Carbon emission accounting method and device

Abstract

The application provides a carbon emission accounting method and device, relates to the technical field of energy management, and comprises the following steps: obtaining multiple energy types of a public transport vehicle, multi-source energy consumption data, energy quality parameters, vehicle state parameters, operating condition parameters and environmental parameters; determining a dynamic carbon emission factor according to the energy quality parameters, the vehicle state parameters, the operating condition parameters, the environmental parameters and the energy types; generating a carbon emission amount based on the energy types, the multi-source energy consumption data and the dynamic carbon emission factor; and performing summary analysis based on the carbon emission amount according to a preset accounting dimension to generate a carbon emission accounting result. The carbon emission accounting method and device provided by the application improve the public transport carbon emission accounting precision and dynamic response capability, realize fine accounting at the vehicle level, the line level, the time period level and the vehicle fleet level, and provide data support for low-carbon scheduling, carbon emission reduction assessment and carbon trading.

Description

Technical Field

[0001] This application relates to the field of energy management technology, and in particular to a carbon emission accounting method and apparatus. Background Technology

[0002] Currently, the demand for carbon emission accounting in the urban public transportation sector is increasing. Public transport vehicles utilize various energy sources, including diesel, natural gas, electricity, and hydrogen, during actual operation. The routes, passenger load factors, operating conditions, energy replenishment methods, and scheduling of different vehicles can all affect actual energy consumption and carbon emission levels. Therefore, the public transport industry needs to establish carbon emission accounting methods that are adaptable to different public transport operation scenarios to support business needs such as low-carbon operation management, carbon reduction assessment, and carbon trading.

[0003] Existing carbon emission accounting methods typically employ fixed emission factors and calculate emissions based on total energy consumption at the enterprise, fleet, or statistical period level. While these methods can obtain total carbon emissions within a certain range, their granularity is relatively coarse, making it difficult to accurately reflect changes in carbon emissions from public transport vehicles during actual operation. Consequently, they struggle to provide reliable data support for low-carbon operation and refined management of public transport.

[0004] This section is intended to provide background or context for the embodiments of the invention set forth in the claims. The description herein is not an admission that it is prior art simply because it is included in this section. Summary of the Invention

[0005] To address the problems in the existing technology, this application provides a carbon emission accounting method and apparatus, which can solve the problems of weak real-time data acquisition capability, static emission factors, insufficient multi-energy coordination, failure to consider the impact of scheduling strategies, and coarse accounting granularity in the existing public transportation carbon emission accounting, resulting in low accounting accuracy and difficulty in supporting low-carbon scheduling and carbon emission reduction assessment.

[0006] One aspect of the present invention provides a carbon emission accounting method, the method comprising: Acquire data on various energy types, multi-source energy consumption, energy quality parameters, vehicle status parameters, operating condition parameters, and environmental parameters of public transport vehicles; The dynamic carbon emission factor is determined based on the energy quality parameters, vehicle status parameters, operating condition parameters, environmental parameters, and energy type. Carbon emissions are generated based on the energy type, the multi-source energy consumption data, and the dynamic carbon emission factor. The carbon emissions are summarized and analyzed according to preset accounting dimensions to generate carbon emission accounting results.

[0007] Further, determining the dynamic carbon emission factor based on the energy quality parameters, vehicle status parameters, operating condition parameters, environmental parameters, and energy type includes: Match the basic carbon emission factors corresponding to each energy type from a pre-built basic carbon emission factor library; The basic carbon emission factor is corrected based on the energy quality parameters, vehicle status parameters, operating condition parameters, and environmental parameters to determine the dynamic carbon emission factor.

[0008] Further, the step of correcting the basic carbon emission factor based on the energy quality parameters, the vehicle status parameters, the operating condition parameters, and the environmental parameters to determine the dynamic carbon emission factor includes: Determine the energy correction factor based on the energy quality parameters; The vehicle aging correction factor is determined based on the vehicle condition parameters. Determine the operating condition correction coefficient based on the aforementioned operating condition parameters; Determine the environmental correction factor based on the environmental parameters; The dynamic carbon emission factor is calculated based on the energy correction factor, the vehicle aging correction factor, the operating condition correction factor, the environmental correction factor, and the basic carbon emission factor.

[0009] Furthermore, the generation of carbon emissions based on the energy type, the multi-source energy consumption data, and the dynamic carbon emission factor includes: Call the carbon emission accounting model corresponding to each energy type from the pre-built carbon emission accounting model library; The multi-source energy consumption data is standardized to obtain effective multi-source accounting data; Based on the carbon emission accounting model, carbon emissions are calculated using the effective accounting data and the dynamic carbon emission factor.

[0010] Furthermore, the accounting dimensions include: vehicle dimension, route dimension, and fleet dimension; the process of summarizing and analyzing carbon emissions according to the preset accounting dimensions to generate carbon emission accounting results includes: The carbon emissions are summarized and analyzed according to the vehicle dimension to generate vehicle-level carbon emission results; The carbon emissions are summarized and analyzed according to the aforementioned route dimension to generate route-level carbon emission results; The carbon emissions are summarized and analyzed according to the fleet dimension to generate fleet-level carbon emission results; Calculate the carbon emission intensity per unit passenger volume based on the aforementioned fleet-level carbon emission results; The carbon emission accounting result is generated based on the vehicle-level carbon emission result, the route-level carbon emission result, the fleet-level carbon emission result, and the carbon emission intensity per unit passenger volume.

[0011] Furthermore, it also includes: The carbon emission accounting results are compared with the obtained actual energy consumption statistics to obtain the accounting error; When the calculation error exceeds a preset error threshold, the correction parameters of the dynamic carbon emission factor and / or the model parameters of the carbon emission calculation model are adjusted. Carbon emission accounting will be re-executed based on the adjusted correction parameters and / or model parameters.

[0012] In another aspect, the present invention provides a carbon emission accounting device, the device comprising: The information acquisition unit is used to acquire data on various energy types and multi-source energy consumption of buses, energy quality parameters, vehicle status parameters, operating condition parameters, and environmental parameters. The factor determination unit is used to determine the dynamic carbon emission factor based on the energy quality parameter, the vehicle status parameter, the operating condition parameter, the environmental parameter, and the energy type. A carbon emission generation unit is used to generate carbon emissions based on the energy type, the multi-source energy consumption data, and the dynamic carbon emission factor. The calculation result generation unit is used to summarize and analyze the carbon emissions according to preset calculation dimensions to generate carbon emission calculation results.

[0013] Furthermore, the factor determination unit includes: The basic factor determination module is used to match the basic carbon emission factors corresponding to each energy type from a pre-built basic carbon emission factor library; The dynamic factor determination module is used to correct the basic carbon emission factor based on the energy quality parameter, the vehicle status parameter, the operating condition parameter, and the environmental parameter to determine the dynamic carbon emission factor.

[0014] Furthermore, the dynamic factor determination module includes: The first correction coefficient determination submodule is used to determine the energy correction coefficient based on the energy quality parameters. The second correction coefficient determination submodule is used to determine the vehicle aging correction coefficient based on the vehicle state parameters. The third correction coefficient determination submodule is used to determine the operating condition correction coefficient based on the operating condition parameters. The fourth correction factor determination submodule is used to determine the environmental correction factor based on the environmental parameters; The dynamic factor determination submodule is used to calculate the dynamic carbon emission factor based on the energy correction coefficient, the vehicle aging correction coefficient, the operating condition correction coefficient, the environmental correction coefficient, and the basic carbon emission factor.

