between the actual carbon emissions of the enterprise at each time period within the preset monitoring period and the corresponding theoretical carbon emissions of the enterprise at each time period. The total deviation rate is obtained by calculating the deviation between the measured carbon emissions of the period and the theoretical carbon emissions of the period. Based on the preset deviation judgment interval in which each of the time-sharing deviation rates falls, the deviation level of the target enterprise park in each of the time periods is determined; Based on the preset deviation judgment interval in which the total deviation rate falls, the deviation level of the target enterprise park in the preset time period is determined; The verification results include the deviation level of the target enterprise park during the preset time period, and the deviation level during each of the time periods.

5. The enterprise carbon emission traceability verification method according to claim 4, characterized in that, The park's electricity consumption data also includes the start / stop status of equipment in each power-consuming unit, and further includes: Based on the total deviation rate and the time-specific deviation rate for each time period, the carbon emission anomaly scenario of the target enterprise park is identified, and corresponding early warning information is generated and pushed; the carbon emission anomaly scenario includes data anomaly scenario, leakage anomaly scenario and operating condition anomaly scenario. When the total deviation rate exceeds the severe deviation threshold in the preset deviation judgment interval, it is determined to be the data abnormal scenario, and a data abnormality warning message is generated and pushed. When the measured carbon emissions at any preset spatial point within the target enterprise park are consistently higher than the theoretical carbon emissions, and the time-of-use electricity consumption of the application unit does not increase, it is determined to be the leakage abnormal scenario, and leakage abnormal warning information is generated and pushed. When the time-of-use deviation rate of any power consumption unit in the target enterprise park exceeds the severe deviation threshold in the preset deviation judgment interval, and the corresponding equipment start-up and shutdown status does not match the carbon emission change trend, it is determined as the abnormal operating condition scenario, and an abnormal operating condition warning message is generated and pushed; the carbon emission change trend is determined by the enterprise's time-of-use theoretical carbon emissions and the enterprise's time-of-use measured carbon emissions in each time period arranged in chronological order. The early warning information includes the type of abnormal scenario, the time period of the abnormality, and the corresponding preset spatial location or power unit identifier.

6. The enterprise carbon emission traceability verification method according to any one of claims 1-5, characterized in that... The process of acquiring and preprocessing multi-source data from the target enterprise park to obtain target multi-source data includes: Acquire multi-source data from the target enterprise park; Outlier cleaning and time alignment are performed on the multi-source data to obtain the target multi-source data.

7. A corporate carbon emission traceability and verification system, characterized in that, include: The multi-source data acquisition and preprocessing module is used to acquire multi-source data of the target enterprise park and preprocess it to obtain target multi-source data, which includes UAV observation data, park electricity consumption data, carbon emission data and meteorological data. The theoretical carbon emission calculation module is used to calculate the theoretical carbon emission using the park's electricity consumption data and the carbon emission data, to obtain the enterprise's time-of-use theoretical carbon emission, and to summarize the carbon emission based on a preset time period to obtain the periodic theoretical carbon emission. The measured carbon emission inversion module is used to perform measured carbon emission inversion using the UAV observation data and the meteorological data to obtain the enterprise's time-sharing measured carbon emissions, and to summarize the carbon emissions based on the preset time period to obtain the periodic measured carbon emissions. The cross-verification module is used to perform time-sharing verification using the enterprise's time-sharing theoretical carbon emissions and the enterprise's time-sharing measured carbon emissions, and to perform total verification using the periodic theoretical carbon emissions and the periodic measured carbon emissions, thereby generating the verification results for the target enterprise park.

8. An electronic device, characterized in that, The system includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the enterprise carbon emission traceability verification method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, it implements the enterprise carbon emission traceability and verification method as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, wherein when the program instructions are executed by a computer, the computer performs the enterprise carbon emission traceability verification method as described in any one of claims 1-6.

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