[0015] Furthermore, the carbon emission generation unit includes: The carbon emission accounting model calling module is used to call the carbon emission accounting model corresponding to each energy type from the pre-built carbon emission accounting model library; The standardization processing module is used to standardize the multi-source energy consumption data to obtain effective multi-source accounting data. The carbon emission generation module is used to calculate carbon emissions based on the carbon emission accounting model, using the effective accounting data and the dynamic carbon emission factor.

[0016] Furthermore, the accounting dimensions include: vehicle dimension, route dimension, and fleet dimension; the accounting result generation unit includes: The vehicle-level result generation module is used to summarize and analyze the carbon emissions according to the vehicle dimension and generate vehicle-level carbon emission results. The line-level result generation module is used to summarize and analyze the carbon emissions according to the line dimension and generate line-level carbon emission results. The fleet-level result generation module is used to summarize and analyze the carbon emissions according to the fleet dimension and generate fleet-level carbon emission results. The carbon emission intensity calculation module is used to calculate the carbon emission intensity per unit passenger volume based on the fleet-level carbon emission results. The calculation result generation module is used to generate the carbon emission calculation result based on the vehicle-level carbon emission result, the route-level carbon emission result, the fleet-level carbon emission result, and the unit passenger volume carbon emission intensity.

[0017] Furthermore, it also includes: The calculation error generation unit is used to compare the carbon emission calculation results with the obtained actual energy consumption statistics to obtain the calculation error; The parameter adjustment unit is used to adjust the correction parameters of the dynamic carbon emission factor and / or the model parameters of the carbon emission accounting model when the calculation error exceeds a preset error threshold. The carbon emission recalculation unit is used to re-perform carbon emission calculations based on the adjusted correction parameters and / or model parameters.

[0018] To achieve the above objectives, according to another aspect of the present invention, a computer device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the carbon emission accounting method described above.

[0019] To achieve the above objectives, according to another aspect of the present invention, a computer-readable storage medium is also provided, on which a computer program / instructions are stored, which, when executed by a processor, implement the steps of the carbon emission accounting method described above.

[0020] To achieve the above objectives, according to another aspect of the present invention, a computer program product is also provided, comprising a computer program / instructions that, when executed by a processor, implement the steps of the carbon emission accounting method described above.

[0021] The beneficial effects of this invention are as follows: The carbon emission accounting method and system based on energy consumption data provided in this application can perform specialized carbon emission accounting for public transportation operation scenarios. It fully considers the actual operation characteristics such as the diversity of public transportation vehicle types, the mixing of energy types, the dynamic changes in operating routes, and the fluctuations in passenger load factor, so that the accounting results are more consistent with the real carbon emission situation in public transportation business scenarios.

[0022] This application collects real-time data on bus energy consumption, as well as multi-source data such as vehicle operating status, energy replenishment, and route location. It also dynamically corrects carbon emission factors by combining data on energy quality, vehicle aging, operating conditions, and environmental factors. This avoids the problem that traditional fixed emission factors cannot reflect actual operational changes, thereby improving the accuracy and real-time performance of bus carbon emission accounting.

[0023] This application establishes a unified carbon emission accounting framework applicable to various energy types such as diesel, natural gas, electricity, and hydrogen energy. It can perform collaborative accounting for the mixed consumption of multiple energy sources in public transport vehicles, accurately reflect the impact of different energy structures on carbon emissions, and provide data support for public transport companies to optimize their energy structure and assess the effectiveness of new energy vehicle substitution.

[0024] This application can summarize and analyze carbon emission results at multiple dimensions such as vehicle level, route level, time period level, and fleet level, which refines the granularity of carbon emission accounting from the traditional coarse statistics to the operational units such as vehicles and routes, thereby supporting refined carbon emission reduction assessment, carbon emission traceability, and public transportation operation management.

[0025] This application can also incorporate bus scheduling strategies into the carbon emission accounting and forecasting process, conduct pre-assessment of the carbon emission impact of scheduling schemes such as route adjustments, departure intervals, and vehicle configuration, and generate low-carbon scheduling recommendations based on the accounting and forecasting results, thereby providing decision support for bus companies to reduce operational carbon emissions.

[0026] Furthermore, this application establishes a closed-loop optimization mechanism through accounting accuracy assessment, model parameter tuning, and emission factor calibration. This mechanism can continuously correct model parameters and emission factors based on actual accounting results, further improving the accounting stability and reliability of the system during long-term operation. It also provides high-precision and traceable data support for public transportation companies to conduct carbon emission reduction assessments, carbon asset management, and carbon trading. Attached Figure Description

[0027] 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 some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings: Figure 1 This is a schematic diagram of the first process of the carbon emission accounting method provided in this embodiment of the invention; Figure 2 This is a schematic diagram of the second process of the carbon emission accounting method provided in the embodiments of the present invention; Figure 3 This is a schematic diagram of the third process of the carbon emission accounting method provided in the embodiments of the present invention; Figure 4 This is a schematic diagram of the fourth process of the carbon emission accounting method provided in the embodiments of the present invention; Figure 5 This is a schematic diagram of the fifth process of the carbon emission accounting method provided in the embodiments of the present invention; Figure 6 This is a schematic diagram of the sixth process of the carbon emission accounting method provided in the embodiments of the present invention; Figure 7 This is a first structural schematic block diagram of the carbon emission accounting device provided in the embodiments of the present invention; Figure 8 This is a schematic block diagram of the second structure of the carbon emission accounting device provided in the embodiments of the present invention; Figure 9 This is a schematic block diagram of the third structure of the carbon emission accounting device provided in the embodiments of the present invention; Figure 10 This is a schematic block diagram of the fourth structure of the carbon emission accounting device provided in the embodiments of the present invention; Figure 11 This is a fifth structural schematic block diagram of the carbon emission accounting device provided in the embodiments of the present invention; Figure 12 This is a sixth structural schematic block diagram of the carbon emission accounting device provided in the embodiments of the present invention; Figure 13 This is a schematic diagram of the structure of the computer device provided in an embodiment of the present invention; Figure 14 This is an overall flowchart of the carbon emission accounting method provided in the embodiments of the present invention; Figure 15 This is a system architecture diagram of the carbon emission accounting method provided in the embodiments of the present invention. Detailed Implementation

[0028] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0029] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0030] It should be noted that the terms "comprising" and "having" and any variations thereof in the specification, claims and accompanying drawings of this invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such processes, methods, products or devices.

[0031] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0032] This invention aims to address the problems of insufficient adaptability of existing public transport carbon emission accounting methods to public transport operation scenarios, low accounting accuracy, and weak dynamic response capability. Specifically, this invention addresses the operational characteristics of public transportation, including multiple vehicle types, multiple energy sources, dynamic routes, and fluctuating passenger load factors. It constructs a dedicated carbon emission accounting method to improve the adaptability of the carbon emission accounting model to public transportation scenarios. By establishing a real-time data acquisition and processing mechanism from multiple sources such as onboard sensors, refueling stations, and charging piles, it achieves real-time acquisition of energy consumption data and dynamic carbon emission accounting. By comprehensively considering factors such as energy quality, vehicle aging, and changes in operating conditions, it constructs a dynamic carbon emission factor adjustment mechanism to improve accounting accuracy. By establishing a unified multi-energy collaborative accounting framework for various energy types such as diesel, natural gas, electricity, and hydrogen, it achieves unified accounting and energy structure optimization analysis under different energy consumption levels. By incorporating public transportation scheduling strategies such as routes, departure intervals, and vehicle configuration into the carbon emission accounting and prediction process, it achieves carbon emission pre-assessment and optimization of scheduling schemes. Simultaneously, by supporting multi-level refined accounting at the vehicle, route, time period, and fleet levels, it provides data support for precise carbon emission reduction assessment, low-carbon public transportation operation management, and carbon trading.

[0033] The following describes the specific implementation process of the carbon emission accounting method provided in this application embodiment, taking the server as the execution subject as an example.

[0034] Figure 1 This is a schematic diagram of the first process of the carbon emission accounting method provided in this embodiment of the invention, as shown below. Figure 1 As shown, in one embodiment of the present invention, the carbon emission accounting method of the present invention includes: S101: Acquire data on various energy types, multi-source energy consumption, energy quality parameters, vehicle status parameters, operating condition parameters, and environmental parameters of public transport vehicles; S102: Determine the dynamic carbon emission factor based on the energy quality parameters, vehicle status parameters, operating condition parameters, environmental parameters, and energy type; S103: Generate carbon emissions based on the energy type, the multi-source energy consumption data, and the dynamic carbon emission factor; S104: Based on the carbon emissions, a summary analysis is performed according to preset accounting dimensions to generate carbon emission accounting results.

[0035] from Figure 1As shown in the flowchart, the carbon emission accounting method provided in this application obtains various energy types, multi-source energy consumption data, energy quality parameters, vehicle status parameters, operating condition parameters, and environmental parameters of public transport vehicles; determines dynamic carbon emission factors based on the energy quality parameters, vehicle status parameters, operating condition parameters, environmental parameters, and energy types; generates carbon emissions based on the energy types, multi-source energy consumption data, and dynamic carbon emission factors; and summarizes and analyzes the carbon emissions according to preset accounting dimensions to generate carbon emission accounting results. This improves the accuracy and dynamic response capability of public transport carbon emission accounting, achieves refined accounting at the vehicle, route, time period, and fleet levels, and provides data support for low-carbon scheduling, carbon emission reduction assessment, and carbon trading.

[0036] Each step is explained in detail below.

[0037] S101: Acquire data on various energy types, multi-source energy consumption, energy quality parameters, vehicle status parameters, operating condition parameters, and environmental parameters of public transport vehicles; Specifically, the server acquires various energy types, multi-source energy consumption data, energy quality parameters, vehicle status parameters, operating condition parameters, and environmental parameters for public transportation vehicles. Energy types can include one or more of diesel, natural gas, electricity, and hydrogen; multi-source energy consumption data can include fuel consumption, gas consumption, electricity consumption, and hydrogen consumption data generated during vehicle operation, as well as energy replenishment data fed back from gas stations, CNG stations, charging stations, or hydrogen refueling stations; energy quality parameters can include fuel calorific value, density, purity, sulfur content, electricity carbon emission-related parameters, or hydrogen source parameters; vehicle status parameters can include vehicle age, cumulative mileage, maintenance records, vehicle model, and vehicle energy consumption performance parameters; operating condition parameters can include vehicle speed, acceleration, idling time, engine speed, route congestion status, operating route, and operating time; environmental parameters can include parameters affecting vehicle energy consumption or emission levels, such as temperature, humidity, altitude, and weather type.

[0038] S102: Determine the dynamic carbon emission factor based on the energy quality parameters, vehicle status parameters, operating condition parameters, environmental parameters, and energy type; Specifically, the server first identifies the type of energy currently used by the bus, and then dynamically adjusts the emission factor corresponding to that energy type by combining changes in energy quality, vehicle aging, real-time operating conditions and environmental conditions. This allows the obtained dynamic carbon emission factor to reflect the emission differences of the bus in actual operation, rather than just using a fixed emission factor for rough calculation.

[0039] Figure 2This is a schematic diagram of the second process of the carbon emission accounting method provided in the embodiments of the present invention, as shown below. Figure 2 As shown, in one embodiment of the present invention, S102 includes: S201: Match the basic carbon emission factors corresponding to each of the energy types from a pre-built basic carbon emission factor library; Specifically, the server first matches the basic carbon emission factors corresponding to each energy type from a pre-built basic carbon emission factor database. This database can store basic emission factors for different energy types, such as diesel, natural gas, electricity, and hydrogen. The server can then search for and retrieve the corresponding basic carbon emission factor from the database based on the energy type currently used by the bus.

[0040] S202: Based on the energy quality parameters, vehicle status parameters, operating condition parameters, and environmental parameters, the basic carbon emission factor is corrected to determine the dynamic carbon emission factor.

[0041] Specifically, the carbon emission levels of public transport vehicles during actual operation are affected by energy quality, vehicle aging, operating conditions, and changes in environmental conditions. Directly using a fixed baseline carbon emission factor would not accurately reflect the actual emissions of the vehicles. Therefore, the server uses energy quality parameters, vehicle status parameters, operating condition parameters, and environmental parameters as correction criteria to dynamically adjust the baseline carbon emission factor, resulting in a dynamic carbon emission factor that adapts to the current public transport operation status.

[0042] In this way, the server can match the corresponding basic carbon emission factors for different energy types, and further dynamically correct the basic carbon emission factors by combining actual operating data, thereby improving the accuracy of carbon emission accounting for public transportation.

[0043] Figure 3 This is a schematic diagram of the third process of the carbon emission accounting method provided in this embodiment of the invention, as shown below. Figure 3 As shown, in one embodiment of the present invention, S202 includes: S301: Determine the energy correction coefficient based on the energy quality parameters; Specifically, the server calculates multiple correction coefficients and then comprehensively corrects the baseline carbon emission factor based on these coefficients. The server determines the energy correction coefficients according to energy quality parameters. These parameters reflect differences in calorific value, density, purity, sulfur content, and whether the energy source is electricity or hydrogen. The server determines the energy correction coefficients based on the deviation between the energy quality parameters and a preset energy quality benchmark, thus characterizing the impact of changes in energy quality on the carbon emission level per unit of energy.

[0044] S302: Determine the vehicle aging correction coefficient based on the vehicle state parameters; Specifically, vehicle status parameters can include vehicle age, cumulative mileage, maintenance records, and vehicle energy efficiency degradation. The server determines the degree of vehicle aging based on these parameters and establishes an aging correction factor to characterize the impact of vehicle aging and changes in maintenance status on actual energy consumption and emissions levels.

[0045] S303: Determine the operating condition correction coefficient based on the aforementioned operating condition parameters; Specifically, operating condition parameters can include vehicle speed, acceleration, idling time, engine speed, congestion status, route type, and station density. The server determines whether the vehicle is in a congested, frequently starting and stopping, smooth-flowing, or high-load operation state based on the operating condition parameters, and determines the operating condition correction coefficient to reflect the impact of different operating conditions on carbon emission levels.

[0046] S304: Determine the environmental correction factor based on the environmental parameters; Specifically, environmental parameters can include temperature, humidity, altitude, weather type, etc. The server uses these environmental parameters to determine the impact on air conditioning load, power system efficiency, or energy consumption levels, and determines environmental correction factors to reflect the impact of external environmental changes on the carbon emissions of public transport vehicles.

[0047] S305: Calculate the dynamic carbon emission factor based on the energy correction factor, the vehicle aging correction factor, the operating condition correction factor, the environmental correction factor, and the basic carbon emission factor.

[0048] Specifically, the server calculates the dynamic carbon emission factor based on energy correction factors, vehicle aging correction factors, operating condition correction factors, environmental correction factors, and basic carbon emission factors. In this way, the dynamic carbon emission factor can simultaneously reflect the combined impact of energy quality, vehicle status, operating conditions, and environmental conditions.

[0049] In one embodiment, basic emission factors matching each energy type are obtained from a basic emission factor library based on industry standards. (Unit: kgCO2e / unit of energy), for example: diesel fuel: ; natural gas: ; electricity: (Based on the regional power grid average emission factor); Hydrogen energy: (Zero emissions during use, hydrogen production needs to be considered); Next, the energy correction factor is determined based on the energy quality parameters. : (1) in, This indicates the actual higher heating value of the energy. This indicates the high calorific value of the energy standard. This indicates the actual sulfur mass fraction in the energy. This indicates the sulfur mass fraction in the energy standard. Indicates the actual density of energy. Indicates energy standard density, The sulfur content influence coefficient is determined based on experimental data (usually). ).

[0050] Based on the vehicle's age mileage Maintenance record rating Calculate the vehicle aging correction factor : (2) in, The influence coefficient is the number of years. This is the mileage impact factor. To maintain the influence coefficient, For the designed service life.

[0051] Based on real-time operating conditions (vehicle speed) acceleration Congestion Index ), calculate the working condition correction factor : (3) in, For the optimal economic speed (usually) ), For maximum acceleration, The coefficient representing the influence of vehicle speed. For acceleration influence coefficient, The congestion impact coefficient, This is the congestion index.

[0052] According to ambient temperature ,humidity ,altitude Calculate the environmental correction factor : (4) in, The reference temperature for the ambient temperature is 25 degrees Celsius. This indicates that the reference humidity for the ambient relative humidity is 50%. , which represents the correction factor for the influence of ambient temperature on the temperature deviation; , representing the correction factor for the environmental humidity effect corresponding to the humidity deviation; , representing the altitude effect correction factor corresponding to the altitude.

[0053] Based on the above correction factors, the dynamic emission factor is calculated. : (5) This dynamic emission factor reflects the impact of energy quality, vehicle status, operating conditions, and environmental factors on carbon emissions in real time, significantly improving the accuracy of the calculation.

[0054] In one embodiment, suppose a diesel bus has a base emission factor The current running parameters are as follows: Energy quality: calorific value (Standard value) sulfur content (Standard value) ),density (Standard value) ); Vehicle condition: years of use Year, mileage 10,000 km (design life) (10,000 km), maintenance rating ; Operating conditions: vehicle speed (Optimal speed) ), acceleration (maximum acceleration) Congestion Index ; Environmental factors: temperature (Reference temperature) ),humidity (Reference humidity) ),altitude ; Calculate the correction factors:

[0055] Dynamic emission factors:

[0056] Compared to static emission factors Dynamic emission factors The increase of 33.1% more accurately reflects the actual carbon emission level.

[0057] S103: Generate carbon emissions based on the energy type, the multi-source energy consumption data, and the dynamic carbon emission factor; Specifically, the server generates carbon emissions based on energy type, multi-source energy consumption data, and dynamic carbon emission factors. For different energy types, the server calculates the individual carbon emissions according to the correspondence between the corresponding energy consumption and the dynamic carbon emission factors, and converts the individual carbon emissions generated by different energy types into carbon dioxide equivalents to obtain the carbon emissions of public transport vehicles within the target accounting period.

[0058] Figure 4 This is a schematic diagram of the fourth process of the carbon emission accounting method provided in this embodiment of the invention, as shown below. Figure 4 As shown, in one embodiment of the present invention, S103 includes: S401: Call the carbon emission accounting model corresponding to each energy type from the pre-built carbon emission accounting model library; Specifically, the server first retrieves the corresponding carbon emission accounting models for each energy type from a pre-built carbon emission accounting model library. This library can include models for diesel, natural gas, electricity, hydrogen, and more. Based on the energy type of the public transport vehicle, the server selects the appropriate carbon emission accounting model to match the energy type, thus adapting to different energy consumption data measurement methods and carbon emission calculation methods.

[0059] S402: Standardize the multi-source energy consumption data to obtain multi-source effective accounting data; Specifically, the server standardizes multi-source energy consumption data to obtain effective multi-source accounting data. Since multi-source energy consumption data may originate from different data sources such as vehicle sensors, energy replenishment equipment, and operation management platforms, the data formats, sampling frequencies, units of measurement, and time granularities may differ between these sources. The server performs data cleaning, anomaly removal, time alignment, vehicle identification matching, unit conversion, and format standardization on the multi-source energy consumption data to obtain effective multi-source accounting data usable for carbon emission calculations.

[0060] S403: Based on the carbon emission accounting model, calculate the carbon emissions using the effective accounting data and the dynamic carbon emission factor.

[0061] Specifically, for a certain energy type, the server inputs the effective energy consumption within the target accounting period into the corresponding carbon emission accounting model, and calculates the carbon emissions corresponding to that energy type by combining the dynamic carbon emission factor; when public buses consume multiple energy sources, the server calculates the carbon emissions of each energy type separately, and performs unified conversion and merging to obtain the carbon emissions of the target public bus or the target statistical object.

[0062] In one embodiment, for each bus During the period Internally collects the consumption of each energy type: Diesel consumption: (Unit: L); Natural gas consumption: (Unit: m) 3 Electricity consumption: (Unit: kWh); Hydrogen energy consumption: (Unit: kg)

[0063] Calculate the carbon emissions per vehicle per energy source based on dynamic emission factors: (6) (7) (8) (9) in, Indicates bus During the period Diesel carbon emissions within the area; Indicates bus During the period Carbon emissions from natural gas within the region; Indicates bus During the period Carbon emissions from electricity within the country; Indicates bus During the period Hydrogen energy and carbon emissions within the country.

[0064] S104: Based on the carbon emissions, a summary analysis is performed according to preset accounting dimensions to generate carbon emission accounting results.

[0065] Specifically, the server summarizes and analyzes carbon emissions according to preset accounting dimensions to generate carbon emission accounting results. The preset accounting dimensions can be set according to the needs of public transportation operation management. For example, carbon emissions can be statistically summarized and analyzed according to dimensions such as vehicle, route, time period, and fleet, ultimately forming carbon emission accounting results that can be used for public transportation low-carbon operation management, carbon emission reduction assessment, or carbon asset management.

[0066] Figure 5 This is a schematic diagram of the fifth process of the carbon emission accounting method provided in this embodiment of the invention, as shown below. Figure 5 As shown, in one embodiment of the present invention, S104 includes: S501: Summarize and analyze the carbon emissions according to the vehicle dimension to generate vehicle-level carbon emission results; Specifically, the server uses vehicle identification as the statistical object to summarize the carbon emissions generated by the same bus within the target accounting period, and obtains vehicle-level results such as total carbon emissions per vehicle, carbon emissions per unit mileage per vehicle, or carbon emissions per unit passenger volume per vehicle.

[0067] S502: Summarize and analyze the carbon emissions according to the aforementioned route dimension to generate route-level carbon emission results; Specifically, the server uses route identifiers as the statistical object to aggregate the carbon emissions of multiple vehicles operating on the same bus route, and obtains route-level results such as total route carbon emissions, carbon emissions per unit mileage of the route, and carbon emissions per unit passenger volume of the route.

[0068] S503: Summarize and analyze the carbon emissions according to the fleet dimension to generate fleet-level carbon emission results; Specifically, the server uses a fleet, branch company, or operating unit as the statistical object, and summarizes the carbon emissions of multiple buses in the fleet to obtain the total carbon emissions of the fleet and the analysis results of the fleet's carbon emission structure.

[0069] S504: Calculate the carbon emission intensity per unit passenger volume based on the aforementioned fleet-level carbon emission results; Specifically, carbon emission intensity per unit of passenger volume can be used to characterize the carbon emission level generated by a fleet when completing a unit of passenger transport service. The server calculates the carbon emission intensity per unit of passenger volume based on the total carbon emissions at the fleet level and the passenger volume, passenger turnover, or passenger transport service volume in the corresponding accounting period.

[0070] S505: The carbon emission calculation result is generated based on the vehicle-level carbon emission result, the route-level carbon emission result, the fleet-level carbon emission result, and the unit passenger volume carbon emission intensity.

[0071] Specifically, the server generates carbon emission accounting results based on vehicle-level carbon emission results, route-level carbon emission results, fleet-level carbon emission results, and carbon emission intensity per unit passenger volume. These carbon emission accounting results can include carbon emission statistics, comparative analysis values, and intensity indicators for different vehicles, routes, and fleets, supporting public transport companies in conducting vehicle energy consumption management, route emission reduction analysis, fleet assessment, and carbon asset management.

[0072] In one embodiment, the total carbon emissions per vehicle The calculation is as follows: (10) For the line During the period Within this section, the carbon emissions of all vehicles on the route are summarized: (11) For the entire convoy, during the time period Internally, the carbon emissions of all routes are summarized: (12) To assess carbon emission efficiency, the carbon emission intensity per unit passenger volume is calculated: (13) in, For time period Passenger volume (number of passengers) within the area. The average transport distance (km) Units are .

[0073] Figure 6 This is a schematic diagram of the sixth process of the carbon emission accounting method provided in this embodiment of the invention, as shown below. Figure 6 As shown, in one embodiment of the present invention, the carbon emission accounting method of the present invention further includes: S601: Compare the carbon emission accounting results with the obtained actual energy consumption statistics to obtain the accounting error; Specifically, the server can also perform closed-loop calibration on the carbon emission accounting results to improve the accuracy and stability of subsequent accounting processes. Actual energy consumption statistics can be derived from energy replenishment ledgers, refueling records, gas refueling records, charging records, hydrogen refueling records, financial settlement data, or manually verified data. The server compares the energy consumption conversion results corresponding to the carbon emission accounting results with the actual energy consumption statistics results, calculates the deviation between the two, and obtains the accounting error.

[0074] S602: When the calculation error exceeds a preset error threshold, the correction parameters of the dynamic carbon emission factor and / or the model parameters of the carbon emission calculation model are adjusted. Specifically, when the calculation error exceeds a preset error threshold, the server adjusts the correction parameters of the dynamic carbon emission factor and / or the model parameters of the carbon emission calculation model. The preset error threshold can be configured according to the calculation accuracy requirements of the public transport company. If the calculation error exceeds this error threshold, it indicates that the current correction parameters or model parameters may not accurately adapt to the actual public transport operation status, and the server adjusts the energy correction parameters, vehicle aging correction parameters, operating condition correction parameters, environmental correction parameters, or the calculation parameters in the carbon emission calculation model.

[0075] S603: Re-execute carbon emission accounting based on the adjusted correction parameters and / or model parameters.

[0076] Specifically, by re-performing carbon emission accounting, the server obtains updated carbon emission accounting results, further reducing the deviation between the accounting results and actual energy consumption statistics. Through continuous comparison, parameter adjustment, and recalculation, a closed-loop optimization mechanism for carbon emission accounting can be formed, enabling the carbon emission accounting method to maintain high accuracy and stability during long-term operation.

[0077] In one embodiment, the method of the present invention further includes: carbon emission prediction and low-carbon scheduling optimization of the scheduling scheme. First, the future passenger flow of the route is predicted based on historical passenger flow data and external influence characteristics. Then, the predicted passenger flow is converted into vehicle energy consumption correction amount, and the predicted carbon emission of the future scheduling scheme is calculated in combination with dynamic emission factors. Finally, under the constraints of passenger flow, departure frequency and budget, the optimal bus scheduling scheme is solved with the goal of minimizing carbon emissions.

[0078] This invention relies on historical passenger flow time-series samples of railway lines, and integrates external features such as weather and date through ARIMA or LSTM time-series models to achieve future... Accurate prediction of passenger flow for different time periods and routes; the predicted passenger flow is used to quantify the energy consumption increase caused by passenger load, providing data support for low-carbon scheduling optimization of the public transport network and carbon emission calculation in all scenarios.

[0079] Specifically, the server first constructs a passenger flow sample dataset and collects data from various routes. Historical time-series passenger flow data, sourced from bus card swipe records, QR code payment data, and passenger flow data collected by onboard passenger flow statistics equipment; the historical window length is set to... Construct a time-series dataset of historical passenger flow sliding window: Simultaneously, multi-dimensional external influence feature factors are introduced, including real-time weather conditions, date attributes (weekdays / public holidays), major events along the route, and road congestion status, which together constitute the input feature set for passenger flow prediction.

[0080] Next, the time series prediction model is built and trained. This invention uses two types of time series models to complete the mining of passenger flow time series patterns: the linear time series model ARIMA and the nonlinear deep learning time series model LSTM. (1) ARIMA model: used to extract the linear daily and weekly cycle variation patterns in passenger flow data and capture the steady time-series evolution trend of passenger flow; (2) LSTM Long Short-Term Memory Network: used to mine nonlinear long-term temporal dependencies in scenarios such as peak passenger flow and sudden changes in passenger flow during holidays.

[0081] The current moment's preceding historical passenger flow time-series data and multi-dimensional external influence features are simultaneously input into the trained time-series model, and the model outputs future... Time-of-day routes Basic predicted passenger flow .

[0082] Predicted passenger flow for routes using time-series models Then, the passenger flow load correction factor is calculated. : (14) in, Forecast passenger flow for line j during time period t+Δt; The rated passenger capacity of vehicle i; The passenger load energy consumption sensitivity coefficient represents the degree to which an increase in vehicle passenger capacity amplifies energy consumption per unit mileage.

[0083] According to the future scheduling plan (Including route set, departure intervals, vehicle configuration, and energy mix), predicted carbon emissions: (15) in, To predict energy consumption (based on route length, projected passenger flow, and vehicle type), the energy consumption of a single vehicle in future time periods is predicted using the following formula: (16) in, This indicates the one-way operating mileage of route j; This represents the baseline energy consumption per unit mileage for Type i vehicles; This represents the energy quality correction factor for vehicle i during the time period t+Δt. This represents the environmental correction factor (temperature, humidity, and altitude correction) for the time period t+Δt. The correction factor for passenger flow load of vehicle i during the time period t+Δt is calculated from the ratio of predicted passenger flow to rated passenger capacity.

[0084] The vehicle energy consumption calculation results are further coupled with dynamic carbon emission factors to achieve quantitative calculation of the overall carbon emissions of the entire route scheduling plan and predict dynamic emission factors. As shown below: (17) in, This indicates the energy baseline emission factor corresponding to vehicle i; This represents the energy quality correction coefficient for vehicle i during the time period t+Δt. This represents the environmental correction factor for the time period t+Δt. Indicates the time-series carbon intensity of energy supply during the period t+Δt (time-of-use carbon emissions from the power grid / fluctuations in upstream carbon emissions from fuel). It represents the time-series periodic correction term, which carries the periodicity of operation (daily cycle, weekly cycle, holiday cycle).

[0085] To minimize carbon emissions, an optimization model is constructed: (18) (19) (20) (twenty one) in, For vehicles Passenger capacity, For the line The frequency of departures, For the line Maximum departure frequency, For the line The minimum departure frequency, For the line Forecast passenger demand, For the line Operating costs, Budget constraints.

[0086] The optimal scheduling scheme is obtained by solving the optimization model using heuristic algorithms (such as genetic algorithms and particle swarm optimization). .

[0087] In one embodiment, the method of the present invention further includes: historical carbon emission trend analysis and multi-scenario carbon emission simulation.

[0088] Historical carbon emission trend analysis includes: based on historical carbon emission data ( (where the historical window length is used), and time series analysis models (such as ARIMA and LSTM) are employed to extract periodic patterns. (twenty two) Multi-scenario carbon emission simulation includes simulating carbon emission scenarios under different scheduling strategies, energy structures, and weather conditions to generate a scenario set. .

[0089] In one embodiment, the key technical parameters are shown in Table 1: Table 1

[0090] In one embodiment, the overall process of the carbon emission accounting method of the present invention is as follows: Figure 14 As shown, firstly, real-time data is collected from the on-board diagnostic system (On-Board Diagnostics). The system collects basic information about buses, including operating status, energy consumption, energy replenishment, and vehicle location, from multiple sources such as OBD sensors, refueling stations, charging piles, hydrogen refueling systems, and GPS / BeiDou positioning. Then, the collected data undergoes cleaning, anomaly detection, anomaly removal, and interpolation to ensure the completeness and accuracy of data required for subsequent calculations. Next, it integrates energy quality, vehicle status, operating conditions, and environmental factors to calculate a dynamic carbon emission factor adapted to the current bus operating status. Subsequently, based on the energy type of the buses, including diesel, natural gas, electricity, and hydrogen, the corresponding carbon emission calculation model is selected, and the carbon emissions of different energy types are uniformly converted into CO2e. Then, the carbon emission results are summarized at the vehicle, route, and fleet levels to support multi-dimensional carbon emission analysis. Furthermore, based on historical data, future carbon emission trends are predicted, carbon emission assessments are performed on different scheduling schemes, and low-carbon optimization suggestions are generated. Finally, the accuracy of the carbon emission calculation is evaluated, and when the error exceeds a preset threshold, the model parameters are adjusted, thus forming a continuously optimized closed-loop calculation mechanism.

[0091] In one embodiment, the system architecture of the carbon emission accounting method of this application is as follows: Figure 15 As shown, the system architecture of this invention includes a data acquisition layer, a data processing layer, an accounting and decision-making layer, an application display layer, and a feedback optimization layer. The data acquisition layer is used to acquire multi-source data related to the operation and energy consumption of public transport vehicles. Specifically, it can collect vehicle operation data such as vehicle speed, acceleration, engine speed, fuel consumption, and electricity consumption in real time through onboard OBD sensors; acquire vehicle energy replenishment data through the refueling station metering system, charging pile data interface, and hydrogen refueling system; and obtain vehicle location and route information through GPS / BeiDou positioning modules. The data processing layer is used to clean, fuse, and detect anomalies in the collected multi-source data, removing abnormal data and storing the standardized data in a time-series database, thereby providing high-quality data support for subsequent accounting and decision-making.

[0092] The accounting and decision-making layer is the core layer of the system, including a dynamic emission factor calculation module, a multi-energy collaborative accounting module, a carbon emission prediction module, and a scheduling optimization suggestion module. The dynamic emission factor calculation module dynamically adjusts carbon emission factors based on factors such as energy quality, vehicle status, and operating conditions, thus overcoming the limitations of traditional static emission factors. Specifically, this module can perform energy quality analysis and adjust the basic emission factor based on batch data provided by energy suppliers, such as diesel sulfur content, calorific value, and density; it can perform vehicle aging correction based on vehicle age, mileage, and maintenance records, calculating the impact coefficient of vehicle aging on emissions; it can perform operating condition adaptation adjustments based on real-time operating conditions such as urban congestion, suburban smooth traffic, and highway cruising; and it can also comprehensively consider the impact of environmental factors such as temperature, humidity, and altitude on emissions, performing environmental factor corrections on the carbon emission factor.

[0093] The multi-energy collaborative accounting module is used to construct a unified carbon emission accounting framework to support collaborative accounting for multiple energy types, including diesel, natural gas, electricity, and hydrogen. This module can automatically identify the energy type used by vehicles and match the corresponding accounting model; it can uniformly convert carbon emissions from different energy types into standard carbon dioxide equivalents (CO2e); it can also analyze the proportion of different energy types and their contribution to total carbon emissions, and further evaluate the carbon emission reduction benefits of electric, hydrogen-powered, and other new energy vehicles replacing traditional energy vehicles.

[0094] The carbon emission prediction module is used to predict carbon emission trends in future periods based on historical carbon emission data and future scheduling plans. This module can analyze the periodic patterns of carbon emissions on daily, weekly, monthly, and quarterly timescales based on historical data; predict carbon emissions based on routes, departure intervals, and vehicle configurations in future scheduling plans; simulate carbon emission scenarios under different scheduling strategies, energy structures, and weather conditions; and trigger early warnings and provide corresponding optimization suggestions when predicted carbon emissions exceed preset thresholds.

[0095] The scheduling optimization suggestion module provides low-carbon optimization recommendations for bus scheduling based on carbon emission accounting and prediction results. This module can identify high-carbon emission routes and propose route optimization suggestions, such as adjusting route directions or reducing congested sections; it can recommend optimal vehicle types and quantities based on route characteristics, such as diesel, electric, or hydrogen-powered vehicle configurations; it can optimize departure intervals based on passenger flow forecasts to reduce empty load rates and carbon emissions; and it can analyze the impact of driving behavior on carbon emissions and provide energy-saving driving suggestions to drivers or operations managers.

[0096] The application presentation layer provides multi-dimensional carbon emission visualization dashboards at the vehicle, route, and fleet levels, and generates carbon emission reports to support public transport companies in conducting carbon trading, carbon reduction assessments, and low-carbon operation management. The feedback optimization layer forms a closed-loop optimization mechanism through accounting accuracy assessment, model parameter tuning, and emission factor calibration, thereby continuously improving the system's carbon emission accounting accuracy and long-term operational stability.

[0097] In one embodiment, the carbon emission accounting of a city's public transport group is taken as an example. The public transport group owns 5,000 buses, including 3,000 diesel buses, 1,500 electric buses, and 500 hydrogen fuel cell buses, operating a total of 300 routes, with an average daily passenger volume of approximately 5 million.

[0098] In terms of system configuration, the data acquisition section equips each bus with an OBD sensor and connects 300 refueling stations, 200 charging piles, and 50 hydrogen refueling stations to the system, enabling unified collection of vehicle operation data, energy consumption data, and energy replenishment data. The data processing section deploys a time-series database with a storage capacity of 100TB and is equipped with a data cleaning and fusion engine for cleaning, fusing, and standardizing the collected multi-source data. The accounting and decision-making section includes a dynamic emission factor calculation module, a multi-energy collaborative accounting module, a carbon emission prediction module, and a scheduling optimization suggestion module to achieve dynamic accounting, prediction, and optimization of bus carbon emissions. The application demonstration section provides vehicle-level carbon emission dashboards, route-level carbon emission analysis, fleet-level carbon emission reports, and a carbon trading support module to meet the daily management, emission reduction assessment, and carbon trading application needs of bus companies.

[0099] The above methods achieve the following technical effects: Compared with traditional static emission factor calculation methods, the relative error of carbon emission calculation is reduced from 15% to less than 3%, and the calculation accuracy is improved by about 80%; the system supports minute-level carbon emission calculation, which can reflect the impact of factors such as vehicle operating status, road conditions, and weather on carbon emissions in real time, improving dynamic response capabilities; the unified calculation framework can support the collaborative calculation of three energy types: diesel, electricity, and hydrogen, accurately reflecting the carbon emission reduction benefits brought about by energy structure optimization, such as increasing the electrification rate from 30% to 50%. When the carbon emission reduction rate is % (percentage missing), it can achieve an annual reduction of approximately 150,000 tons of CO2e. Through carbon emission prediction and scheduling optimization, 10 high-carbon emission routes can be optimized, reducing annual CO2e by approximately 20,000 tons and lowering carbon emissions by approximately 12%. Based on high-precision and traceable carbon emission data, the bus group can participate in the carbon trading market, with annual carbon trading revenue of approximately 8 million yuan. At the same time, the system supports multi-dimensional carbon emission accounting at the vehicle level (5,000 vehicles), route level (300 routes), minute-level time periods, and fleet level, which can support accurate carbon emission reduction assessment and low-carbon operation management of public transportation.

[0100] In one embodiment, 100 diesel buses were selected for a three-month verification of the dynamic emission factor calculation results. Control group: using static emission factors (…). Experimental group: The dynamic emission factor calculation algorithm of this invention was used; baseline value: Carbon emissions measured by exhaust gas analyzer.

[0101] The test results are shown in Table 2: Table 2

[0102] As shown in Table 2, the dynamic emission factor calculation algorithm comprehensively considers energy quality, vehicle aging, operating conditions, and environmental factors, significantly improving the calculation accuracy. The average relative error is reduced by 73.2%, and R² is increased from 0.72 to 0.95.

[0103] In one embodiment, a bus route (20km long) was equipped with 10 buses (5 diesel buses and 5 electric buses) for a one-month scheduling optimization test. Control group: traditional scheduling scheme (10-minute departure interval, fixed vehicle configuration); Experimental group: optimized scheduling scheme based on carbon emission prediction (dynamic departure interval of 8-12 minutes, vehicle configuration optimized according to passenger flow and carbon emissions).

[0104] The test results are shown in Table 3: Table 3

[0105] As shown in Table 3, the optimized scheduling scheme based on carbon emission prediction reduces carbon emissions by 14.6%, empty load rate by 25.0%, and carbon emission intensity per unit passenger volume by 16.5% even with a slight increase in passenger waiting time (+11.5%), thus achieving the goal of low-carbon operation.

[0106] This application provides a carbon emission accounting method that acquires various energy types, multi-source energy consumption data, energy quality parameters, vehicle status parameters, operating condition parameters, and environmental parameters of public transport vehicles; determines dynamic carbon emission factors based on the energy quality parameters, vehicle status parameters, operating condition parameters, environmental parameters, and energy types; generates carbon emission amounts based on the energy types, multi-source energy consumption data, and dynamic carbon emission factors; and summarizes and analyzes the carbon emission amounts according to preset accounting dimensions to generate carbon emission accounting results. This method improves the accuracy and dynamic response capability of public transport carbon emission accounting, achieves refined accounting at the vehicle, route, time period, and fleet levels, and provides data support for low-carbon scheduling, carbon emission reduction assessment, and carbon trading.

[0107] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0108] Based on the same inventive concept, embodiments of the present invention also provide a carbon emission accounting device, which can be used to implement the carbon emission accounting method described in the above embodiments, as described in the following embodiments. Since the principle by which the carbon emission accounting device solves the problem is similar to that of the carbon emission accounting method, embodiments of the carbon emission accounting device can refer to embodiments of the carbon emission accounting method, and repeated details will not be elaborated further. As used below, the terms "unit" or "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0109] Figure 7 This is a schematic block diagram of the first structure of the carbon emission accounting device provided in the embodiments of the present invention, as shown below. Figure 7 As shown, in one embodiment of the present invention, the carbon emission accounting device of the present invention includes: The information acquisition unit 701 is used to acquire data on various energy types, multi-source energy consumption, energy quality parameters, vehicle status parameters, operating condition parameters, and environmental parameters of the bus. The factor determination unit 702 is used to determine the dynamic carbon emission factor based on the energy quality parameter, the vehicle status parameter, the operating condition parameter, the environmental parameter and the energy type. Carbon emission generation unit 703 is used to generate carbon emissions based on the energy type, the multi-source energy consumption data and the dynamic carbon emission factor. The calculation result generation unit 704 is used to summarize and analyze the carbon emissions according to the preset calculation dimensions to generate carbon emission calculation results.

[0110] Figure 8 This is a schematic block diagram of the second structure of the carbon emission accounting device provided in the embodiments of the present invention. Figure 7 Based on the embodiments, further, such as Figure 8 As shown, in one embodiment of the present invention, the factor determination unit 702 includes: The basic factor determination module 801 is used to match the basic carbon emission factors corresponding to each energy type from a pre-built basic carbon emission factor library; The dynamic factor determination module 802 is used to correct the basic carbon emission factor based on the energy quality parameter, the vehicle status parameter, the operating condition parameter, and the environmental parameter to determine the dynamic carbon emission factor.

[0111] Figure 9 This is a schematic block diagram of the third structure of the carbon emission accounting device provided in the embodiments of the present invention. Figure 8 Based on the embodiments, further, such as Figure 9 As shown, in one embodiment of the present invention, the dynamic factor determination module 802 includes: The first correction coefficient determination submodule 901 is used to determine the energy correction coefficient based on the energy quality parameters. The second correction coefficient determination submodule 902 is used to determine the vehicle aging correction coefficient based on the vehicle state parameters. The third correction coefficient determination submodule 903 is used to determine the operating condition correction coefficient based on the operating condition parameters. The fourth correction factor determination submodule 904 is used to determine the environmental correction factor based on the environmental parameters; The dynamic factor determination submodule 905 is used to calculate the dynamic carbon emission factor based on the energy correction coefficient, the vehicle aging correction coefficient, the operating condition correction coefficient, the environmental correction coefficient, and the basic carbon emission factor.

[0112] Figure 10 This is a schematic block diagram of the fourth structure of the carbon emission accounting device provided in the embodiments of the present invention. Figure 7 Based on the embodiments, further, such as Figure 10 As shown, in one embodiment of the present invention, the carbon emission generation unit 703 includes: The carbon emission accounting model calling module 1001 is used to call the carbon emission accounting model corresponding to each energy type from the pre-built carbon emission accounting model library; The standardization processing module 1002 is used to standardize the multi-source energy consumption data to obtain multi-source effective accounting data. The carbon emission generation module 1003 is used to calculate carbon emissions based on the carbon emission accounting model, using the effective accounting data and the dynamic carbon emission factor.

[0113] Figure 11 This is a fifth structural schematic block diagram of the carbon emission accounting device provided in the embodiments of the present invention. Figure 7 Based on the embodiments, further, such as Figure 11 As shown, in one embodiment of the present invention, the accounting dimensions include: vehicle dimension, route dimension, and fleet dimension; the accounting result generation unit 704 includes: The vehicle-level result generation module 1101 is used to summarize and analyze the carbon emissions according to the vehicle dimension and generate vehicle-level carbon emission results. The line-level result generation module 1102 is used to summarize and analyze the carbon emissions according to the line dimension and generate line-level carbon emission results. The fleet-level result generation module 1103 is used to summarize and analyze the carbon emissions according to the fleet dimension to generate fleet-level carbon emission results; Carbon emission intensity calculation module 1104 is used to calculate the carbon emission intensity per unit passenger volume based on the fleet-level carbon emission results. The calculation result generation module 1105 is used to generate the carbon emission calculation result based on the vehicle-level carbon emission result, the route-level carbon emission result, the fleet-level carbon emission result, and the unit passenger volume carbon emission intensity.

[0114] Figure 12 This is a sixth structural schematic block diagram of the carbon emission accounting device provided in the embodiments of the present invention. Figure 7 Based on the embodiments, further, such as Figure 12 As shown, in one embodiment of the present invention, the carbon emission accounting device of the present invention further includes: The calculation error generation unit 1201 is used to compare the carbon emission calculation result with the obtained actual energy consumption statistics to obtain the calculation error; The parameter adjustment unit 1202 is used to adjust the correction parameters of the dynamic carbon emission factor and / or the model parameters of the carbon emission accounting model when the calculation error exceeds a preset error threshold. Carbon emission recalculation unit 1203 is used to re-perform carbon emission recalculation based on adjusted correction parameters and / or model parameters.

[0115] This application provides a carbon emission accounting method and apparatus. It acquires various energy types, multi-source energy consumption data, energy quality parameters, vehicle status parameters, operating condition parameters, and environmental parameters of public transport vehicles. Based on the energy quality parameters, vehicle status parameters, operating condition parameters, environmental parameters, and energy types, a dynamic carbon emission factor is determined. Carbon emissions are generated based on the energy types, multi-source energy consumption data, and dynamic carbon emission factors. The carbon emissions are then summarized and analyzed according to preset accounting dimensions to generate carbon emission accounting results. This improves the accuracy and dynamic response capability of public transport carbon emission accounting, enabling refined accounting at the vehicle, route, time period, and fleet levels, and providing data support for low-carbon scheduling, carbon reduction assessment, and carbon trading.

[0116] Figure 13 This is a schematic diagram of the structure of the computer device provided in an embodiment of the present invention, such as... Figure 13 As shown, the electronic device may include a processor 1301, a communications interface 1302, a memory 1303, and a communication bus 1304. The processor 1301, communications interface 1302, and memory 1303 communicate with each other via the communication bus 1304. The processor 1301 can call logical instructions in the memory 1303 to execute the following methods: acquiring multiple energy types, multi-source energy consumption data, energy quality parameters, vehicle status parameters, operating condition parameters, and environmental parameters of the bus; determining a dynamic carbon emission factor based on the energy quality parameters, vehicle status parameters, operating condition parameters, environmental parameters, and energy type; generating carbon emissions based on the energy type, multi-source energy consumption data, and dynamic carbon emission factor; and summarizing and analyzing the carbon emissions according to a preset accounting dimension to generate carbon emission accounting results.

[0117] Furthermore, the logical instructions in the aforementioned memory 1303 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a 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, a top-drive control center server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. 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.

[0118] This embodiment discloses a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions, and when the program instructions are executed by a computer, the computer can execute the methods provided in the above-described method embodiments, such as: acquiring multiple energy types, multi-source energy consumption data, energy quality parameters, vehicle status parameters, operating condition parameters, and environmental parameters of a public transport vehicle; determining a dynamic carbon emission factor based on the energy quality parameters, vehicle status parameters, operating condition parameters, environmental parameters, and energy type; generating carbon emissions based on the energy type, multi-source energy consumption data, and dynamic carbon emission factor; and summarizing and analyzing the carbon emissions according to a preset accounting dimension to generate a carbon emission accounting result.

[0119] This embodiment provides a computer-readable storage medium storing a computer program that causes a computer to execute the methods provided in the above-described method embodiments. For example, the methods include: acquiring multiple energy types, multi-source energy consumption data, energy quality parameters, vehicle status parameters, operating condition parameters, and environmental parameters of a public transport vehicle; determining a dynamic carbon emission factor based on the energy quality parameters, vehicle status parameters, operating condition parameters, environmental parameters, and energy type; generating carbon emissions based on the energy type, multi-source energy consumption data, and dynamic carbon emission factor; and summarizing and analyzing the carbon emissions according to a preset accounting dimension to generate a carbon emission accounting result.

[0120] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0121] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0122] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0123] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0124] In the description of this specification, the references to terms such as "an embodiment," "a specific embodiment," "some embodiments," "for example," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0125] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of this application. It should be understood that the above descriptions are merely specific embodiments of this application and are not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A carbon emission accounting method, characterized in that, include: Acquire data on various energy types, multi-source energy consumption, energy quality parameters, vehicle status parameters, operating condition parameters, and environmental parameters of public transport vehicles; The dynamic carbon emission factor is determined based on the energy quality parameters, vehicle status parameters, operating condition parameters, environmental parameters, and energy type. Carbon emissions are generated based on the energy type, the multi-source energy consumption data, and the dynamic carbon emission factor. The carbon emissions are summarized and analyzed according to preset accounting dimensions to generate carbon emission accounting results.

2. The carbon emission accounting method according to claim 1, characterized in that, The determination of the dynamic carbon emission factor based on the energy quality parameters, vehicle status parameters, operating condition parameters, environmental parameters, and energy type includes: Match the basic carbon emission factors corresponding to each energy type from a pre-built basic carbon emission factor library; The basic carbon emission factor is corrected based on the energy quality parameters, vehicle status parameters, operating condition parameters, and environmental parameters to determine the dynamic carbon emission factor.

3. The carbon emission accounting method according to claim 2, characterized in that, The step of correcting the basic carbon emission factor based on the energy quality parameters, vehicle status parameters, operating condition parameters, and environmental parameters to determine the dynamic carbon emission factor includes: Determine the energy correction factor based on the energy quality parameters; The vehicle aging correction factor is determined based on the vehicle condition parameters. Determine the operating condition correction coefficient based on the aforementioned operating condition parameters; Determine the environmental correction factor based on the environmental parameters; The dynamic carbon emission factor is calculated based on the energy correction factor, the vehicle aging correction factor, the operating condition correction factor, the environmental correction factor, and the basic carbon emission factor.

4. The carbon emission accounting method according to claim 1, characterized in that, The generation of carbon emissions based on the energy type, the multi-source energy consumption data, and the dynamic carbon emission factor includes: Call the carbon emission accounting model corresponding to each energy type from the pre-built carbon emission accounting model library; The multi-source energy consumption data is standardized to obtain effective multi-source accounting data; Based on the carbon emission accounting model, carbon emissions are calculated using the effective accounting data and the dynamic carbon emission factor.

5. The carbon emission accounting method according to claim 1, characterized in that, The accounting dimensions include: vehicle dimension, route dimension, and fleet dimension; the carbon emission calculation results are generated by summarizing and analyzing the carbon emissions according to the preset accounting dimensions, including: The carbon emissions are summarized and analyzed according to the vehicle dimension to generate vehicle-level carbon emission results; The carbon emissions are summarized and analyzed according to the aforementioned route dimension to generate route-level carbon emission results; The carbon emissions are summarized and analyzed according to the fleet dimension to generate fleet-level carbon emission results; Calculate the carbon emission intensity per unit passenger volume based on the aforementioned fleet-level carbon emission results; The carbon emission accounting result is generated based on the vehicle-level carbon emission result, the route-level carbon emission result, the fleet-level carbon emission result, and the carbon emission intensity per unit passenger volume.

6. The carbon emission accounting method according to any one of claims 1 to 5, characterized in that, Also includes: The carbon emission accounting results are compared with the obtained actual energy consumption statistics to obtain the accounting error; When the calculation error exceeds a preset error threshold, the correction parameters of the dynamic carbon emission factor and / or the model parameters of the carbon emission calculation model are adjusted. Carbon emission accounting will be re-executed based on the adjusted correction parameters and / or model parameters.

7. A carbon emission accounting device, characterized in that, include: The information acquisition unit is used to acquire data on various energy types and multi-source energy consumption of buses, energy quality parameters, vehicle status parameters, operating condition parameters, and environmental parameters. The factor determination unit is used to determine the dynamic carbon emission factor based on the energy quality parameter, the vehicle status parameter, the operating condition parameter, the environmental parameter, and the energy type. A carbon emission generation unit is used to generate carbon emissions based on the energy type, the multi-source energy consumption data, and the dynamic carbon emission factor. The calculation result generation unit is used to summarize and analyze the carbon emissions according to preset calculation dimensions to generate carbon emission calculation results.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 6.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 6.

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